Big Data Predictions for 2017


For the past couple of years I have cited IDC’s prediction that by 2017, a unified data platform will become the foundation of Big Data Analytics strategy. The reason this makes sense is that “Big Data Analytics” is all about developing insights based on any data.

I’m not sure how many companies will achieve this state in 2017. Most companies are at various stages along their journeys to a unified approach. Another analyst firm, Gartner, has said IT is bifurcating into an “I” focused on insights and a “T” focused on operational technology.

The driver behind this separation is that the skill sets and organizational model for analytics versus operational technology are diverging rapidly among companies getting the most value from Big Data. However, both “I” and “T,” Gartner says, should rely on a common foundation so that insights can be operationalized to affect business outcomes.

You can see there is a complexity here for companies of nearly any size and scale that can be hard to overcome.

Despite this, companies continue attempts to emulate the Netflix’s and Amazon’s of the world. Today, there are few companies that have not already gone down the path of deploying Hadoop somewhere, but results vary widely.

As a technology market, analytics is unique. To work well, business people must be involved in the fitment of the solution to the business. By “fitment” I mean the use cases the analytics supports. It’s not an application but also isn’t a custom solution. It’s somewhere in between.

IT cannot simply acquire an on-premise technology or provision space in the cloud and expect business people to come to the well. Fortunately, stories are emerging of companies successfully addressing the complexity, skills, and cultural issues which others can model to course correct.

For 2017, these lessons serve as predictions to describe what winners will do right this coming year to realize their Big Data value dreams.

Prediction 1: Winners will reconcile and unify disparate Big Data technology decisions of the past

Whether Hadoop plays a strategic role in your company’s architecture or a single node server hides beneath someone’s desk, this technology needs to be considered within a broader context.

Winners take a “business question” first approach to analytics which leverages Hadoop and its related open source technologies. By doing so, implied in the answer are the connections to existing data management, business intelligence, and operational applications necessary to take an insight to action. The action is where Big Data insights pay off and demonstrate value throughout the C-suite.

What falls out of business-led Big Data strategy is a rational Big Data architecture which creates value for the enterprise.

Prediction 2: Winners will adopt Artificial Intelligence capabilities in perfect harmony with their Big Data platforms

AI is the hottest buzzword in technology today and promises to bring the benefits of advanced analytics to the masses. Unfortunately, these are not shortcuts for companies failing to get it right with Big Data. In fact, the two are complementary.

AI capabilities coming to market today promise to shortcut the data science process by bringing best practice analytics to questions like “what offer should I present to a customer based on their history with my business?”

What I expect to happen is winning companies looking beneath the covers of AI to examine the assumptions behind them to look for ways to tweak and improve the logic, leveraging their Big Data platform. That is where meaningful differentiation lives.

Prediction 3: Winners will leverage cloud services to improve their analytics processes

Speed to insight is where many companies struggle today. Data wrangling continues to be the biggest time sink hole with analytics projects. So examining the efficiency by which use cases unfold is a good way to understand your opportunities to extract greater value from these investments.

The cloud offers the chance to blend capabilities to speed up a use case. From data management and security to data integration, preparation, and enhancement, through discovery, modeling, and visualization, modular cloud services can be employed which target steps in the analytics process where things bog down.

For companies well down the path on their journeys, this simplifies benchmarking. For those just beginning and maybe lacking advanced analytics skill, cloud helps you catch up to the competition by focusing first on descriptive Big Data analytics.

Prediction 4: Winners will monetize Big Data with Data Products

Data monetization is another buzzword that has the ear of executives interested in new revenue streams. In certain industries the monetization opportunity is clear. For most companies, however, the monetization use case is less so. This is where Data Products come into play.

The beauty of developing a productive Big Data use case execution engine is being able to attack more than hard questions about the business. It allows the creation of analytic assets with monetary value, or data products.

Winners in 2017 will look to investigate new ways of instrumenting various aspects of the business leveraging any data source. Such as creating indexes or scores which offer a more accurate and realistic gauge of customer satisfaction than surveys alone and which informs customer facing and internal decisions across the company.

Throughout 2016 much of the Big Data news described the complexity and challenges companies face getting value from Big Data. In 2017, the gap between the Big Data “haves” and “have-nots” will start to close if these predictions come to fruition.

Gib Bassett
Retail and Consumer Goods Industry Principal with Oracle Corporation
Twitter @gibbassett


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