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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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Insights

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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How the IoT Can Bring Down Healthcare Costs

Healthcare is a multi-billion dollar industry, and that’s not going to change anytime soon. The financial figures go both ways – revenues and costs – but for most of the people involved in healthcare especially consumers, it boils down to the latter.

Healthcare costs are high for a reason. The processes, products and technologies used in the industry undergo strict quality control checks to ensure their effectiveness and resources are needed to create and deploy such components.

From a business standpoint, if stocks were to be used as a basis for healthcare costs, even people with limited knowledge on financial markets can understand how massive this industry is and why the costs of medicine increase annually. In an article by Business Insider, it stated healthcare stocks have remained strong even after several other stocks fell after the Presidential Inauguration. And according to FXCM’s article on how to value a stock, they suggest while a stock’s valuation may differ from its intrinsic value, healthcare remains a compelling sector as baby boomers are now entering their senior years.

Fortunately, technology is also becoming a means to cut healthcare costs. Among the most promising innovations that could potentially make this possible is the Internet of Things (IoT).

The tech titan IBM enumerated the advantages of integrating the IoT into healthcare and the first on the list is reduced costs. A concrete example was given: real time patient monitoring. Non-critical patients can be monitored even at home, thereby decreasing hospital admissions and unnecessary costs.

Mubaloo revealed IoT-dependent technologies can be implemented in medical products such as RFID tags, beacons and even ‘smart beds’. Due to the large amount of equipment used by medical personnel, it’s a costly – not to mention time-consuming – task to track every piece, but with tiny modifications such as the installation of RFID chips, the process becomes much more efficient.

Beacons, on the other hand, can be placed near patient rooms or hospital wards, which can then be updated with the corresponding patient data or any relevant info to reduce costs on printed materials and other similar articles. ‘Smart beds’ can be used to notify doctors or nurses regarding the activity of their patients, which then lessens the need for frequent hospital rounds.

Moreover, Aranca discussed the prevalence of tech wearables in the US and Europe. Wearable devices are now specifically developed for functions such as tracking vital signs. This adds to the potential of remote patient monitoring as well as managing particular diseases. For instance, a wearable tracker may be used to measure a person’s glucose levels to help avoid or manage diabetes. Apple is reportedly developing this technology, and CNBC revealed that the first person to be tested is the firm’s CEO, Tim Cook.

More and more devices are getting connected each year, and experts estimate that around 20 billion devices will be interconnected by 2020 based on research. With such a rapid phase of development, it’s only a matter of time before innovations such as the aforementioned wearables get officially rolled out across the industry.

As global healthcare turns more reliant on technology and connectivity, the Internet of Things will be utilized further in various parts of the industry. And with reduced costs now highly feasible, hopefully more people will be able to have access to the quality healthcare that they deserve.

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Digital Transformation Fatigue – Getting the Most Out of Your Data

In 2011 Ken Perlman from Kotter International, conducted a workshop on change and innovation and saw how continual change was taking a toll on employees as they were exhausted and fatigued. This research from Perlman concluded that 70 percent of transformation efforts failed. Not much has changed since this study.

The rapid rate of technological advancement has resulted in a constant game of catch up. Businesses have become increasingly dependent on new change program that are designed to drive efficiency . With good intentions at the core, this change can lead to “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 year.”

As the importance of big data continues to increase for businesses in terms of marketing and sales, there are constant efforts to access a more productive data management platform. While companies hope to get the most out of their data management platforms, they can sometimes run into problems. With continuous changes, employees often experience burnout which can create a sense of frustration within a company.

Why are Data Management Platforms Important?

In the digital age, data management platforms (DMPs) are the backbone that help businesses connect and build their audience segments. These platforms are effective in storing and managing data on audiences, sentiment, and engagement. The analyses from data management platforms are designed to create campaigns that can be continually developed to reach certain audience segments.

Many businesses have adopted data management platforms as they have seen quantifiable results. However, the implementation of these platforms has been problematic. A report from the Oracle Marketing Cloud reveals how many companies are experiencing Transformation Fatigue as their employees are not equipped to handle the transition and adoption of new data management platforms.

-Oracle Marketing Cloud and E consultancy

As data management platforms become essential for an effective business, companies will have to understand and organize the incoming data that is presented. According the chart above, 32% of companies are not using a DMP due to a lack of internal expertise. Organizations should strive to maximize their market share relative to their competitors, and the ability to use business intelligence to boost productivity and influence ROI becomes notably important.

The Synaptik platform has been at the forefront of providing strong business intelligence that combines structured and unstructured data. Synaptik connects businesses with services for a variety of purposes such as brand sentiment, campaign effectiveness, and customer experience. The user-friendly platform allows you to create new combinations of pivot tables without the back and forth communication of the IT Department.

The process of acquiring internal and external/3rd party quantitative and qualitative data can be time-consuming and challenging. Different sources like websites, social media channels, video content sites, government databases, APIs & SQL databases require different techniques and have their limitations. This can make sorting and analyzing data very difficult especially without the right technical expertise. Fortunately, Synaptik as a platform comes with data professionals who can assist in building and configuring data agents for 1-click ease of use.

As you can leverage new data analytic processes new “business and data revenue” opportunities can present themselves.

By Joe Sticca

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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

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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

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The Technology Solution to the IoT Conundrum

Unhindered in its incessant growth, the Internet of Things (IoT) continues to increase its network of connected devices exponentially. Gartner predicts that a staggering 20 billion connected devices will be in existence by 2020. To put into further context, the current trajectory of the IoT will soon usher in an age where there are, on average, three connected devices for every living person. In keeping with Gartner’s research, this fast-growing industry will soon be powering a market worth upwards of $3 trillion. An explosive growth in any industry is always accompanied with a barrage of data, a challenge that we here at True Interaction understand well. The issues associated with capturing vast amounts of data easily, both structured and unstructured, is a critical barrier point in the pursuit of data discovery and we provide solutions to these data management difficulties by means of our platform Synaptik.

While the unimpeded growth of IoT is indeed quite promising, we cannot simply dismiss the unique set of challenges that accompany such a sudden and chaotic influx of devices. The current infrastructure and infrastructure of Internet and online services are hardly primed to handle the identifying, connecting, securing, and managing of so many devices. The difficulties posed by large IoT ecosystems may be considered as a problem for the future, but the technology that can address these issues potentially exists now.

Blockchain as a Solution?

Hailed for the transparency, accuracy, and permanence that is inherent in its process, our previous post explains that blockchain “create[s] a digital ledger of transactions that can be shared among a distributed network of computers.” The utilization of cryptography allows each account on the network to access and manipulate the ledger securely, decentralizing the process and essentially eliminating the need for a middleman. Finance had one of the first and most notably successful implementation of blockchain through Bitcoin, and the industry is seemingly eager to embrace the technology even more. We have covered the meteoric rise in importance and interest that blockchain technology has been attracting in a comprehensive post, as well as its applications in the media and entertainment industry. However, this instance, with its suggested applications in the IoT, is especially momentous in that it observes the convergence of two recently developed technological sectors. So how will the blockchain model assist the IoT industry in its promising ascent?

Security

IoT’s impressive growth is an assuring testament to its bright future as well as its crux. A centralized model of security has been effective in the past, but it is not nearly equipped to handle network nodes that balloon up to the millions from devices that also conduct billions of transactions. Not only will the computational requirements (and costs!) skyrocket, but expanding a network to that size will inevitably cause servers to become a bottleneck. The chaotic and highly vulnerable state that this puts servers in will make it susceptible to Denial of Service (DoS/DDoS) attacks, where servers are targeted and brought down by being flooded with traffic from compromised devices.

The organization inherent in the system gives blockchain the ability to create secure mesh networks that allow devices to connect reliably to one another. Once the legitimacy of a node has been secured and registered on the blockchain, devices will be able to identify and authenticate each other without the need for central brokers. An added benefit of this model is its scalability and the way it can be expanded to support a billion devices while barring the need for additional resources. IBM extrapolates the results of blockchain’s impeccably accurate and transparent record-keeping capabilities by anticipating more trust to form between people and parties, thus making transactions run more seamlessly. Chris O’Connor, General Manager, Internet of Things Offerings for IBM, illustrates the concept:

 

“While Person A may not know device B and may not trust it implicitly, the indelible record of transactions and data from devices stored on the blockchain provide proof and command the necessary trust for businesses and people to cooperate.”

 

Self-Regulation

What is a common feature in the most expansive imaginings of a technologically unmatched world? Typically, the height of technological success is marked by the existence of self-sustaining machines. It may astonish people to learn that the means for creating devices that have little to no need for human interference already exists. IBM and Samsung have partnered together in developing a concept known as ADEPT (Autonomous Decentralized Peer-to-Peer Telemetry). The project chose three protocols (file sharing, smart contracts, and peer-to-peer messaging) to underpin the seminal concept.

One of the most interesting proposals for the use of this technology is the enabling of devices to autonomously maintain themselves. IBM’s draft paper features lofty goals that include devices not only being capable of signaling operational problems, but also being able to retrieve software updates and potentially address its self-diagnosed issues. The ADEPT technology is intended to accomplish the incredible feat of allowing devices to communicate with other nearby devices in order to facilitate power bartering and energy efficiency. Machines that work in accordance with consumables will be able to restock their own supplies as well. This feature will be available in a Samsung W9000 washing machine. Wielding the ADEPT system, this Samsung washing machine will use smart contracts to issue commands to a detergent retailer that gives the device the ability to pay for an order itself and later receive word from the retailer that the detergent has been paid for and shipped.

Smart Contracts

In the digital age, with the emergence of a slew of transaction systems, blockchain is being heralded as the next logical step. At the heart of blockchain technology is its unique penchant for transparency and demand for accountability from its users. Moreover, its decentralized process negates the need for intermediaries. These unique features make blockchain a feasible platform on which to conduct smart contracts. Co-opting the technology’s intended transactional use, “contracts could be converted to computer code, stored and replicated on the system and supervised by the network of computers that run the blockchain.” The exchange of money, property, or anything of value in a conflict-free manner sans a middleman to broker the deal exists because of blockchain technology.

Blockchain is just one of many frameworks and sources of data into which Synaptik can be easily integrated. Data management is the most critical piece in the seamless execution of a successful data discovery process that is capable of gleaning answers to questions you may not have known to ask.

For more information to empower your data science initiatives please visit us at www.Synaptik.co. We pride ourselves in our ability to empower every day users to do great data discovery without the need for deep core technical development skills.

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

by Joe Sticca

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Evolution of Big Data Technologies in the Financial Services Industry

Our previous post provides an industry analysis that examines the maturity of banking and financial markets organizations. The significant deviations from the traditional business model within the financial services industry in the recent years emphasize the increasing need for a difference in how institutions approach big data. The long-standing industry, so firmly entrenched in its decades-long practices, is seemingly dipping its toes into the proverbial pool of big data as organization recognize that its implementation is integral to a firm’s survival, and ultimately its growth. IBM’s Big Data @ Work survey reports that 26 percent of banking and financial markets companies are focused on understanding the concepts surrounding big data. On the other end of the spectrum, 27 percent are launching big data pilots, but the majority of the companies surveyed in this global study (47 percent) remains in the planning stage of defining a road map towards the efficient implementation of big data. For those organizations still in the stage of planning and refinement, it is crucial to understand and integrate these observed trends within financial technologies that can bolster a company’s big data strategy.

Customer Intelligence

While banks have historically maintained the monopoly on their customer’s financial transactions, the current state of the industry, with competitors flooding the market on different platforms, prevents this practice to continue. Banks are being transformed from product-centric to customer-centric organizations. Of the survey respondents with big data efforts in place, 55 percent report customer-centric objectives as one of their organization’s top priorities, if not their utmost aim. In order to engage in more customer-centric activities, financial service companies need to enhance their ability in anticipating changing market conditions and customer preferences. This will in turn inform the development and tailoring of their products and services towards the consumer, swiftly seizing market opportunities as well as improving customer service and loyalty.

Machine Learning

Financial market firms are increasingly becoming more aware of the many potential applications for machine learning and deep learning, two of the most prominent uses being within the fraud and risk sectors of this industry. The sheer volume of consumer information collected from the innumerable amount of transactions conducted through a plethora of different platforms daily calls for stronger protocols around fraud and risk management. Many financial services companies are just beginning to realize the advantageous inclusion of machine learning within an organization’s big data strategy. One such company is Paypal, which, through a combination of linear, neural network, and deep learning techniques, is able to optimize its risk management engines in order to identify the level of risk associated with a customer in mere milliseconds. The potential foreshadowed by these current applications is seemingly endless, optimistically suggesting the feasibility of machine learning algorithms replacing statistical risk management models and becoming an industry standard. The overall value that financial institutions can glean from the implementation of machine learning techniques is access to actionable intelligence based on the previously obscured insights uncovered by means of such techniques. The integration of machine learning tactics will be a welcome catalyst in the acceleration towards more real-time analysis and alerting.

IoT

When attempting to chart the future of financial technology, many point to the Internet of Things (IoT) as the next logical step. Often succinctly described as machine-to-machine communication, the IoT is hardly a novel concept, with the continual exchange of data already occurring between “smart” devices despite the lack of human interference. As some industries, such as in retail and manufacturing, already utilize this technology to some extent, it is not a far-fetched notion to posit that the financial service industry will soon follow suit. While there are those who adamantly reject the idea due to the industry being in the business of providing services as opposed to things, this would be a dangerously myopic view in this day and age. Anything from ATMs to information kiosks could be equipped with sensing technology to monitor and take action on the consumer’s’ behalf. Information collected from real-time, multi-channel activities can aid in informing how banks provide the best, most timely offers and advice to their customers.

For more information to empower your data science initiatives please visit us at www.Synaptik.co. We pride ourselves to empower every day users to do great data discovery without the need for deep core technical development skills.

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

By Justin Barbaro

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Technological Disruptions in the Financial Services Industry

The financial services industry has long boasted a resilient and steadfast business model and has proven to be one of the most resistant sectors when it comes to disruption by technology. In the recent years, however, a palpable change is overturning the ways and processes that have upheld this institution for so long. Organizations within this historically traditional and unyielding sector are realizing the need to not only assimilate into the digital era, but to embrace and incorporate it fully or else be overtaken but others who have opted to innovate rather than succumb under the pressure of this increasingly competitive industry.

Changing Dynamics

The relationship between banks and their customers have drastically changed from the time during which the traditional banking model was formulated. Perhaps more than any other commercial enterprise, banks retained control over the relationships they had with their customers. For the most part, the bank with which an individual was aligned often determined his or her financial identity as nearly all transactions were administered through one’s bank. Moreover, McKinsey reports that, historically, consumers very rarely flitted between different service providers because of the promising image of stability that the industry has worked hard to maintain. While this may have been the case in the past, the relational dynamics between banks and their customers are not the same today as they were nearly a decade or so ago.

Instead of being reliant to a single bank for all financial dealings, consumers have more options at their disposal, which they are taking full advantage of by engaging in transient relationships with multiple banks such as “a current account at one that charges no fees, a savings accounts with a bank that offers high interest, a mortgage with a one offering the best rate, and a brokerage account at a discount brokerage.” Competition between financial institutions is undeniably fiercer than ever, and it turns out that consumers are also being courted by new peer-to-peer services, such as PayPal, that allow those who opt to use these services to conduct financial transactions beyond the traditional banking means and organizations.

Data Growth

The sheer rise in players and competitors within this industry alone is enough to indicate another glaring issue: the sudden growth in volume of financial transactions. More transactions leads to an explosion of data growth for financial service providers, a predicament for which not many organizations are adequately prepared to handle. A study conducted by the Capgemini/RBS Global Payments estimates that the global volume for electronic payments is about 260 billion and growing between 15 and 22% for developing countries. The expansion of data points stored for each transaction, committed on the plethora of devices that are available to the consumer, is causing difficulties in the active defense against fraud and detection of potential security breaches. Oracle observes a shift in the way fraud analysis is being conducted, with it previously being performed over a small sample of transactions but which now necessitates the analysis of entire transaction history data sets.

Timely Insight

Shrinking revenues is one of the most prominent challenges for financial institutions to date, calling for a need to improve operational cost efficiencies. New financial technologies are being developed to address issues like this by leveraging the amount of available data that these financial institutions have access to and monetize it. Traditional Business Intelligence tools have been a staple in the industry for years, but it is usually limited in its capacity. A lot of the available BI tools work well in conjunction with business analysts when they are looking to find answers and solutions to specific conundrum. The key to revamping traditional banking frameworks in order to make it more competitive and agile in the current environment is to build and incorporate processes that are capable of revealing patterns, trends, and correlations in the data. Oracle posits that disruptive technologies in the financial sectors need to be able to do more than report, but also uncover. The technological ramifications of an evolving financial industry with a continuously expanding amount of data and the demand for real-time, data-driven decisions include the ability to detect unanticipated questions and simultaneously provide tangible solutions.

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

By Joe Sticca

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Leveraging Data and Revenue Opportunities in Media Syndication

In the modern digital era where people are constantly bombarded by web and mobile content, any expectation of success for digital media creators requires advantageous placement of their content. In most cases, that means disseminating media across as many platforms as can conceivably host it. The benefits are innumerable, not the least of which is the expanded opportunities for scale and the considerable financial implications of an increase in revenue generation as a result of growing one’s audience. However, this process is not without its drawbacks. Our previous blog post delineates some key issues that creators are often forced to address once they decide to showcase their content through different online channels.

Analytics

Building a brand by means of video content requires a close eye on the full range of analytics concerning one’s media. The reach and impact of one’s online presence is paramount to a creator’s ability to make strategic, data driven decisions. Though it might seem tempting, it is not strategic, nor is it actually feasible, to publish content on any platform indiscriminately. Some video content perform exponentially better in certain online environments and in some cases, its existence on a certain platform could prove to be detrimental to a creator’s overall brand, which makes its continued presence on that platform counterintuitive.

The key to combatting this scenario is for the supplier to maintain unhindered visibility into the data surrounding and generated by their content. Analytics on this scale, across different channels is understandably a massive feat. As a response to this challenge, media creators are exploring blockchain technologies (as we detail in a previous post) and leveraging its transparency feature in order to retain control of data tracking and reporting capabilities despite adopting a multi-channel distribution strategy.

Contract Compliance and Rights Management

Once a creator adopts a multi-platform syndication strategy, the increase in exposure is accompanied by the intensification of complexity in regards to contract agreements. There does exist a much simpler distribution strategy in which a creator simply sends the content files to a third party host and is then awarded a license fee, revenue share, or a combination of the two for their contribution. However, the tradeoff for the presumed ease of this distribution process is that the creator effectively loses control of their content. This concession of control does make a simple, cut-and-dry third party distribution less enticing for content suppliers and may deem the management of multifaceted contractual agreements worth it if it means that they retain the rights to their media.

EY astutely posits that “opportunities, across all sectors of the global economy, have at least one thing in common: they require multiple corporations to partner.” In adhering to that model, media syndication requires the interwoven partnerships between the content supplier, syndication partners, and advertising networks. The amount of involved parties can be daunting even from the beginning, as each one must find enough mutual benefit to forge on with an agreement. The resulting mass of contracts is irrefutably overwhelming. Establishing the contract will be inherently difficult with the sheer amount of parties and agents involved, but AdMonster suggests focusing on the terms of your syndication relationships, your business priorities, and the requirements of your advertisers when determining the terms of a syndication agreement.

Beyond the initial hurdle of contract creation lies the cumbersome task of enforcing the established contract. The agreement will cover everything from ad agency contract compliance to the rules of digital distribution as agreed upon by the content supplier and the platform that will host it. For many video creators, this is beyond what they are capable or willing to do, hence the common employment of syndication services such as Castfire or Grab Networks. Streaming Media reports that content suppliers value these services’ capability to manage relationships, especially with advertisers, and keep “track of advertising agreements and cross-promotion rules for the various sites that carry a video.” With the release of these video inventory and contract management systems, video creators are being spurred into creating more content and syndicating it to more platforms.

On a broader level, EY continues to laud the disruptive capabilities of blockchain technology, particularly in the media industry. The technology is brimming with potential as EY notes the advent of a blockchain-based music ecosystem “in which artists can place their songs and control song data and terms of usage, with transaction royalties distributed in real time to the artists, producers, writers and engineers involved in a song’s production.” Once this system is modified for video media, the means with which contracts and the terms regarding the rights to a piece of content are enforced will be conducted with significant ease and transparency.

Dealing with countless data repositories in a multitude of formats of structured and unstructured data can make the efforts not worth the ROI for many content suppliers. But True Interaction’s Synaptik platform has the ability to automate and aggregate your disparate structured and unstructured data across internal and external sources that will usher in a transformational return. It is becoming imperative for creators to learn more about the benefits of blockchain technology and the many ways it can be integrated into your business processes in order to steer your organization to success.

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

By Justin Barbaro

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Media Distribution and Syndication

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With the advent of television syndication, no viewer is left unreached and no potential market left untapped by advertisers. In 2016, advertising revenue resulting directly from television syndication approached $1.86 billion. This figure, while still significant on its own, is notably less than previous years as profits have been steadily declining since its most recent peak of $2 billion in annual revenue recorded in 2013. As television is finding less and less areas to expand to even on a global scale, the digital space is experiencing sustained and relentless growth. The expansion of the digital universe includes the development of new platforms on which to consume a plethora of video content, some of which were previously only available on television. The transition in preference away from the traditional television set is liberating for consumers of video media, but poses a new set of difficulties for content creators.

Distribution

Following the model of television syndication transcending geographic limitations, any video media must be able to traverse the saturated digital space well in order to maximize exposure. However, the means of disseminating content across the web are much more varied and tedious than in the days of just television broadcasting. The concepts of distribution and syndication in regards to digital media are often conflated together. The two terms, however, convey different ideas. According to Kaltura, distribution is perceived to be the simpler of the two processes as it only entails the submission of content to a third party host, who is then responsible for plugging it into their own player. The most recognizable of these third party hosts and an apt example would be YouTube.

Many content creators who opt to use third party hosts to distribute their media are drawn to the simplicity and ease of the process. The need to choose and design a player is eliminated and they are also not responsible for selling advertising. Hosts that have native apps would also allow video media to be viewed on more devices, and since the creator does not pay to stream the content, there are fewer upfront distribution costs involved.

The problem with distribution often lies in control, or lack thereof. Essentially, this option requires the one submit their content as a single, unaccompanied unit. The host determines how the media will be displayed and presented and any further alterations will have to be conducted through the platform. Even more critical is the absence of the full range of analytics available to the creator once the content has been handed over to the host. The shortage of relevant data can inhibit strategic decisions.

Syndication

Video syndication is a much more complex and tedious process. Simply put, “When you syndicate your content, you embed the entire thing as a package—the content itself and a video player of your choosing.” The level of difficulty may be higher, but the control one retains and the design offerings are much more comprehensive and advertising decisions lie securely within the creator’s purview. A centralized hub for all of one’s media is also a key feature, such that once content is removed from one place, it disappears from all players, everywhere.

Video syndication, despite its wide array of advantages, is plagued with its own difficulties, and any content creator must be prepared by remaining vigilant of the following key issues:

1. Awareness of capability. The allure of syndicating to the every platform that will accept one’s content is hard to resist, especially with the goal of distributing content to as many people as possible. However, it does not take long for things to get unwieldy as far as enforcing complex business rules with advertisers and syndication partners. With the aim of maximizing revenue in mind, AdMonsters details the issues associated with fulfilling one’s agreements with advertisers as far as reformatting ads for the different screens and devices they are running on. Analytics and data discovery has been predominantly weighted to the syndicator/distributor though with the burgeoning organic growth in small to mid tier providers of content. It is becoming clear that the creators need analytics for creative purposes as well as control/contract use purposes.

2. Technological congruence. Online video syndication is constantly uncovering issues as it continues to develop. More recently, the lack of incompatible technologies utilized by those supplying the content and the distributing platforms have been cited as a glaring concern. TechTarget asserts that progress within this industry sector should move towards addressing the “need for a standardized data exchange mechanism, and the need for a standardized metadata vocabulary.”

3. Tracking and reporting. Syndication is hardly a hands-off process. On the contrary, AdMonsters proclaims that the inclusion and integration of syndication partners further necessitates an attentive eye and data driven approach to managing these partnerships. Content suppliers must be able to track how well the video media is performing as well as the revenue generation on each partner’s platform and adherence to contract use terms. In some ways content suppliers are trying to take back control of data tracking with new trends in blockchain technologies. Comprehensive and coherent reporting that is capable of tracking every aspect of one’s syndicated content is critical in framing an informed distribution strategy.

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

by Joe Sticca