Tag: data management

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So, what exactly is a DMP?

EDITOR’S NOTE: This article is about how data management platforms (DMPs) can assist decision-makers in organizing their data in ways leading to strategic insights. True Interaction built SYNAPTIK, our Data Management, Analytics, and Data Science Simulation DMP, specifically to make it easy for leaders to collect and manage data to get to insights faster. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design.

A data management platform, or DMP, imports, stores and compiles customer or target audience data from various sources and makes it actionable. It can ingest, sanitize, sort and format data. Most importantly, it can analyze and segment this data and present it in a visual format that is easily understood and made use of by executive decision makers.

In the AdTech/Martech world, there’s a common misconception that DMPs are somehow exclusive to the digital advertising ecosystem where DMPs produce audience segments that are syndicated to external ad targeting and content delivery platforms and compared across channels. The reality is that everyone from toy stores to hedge funds and even government agencies are employing DMPs for internal data management. Finance firms may use a DMP for data forensics. Retail giants are increasingly employing DMPs as 1:1 data engines that personalize the e-commerce experience with recommendation engines and displays based upon rich user profiles. Enterprise organizations and SMBs alike utilize DMPs for non-advertising/marketing tasks such as aggregation of scraped and purchased data sets, business intelligence, product management and inventory. In fact, DMPs utilizing AI have been replacing traditional supply chain management departments at a rapid pace in 2017.

Both B2B and B2C organizations leverage a DMP to understand customer audiences based upon conversion, engagement and purchase rates and to target audiences with personalized and therefore more effective messaging. A typical B2B use case involves the matching and correlation of 1st party data with 3rd party data for look alike modeling which provides channel clarity and really enables business to build profiles at a company level that groups together individuals associated with or employed by organizations that are sales targets.

The first thing an organization that is considering purchasing a DMP should do is establish what the process and flow will look like for the intake of data from multiple sources stored in various locations. Run a test with a month’s worth of data. See what kind of issues you encounter getting your data ingested and normalized. Concurrent to establishing a process for input, you want to understand your own business goals and ultimately identify what is of value to your team’s mission. This could be new registrations and cohort starts and the VPA Value Per Acquisition or CLV Customer Lifetime Value. An organization could simply want to understand what the impact of their newly refurbished, responsive website is on their e-commerce and what platform, device or browser are their most avid customers using. Prioritize the top 4 or 5 data points and make sure these are stood up as part of the initial integration.

In the digital advertising industry, it can be a challenge to differentiate between a DMP and a demand-side platform (DSP) as the lines are continually blurred. Some DSPs have begun to offer DMP functionality to inform their ad purchasing and to avoid lag and integration problems that typically result from today’s fragmented martech/adtech stack. Some have morphed into SSPs that integrate with multiple DMPs while simultaneously offering their own DMP service. Bottom line though: DMPs store and process data, sort it and provide context and insight. Beyond that, they don’t function as an exchange or DSP. A DMP does not coordinate programmatic ad campaigns for you.

A DMP can be used to map different SourceIDs and cookie IDs across the ecosystem. This is major problem that needs resolution in that an industry standard does not exist. So, you have Ad networks, mobile exchanges, middle man measurement tools, data management platforms, fraud vendors, SSPs, agency trading desks and DSPs all using various IDs to track transactions. Attribution can get quite complicated.

A good DMP can cleanse and process structured and unstructured data alike and generate visual analytics for the data from multiple departments, programs and campaigns. Ideally, the data becomes actionable and decisions become validated, justified and quantified by the insights produced. Data is compartmentalized and segments may be produced. As we approach 2018, I can’t imagine recommending a DMP that is not 100% cloud based, as it needs to scale. Similarly, it should possess an intelligent layer of machine learning. A good DMP offers their users the option of either API stream or S3 data bucket upload, whichever is preferred by the customer.

Clearly the point is to manage one’s data but also to merge it and make sense of it. Ultimately, a DMP should enable the monetization of an organization’s data. A good DMP will create one holistic view of all data within an organization. Synaptik is a DMP that is flexible enough to address you strategic data needs across a number of organizational functions: Finance, Analytics, IT, Marketing, Operations and Customer Relation Management, among others. Synaptik’s advanced intelligent layer can even draw correlations between the different data. While most businesses are overwhelmed by the sheer volume of data that they are failing to leverage, others may be intimidated by the thought of purchasing a DMP because they don’t think they have the capacity, or the technical DNA in house to take this kind of thing on. Well, a DMP is supposed to minimize both labor and angst and should come with frictionless on-boarding and attentive support for rule mapping and customization. The DMP staff should be falling over themselves to meet your terms. Pointing customers to a rabbit hole of self help technical article links and leaving it up to the customer to figure out how best to get things up and running is not acceptable. The DMP you select should be intuitive enough for you to figure out how to configure it on your own once it’s been deployed. Lastly, a good DMP should feel be agnostic and customizable.

At True Interaction, we pride ourselves in our Digital Transformation Services along with our Data Intelligence acumen. Please schedule a time to have a discovery conversation today.

by David Sheihan Hunter Lindez

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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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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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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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Securing The Future Of ROI With Simulation Decision Support

EDITOR’S NOTE: This article is about how to approach and think about Decision Simulation. True Interaction built SYNAPTIK, our Data Management, Analytics, and Data Science Simulation Platform, specifically to make it easy to collect and manage core and alternative data for more meaningful data discovery. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design

EXCERPT

Simulation is simply the idea of imitating human or other environmental behaviors to test possible outcomes. It is obvious a business will want to take advantage of such Simulation technologies in order to maximize profits, reduce risks and/or reduce costs.

Simulation decision support is the backbone of many cutting edge companies these days. Such simulations are used to predict financial climates, marketing trends, purchasing behavior and other tidbits using historical and current market and environmental data.

Managing ROI

Data management is a daunting task that is not to be trusted in the hands of lose and unruly processes and technology platforms. Maximizing profit and/or reducing risks using simulated information will not be an automatic process but rather a managed task. Your business resources should be leveraged for each project needing long term ROI planning; computer simulations are just some pieces to the overall puzzle. Simulation decision support companies and platforms are not exactly a dime a dozen but should still be evaluated thoroughly before engaging.

Scaling Your Business

Modern software platforms exist to assist in the linear growth of your business initiatives. Algorithms have been produced thanks to years of market data and simulations in order to give a clear picture to your expectations and theories. Machine learning has also been rapidly improving over that past several years, making market simulations even more accurate when it comes to short and long-term growth. There is no lack of Algorithms or libraries of Data science modules, it is the ability to easily scale your core and alternative data sets into and easy to use platform that is configured to your business environment. Over the last several years these Data Science platforms, such as Synaptik.co, has allowed companies with limited resources to scale their operations to take advantage of decisions simulation processes that were once too expensive and required specialized, separate resources to manage.

Non-tech Based Departments Can No Longer Hide

All branches of companies are now so immersed in software and data that it is difficult to distinguish the IT and non-IT departments. Employees will plug away at their company designated computing resources in order to keep records for the greater good of the corporation. These various data pools and processes are rich in opportunities to enable accurate business simulations. In turn, simulation findings can be shared with different departments and partners to enrich a collaborative environment to amplify further knowledge for a greater propensity for success. It is no joking matter that big or small companies will need simulation decision support processes to ensure they not only stay competitive but excel in their growth initiatives.

Data and Knowledge Never Sleeps

In 2016, the Domo research group produced data visualizing the extent of data outputted by the world. By 2020, the group predicts that we will have a data capacity of over 44 trillion gigabytes. This overwhelming amount of data available to the average human has companies on their toes in order to grasp the wild change in our modern world. The data produced is neutral to the truth, meaning accurate and inaccurate ideas are influencing the minds of your customers, partners and stakeholders. Scaling profits and reducing risk will become an increasingly involved activity, which gives us another reason to embark on Decision Simulation processes to deal with the overwhelming amount of data and decisions needed in this fluid data rich world.

EDITOR’S NOTE: This article is about how to approach and think about Decision Simulation. True Interaction built SYNAPTIK, our Data Management, Analytics, and Data Science Simulation Platform, specifically to make it easy to collect and manage core and alternative data for more meaningful data discovery. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design

By Joe Sticca

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Shocking? Predictive Analytics Might Be Right For Your Future

EDITOR’S NOTE: This article is about how to approach and think about Predictive Analytics. True Interaction built SYNAPTIK, our Data Management, Analytics, and Data Science Simulation Platform, specifically to make it easy to collect and manage core and alternative data for more meaningful data discovery. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design

EXCERPT

“What is marketing?” Isn’t it the attempt to sell products and services to people who are most likely to buy them? Would you be shocked to learn that Predictive Analytics is useful for completing sales? We have a tendency to think of our processes/departments and data in silo-ed terms. Though, with today’s platforms it is critical to harness insights across silos as well as bring in “alternative data”.

How is your Data Management? Can your sales and marketing staff use your data sets to up-sell products or services?” Data management is the biggest barrier as well as the biggest opportunity to surpassing internal KPIs.

Know Your Customer.

“Have you ever heard of someone lamenting about things they should have done as youth to be successful adults?” They might read a good book and suggest “they could have written that themselves.” They think that the path to success is “obvious.” Simply know everything about your customer and provide him or her with valuable products or services. That is the secret to success. “But how do you get to know your customer?” The answer is Data Management and Predictive Analytics.

What Do You Know?

Customer Relationship Management (CRM) software has become very popular because it allows you to accumulate, manage and act upon client data. This can be an automatic data management system. You could even review past buying habits and automatically send an email for a hot new product, which might be appealing. Up Selling can increase your profits per customer. CRM is Business Analytics – giving you a deeper understanding of who your customer is, what he wants and where he is going. “Why do you think so many websites want to place cookies on your computer?” They want to track your behavior and anticipate your next buying action.

When Did You Know It?

“If you don’t know what your customer bought yesterday, how will you know what they will buy tomorrow?” The most agile business understands their customer in real-time. The Twitter world is about immediate gratification. People want to say “Hi,” see your pictures and plan your future together within the first 3 seconds, you meet. The profitable business knows the answers before the customer asks them. Predictive Analytics might be right for your future because it gives you the power to anticipate consumer buying trends and or behaviors across channels (Social, video, mobile, etc.). Your competitor might already be using these Business Analytics; you might be leaving “money on the table.” Sign up for a discussion, demo or strategy session today Hello@TrueInteraction.com.

EDITOR’S NOTE:This article is about how to approach and think about Predictive Analytics. True Interaction built SYNAPTIK, our Data Management, Analytics, and Data Science Simulation Platform, specifically to make it easy to collect and manage core and alternative data for more meaningful data discovery. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design

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How Alternative Data Can Transform Your Business Intelligence

EDITOR’S NOTE: This article is about harnessing new sources of Alternative Data. True Interaction built SYNAPTIK, our Data Management, Analytics, and Machine Learning Platform, specifically to make it easy to collect and manage core and alternative data/media types for more meaningful data discovery. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design

Big data has been commonly described over the last few years through properties known as the “3 V’s”: Volume, Velocity, and Variety. If you are a human being just about anywhere in the world today, it’s patently obvious to you that these three dimensions are increasing at an exponential rate.

We’ve seen the staggering statistics with regards to Volume and Velocity reported and discussed everywhere:

Big Volume
IDC reported that the data we collectively create and copy globally is doubling in size every two years. Calculated at 4.4 zettabytes in 2014, the organization estimates global data will reach 44 zettabytes — that’s 44 trillion gigabytes — by 2020.
Cisco forecasts that overall mobile data traffic is expected to grow to 49 exabytes per month by 2021, a seven-fold increase over 2016. Mobile data traffic will grow at a compound annual growth rate (CAGR) of 47 percent from 2016 to 2021.

Big Velocity

Facebook’s 1.97 billion monthly active users send an average of 31.25 million messages and view 2.77 million videos every minute.

Twitter’s 308 million monthly active users send, on average, around 6,000 tweets every second. This corresponds to over 350,000 tweets sent per minute, 500 million tweets per day and around 200 billion tweets per year.

Big Variety = Alternative, Non-traditional, Orthogonal Data

These well-touted figures often leave one feeling aghast, small, and perhaps powerless. Don’t worry, the feeling is mutual! So today, let’s get ourselves right-sized again, and shift our focus to the 3rd dimension — of big data, that is — and examine a growing, more optimistic, and actionable business trend concerning big data that is materializing in organizations and businesses of all kinds, across just about any industry that you can imagine, without regard for business size or scope. Let’s examine the explosion of big data Variety, specifically with regards to harnessing new and emerging varieties of data to further inform reporting, forecasting, and the provision of actionable BI insights.

In a pattern similar to online retail’s “Long Tail” — the emphasis of niche products to consumers providing that emerged in the 2000’s — more and more future-leaning businesses are incorporating outside, alternate “niches” of data that differ from the traditional BI data sources that standard BI dashboards have commonly provided.

In a recent interview in CIO, Krishna Nathan, CIO of S&P Global explained that “Some companies are starting to collect data known as alternative, non-traditional or orthogonal.” Nathan further describes Alternative Data as the various data “that draw from non-traditional data sources, so that when you apply analytics to the data, they yield additional insights that complement the information you receive from traditional sources.” Because of the increasing prevalence of data from mobile devices, satellites, IoT sensors and applications, huge quantities of structured, semi-structured and unstructured data have the potential to be mined for information and potentially help people make better data-driven decisions. “While it is still early days for this new kind of data”, Nathan says, “CIOs should start to become familiar with the technologies now. Soon enough, alternative data will be table stakes.”

In The Field

Let’s examine the various applications of these new data sources that are manifesting themselves in parallel with the burgeoning technological advancements in our world today.

VC and Credit

Alternative data is increasingly wielded by VC firms as well as the credit industry to lend insight into backing startups, businesses, and technologies. Many small businesses, especially those with a limited credit history, have difficulty demonstrating creditworthiness and may be deemed as high risk when viewed through the lens of traditional data sources.

However, Experian recently described the growing number number of online marketplace lenders, or nonbank lenders, that have already begun taking a nontraditional approach by leveraging a wealth of alternative data sources, such as social media, Web traffic, or app downloads to help fill the void that a business with limited credit history might have. By combining both traditional and nontraditional data sets, these lenders are able to help small businesses access financial resources, while expanding their own portfolios.

Health

Patient information continues to be collected through traditional public health data sources, including hospital administration departments, health surveys and clinical trials. Data analysis of these sources is slow, costly, limited by responder bias, and fragmented.

However, According to MaRS DD, a research and science-based entrepreneurial venture firm, with the growing use of personal health applications among the public, self-reported information on prescription drug consumption and nutritional intake can be analyzed and leveraged to gain insight into patient compliance and use patterns, as well as into chronic disease management aptitude in between visits to frontline healthcare practitioners. In addition, social media platforms can be used as both monitoring tools and non-traditional methods of increasing patient engagement, as they allow healthcare professionals to interact with populations that under-utilize services. Healthcare organizations can mine social media for specific keywords to focus and project initiatives that track the spread of influenza, zika, or opioid addiction, for example, or even to provide real-time intervention.

Retail, Dining, Hospitality and Events

Several different kinds of data sources can give these industries a bigger picture and aid in both more granular reporting, but also more accurate forecasting. For example, Foursquare famously predicted that Chipotle same-store sales would fall 29 percent after the Mexican chain was hit with E. coli outbreaks, based upon check-ins on their application. The actual decline announced by Chipotle ended up being a spot-on 30 percent. It’s no coincidence that Foursquare recently announced Foursquare Analytics, a foot traffic dashboard for brands and retailers.

In addition, by making use of CCTV or drone imagery, incredible insight can be garnered from examining in-store foot traffic or the density of vehicles in a retailer’s parking lot over time. Today, a combination of Wi-Fi hotspots and CCTV cameras can compile numbers about in-store customer traffic patterns in the same way that online stores collect visitor and click information. For example, by using a modern CCTV system to count the number of people in each part of the store, heatmap analytics can visualize “hot zones” — to help maximize in-store promotional campaigns, and identify “cold zones” to determine how store layout changes can improve customer traffic flow.

Don’t forget the weather! By leveraging a real-time weather data analytics system in order to process historical, current, and forecasted weather data, retailers can predict how shifting demands will affect inventory, merchandising, marketing, staffing, logistics, and more.

Wall Street

You can bet your life that investment firms are early adopters of alternative data sources such as in the Chipotle-Foursquare story mentioned earlier. Consider the incredible resource that satellite imagery is becoming — it’s not just for government intelligence anymore: Because satellite imagery now enables organizations to count cars in retailers’ parking lots, it is possible to estimate quarterly earnings ahead of a business’ quarterly reports. Data analysts can use simple trigonometry to measure the shadows cast by floating oil tank lids in order to gauge the world’s oil supply. By monitoring vehicles coming and going from industrial facilities in China, it’s even possible to create a nascent China manufacturing index. Infrared sensors combined with satellite images can detect crop health far ahead of the USDA. All of this makes a boon for traders and investors.

What About Your Organization?

No matter the size of your business, now is the time to consider upgrading your 2000’s-era BI Dashboards to incorporate alternative data sources — remember, the convergence of IoT, cloud, and big data are creating new opportunities for analytics all the time. Data is expected to double every two years, for the next decade. Furthermore, it is essential to integrate all of these data opportunities with traditional data sources in order to create a full spectrum of analytics, and drive more intelligent, more actionable insights.

The Right Analytics Platform

Legacy data management systems that have not optimized their operations will not be able to process these new and disparate sources of alternative data to produce relevant information in a timely manner. The lack of machine learning mechanisms within these sub-optimal systems will hinder businesses in their knowledge discovery process, barring organizations from making data-driven decisions in real time.

According to Joe Sticca, Senior Executive of Digital Transformation & Data Science for True Interaction, “The most deleterious disadvantage of failing to address these pressing issues… is the careless neglect of invaluable business insight that is concealed in the mass of available data. Now, more than ever, businesses of all size need the ability to do great data discovery, but without necessitating a deep core technical development and data analyst skillset to do so.”

One solution path? Cutting-edge fully-managed data and machine learning platforms like Synaptik, that make it easy to connect with dozens of both structured and unstructured data services and sources, in order to gain the power of algorithms, statistical analysis, predictive modeling and machine learning, for a multitude of purposes, and metrics such as brand sentiment, campaign effectiveness and customer experience. Synaptik helps businesses transform via an adaptive, intuitive and accessible platform – using a modern mix of lightweight frameworks, scalable cloud services, and effective data management and research tools. More importantly, it works with non-IT skill sets to propagate better pattern recognition across your organization’s people and divisions.

(infographic by Quandl.com)

by Michael Davison

Categories
Insights

As Online Video Matures, New Data Challenges Emerge

As 2017 drives on, we’ve seen the continued evolution of digital media, mostly surrounding video, especially with regards to live streaming and mobile. It’s paramount for any organization, regardless of size, to be aware of these trends on order to best take action and capitalize on them.

Mobile, OTT, Live

More and more video is being produced for and consumed on mobile. The weekly share of time spent watching TV and video on mobile devices has grown by 85% since 2010. Mobile will account for 72% of US digital ad spend by 2019. Traditional plugged-in cable TV continues to decline, as audiences demand to consume their media, wherever and whenever they want.

Over-the-top content (OTT) is audio, video, and other media content delivered over the Internet without the involvement of a multiple-system operator (MSO) in the control or distribution of the content – think Netflix and Hulu over your traditionally HBO cable subscription. It’s becoming an increasingly important segment of the video viewing population, and the rising popularity of multiple OTT services beyond Netflix only suggests that the market is poised for more growth. According to comScore, 53% of Wi-Fi households in the U.S. are now using at least one over-the-top streaming service, with Netflix being the primary choice.

Meanwhile, the Live streaming market continues to explode, expected to grow to $70.05B by 2021, from $30.29B in 2016. Breaking news makes up 56% of most-watched live content, with conferences and speakers tied with concerts and festivals in second place at 43%.

The usual giants are leading the charge with regards to propagating and capitalizing on live streaming; in June of 2016, it was reported that Facebook had paid 140 media companies a combined $50m to create videos for Facebook Live. Industry influencers predict that we will see major brands partner with live broadcasting talent to personalize their stories, as well as innovate regarding monetization with regards to live video. We might even see the resurgence of live advertising, according to food blogger Leslie Nance in a report by Livestream Universe. “I think we will be seeing more of live commercial spots in broadcast. Think Lucy, Vita Vita Vegimen. We will come full circle in the way we are exposed to brands and their messages.”

However, one of the greatest advantages of live streaming is its simplicity and affordability – even small business owners can – and should – leverage its benefit. Says Content Strategist Rebekah Radice,

“Live streaming has created a monumental shift in how we communicate. It took conversations from static to live, one-dimensional to multi-faceted. I predict two things. Companies that (1) haven’t established relationships with their social media audience (invested in their community – optimized the experience) and (2) don’t extend that conversation through live streaming video (created an interactive and open communication channel) will lose massive momentum in 2017.“

The Social Media Connection

Social Media is used especially in concert with live video. Because live streaming propagates a feeling of connectedness – very similar to the eruptions of activity on Twitter during unfolding current events – live streaming also inspires more simultaneous activity, especially with regards to communication. Consumers conduct more email, texting, social media use and search while streaming live video than on-demand or traditional TV viewing.
At the beginning of 2016, Nielsen introduced Social Content Ratings, the most comprehensive measure of program-related social media activity across both Facebook and Twitter to account and capture this trend. “With social media playing an increasing role in consumers’ lives and TV experiences, its value for the media industry continues to mature,” said Sean Casey, President, Nielsen Social, in a press release for the company.
By measuring program-related conversation across social networking services, TV networks and streaming content providers can better determine the efficacy of social audience engagement strategies, as well as bring more clarity to the relationship between social activity and user behaviors while watching.

Nielsen says that the ratings system will support agencies and advertisers in making data-driven media planning and buying decisions as they seek to maximize social buzz generated through ad placements, sponsorships, and integrations.

Deeper Analytics, More Challenges

Besides Nielsen’s new Social Content Ratings, we are already seeing major tech platforms like Google and Facebook roll new analytics features that allow users to filter audiences by demographics like age, region, and gender. In the near future, these analytics will become even more complex. Certainly, more sophisticated forms of measuring user engagement will enable advertisers to learn more about how users respond to messaging, with a benefit of building campaigns more cost efficiently, provided they have the ability to see, compare, and take action on their disparate data. One of the main challenges being discussed that faces the market today is the effective integration of digital data with traditional data sources to create new and relevant insights.

There is a deluge of data that is generated through non-traditional channels for media and broadcasting industry such as online and social media. Given the volumes, it is impossible to process this data unless advanced analytics are employed. ~Inteliment

The Proper Data Solution

As we become more accustomed to this “live 24/7” paradigm, the onus is on organizations to ensure that they are properly deriving actionable data from this increasing myriad of sources, so that they may better:

-Assess audience participation and engagement
-Measure the efficacy of media content
-Predict and determine user behaviors
-Plan advertising budget

According to Joe Sticca, Senior Executive of Digital Transformation & Data Science for True Interaction, “…the most deleterious disadvantage of failing to address these pressing issues… is the careless neglect of invaluable business insight that is concealed in the mass of available data.”

Thus, data management systems that have not optimized their operations will not be able to process data to produce relevant information in a timely manner. The lack of machine learning mechanisms within these sub-optimal systems will hinder businesses in their knowledge discovery process, barring organizations from making data-driven decisions in real time. Mr. Sticca concludes that “…now, more than ever, businesses of all size need the ability to do great data discovery, but without necessitating a deep core technical development and data analyst skillset to do so.”

One solution path? Cutting-edge fully-managed data and machine learning platforms like Synaptik, that make it easy to connect with dozens of both structured and unstructured data services and sources, in order to gain the power of algorithms, statistical analysis, predictive modeling and machine learning, for a multitude of purposes, and metrics such as brand sentiment, campaign effectiveness and customer experience. Synaptik helps businesses transform via an adaptive, intuitive and accessible platform – using a modern mix of lightweight frameworks, scalable cloud services, and effective data management and research tools. More importantly, it works with non-IT skillsets to propagate better pattern recognition across your organization’s people and divisions.

By Michael Davison

Categories
Insights

3 Issues with Data Management Tools

The market is currently awash with BI tools that advertise lofty claims regarding their ability to leverage data in order to ensure ROI. It is evident, however, that these systems are not created equally and the implementation of one could adversely affect an organization.

Cost

While consistent multifold increases of the digital universe is ushering in lower costs for data storage, a decline reported to be as much as 15-20 percent in the last few years alone, it is also the catalyst for the the rising cost of data management. It seems that the cause for concern regarding data storage does not lie in the storage technologies themselves, but in the increasing complexity of managing data. The demand for people with adequate skills within the realm of data management is not being sufficiently met, resulting in the need for organizations to train personnel from within. The efforts required to equip organizations with the skills and knowledge to properly wield these new data management tools demand a considerable portion of a firm’s time and money.

Integration

The increased capacity of a new data management system could be hindered by the existing environment if the process of integration is not handled with the proper care and supervision. With the introduction of a different system into a company’s current technological environment as well as external data pools( i.e. digital, social, mobile, devices, etc.), the issue of synergy between the old and new remains. CIO identifies this as a common oversight and advises organizations to remain cognizant of how data is going to be integrated from different sources and distributed across different platforms, as well as closely observe how any new data management systems operate with existing applications and other BI reporting tools to maximize insight extracted from the data.

Evan Levy, VP of Data Management Programs at SAS, shares his thoughts on the ideal components of an efficient data management strategy as well as the critical role of integration within this process, asserting that:

“If you look at the single biggest obstacle in data integration, it’s dealing with all of the complexity of merging data from different systems… The only reasonable solution is the use of advanced algorithms that are specially designed to support the processing and matching of specific subject area details. That’s the secret sauce of MDM (Master Data Management).”

Reporting Focus

The massive and seemingly unwieldy volume is one major concern amidst this rapid expansion of data, the other source of worry being that most of it is largely unstructured. Many data management tools offer to relieve companies of this issue by scrubbing the data clean and meticulously categorizing it. The tedious and expensive process of normalizing, structuring, and categorizing data does admittedly carry some informational benefit and can make reporting on the mass of data much more manageable. However, in the end, a lengthy, well-organized report does not guarantee usable business insight. According to research conducted by Gartner, 64% of business and technology decision-makers have difficulty getting answers simply from their dashboard metrics. Many data management systems operate mostly as a visual reporting tool, lacking the knowledge discovery capabilities imperative to producing actionable intelligence for the organizations that they serve.

The expenses that many of these data management processes pose for companies and the difficulties associated with integrating them with existing applications may prove to be fruitless if they are not able to provide real business solutions. Hence, data collection should not be done indiscriminately and the management of it conducted with little forethought. Before deciding on a Business Intelligence system, it is necessary to begin with a strategic business question to frame the data management process in order to ensure the successful acquisition and application of big data, both structured and unstructured.

Joe Sticca, Chief Operating Officer of True Interaction, contributed to this post.

By Justin Barbaro

Categories
Insights

Ensure Data Discovery ROI with Data Management

The explosion of data is an unavoidable facet of today’s business landscape. Domo recently released its fourth annual installment of its Data Never Sleeps research for 2016, illustrating the amount of data that is being generated in one minute on a variety of different platforms and channels. The astounding rate in which data has been growing shows no indication of slowing down, anticipating a digital universe saturated in nearly 44 trillion gigabytes of data by the year 2020. With data being produced in an increasingly unprecedented rate, companies are scrambling to set data management practices in place to circumvent the difficulties of being overwhelmed and eventually bogged down by the deluge of information that should be informing their decisions. There are a plethora of challenges with calibrating and enacting an effective data management strategy, and according to Experian’s 2016 Global Data Management Benchmark Report, a significant amount of these issues are internal.

Inaccurate Data

Most businesses strive for more data-driven insights, a feat that is rendered more difficult by the collection and maintenance of inaccurate data. Experian reports that 23% of customer data is believed to be inaccurate. While over half of the companies surveyed in this report attribute these errors to human error, a lack of internal manual processes, inadequate data strategies, and inadequacies in relevant technologies are also known culprits in the perpetuation of inaccurate data. While the reason for the erroneous input of data is still largely attributed to human oversight, it is the blatant lack of technological knowledge and ability that is barring many companies from leveraging their data, bringing us to our next point.

Data Quality Challenges

Inescapable and highly important, the sheer volume of information being generated by the second warrants a need for organizations to improve data culture. Research shows that businesses face challenges in acquiring the knowledge, skills, and human resources to manage data properly. This is reflective of organizations of all sizes and resources, not just large companies, as a baffling 94% of surveyed businesses admit to having experienced internal challenges when trying to improve data quality.

Reactive Approach

Experian’s data sophistication curve identifies four different levels of data management sophistication based on the people, processes, and technology associated with the data: unaware, reactive, proactive, and optimized. While the ultimate goal is ascending to the optimized level of performance, 24% of the polled businesses categorize their data management strategies as proactive, while the majority (42%) admits to merely reaching the reactive level of data management sophistication. The reactive approach is inefficient in many ways, a prominent one being the data management difficulties, both internal and external, espoused by waiting until specific issues with data crops up before addressing and fixing them as opposed to detecting and resolving such problems in a timely manner.

The most deleterious disadvantage of failing to address these pressing issues as they are detected is the careless neglect of invaluable business insight that is concealed in the mass of available data. Data management systems that have not optimized their operations will not be able to process data to produce relevant information in a timely manner. The lack of machine learning mechanisms within these sub-optimal systems will hinder businesses in their knowledge discovery process, barring organizations from making data-driven decisions in real time.

Denisse Perez, Content Marketing Analyst for True Interaction, contributed to this post.

by Joe Sticca

Categories
Insights

Wrangling Data for Compliance, Risk, and Regulatory Requirements

N.B. This article addresses the financial services industry, however, the insight and tips therein are applicable to nearly any industry today. ~EIC)

The financial services industry has always been characterized by its long list of compliance, risk, and regulatory requirements. Since the 2008 financial crisis, the industry is more regulated than ever, and as organizations undergo digital transformation and financial services customers continue to do their banking online, the myriad of compliance, risk, and regulatory requirements for financial institutions will only increase from here. In a related note, organizations are continuing to invest in their infrastructure to meet these requirements. IDC Financial Insights forecasts that the worldwide risk information technologies and services market will grow from $79 billion in 2015 to $96.3 billion in 2018.

All of this means reams of data. Financial firms by nature produce enormous amounts of data, and due to compliance requirements, must be able to store and maintain more data than ever before. McKinsey Global Institute reported in 2011 that the financial services industry has more digitally stored data than any other industry.

To succeed in todays financial industry, organizations need to take a cumulative, 3-part approach to their data:

1. Become masters at data management practices.

This appears obvious, but the vast amount of compliance, risk, and regulatory requirements necessitate that organizations become adept at data management. Capgemini identified 6 aspects to data management best practices:

Data Quality. Data should be kept optimal through periodic data review, and all standard dimensions of data quality– completeness, conformity, consistency, accuracy, duplication, and integrity must be demonstrated.

Data Structure. Financial services firms must decide whether their data structure should be layered or warehoused. Most prefer to warehouse data.

Data Governance. It is of upmost importance that financial firms implement a data governance system that includes a data governance officer that can own the data and monitor data sources and usage.

Data Lineage. To manage and secure data appropriately as it moves through the corporate network, it needs to be tracked to determine where it is and how it flows.

Data Integrity. Data must be maintained to assure accuracy and consistency over the entire lifecycle, and rules and procedures should be imposed within a database at the design stage.

Analytical Modeling. An analytical model is required to parcel out and derive relevant information for compliance.

2. Leverage risk, regulatory, and compliance data for business purposes.

There is a bright side to data overload; many organizations aren’t yet taking full advantage of the data they generate and collect. According to PWC, leading financial institutions are now beginning to explore the strategic possibilities of the risk, regulatory, and compliance data they own, as well as how to use insights from this data and analyses of it in order to reduce costs, improve operational efficiency, and drive revenue.

It’s understandable that in today’s business process of many financial institutions, the risk, regulatory, and compliance side of the organization do not actively collaborate with the sales and marketing teams. The tendency toward siloed structure and behavior in business make it difficult to reuse data across the organization. Certainly an organization can’t completely change overnight, but consider these tips below to help establish incremental change within your organization:

Cost Reduction: Eliminate the need for business units to collect data that the risk, regulatory, and compliance functions have already gathered, and reduce duplication of data between risk regulatory, compliance, and customer intelligence systems. Avoid wasted marketing expenses by carefully targeting marketing campaigns based upon an improved understanding of customer needs and preferences.

Increased Operational Efficiency: Centralize management of customer data across the organization. Establish a single source of truth to improve data accuracy. Eliminate duplicate activities in the middle and back office, and free resources to work on other revenue generating and value-add activities.

Drive Revenue: Customize products based upon enhanced knowledge of each customer’s risk profile and risk appetite. Identify new customer segments and potential new products through better understanding of customer patterns, preferences, and behaviors. Enable a more complete view of the customer to pursue cross-sell and up-sell oppportunities.

3. Implement a thorough analytics solution that provides actionable insight from your data.

Today, it’s possible for financial organizations to 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.

Machine Learning Platforms like SYNAPTIK enable 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.

By Michael Davison