Intelligent influence: How to use Google APIs to bolster your social media content

As the old adage goes, a picture is worth a thousand words. For social media influencers, however, a picture or video can be worth thousands of dollars.

Social media influencing, driven primarily by sharing photo and video content across popular social media channels, represents a $1B USD industry with expectations of doubling in the next two years. Influencers need to seek out every advantage in order to survive in this growing yet infamously competitive marketplace. So how can social media influencers leverage technology in new ways to maximize the value of sharing media with their followers?

Below is a sample use case of how a social media influencer can leverage the power of Google Cloud APIs to bolster their existing media library. In this use case, it is important to remember to consolidate strategies into corresponding tool sets and try to keep them under one application as to not create disparate processes and data sets. One leading solution for this is the Synaptik framework, which will be discussed later in the post.

Tools: Google Cloud Video Intelligence API + Natural Language API

The Google Video Cloud Intelligence API allows users to, for a nominal fee, search every moment of every video file in a user’s catalog and find every occurrence as well as its significance. It quickly annotates videos stored in Google Cloud Storage, and helps users identify key noun entities of each video, as well as when they occur within the video.

The Google Cloud Natural Language API provides natural language understanding technologies to developers. Examples include sentiment analysis, entity recognition, entity sentiment analysis and text annotations.

Use Case: A fitness social media influencer wants to drive more insights from his collection of workout videos while better meeting the needs his current and potential followers.

Action Steps:

1.) Identification of nouns within video content. The influencer wants to make sure that videos are tracked by workout type. He uses the Google Cloud Video Intelligence API to identify noun keywords within each video including treadmill, kettlebell, barbell and bicycle.

2.) Organization of video content. The influencer is now able to take several action steps following noun identification. He chooses to crop videos showing certain activities to share daily on social media: one day highlighting kettlebells, another for a treadmill routine. Second, he makes his YouTube videos searchable by activity so his followers and visitors to his videos can easily find specific routines within larger workouts.

3.) Tracking of follower response. Using the Google Cloud Natural Language API, the influencer can also track the positive and negative sentiment of followers towards each social media posting. This allows him to drive his digital video content strategy to better serve his customers and ultimately continue to grow his social influence!

This work can seem daunting for a social media influencer, or really any small- to medium-sized business owner. However, Synaptik has the power to integrate Google Video Cloud Intelligence, Natural Language and dozens of other Google Cloud-based APIs into one easy-to-use, customizable platform. Sign up for a 30 minute consultation and we can show you what customers are saying about your products and services across multiple social media channels online (Facebook, Twitter, LinkedIn, etc.).

By Joe Sticca

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

Blockchain 101 Self-Assessment

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

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

BLOCKCHAIN FREQUENTLY ASKED QUESTIONS:

What Is the Blockchain?

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

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

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

Blockchain: Who controls the risk?

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

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

Could Blockchain be the structural change the market needs?

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

Blockchain: What does the future hold?

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

Next, get started with the Blockchain Self-Assessment:

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

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

Get the Blockchain Quick Exploratory Self-Assessment eBook here:

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

About the Author

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

By Gerard Blokdijk

Can Artificial Intelligence Catalyze Creativity?

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

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

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

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

So, how can technology augment your innovation ecosystem?

Stop

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

Collaborate

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

Listen

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

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

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

By Nina Robbins

Improving the Fan Experience through Big Data and Analytics

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

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

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

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

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

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

Beacon Stadium App
Beacon Stadium App-Courtesy of Umbel

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

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

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

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

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

Contributors:

Joe Sticca, Chief Operating Officer at True Interaction

Kiran Prakash, Content Marketing at True Interaction

Real Estate: Climate-proof your Portfolio

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

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

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

Home Valuation

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

Design

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

Marketing

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

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

By Nina Robbins

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

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

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

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

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

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

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

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

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