Tag: Digital Transformation

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What you need to know: The Future of FinTech, RegTech and Wealth Management in the Digital Space

The tipping point is here. High tech business intelligence tools with built-in machine learning algorithms and big data inputs were once reserved for the Fortune 500. Now, the FinTech fad has shifted from early stage adopter to mainstream money manager and former technophobes are starting to digitize their businesses from end to end. New low cost, user-friendly self-service tools that can produce rapid-fire insights and on-demand customer service are finally within reach and can provide family wealth managers the brain power they need without the additional headache. Synaptik, True Interaction’s “Plug, Play, Predict” machine platform is already serving companies in the space, providing value more quickly than industry norms.

Reuters white paper on the digitization of wealth management identified three drivers behind the mainstream movement towards FinTech:

– New tools for investment research, risk management, trade processing, compliance, and reporting
– New business models offering better, faster, cheaper variants of existing services in investment management and brokerage
– New marketplaces, new managers, and new financial products that are changing the way capital and risk are allocated

In this blog post, we’ll explore disruptive technologies traditional firms with limited IT expertise can use to beat the market, improve existing services and stay on top of an increasingly complex regulatory environment. By leveraging cloud, open source, big data, Artificial Intelligence, API and Chatbots, companies can create robust digital ecosystems that will win younger clients and increase profits across the board. Companies that continue to resist digital transformation run the risk of becoming less competitive while those that embrace the opportunity will benefit from supplementing talented human capital with technological know-how.

Courtesy of PWC

Beat the Market

Big name hedge funds and investment firms deploy AI to comb through the internet for new investment opportunities. The elusive “super-algo” can swallow huge amounts of information from news reports, databanks and social media platforms and quickly optimize portfolios to profit from microscopic ripples and seismic shifts in the market. While private family wealth managers have relied on traditional methods and experience to pinpoint good investment opportunities, machine learning can provide the edge they need to compete in a volatile world. Now, building data ecosystems that provide real-time information and time series data on company performance and consumer trends no longer requires a Ph.D. in data analytics or computer science.

When considering investment management software, companies should look for some key features including scenario simulation, modeling, portfolio rebalancing, performance metrics, yield curve analysis and risk analytics. Your software should also be flexible, adaptable and able to ingest structured and unstructured data. The costs of professional investment programs range from $1300 to $8000 but as the market matures costs are likely to go down.

Money Management on Demand

Wealth management firms have relied on traditional relationship-driven business models for decades. But the personal touch that keeps more senior clients happy may repel the next generation. To attract younger clientele, companies need to invest in on-demand, low-touch digital customer service models that provide better transparency and more autonomy to their clients. Creating a flexible digital strategy that allows different client segments to engage with their portfolio independently and with their advisor as little or as often as they want is key to success. EY’s report “Advice goes virtual” looks at the range of innovative wealth management models that are now available and highlights firms that have struck the perfect balance between automation and human capital. Companies like Personal Capital, Future Advisor and LearnVest provide digital platforms with phone-based financial advisor services to meet the needs of busy millennials and satisfy the clients that prefer a dedicated human that knows the future they want to make for themselves. EY’s chart on innovations in wealth management sums up the range of digital opportunities that clients are gravitating towards.


Courtesy of EY

Automated Compliance

Since the financial crisis, the cost of compliance has risen steeply. Tech Crunch reports that “the global cost of compliance is an estimated $100 billion per year. For many financial firms, compliance is 20% of their operational budget.” Innovations in RegTech, an offspring of FinTech, can automate certain components of the compliance process and have the potential to dramatically reduce the cost of doing business. The Institute for International Finance (IIF) defines “RegTech” as “the use of new technologies to solve regulatory and compliance requirements more effectively and efficiently.”

Since 2008, the increasing speed of regulatory change has kept wealth management firms in a state of paralysis. Companies are constantly playing catch up and readjusting procedures to meet new requirements. In the not so distant future, integrated RegTech solutions will connect directly with regulatory systems and automatically update formulae, allowing wealth management firms to refocus their resources on revenue generating activities.

Instead of producing lengthy paper reports for regulators, new RegTech solutions can generate and communicate required reports automatically. Instead of scouring hundreds of documents and spreadsheets on a quarterly basis, RegTech solutions will alert compliance managers to risks in real-time so they can be eliminated immediately. The possibilities are endless and the cumbersome and costly task of navigating the increasingly complex regulatory environment will continue to generate more innovations in this field. While RegTech is still in its infancy, small family wealth management firms should start investigating this growing subsector and use this disruptive technology to their advantage.

Traditional wealth management firms that continue to resist the digital revolution will begin to look antiquated, even to their most senior clientele. True Interaction specializes in building and executing digital transformation strategies for companies that don’t have IT expertise. Synaptik, True Interaction’s CMS for data, is already providing firms in the FinTech, RegTech and AdTech spaces with easy-to-use data management, visualization, and deep learning insights. Our experts are providing free consultations to help them assess their needs and start planning their digital future. Schedule your custom consultation here.

By Nina Robbins

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Big Data Definition, Process, Strategies and Resources

Are we at the Big Data tipping point?

The Big Data space is warming up – to the point that various experts by now perceive it as the over-hyped successor to cloud. The publicity might be a bit much, however Big Data is by now living up to its prospective, changing whole business lines, such as marketing, pharmaceutical research, and cyber-security. As a business gains experience with concrete kinds of information, certain issues tend to fade, however there will on every relevant occasion be another brand-new information source with the same unknowns awaiting in the wings. The key to success is to start small. It’s a lower-risk way to see what Big Data may do for your firm and to test your businesses’ preparedness to employ it.

In nearly all corporations, Big Data programs get their start once an executive becomes persuaded that the corporation is missing out on opportunities in data. Perhaps it’s the CMO looking to glean brand-new perceptiveness into consumer conduct from web data, for example. That conviction leads to a comprehensive and laborious procedure by which the CMOs group could work with the CIOs group to state the exact insights to be pursued and the related systematic computational analysis of data or statistics to get them.

Big Data: Find traffic bottlenecks?

The worth of Big Data for network traffic and flow analysis is in the capacity to see across all networks, applications and users to comprehend in what way IT assets, and in particular net-work bandwidth, is being dispersed and devoured. There are several tools with which customers can finally see precisely whoever is doing what on the net-work, down to the concrete application or smartphone in use. With this real-time perceptiveness, associated with prolonged term use history, clients can spot tendencies and outliers, identifying wherever performance difficulties are starting and why.

Big Data has swished into any industry and at the moment plays an essential part in productivity development and contention competition. Research indicates that the digital cluster of data, data processing power and connectivity is ripe to shake up many segments over the next 10 years.

Big Data: What type of work and qualifications?

Big Data’s artificial intelligence applications of tools and methods may be applied in various areas. For example, Google’s search and advertisement business and its new robot automobiles, which have navigated 1000s of miles of California roads, both employ a package of artificial intelligence schemes. Both are daunting Big Data challenges, parsing huge amounts of information and making decisions without delay.

A Big Data specialist should master the different components of a Hadoop ecosystem like Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark. They should also get hands-on practice on CloudLabs by implementing real life programs in the areas of banking, electronic communication telecommunication, social media, insurance, and e-commerce.


Image: Erik Underwood/TechRepublic

How can the value of Big Data be defined?

The Big Data wave is altogether about detecting hidden worth in information resources. It characteristically is thought of as a large organization bringing all their different sources of information together (big and complex). Then boiling this data down to, still sizable, however a lot more controllable, data sets. This data can additionally be attacked with advanced systematic computational analysis of data or statistics, machine learning, and all types of out there mathematics. From this, brand new and unforeseen insights can be found.

Experts say that when Big Data programs disappoint, it’s frequently since businesses have not plainly described their objectives, the systematic computational analysis of data or statistics analytics problem they desire to answer, or the quantifications they’ll use to measure success. An illustration of a program with a plainly described and quantifiable objective is a retail merchant desiring to improve the precision of inventory in its stores. That lessens waste and betters profitability. Measuring before and after precision is easy; so is calculating ROI founded on the resulting increased profitability.

Big Data: Who should receive measurement reports?

The boom in the B2B Big Data market (from a sub-$100m business in 2009 to $130bn today) reflects an enterprise-led agglomerate scramble to invest in information mining, suggestive of the California gold rush, accompanied by a similar media buzz. Big Data is one of those specifications that gets flung about lots of businesses – without much of an agreement as to what it means. Technically, Big Data is whatever pool of data that is assembled from more than a single source. Not only does this trigger the technological interoperability problems that make data interchange so thwarting, but it as well makes it hard to know what information is available, what format it’s in, in what way to synthesize aged and brand-new data, and in what way to architect a practical way for end-users to communicate with Big Data tools.

In addition to the right applications of tools and methods, suppliers should invest time and manpower in obtaining the capabilities to make systematic computational analysis of data or statistics work for them. This includes crafting a committed group of specialists to supervise Big Data programs, implement and enhance software, and persuade users that those brand new strategies are worth their while. Given the extensive potential in the marketing industry, stakeholders need to create clever methods to manage the Big Data in their audience metrics. The creation of a united public metric standard is a hard, however essential objective, and stakeholders ought to strive to supply complete transparency to users with regard to tracking information as well as opt-out systems.

Robust metadata and forceful stewardship procedures as well make it simpler for corporations to query their information and get the answers that they are anticipating. The capacity to request information is foundational for reporting and systematic computational analysis of data or statistics, however corporations must characteristically overcome a number of challenges before they can engage in relevant examination of their Big Data resources. Businesses may do this by making sure that there is energetic participation and backing from one or more business leaders when the original plan of action is being elaborated and once the first implementations take place. Also of vital significance here is continuing collaboration amid the business and IT divisions. This ought to ensure that the business value of all ventures in Big Data systematic computational analysis of data or statistics are correctly comprehended.

A recent KPMG study showed only 40% of senior managers have a high level of trust in the user insights from their systematic computational analysis of data or statistics, and nearly all indicated their C-suite did not completely aid their current information analytics plan of action. 58% of organizations report that the influence of Big Data analytics on earnings was 3% or smaller. The actual Bonanza appears limited to banking, supply chains, and technical performance optimization – understandably some organizations feel left behind.

Big Data: How much value is created for each unit of data (whatever it is)?

The big part of Big Data alludes to the capacity of data accessible to examine. In the supply chain realm, that could include information from point-of-sale setups, bar-code scanners, radio frequency identification readers, global positioning system devices on vehicles and in cell phones, and software systems used to run transportation, warehousing, and additional operations.

CIOs and other Information Technology decision makers are used to needing to do more with less. In the world of Big Data, they might be able to achieve cost savings and efficiency gains, IT Ops and business intelligence (BI) strategies, exploiting advancements in open source software, distributed data processing, cloud economic science and microservices development.

Consultants who work with businesses on systematic computational analysis of data or statistics projects cite additional supply chain advancements that result from Big Data programs. For example, an online retailer that uses sales information to forecast what color sweaters sell the most at different times of the year. As a result of that data, the company at the moment has its providers create sweaters without color, then dye them later, based on consumer demand determined in near-real time.

Data experts in science and information experts as well as architects and designers with the expertise to work with Big Data applications of tools and methods are in demand and well-compensated. Want an extra edge looking for your following assignment? Get Big Data certified.

Is senior management in your organization involved in Big Data-related projects?

As with any business initiative, a Big Data program includes an element of risk. Any program may disappoint for whatever number of reasons: poor management, under-budgeting, or a lack of applicable expertise. However, Big Data projects carry their own specific risks.

The progressively rivalrous scenery and cyclical essence of a business requires timely access to accurate business data. Technical and organizational challenges associated with Big Data and advanced systematic computational analysis of data or statistics make it hard to build in-house applications; they end up as ineffective solutions and businesses become paralyzed.

Large-scale information gathering and analytics are swiftly getting to be a brand-new frontier of competitive distinction. Financial Institutions want to employ extensive information gathering and analytics to form a plan of action. Data-related threats and opportunities can be subtle.

To support Big Data efforts there are 2 fundamental types of PMOs: one that acts in an advising capacity, delivering project managers in business units with training, direction and best practices; and a centralized variant, with project managers on staff who are lent out to business units to work on projects. How a PMO is organized and staffed depends on a myriad of organizational circumstances, including targeted objectives, customary strengths and cultural imperatives. When deployed in line with an organization’s intellectual/artistic awareness, PMOs will help CIOs provide strategic IT projects that please both the CFO and internal clients. Over time, and CIOs ought to permit 3 years to obtain benefits, PMOs can save organizations money by enabling stronger resource management, decreasing project failures and supporting those projects that offer the largest payback.

Next, get started with the Big Data Self-Assessment:

The Big Data Self-Assessment covers numerous criteria related to a successful Big Data project – a quick primer eBook is available for you to download, the link is at the end of this article. In the Big Data Self Assessments, we find that the following questions are the most frequently addressed criteria. Here are their questions and answers.

The Big Data Self-Assessment Excel Dashboard shows what needs to be covered to organize the business/project activities and processes so that Big Data outcomes are achieved.

The Self-Assessment provides its value in understanding how to ensure that the outcome of any efforts in Big Data are maximized. It does this by securing that responsibilities for Big Data criteria get automatically prioritized and assigned; uncovering where progress can be made now.

To help professionals architect and implement the best Big Data practices for your organization, Gerard Blokdijk, head and author of The Art of Service’s Self Assessments provides a quick primer of the 49 Big Data criteria for each business, in any country, to implement them within their own organizations.

Take the abridged Big Data Survey Here:

Big Data Mini Assessment

Get the Big Data Quick Exploratory Self-Assessment eBook:

https://189d03-theartofservice-self-assessment-temporary-access-link.s3.amazonaws.com/Big_Data_Quick_Exploratory_Self-Assessment_Guide.pdf

by Gerard Blokdijk

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Blockchain 101 Self-Assessment

Blockchain is the new black. We’ve heard the term in conference calls, seen it on the cover of magazines and know it’s a hot topic on CNBC but the barrage of information makes it difficult to distinguish hype from reality. It’s clear that Blockchain will revolutionize the world but understanding how is mission critical. In this blog post we’ll cover the Blockchain essentials and the most frequently asked questions we’ve come across.

At The Art of Service we’ve developed a Blockchain self-assessment tool that professionals use to test the depth of their knowledge on the Blockchain concept and its potential. The Blockchain self-assessment covers numerous criteria related to a successful project – a quick primer version is available for you to download at the end of the article.

BLOCKCHAIN FREQUENTLY ASKED QUESTIONS:

What Is the Blockchain?

The problem with nearly all Blockchain explanations is that they supply too much detail upfront and use lingo that winds up leaving folks more confused than when they started. We are in the nascent stages of this technological revolution and it’s hard to predict how Blockchain will impact our institutions and our lives. Brand new Blockchain-related technologies are being built every day and the framework is evolving.

Here are some key definitions and ideas to help you understand the fundamental pillars behind this insurgent technology:

1. Blockchain is a technology that essentially disperses an account ledger. For those of you in the monetary management world, you know an account ledger as the trusted source of transactions or facts. The same is true with Blockchain but in lieu of existing in a great buckskin bound book or in a financial management program, Blockchains are run by a dispersed set of information handling resources working together to maintain that account ledger.
2. The Blockchain procedure of securely and permanently time-stamping and recording all transactions makes it very hard for a user to change the account book once a block in a Blockchain has been added.
3. Private Blockchains allow for distributing identical copies of an account book but only to a restricted amount of trusted contributors. This set of techniques, practices, procedures and rules is better suited for applications needing simplicity, speed, and greater clarity.
4. Users of the Distributed Account Ledger Technology (DLT) notably benefit from the efficiencies by generating a more robust ecosystem for real-time and secure data sharing.
5. Blockchain is only one of the various kinds of data constructions that provide secure and valid achievement of distributed agreement. The Bitcoin Blockchain, which uses Proof-of-Work mining, is the most common approach being used today. However, additional forms of DLT consensus exist such as Ethereum, Ripple, Hyperledger, MultiChain and Eris.

Blockchain: Who controls the risk?

Each party on a Blockchain has access to the entire database and its complete past. No single party controls the data or the information. Every party can substantiate the records of its transaction associates directly, without a mediator.

For public businesses, the conditions of Blockchain are very different. The identity of contributors must be known while permissioned Blockchains require no evidence of work. Over the next few years, Blockchain growing pains will hit the industry and support systems will begin to take shape. Today, Blockchain needs supporting infrastructure available for cloud or traditional database setups – there are no systems management tools, reporting tools or legacy configuration integrations in place.

Could Blockchain be the structural change the market needs?

Blockchain’s foundational technology is the biggest innovation computer science has seen in a long time. The thought of a dispersed database where trust is established through mass collaboration and clever code rather than a powerful institution is game-changing. Now it will be up to the larger business community to determine whether it will become the building block for the digitized economy or if it will be disregarded and perish. Now, building formidable and trustworthy Blockchain standards is the next step to turn this global opportunity into a reality.

Blockchain: What does the future hold?

There are many Blockchain and distributed account ledger setups emerging in the market including: BigchainDB, Billon, Chain, Corda, Credits, Elements, Monax, Fabric, Ethereum, HydraChain, Hyperledger, Multichain, Openchain, Quorum, Sawtooth, Stellar. The Block chain use cases span a number of industries including insurance, healthcare and finance but we are only scratching the surface of what’s possible.

Next, get started with the Blockchain Self-Assessment:

The Blockchain Self-Assessment Excel Dashboard provides a way to gauge performance against planned project activities and achieve optimal results. It does this by ensuring that Blockchain criteria are automatically prioritized and assigned; uncovering where progress can be made now; and what to plan for in the future.

To help professionals architect and implement best Blockchain practices for your organization, Gerard Blokdijk, author of The Art of Service’s Self Assessments provides a quick primer of the 49 Blockchain criteria for any business in any country.

Get the Blockchain Quick Exploratory Self-Assessment eBook here:

https://189d03-theartofservice-self-assessment-temporary-access-link.s3.amazonaws.com/Blockchain_Quick_Exploratory_Self-Assessment_Guide.pdf

About the Author

Gerard Blokdijk is the CEO of The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information specialist. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.

By Gerard Blokdijk

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Can Artificial Intelligence Catalyze Creativity?

In the 2017 “cerebral” Olympic games, artificial intelligence defeated the human brain in several key categories. Google’s AlphaGo beat the best player of Go, humankind’s most complicated strategy game; algorithms taught themselves how to predict heart attacks better than the AHA (American Heart Association); and Libratus, an AI built by Carnegie Mellon University, beat four top poker players at no-limit Texas Hold ‘Em. Many technologists agree that computers will eventually outperform humans on step-by-step tasks, but when it comes to creativity and innovation, humans will always be a part of the equation.

Inspiration, from the Latin inspiratus, literally means “breathed into.” It implies a divine gift – the aha moment, the lightning bolt, the secret sauce that can’t be replicated. Around the globe, large organizations are attempting to reculture their companies to foster innovation and flexibility, two core competencies needed to survive the rapid-fire rate of change. Tom Agan’s HBR article titled “The Secret to Lean Innovation” identified learning as the key ingredient, while Lisa Levey believes that seeing failure as a part of success is key.

At the same time, although innovation is a human creation, machines do play a role in that process. Business leaders are using AI and advanced business intelligence tools to make operations more efficient and generate higher ROI, but are they designing their digital ecosystems to nurture a culture of innovation? If the medium is the message, then they should be.

“If you want to unlock opportunities before your competitors, challenging the status quo needs to be the norm, not the outlier. It will be a long time if ever before AI replaces human creativity, but business intelligence tools can support discovery, collaboration and execution of new ideas.” – Joe Sticca, COO at Synaptik

So, how can technology augment your innovation ecosystem?

Stop

New business intelligence tools can help you manage innovation, from sourcing ideas to generating momentum and tracking return on investment. For instance, to prevent corporate tunnel vision, you can embed online notifications that superimpose disruptive questions on a person’s screen. With this simple tool, managers can help employees step outside the daily grind to reflect on the larger questions and how they impact today’s deliverable.

Collaborate

The market is flooded with collaboration tools that encourage employees to leverage each other’s strengths to produce higher quality deliverables. The most successful collaboration tools are those that seamlessly fit into current workflows and prioritize interoperability. To maximize innovation capacity, companies can use collaboration platforms to bring more diversity to the table by inviting external voices including clients, academics and contractors into the process.

Listen

Social listening tools and sentiment analysis can provide deep insights into the target customer’s needs, desires and emotional states. When inspiration strikes, innovative companies are able to prototype ideas quickly and share those ideas with the digital universe to understand what sticks and what stinks. By streamlining A/B testing and failing fast and often, agile companies can reduce risk and regularly test their ideas in the marketplace.

While computers may never birth the aha moments that drive innovation, advanced business intelligence tools and AI applications can capture sparks of inspiration and lubricate the creative process. Forward-thinking executives are trying to understand how AI and advanced business intelligence tools can improve customer service, generate higher ROI, and lower production costs. Companies like Cogito are using AI to provide real-time behavioral guidance to help customer service professionals improve the quality of their interactions while Alexa is using NLP to snag the full-time executive assistant job in households all over the world.

Creativity is the final frontier for artificial intelligence. But rather than AI competing against our innovative powers, business intelligence tools like Synaptik can bolster innovation performance today. The Synaptik difference is an easy user interface that makes complex data management, analytics and machine learning capabilities accessible to traditional business users. We offer customized packages that are tailored to your needs and promise to spur new ideas and deep insights.

By Nina Robbins

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Improving the Fan Experience through Big Data and Analytics

As consumer electronics companies produce bigger and better HD televisions, sports fans have enjoyed the ability to feel the excitement of the stadium from the comfort of their own homes. Broadcast companies like NBC, FOX, CBS and ESPN have further enhanced the viewing experience by engaging fans on social media platforms and producing bingeworthy content. The downside of high ratings are stagnating stadium attendance levels.

With the convenience of the at-home viewing experience, how can professional sport leagues bring fans back to the stadium? In a 1998 poll conducted by ESPN, 54% of fans revealed that they would rather be at a game than at home. However, when that poll was taken again in 2012, only 29% of fans wanted to be at the game.

Now, professional football teams are betting big data can provide insights that will help them get fans back in the seats. For instance, The New England Patriots have partnered with data science experts to better understand the needs of their fanbase. By investing in big data and high-power analytics tools, the New England Patriots are uncovering new insights on consumer behavior such as in-store purchases, ticket purchase information, and click rates – information that will help them optimize marketing and sales tactics.

While most Patriot games do sellout, there are instances where season ticket holders do not show up. With tools from Kraft Analytics Group (KAGR), The New England Patriots can access data from every seat in the stadium to see who will be attending and how many season ticket holders came to the game. By tracking all of this data the New England Patriots are able to uncover insights into their fanbase that were previously unknown. Robert Kraft, owner of the New England Patriots, was asked about fan turnout and how valuable it was for the team.

“If somebody misses a game, they get a communication from us and we start to aggregate the reasons why people miss one, two, or three games. At the end of the year, I can know everything that took place with our ticket-holders during that season. It’s incredibly valuable to adjust your strategy going forward depending on what your goals are.“-Robert Kraft, Owner of the New England Patriots

Many teams are also turning to IoT (Internet of Things) solutions to optimize their fan experience. With IoT solutions, devices can be connected to the internet with a click of a button. Professional sports teams have taken advantage of these opportunities by using platforms such as iBeacon. This app uses bluetooth connections in order to connect with mobile devices to create a new type of stadium experience. With this technology connecting to concession stands and areas around the ballpark, fans can find the closest pizza discount and the shortest bathroom line.

Beacon Stadium App
Beacon Stadium App-Courtesy of Umbel

IoT stadiums will eventually become the new norm. The San Fransisco Giants have become leaders in the revolution. Bill Schlough CIO of the San Fransisco Giants commented on this trend,

“Mobile and digital experiences are paramount to our fan experience,” according to Schlough, “and they have played a role in the fact that we’ve had 246 straight sellouts.”

Schlough and the Giants organization have taken an active role to offer their fans a unique viewing experience. Cell phone coverage was introduced in the early 2000s, and in 2004 they introduced a plan to make AT&T Park a mobile hotspot. With WiFi antennas across the stadium, fans have the ability to watch videos and use social media to interact with other fans in the stadium.

As owners and cities continue to spend billions of dollars for new stadiums, meeting consumer demand will be crucially important in a digital world. Teams like the New England Patriots and the San Fransisco Giants have already started using technological tools like analytics and the Internet of Things in order to cater to the needs of their fans. With more innovators in the tech industry, other sports teams will likely follow the path of the Patriots and Giants in order to provide a memorable experience at the game for their customers.

With Synaptik’s social listening tools and easy data management integration, companies have the advantage to track conversations and data around secific topics and trends. Sign up for a 30 minute consultation.

Contributors:

Joe Sticca, Chief Operating Officer at True Interaction

Kiran Prakash, Content Marketing at True Interaction

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Sparking Digital Communities: Broadcast Television’s Answer to Netflix

In the late 1990s and early 2000s network television dominated household entertainment. In 1998, nearly 30% of the population in the United States tuned into the NBC series finale of “Seinfeld”. Six years later, NBC’s series finale of the popular sitcom “Friends” drew 65.9 million people to their television screen, making it the most watched episode on US network TV in the early aughts. Today, nearly 40% of the viewers that tuned into the “Game of Thrones” premier viewed the popular show using same-day streaming services and DVR playback. The way people watch video content is changing rapidly and established network television companies need to evolve to maintain their viewership.

While linear TV is still the dominant platform amongst non-millenials, streaming services are quickly catching up. As young industry players like Hulu, Netflix and Youtube transform from streaming services to content creators and more consumers cut ties with cable, established network broadcasters need to engage their loyal audience in new ways. The challenge to stay relevant is further exacerbated by market fragmentation as consumer expectations for quality content with fewer ad breaks steadily rise.


Courtesy of Visual Capitalist

One advantage broadcast television still has over streaming services is the ability to tap into a network of viewers watching the same content at the same time. In 2016, over 24 million unique users sent more than 800 million TV related tweets. To stay relevant, network television companies are hoping to build on this activity by making the passive viewing experience an active one. We spoke with Michelle Imbrogno, Advertising Sales Director at This Old House about the best ways to engage the 21st century audience.

“Consumers now get their media wherever and whenever it’s convenient for them. At “This Old House”, we are able to offer the opportunity to watch our Emmy Award winning shows on PBS, on thisoldhouse.com or youtube.com anytime. For example, each season we feature 1-2 houses and their renovations. The editors of the magazine, website and executive producer of the TV show work closely together to ensure that our fans can see the renovations on any platforms. We also will pin the homes and the items in them on our Pinterest page. Social media especially Facebook resonates well with our readers.“– Michelle Imbrogno, Advertising Sales Director, This Old House

Social media platforms have become powerful engagement tools. According to Nielsen’s Social Content Ratings in 2015, 60% of consumers are “second screeners” – using their smartphones or tablets while watching TV. Many “second screeners” are using their devices to comment and interact with a digital community of fans. Games, quizzes and digital Q & A can keep viewers engaged with their favorite programming on a variety of platforms. The NFL is experimenting with new engagement strategies and teamed up with Twitter in 2016 to livestream games and activate the digital conversation.

“There is a massive amount of NFL-related conversation happening on Twitter during our games and tapping into that audience, in addition to our viewers on broadcast and cable, will ensure Thursday Night Football is seen on an unprecedented number of platforms.”-NFL Commissioner Roger Goodell ,”

With social media optimization (SMO) software, television networks can better understand their audience and adjust their social media strategy quickly. Tracking website traffic and click rates simply isn’t enough these days. To stay on trend, companies need to start tracking new engagement indicators using Synaptik’s social media intelligence checklist:

Step 1: Integrate Social Listening Tools

The key to understanding your audience is listening to what they have to say. By tracking mentions, hashtags and shares you can get a better sense of trending topics and conversations in your target audience. Moreover, this knowledge can underpin your argument for higher price points in negotiations with media buyers and brands.

Step 2: Conduct a Sentiment Analysis

Deciphering a consumer’s emotional response to an advertisement, character or song can be tricky but sentiment analysis digs deeper using natural language processing to understand consumer attitudes and opinions quickly. Additionally, you can customize outreach to advertisers based on the emotional responses they are trying to tap into.

Step 3: Personality Segmentation

Understanding a consumer’s personality is key to messaging. If you want to get through to your audience you need to understand how to approach them. New social media tools like Crystal, a Gmail plug-in, can tell you the best way to communicate with a prospect or customer based on their unique personality. This tool can also help you customize your approach to media buyers and agents.

By creating more accessible content for users and building a digital community around content, television networks can expect to increase advertising revenue and grow their fan base. With Synaptik’s social listening tools, companies have the advantage to track conversations around specific phrases, words, or brands. Sign up for a 30 minute consultation and we can show you what customers are saying about your products and services across multiple social media channels online (Facebook, Twitter, LinkedIn, etc.).

Contributors:

Joe Sticca, Chief Operating Officer at True Interaction

Kiran Prakash, Content Marketing at True Interaction

by Nina Robbins

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Real Estate: Climate-proof your Portfolio

The real estate industry is built on the power to predict property values. With sea levels on the rise, smart investors are thinking about how to integrate climate science into real estate projections. Complex algorithms and regression models are nothing new to developers and brokerage firms but the rapidly evolving data ecosystem offers breakthrough opportunities in resiliency marketing, valuation and forecasting.

In Miami, investors are starting to look inland for property deals on higher ground. According to a New York Times article by Ian Urbina, “home sales in flood-prone areas grew about 25% less quickly than in counties that do not typically flood.” To get in front of the wave, real estate investors and appraisers need to regularly update their forecasting models and integrate new environmental and quality of life data sets. Third party data can be expensive but as municipal governments embrace open data policies, costs may go down.

Today, no fewer than 85 cities across the U.S. have developed open data portals that include data on everything from traffic speed to air quality to SAT results. Real estate professionals are using data to do more than just climate-proof their portfolios. With high-powered business intelligence tools, businesses can turn this rich raw data into better insights on:

Home Valuation

Zillow, an online real estate marketplace is leading the charge on better home valuation data models. The company’s ‘zestimate’ tool is a one-click home value estimator based on 7.5 million statistical and machine learning models that analyze hundreds of data points on each property. Now, they’ve launched a $1 million dollar prize competition calling on data scientists to create models that outperform the current Zestimate algorithm.

Design

According to the Census Bureau, in 1960, single-person households made up about 13% of all American households. Now, that number has jumped to 28% of all American households. Additionally, a survey by ATUS cited in a Fast Company article by Lydia Dishman revealed that the number of people working from home increased from 19% in 2003 to 24% in 2015. The rapid rate of technological change means a constant shift in social and cultural norms. The micro-apartment trend and the new WeLive residential project from WeWork are signs of changing times. For developers, the deluge of data being created by millennials provides incredible insight into the needs and desires of tomorrow’s homebuyers.

Marketing

Brokerage firms spend exorbitant amounts of money on marketing but with big data in their pocket, real estate agents can narrow in on clients ready to move and cut their marketing spend in half. According to this Wall Street journal article by Stefanos Chen, saavy real estate agents use data sources like grocery purchases, obituaries and the age of children in the household to predict when a person might be ready to upsize or downsize. This laser-sharp focus allows them to spend their marketing budget wisely and improve conversion rates across the board.

In today’s competitive marketplace, real estate professionals need a self-service data management and analytics platform that can be applied to any use case and doesn’t require advanced IT skills. Synaptik is designed to adapt to your needs and can easily integrate quantitative and qualitative data from websites, social media channels, government databases, video content sites, APIs and SQL databases. Real estate is big business and better intelligence mean better returns. Sign up for a demo and find answers to questions you didn’t even know to ask.

By Nina Robbins

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Top 3 CTO Secrets to Success

As technology becomes integrated into every aspect of traditional business, CTOs are taking on more and more responsibilities. CTOs are no longer back office administrators that are called in to put out fires, they are front line leaders that require business acumen, top notch communication skills, and a deep understanding of every part of business from the sales cycle to the supply chain. Externally, CTOs are expected to stay on top of the latest and greatest tech products in the market. They are constantly weighing the pros and cons of system redesign and held responsible if product deployments slow down productivity.

So how do successful CTOs navigate the waters in constant sea change? Greg Madison, CTO at Synaptik, provides insight into what it takes to succeed in the 21st century startup:

1. Know your needs

Understanding the scope of a project or product is critical to identifying what your needs are and will help in the evaluation of new technologies. There is an overwhelming amount of new tech solutions to problems, and all marketing sells a specific technology as “the next big thing that you need,” but if you’re really not in need of it, don’t use it. Correctly identify what you’re needs are, and what the capabilities of your current technologies may be. If some new tech doesn’t solve a problem, then it’s not worth an in-depth evaluation.

2. Know your team

Most of us get into the tech industry to work with computers and we’re shocked to find out that we have to work with people instead. Knowing those above you, in your charge, and your peers, can help in avoiding personality conflicts, as well as increase efficiency of task completion and cooperation. Not to say that all things should be tailored to an individual, only that knowing the preference or passion of the individual can be of a benefit when taking an idea from your CEO, translating that into actionable tasks, and assigning those tasks to the right team member.

3. Know your code

As your dev team grows, you code less and less as a CTO. Though this may be a difficult reality at times, it’s necessary. However, that doesn’t mean that you should lose touch with the codebase. Though a CTO should be looking for new technologies, you also can’t forget to maintain and refactor existing code. Not many people will code it right the first time, and so it must be refactored and maintained without the mentality that you can just scrap it and start over if it gets too out of control. Establishing and maintaining a set cycle for code evaluation and maintenance is key to ensuring a stable product.

To learn more about Greg’s work at Synaptik, sign up for a demo and explore the best in class data management platform that is designed to adapt by providing a lightweight ready-to-go module-based software framework, for rapid development.

“Synaptik offers traditional business users access to high-powered data analytics tools that don’t require advanced IT skills. Whether you are working in insurance, media, real estate or the pharmaceutical industry, Synaptik can provide deep insights that put you ahead of the competition.” – Greg Madison, CTO at True Interaction and Synaptik

By Nina Robbins

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Why Third Party Data Will Transform the Insurance Industry

Insurance Outlook

Insurance companies have always been able to navigate their way through an evolving marketplace. However, according to the Deloitte Insurance Outlook 2018, macroeconomic, social, and regulatory changes are likely to impact insurance companies. In the digital age, insurance companies are dealing with disruptive forces like climate change, the development of autonomous vehicles and the rising threat of cyber attacks. While these trends may seem troublesome, high-tech business intelligence tools can provide more clarity in an increasingly unpredictable world.

With stagnant growth across the industry, insurance companies are investing in new products and business models to gain an advantage in a highly competitive market. The financial goals of every insurance company remains the same – cut costs while improving productivity. These financial goals have become difficult to reach as 1-click digital service has increased consumer expectations. With this in mind, insurance companies are intent on adopting business intelligence and analytical tools that are designed to promote growth and efficiency.

How Can Business Intelligence and Analytics help the Insurance Industry?

Insurance companies have traditionally used CRM software to connect and maintain contact with their potential customers. Now, complicated service industries like healthcare and insurance are starting to see the benefits of using more powerful business intelligence and analytics platforms.

In an unpredictable world, the use of analytics and business intelligence tools can reduce risk and improve decision-making. In 2015, Bain and Company surveyed 70 insurers and found that annual spending on growth on Big Data analytics will reach 24% in life insurance and 27% in P&C (Property and Casualty) insurance. While this information demonstrates the rapid adoption of business intelligence tools, this survey also revealed that 1 in 3 life insurers and 1 in 5 P&C insurers do not use advanced analytics for any function of their business. This leaves an opportunity in the marketplace for insurance companies to utilize business intelligence tools to gain a competitive advantage.

BI allows insurers to gain better insights on their customers in order to create a better experience. These tools not only help companies paint a whole picture of their customers, but they also help strengthen client relationships, market share, and revenue. According to Mckinsey and Company, companies that use data analytics extensively are more than twice as likely to generate above average profits.

The Takeaway

Working in the insurance industry can be exciting and challenging. The individual sales process can be rewarding as the success of a sale is the responsibility of a single agent. Insurance agents are often fully occupied with meetings and phone calls. While insurance agents normally have access to basic demographic data, third party data vendors have become increasingly popular because of their capability to combine data sets and provide new insights that were previously unknown. Additionally, third party data has been a useful resource for insurance companies to understand the motivations of their prospects. By analyzing the social trends and life events of their prospects, insurance agents have the tools to make a stronger sales pitch.

At Synaptik, we pride ourselves on customer service. Our in-house data scientists are to happy to help you identify third party data sets that can be integrated into your current performance management system and put you ahead of the competition. According to the Everest Research Group, adoption of third party data analytics is expected to quadruple in size by 2020. In an increasingly volatile market, third party data will be critical to better planning, decision-making and customer satisfaction.

By Kiran Prakash

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New York Civic Tech Innovation Challenge – Finalist

The Neighborhood Health Project is a 360° urban tech solution that takes the pulse of struggling commercial corridors and helps local businesses keep pace with competition.

New York City’s prized brick-and-mortar businesses are struggling. With the rise of e-commerce, sky high rents and growing operational costs, the small businesses that give New York City Streets their distinctive character face mass extinction.

This year’s NYC Department of Small Business Services Neighborhood Challenge 5.0 paired nonprofit community organizations and tech companies to create and implement tools that address specific commercial district issues. On June 15th, community-based organizations from across the city from the Myrtle Avenue Brooklyn Partnership to the Staten Island Economic Development Corporation, presented tech solutions to promote local business and get a deeper understanding of the economic landscape.

The Wall Street Journal reports that “the Neighborhood Challenge Grant Competition is a bit like the Google Lunar XPrize. Except rather than top engineers competing to put robots on the moon, it has tiny neighborhood associations inventing new methods to improve business, from delivery service to generating foot traffic.”

Synaptik, the Manhattan Chamber of Commerce and the Chinatown BID were thrilled to have their Neighborhood Health Project chosen as a finalist in this year’s competition.

The Neighborhood Health Projects aims to preserve the personality of our commercial corridors and help our small businesses and community at large adapt to the demands of the 21st century economy. By optimizing data collection, simplifying business engagement and integrating predictive analytics, we can get a better understanding of the causes and effects of commercial vacancies, the impacts of past policies and events and create an open dialogue between businesses, communities and government agencies.

“With Synaptik, we can provide small businesses user-friendly tools and data insights that were previously reserved for industry heavy weights with in-house data scientists and large resource pools” said Liam Wright, CEO of Synaptik.

The Neighborhood Health Project Team was honored to have had the opportunity to share the stage with such innovative project teams. “It is great to see civic organizations take an innovative role in data intelligence to serve community constituents and local businesses. We came far in the process and hope to find alternative ways to bring this solution to New York City neighborhoods ” said Joe Sticca, Chief Operating Officer of Synaptik.

By Nina Robbins

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Big Data – The Hot Commodity on Wall Street

Imagine – The fluorescent stock ticker tape speeding through your company stats – a 20% increase in likes, 15% decrease in retail foot traffic and 600 retweets. In the new economy, net worth alone doesn’t determine the value of an individual or a business. Social sentiment, central bank communications, retail sentiment, technical factors, foot traffic and event based signals contribute to the atmospheric influence encasing you company’s revenue.

NASDAQ recently announced the launch of the “NASDAQ Analytics Hub” – a new platform that provides the buy side with investment signals that are derived from structured and unstructured data, and unique to Nasdaq. Big Data is the new oil and Wall Street is starting to transform our crude data material into a very valuable commodity.

What does this mean for the future of business intelligence?

It means that businesses that have been holding on to traditional analytics as the backbone of boardroom decisions must evolve. Nasdaq has pushed big data BI tech squarely into the mainstream. Now, it’s survival of the bittest.

An early majority of businesses have already jumped onto the Big Data bandwagon, but transformation hasn’t been easy. According to Thoughtworks, businesses are suffering from “transformation fatigue – the sinking feeling that the new change program presented by management will result in as little change as the one that failed in the previous fiscal year.” Many companies are in a vicious cycle of adopting a sexy new data analytics tool, investing an exorbitant amount of time in data prep, forcing employees to endure a cumbersome onboarding process, getting overwhelmed by the complexity of the tool, and finally, giving up and reverting to spreadsheets.


“There is a gap and struggle with business operations between spreadsheets, enterprise applications and traditional BI tools that leave people exhausted and overwhelmed, never mind the opportunities with incorporating alternative data to enhance your business intelligence processes.” – Joe Sticca COO TrueInteraction.com – Synaptik.co

Now, the challenge for data management platforms is to democratize data science and provide self-service capabilities to the masses. Luckily, data management platforms are hitting the mark. In April, Harvard Business Review published results of an ongoing survey of Fortune 1000 companies about their data investments since 2012, “and for the first time a near majority – 48.4% – report that their firms are achieving measurable results for their big data investments, with 80.7% of executives characterizing their big data investments as successful.”

As alternative data like foot traffic and social sentiment become entrenched in the valuation process, companies will have to keep pace with NASDAQ and other industry titans on insights, trends and forecasting. Synaptik is helping lead the charge on self-service data analytics. Management will no longer depend on IT teams to translate data into knowledge.

“Now, with the progression of cloud computing and easy to use data management interfaces with tools like Synaptik, your able to bring enterprise control of your data analytics processes and scale into new data science revenue opportunities.” – Joe Sticca COO TrueInteraction.com – Synaptik.co

Synaptik’s fully-managed infrastructure of tools makes big-data in the cloud is fast, auto-scalable, secure and on-demand when you need it. With auto-ingestion data-transfer agents, and web-based interfaces similar to spreadsheets you can parse and calculate new metadata to increase dimensionality and insights, using server-side computing, which is a challenge for user-side spreadsheet tools.

By Nina Robbins

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Digital Transformation Capability and the Modern Business Landscape

Yesterday morning, The Wall Street journal announced that Goldmann Sachs Group Inc. dropped out of R3CEV LLC blockchain group. R3 has been notable in its corralling of 70 different banks and financial firms to join their group since 2014, including Bank of America, J.P. Morgan and State Street. A spokesperson commented on the company’s departure:

Developing technology like this requires dedication and significant resources, and our diverse pool of members all have different capacities and capabilities which naturally change over time.

For the record, Goldmann Sachs will continue to invest in blockchain technology including the startups Circle and Digital Asset Holdings, but there is only speculation as to exactly why Goldmann Sachs’ membership with R3 expired. Certainly it may have been related to disagreement as to the equity distribution models between R3 and its members, but just a month earlier, when R3 announced their blockchain proof-of-concept prototype exercise, R3 CEO David Rutter commented:

Quality of data has become a crucial issue for financial institutions in today’s markets. Unfortunately, their middle and back offices rely on legacy systems and processes – often manual – to manage and repair unclear, inaccurate reference data.

The truth is, that there’s still quite a bit of latitude of digital capability across, within, and without businesses, big or small.

Getting the whole gang onboard

Perhaps Goldmann Sachs’ departure is due to exactly this: some aspect of their business units are behind the power curve in their digitization transformation and data management efforts.

Digital transformation can be a painstakingly complicated process, partially because, according to Computer Weekly, some parts of the transformation process aren’t even executed by the organization itself, yet still require all the vigilance their CIO and IT units can muster, being ultimately their responsibility:

Companies of all kinds are increasingly using technology partners, channel partners, contract manufacturers, warehousing and logistics partners, service partners and other outside services to handle all or part of a business process. Most enterprises come to view these partners as the extended enterprise, and look for ways to have tight integration and collaboration with them.

To achieve effective, successful transformation, digital business leaders must get their whole business ecosystem onboard with a clear, discernable, comprehensive strategic digital transformation plan that touches upon all of the extended enterprise. To act and assess digital transformation opportunity, McKinsey suggests 4 steps:

1. Estimate the value at stake. Companies need to get a clear handle on the digital-sales and cost-reduction opportunities available to them. Digital—and digitally influenced—sales potential should be assessed at the product level and checked against observed internal trends, as well as competitor performance. On the cost side, administrative and operational processes should be assessed for automation potential, and distribution should be rightsized to reflect digital-sales growth. The aggregate impact should be computed and turned into a granular set of digital targets to monitor progress and drive value capture.

2. Prioritize. Most organizations don’t have the ability, resources, or risk tolerance to execute on more than two or three big opportunities at any one time. Be selective. Figure out what areas are likely to deliver the greatest return on investment and the best customer outcomes and start there. While digital requires some experimentation, too many ad hoc demos and showcases lead to scattershot investments that fail to deliver sustained value. One retailer, for instance, ended up with 25 subscale digital offerings by not culling in the right places.

3. Take an end-to-end view. One financial-services firm built a world-class digital channel but failed to update the paper-based processes that supported it—processes that were prone to error. That false veneer of speed and efficiency eroded trust and turned off customers. The moral? Although it may seem counterintuitive, overinvestment in a slick front end that is not matched with the corresponding high-quality fulfillment that customers now expect may actually lead to increased customer frustration.

4. Align the business portfolio accordingly. In the long run, some lines of business will simply be destroyed by digital. Hanging on and tweaking them is futile. Companies need to act purposefully and divest where it makes sense, identifying what holdings are likely to be cannibalized or likely to underperform in the new environment and sloughing them off. Conversely, some areas will clearly need new capabilities and assets, which companies often do not have the luxury to build up organically over time. One retailer used targeted acquisitions to rapidly build out its e-commerce capabilities, allowing it to focus on defining strategy and aspirations rather than tinkering with the “plumbing.” Source.

Creating new monetizeable value

A recent report by Gartner revealed that often organizations are missing out on a bevy of monetizeable value due to overemphasizing traditional silos and markets (marketing, social media, mobile applications, etc.). A too-narrow focus means organizations are getting only a small share of the full value that digital transformation can provide. Saul Judah, research director at Gartner says,

All too often IT leaders focus value creation more narrowly, with the result that most digital initiatives are aimed at operational improvements, rather than value transformation. While this tactical approach to digital value can result in very real process and financial improvements, the greatest potential for digital value lies in more strategic initiatives, such as creating new markets, empowering employees, changing the basis of competition and crossing industry boundaries.

IT leaders need to work with the business side of the house to identify and exploit these high-value initiatives.

Algorithms and analytics offer accelerators of value and are themselves of exchangeable and monetizable value. An analytics process may use algorithms in its creation, which could also be monetizable through an algorithmic marketplace, making it available to enterprises of all types and sizes to use.

For example, True Interaction’s data agnostic machine learning analytics platform, SYNAPTIK, is rolling out a data marketplace where organizations can syndicate and distribute new data revenue opportunities and actions to their clients, as well as other platforms.

Digital transformation and the modern enterprise landscape

The Blockchain endgame?

Blockchain technology offers several benefits to an organization. The technology uses new methods of encryption which enables anonymous sharing of information in a data-rich environment. They are further characterised as Smart Contracts, computer protocols that facilitate, verify, or enforce the negotiation of contract cases or terms. And with blockchain, the dataset remains updated and intact at all times, without the need or use of a central governing authority.

Decentralised systems using blockchain technology can manage the data relationships and sequence of events where all parties share the same data source. Furthermore, with the impending intersection of the Internet of Things with blockchain technology, digitally tenacious organizations will soon be able to connect, conceivably, anything with anything, and get them to communicate intelligently and securely. Enterprises that embrace this phenomenon will be able to provide a better user experience and value-added services, as well as gain competitive advantage and differentiation.

Seeing the rumble between Goldmann Sachs and R3 shows us that we are still a ways off as far as describing the exact standards of blockchain in business. With certainty, some markets need to topple age-old paradigms of strategic thinking that are no longer relevant in a digital world. But the promise is quite exciting.

by Michael Davison

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Achieving Continuous Business Transformation Through Machine Learning

In the past, I’ve blogged about applying Agile methodology to businesses at large: what we are seeing in business today is that the concept of “business transformation” isn’t something that is undergone once or even periodically – business transformation is becoming a continuous process.

Today, businesses at large – not just their creative and development silos – benefit from operating in an Agile manner, most importantly in the area of responding to change over following a plan. Consider the words of Christa Carone, chief marketing officer for Xerox:

“Where we are right now as an enterprise, we would actually say there is no start and stop because the market is changing, evolving so rapidly. We always have to be aligning our business model with those realities in the marketplace.”

This is an interesting development, but how, technically, can businesses achieve True Interaction in a continually transforming world?

Business Transformation: Reliance upon BI and Analytics

In order for an organization’s resources to quickly and accurately make decisions regarding a product, opportunity or sales channel, they must rely upon historic and extrapolated data, provided by their organization’s company’s Data Warehouse/Business Analytics group.

In a 2016 report by ZS Associates that interviewed 448 senior executives and officials across a myriad of industries, 70% of the respondents replied that sales and marketing analytics is already “very important” or “extremely important” to their business’ competitive advantage. Furthermore the report reveals that in just 2 years time, 79% of respondents expect this to be the case.

However, some very interesting numbers reveal cracks in the foundation: Only 12% of the same respondents could confirm that their organization’s BI efforts are able to stay abreast of the continually changing industry landscape. And only 2% believe business transformation in their company has had any “broad, positive impact.”

5 Reasons for lack of BI impact within an organization

1) Poor Data Integration across the business

Many Legacy BI systems include a suite (or a jumbled mess) of siloed applications and databases: There might be an app for Production Control, MRP, Shipping, Logistics, Order Control, for example, with corresponding databases for Finance, Marketing, Sales, Accounting, Management Reporting, and Human Resources – in all, creating a Byzantine knot of data hookups and plugins that are unique, perform a singular or limited set of functions, and are labor intensive to install, scale and upgrade.

2) Data collaboration isn’t happening enough between BI and Business Executives

Executives generally don’t appear to have a firm grasp of the pulse of their BI: only 41% of ZS Associates’ report participants thought that a collaborative relationship between professionals working directly with data analytics and those responsible for business performance exists at their company.

3) Popular Big Data Solutions are still siloed

Consider Ed Wrazen’s critique of Hadoop: During an interview at Computing’s recent Big Data and Analytics Summit, the Vice-President of Product Management at data quality firm Trillium Software revealed:

“My feeling is that Hadoop is in danger of making things worse for data quality. It may become a silo of silos, with siloed information loading into another silo which doesn’t match the data that’s used elsewhere. And there’s a lot more of it to contend with as well. You cannot pull that data out to clean it as it would take far too long and you’d need the same amount of disk storage again to put it on. It’s not cost-effective or scalable.”

4) Data Integration is still hard to do.

Only 44% of ZS Associates’ report ranked their organizations as “good” or “very good” at data aggregation and integration. 39% said that data integration and preparation were the biggest challenges within the organization, while 47% listed this as one of the areas in their organization where its improvement would produce the most benefit.

5) Only part of the organization’s resources can access BI

Back in the day, BI used to be the sole province of data experts in IT or information analyst specialists. Now companies are seeing the benefits of democratizing the access and analysis of data across the organization. Today, a data analyst could be a product manager, a line-of-business executive, or a sales director. In her book Successful Business Intelligence, Cindi Howson, author and instructor for The Data Warehousing Institute (TDWI) famously remarked:

“To be successful with BI, you need to be thinking about deploying it to 100% of your employees as well as beyond organizational boundaries to customers and suppliers… The future of business intelligence centers on making BI relevant for everyone, not only for information workers and internal employees, but also beyond corporate boundaries, to extend the reach of BI to customers and suppliers.”

Business leaders should examine these symptoms in the context of their own organizations.

Is there a solution to these issues?

True Interaction CEO O. Liam Wright has a novel approach to a new kind of BI solution. One that involves machine learning.

“In my 20 years in the business I’ve discovered several worlds in business that never spoke to each other properly, due to siloed information spaces, communications, platforms and people. Today’s fluid world necessitates changing the BI game completely: If you want to have a high level of true interaction between your systems, platforms, customers, internal multiple hierarchical department structures, then you NEED to flip the game around. SQL-based Dashboards are old news; they are so 2001.

You can’t start with a structured, SQL based situation that inevitably will require a lot of change over time – organizations don’t have the IT staff to continually support this kind of situation – it’s too expensive.”

Instead Liam took a different approach from the beginning:

I thought, what if we capitalized on the sheer quantity and quality of data today, and captured data in unstructured (or structured) formats, and put this data into a datastore that doesn’t care what type of data it is. Then – as opposed to expensive rigid, SQL-based joins on data types, instead we implement lightweight “builds” on top of the data. These lightweight builds enable businesses to start creating software experiences off of their base dataset pretty quickly. They also enable organizations to get Business Intelligence right out of the box as soon as they perform a build – dynamic dashboards and data visualizations which can be come much more sophisticated over time, as you pull in and cross-pollinate more data. Then, when the data is further under control, you can create software agents that can assist you in daily processes of data, or agents that get those controls out the door.

What is this BI and Machine Learning marriage, exactly?

So what exactly is Liam describing? Modern Business has just crossed over the threshold into an exciting new space – organizations can routinely implement an integrated Machine Learning component that runs in the background, that can ingest data of all types from any number of people, places, and platforms, intelligently normalize and restructure it so it is useful, run a dynamic series of actions based upon data type and whatever specified situations contexts your business process is in, and create dynamic BI data visualizations out-of-the-box.

True Interaction’s machine learning solution is called SYNAPTIK.

SYNAPTIK involves 3 basic concepts:

DATA: SYNAPTIK can pull in data from anywhere. Its user-friendly agent development framework can automate most data aggregation and normalization processes. It can be unstructured data, from commerce, web, broadcast, mobile, or social media. It can be audio, CRM, apps, or images. It also can pull in structured data for example, from SAP, Salesforce, Google, communications channels, publications, Excel sheets, or macros.

AGENT: A software agent is a program that acts for a user or other program in a relationship of agency, to act on one’s behalf. Agents can be configured to not only distribute data intelligence in flexible ways but also directly integrate and take action in other internal and external applications for quicker transformation to enhance your business processes and goals.

An Agent is composed of two parts: The operator and the controls. Think of an operator as the classic Telephone Operator in the 1940s that manually plugged in and unplugged your calls in the background. SYNAPTIK enables you to see how the operator works:

Operators can be written in several forms, such as Javascript, PHP / cURL, or Python. Organizations can write their own operators, or True Interaction’s development team can write them for you. An Agent also gives the user a control interface – a form field, or drag and drop functionality, in order to add specific assets, or run any variety of functions. In addition SYNAPTIK makes it easy to connect to a REST API, enabling developers to write their own own software on top of it.

BUILD: A build simply brings the DATA & AGENT components together, ultimately to enable you to better understand your organizations various activities within your data space.

A new model of BI: What is the Return?

– Machine Learning Platforms like SYNAPTIK enables organizations to create wide and deep reporting, analytics and machine learning agents without being tied to expensive specific proprietary frameworks and templates, such as Tableau. SYNAPTIK allows for blending of internal and external data in order to produce new valuable insights. There’s no data modeling required to drop in 3rd party data sources, so it is even possible to create reporting and insight agents across data pools.

– With traditional methods, the process of data normalization requires hours and hours of time – indeed the bulk of time is spent on this today. This leaves very little time for deep analysis and no time for deep insight. With SYNAPTIK, what takes 400 monthly hours to data manage now takes 2 minutes, opening up 399.99 hours of analysis, discovery, and innovation time to deliver the results you need.

– Not only is it possible create your own custom reports/analytic agents, SYNAPTIK enables organizations to share their reporting agents for others to use and modify.

– The inherent flexibility of SYNAPTIK enables businesses to continually provide new data products/services to their customers.

– Not so far down the line: Establishment of The Synaptik Marketplace, where you can participate, monetize, and generate additional revenue by allowing others to subscribe to your Agents and/or Data.

All of these returns contribute to not only augmenting organizational leadership and innovation throughout its hierarchy, but also producing incredibly valuable intelligence monetization, break-thru revenue, as well as improved “client stickiness” with the rollout of new data products and services. And, best of all, it puts businesses into a flexible data environment that does, quite elegantly, enable continuous transformation as industries, markets, and data landscapes continue to change.

We’ve got the Experience you Need

True Interaction has logged 100,000’s of hours of research, design, development, and deployment of consumer products and enterprise solutions. Our services directly impact a variety of industries and departments through our deep experience in producing critical business platforms.

We Can Integrate Anything with… Anything
Per the endless variable demands of each of our global clients, TI has seen it all, and has had to do it all. From legacy systems to open source, we can determine the most optimal means to achieve operational perfection, devising and implementing the right tech stack to fit your business. We routinely pull together disparate data sources, fuse together disconnected silos, and do exactly what it takes for you to operate with tight tolerances, making your business engine hum. Have 100+ platforms? No problem. Give us a Call.

by Michael Davison

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Data, Automation & AI Featured

AI and the Classroom: Machine Learning in Education

Situation

For years schooling has been typified by its aspect of the physical grind on the part of both students and their teachers: teachers cull and prepare educational materials, manually grade students’ homework, and provide feedback to the students (and the students’ parents) on their learning progress. They may be burdened with an unmanageable number of students, or a wide gulf of varying student learning levels and capabilities in one classroom. Students, on the other hand, have generally been pushed through a “one-size-fits-all” gauntlet of learning, not personalized to their abilities, needs, or learning context. I’m always reminded by this quote by world-renowned education and creativity expert Sir Ken Robinson:

“Why is there this assumption that we should educate children simply according to how old they are? It’s almost as if the most important thing that children have in common is their date of manufacture.”

But as the contemporary classroom has become more and more digitized, we’ve seen recent advances in AI and machine learning that are closing in on being able to finally address historical “hand-wrought” challenges – by not only collecting and analyzing data that students generate (such as e-learning log files) when they interact with digital learning systems, but by pulling in large swaths of data from other areas including demographic data of students, educator demographic and performance data, admissions and registration info, human resources information, and so forth.

Quick Review: What is Machine Learning?

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look Machine learning works especially well prediction and estimation when the following are true:

-The inputs are well understood. (You have a pretty good idea of what is important but not how to combine them.)
-The output is well understood. (You know what you are trying to model.)
-Experience is available. (You have plenty of examples to train the data.)

The crucible of machine learning consists of capturing and maintaining a rich set of data, and bringing about the serendipitous state of knowledge discovery: the process of parsing through the deluge of Big Data, identifying meaningful patterns within it, and transforming it into a structured knowledge base for future use. As long as the data flows, its application is endless, and we already see it everywhere, from Facebook algorithms to self-driving cars. Today, let’s examine machine learning and its implementation in the field of Education.

Application of Machine Learning in Education

Prediction

A few years ago, Sotiris Kotsiantis, mathematics professor at the University of Patras, Greece presented a novel case study describing the emerging field of educational data mining, where he explored using students’ key demographic characteristic data and grading data in a small number of written assignments as the data set for a machine learning regression method that can be used to predict a student’s future performance.

In a similar vein, GovHack, Australia’s largest open government and open data hackathon included several projects in the education space, including a project that aims to develop a prediction model that can be used by educators, schools, and policy makers to predict the risk of a student to drop out of school.

Springboarding from these two examples, IBM’s Chalapathy Neti recently shared IBM’s vision of Smart Classrooms: cloud-based learning systems that can help teachers identify students who are most at risk of dropping out, why they are struggling, as well as provide insight into the interventions needed to overcome their learning challenges:

The system could also couple a student’s goals and interests with data on their learning styles so that teachers can determine what type of content to give the student, and the best way to present it. Imagine an eighth grader who dreams of working in finance but struggles with quadratic and linear equations. The teacher would use this cognitive system to find out the students learning style and develop a plan that addresses their knowledge gaps.

Process efficiency: Scheduling, grading, organization

Elsewhere, several Machine Learning for Education ICML (international machine learning conference) workshops have explored novel machine learning applications designed to benefit the education community, such as:

-Learning analytics that build statistical models of student knowledge to provide computerized and personalized feedback on learning the students’ progress and their instructors
-Content analytics that organize and optimize content items like assessments, textbook sections, lecture videos, etc.
-Scheduling algorithms that search for an optimal and adapted teaching policy that helps students learn more efficiently
-Grading systems that assess and score student responses to assessments and computer assignments at large scale, either automatically or via peer grading
-Cognitive psychology, where data mining is becoming a powerful tool to validate the theories developed in cognitive science and facilitate the development of new theories to improve the learning process and knowledge retention
-Active learning and experimental design, which adaptively select assessments and other learning resources for each student individually to enhance learning efficiency

Existing Platforms

Recently, digital education venture capitalist Tom Vander Ark shared 8 different areas where leading-edge platforms are already leveraging machine learning in education:

1. Content analytics that organize and optimize content modules:
a. Gooru , IBM Watson Content Analytics

2. Learning analytics that track student knowledge and recommend next steps:
a. Adaptive learning systems: DreamBox, ALEKS, Reasoning Mind, Knewton
b. Game-based learning: ST Math, Mangahigh

3. Dynamic scheduling matches students that need help with teachers that have time:
a. NewClassrooms uses learning analytics to schedule personalized math learning experiences.

Grading systems that assess and score student responses to assessments and computer assignments at large scale, either automatically or via peer grading:
a. Pearson’s WriteToLearn and Turnitin’s Lightside can score essays and detect plagiarism.

5. Process intelligence tools analyze large amounts of structured and unstructured data, visualize workflows and identifying new opportunities:
a. BrightBytes Clarity reviews research and best practices, creates evidence-based frameworks, and provides a strength gap analysis.
b. Enterprise Resource Planning (ERP) systems like Jenzabar and IBM SPSS helps HigherEd institutions predict enrollment, improve financial aid, boost retention, and enhancing campus security.

6. Matching teachers and schools:
a. MyEdMatch and TeacherMatch are eHarmony for schools.

7. Predictive analytics and data mining to learn from expertise to:
a. Map patterns of expert teachers
b. Improve learning, retention, and application.

8. Lots of back office stuff:
a. EDULOG does school bus scheduling
b. Evolution , DietMaster.

Reflection

As the modern classroom becomes more and more digitized, we are able to gather myriad sets of data. The trick is, of course, being able to purpose it. The prize at heart of machine learning is knowledge discovery, the process of parsing through the deluge of Big Data, identifying meaningful patterns within it, and transforming it into a structured knowledge base for future use. In this article, we’ve seen examples utilizing machine learning in the education sector for prediction, scheduling, grading, and organization. We’ve also listed existing education-related platforms that use a machine learning component.

What does it mean to me?

Big Data have swept into every industry and business function and are now an important factor in production, alongside labor and capital. In a decision making system, the bigger the data, the higher the likelihood is of making good decisions. The time is now for organizations, in education or otherwise, to research how a cost-efficient machine learning component can transform your operational output. For more information, Check out this detailed guide by Jesse Miller on the amazing benefits of technology in the classroom and suggestions on ways to incorporate technology in the classroom.

“Parents are continually exposed to new technology via their children. Whether it be iPad App usage tricks, to the advent of robotics competitions, and perhaps now “new ways of thinking” as a result of interaction with Machine Learning based educational environments. Siloed educational content may give way to a topology of learning experiences.” O. Liam Wright – CEO, True Interaction

True Interaction produces custom full-stack end-to-end technology solutions across web, desktop and mobile, integrating multiple data sources to create a customized data solution. True Interaction can determine the most optimal means to achieve operational perfection, devising and implementing the right tech stack to fit the specific school and or district need. True Interaction pulls together disparate data sources, fuses together disconnected silos, and does exactly what it takes for school data systems to operate with high levels of efficiency and efficacy, ultimately leading to improved student achievement outcomes.

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The Changing Terrain of Media in the Digital Space

The rapid digitization of the media industry does not merely address the immediate needs posed by the market, but also anticipates the constantly changing consumer behavior and rising expectations of an increasingly digital customer. The World Economic Forum points to a growing middle class, urbanization, the advent of tech savvy millennials demanding instantaneous access to content on a variety of platforms, and an aging world population that is invariably accompanied by the need for services designed for an older audience as the most pronounced demographic factors that are currently contributing to the reshaping of the media landscape. The expanding list of accommodations that customers are coming to expect from the media industry more or less fall within the realms of the accessibility and personalization of content.

The Path to Digital Transformation

Average weekday newspaper circulation has been on a steady decline, falling another 7% in 2015 according to the most recent Pew Research Center report. This inevitable dwindling of interest in print publications could be ascribed to the rising demand for media companies to adopt a multi-channel strategy that enables the audience to access content across different platforms. Companies remedy their absence of a formidable digital presence in a variety of ways. One of the most common resolutions that companies have resorted to involve redesigning their business model by bundling print subscriptions with mobile device access, a measure enacted to address the 78% of consumers who view news content on mobile browsers. A more radical approach could be opting for a complete digital transformation, a decision reached by The Independent earlier this year when it became the “first national newspaper title to move to a digital-only future.” The appeal of having information become readily available on any screen of the customer’s choosing is magnified by the expectation of uniformity and equally accessible and engaging user interfaces across all devices. Of course, convenience to the customer does not only rely on their ability to access content on the platform of their choice, but also at any point they desire, hence the focus on establishing quick response times and flexibility of content availability.

Another expectation that consumers have come to harbor aside from unhindered access to content: the minimization, if not the complete elimination of superfluous information. According to the 2016 Digital News Report by the Reuters Institute, news organizations, such as the BBC and the New York Times, are striving to provide more personalized news on their websites and applications. In some cases, people are offered information and clips on topics in which they have indicated an interest. Additionally, companies are also employing a means of developing “auto-generated recommendations based in part on the content they have used in the past.” Transcending written material, streaming platforms like Pandora and Netflix utilize Big Data in order to analyze and discern the characteristics and qualities of an individual’s preferences, thus feeding information into a database that then determines content using predictive analytics that the individual would be predisposed to enjoying. In previous blog posts, we have divulged the value of understanding Big Data, emphasizing how execution based on the insight gleaned from Big Data could be as crucial to a company’s profitability as the insight itself. As evidenced by this growing practice of collecting consumer data in order to cultivate personalized content for consumers, it is obvious that the media industry has not been remiss in its observation of the discernible success that data-driven companies boast relative to competitors that are not as reliant on data. Finally, perhaps as equally satisfying as being able to browse through personalized, recommended content based on one’s past likes and preferences is the exclusion of repetitive content, as informed by one’s viewing history.

Media companies embrace their ascent into digital space in a plethora of ways. Some elect for a complete digital transformation, conducting a substantial part if not all of their business within browsers and applications rather than in print. There are also those that focus on enhancing the customer experience by maintaining contact with consumers through all touch points and following them from device to device, all the while gathering data to be used in optimizing the content provided. Another means through which media companies are realizing their full digital potential is through the digitizing of their processes and operations. These businesses are initiating a shift towards digital products; a decision that is both cost-effective (cutting costs up to 90% on information-intensive processes) and can bolster the efficacy of one’s data mining efforts. Warner Bros was one of the first in the industry to transform the ways of storing and sharing content into a singled, totally integrated digital operation that began with Media Asset Retrieval System (MARS). This innovative digital asset management system ushered in a transformation that effectively lowered Warner Bros’ distribution and management costs by 85%.

A Glimpse into the Future

So what’s next in this journey to digital conversion? According to the International News Media Association (INMA), all roads lead to the Internet of Things (IoT). By 2018, the Business Insider Intelligence asserts that more than 18 billion devices will be connected to the Web. The progression into this new era of tech where information can be harvested from the physical world itself will not go unobserved by the media industry. Media companies are tasked with having to evolve beyond the screen.

Mitch Joel, President of Mirium Agency, writes:

“Transient media moments does not equal a strong and profound place to deliver an advertising message… the past century may have been about maximizing space and repetition to drive brand awareness, but the next half century could well be about advertising taking on a smaller position in the expanding marketing sphere as brands create loyalty not through impressions but by creating tools, applications, physical devices, true utility, and more robust loyalty extensions that makes them more valuable in a consumer’s life.”

Big Data anchors the efforts into the Digital Age and the IoT will provide new, vital networks of information to fortify this crusade.
Contact our team to learn more about how True Interaction can develop game-changing platforms that cut waste and redundancy as well as boost margins for your media company.

By Justin Barbaro