Top six BI and analytics trends for 2016

December 01, 2015

TARGIT’s predictions for the new and growing trends in the world of business intelligence and analytics in the coming year.
Around this time every year I like to take a look at how far the business intelligence and analytics industry has come in the past year and how well TARGIT has done in predicting, keeping up with, and in many cases surpassing evolving industry trends and standards. Towards the end of 2014, I predicted 2015 would see an increase in data governance, a rise in embedded BI, simplified data visualizations, and increased data experimentation. And if the popularity of Decision Suite 2015 in the market is telling, I’d say I was right on the money.

While I predict some trends, such as increased data governance, will continue to grow, I see a few new shifts in the BI and analytics market taking center stage in 2016.

Want a copy of this to hold on to? Download the PDF version.

1. Self-service big data discovery takes the front seat

Historically, self-service data discovery and big data analyses were two separate capabilities of business intelligence. I predict we will soon see an increased shift in the blending of these two worlds. In the past, data discovery was known for ease of use, but limited depth of exploration.

But the type of data science needed to provide powerful analysis for big data is typically slow, complex, and difficult to implement. 2016 will see an expansion of big data analytics with tools that make it possible for business users to perform comprehensive self-service exploration with big data when they need it, without major hand holding from IT. Wider adoption is coming.

Learn more about self-service data discovery in this short video:

2. Explosion in advanced analytics projects

Corresponding with my first predication, I anticipate a huge increase in advanced analytics projects across industries. However, that doesn’t mean they’ll be successful. Taking on a strategy that centers around a great increase in analytics projects—especially those involving big data—can be a daunting task for companies of any size. I wouldn’t be surprised to hear of many vendors and customers struggling to implement successfully. 

Learn about how to design an approachable analytics project: The Action Loop

3. Smarter thinking about data inclusion

That said, I believe 2016 will be the year for smarter data inclusion. Many of the companies that were some of the early adopters of big data analytics when that was a new and exciting buzzword couldn’t see the forest for the trees. With tools suddenly making it possible to analyze everything, users were doing just that. The result was a lot of reports and analyses that told a little bit about too many things, and failing to give any type of realistic picture of the company at all.

This is the time those who have a little experience are going to get smarter about the big and external data that is critical to their business decisions. BI big data discovery projects will become more focalized and realistic.

Learn more about smarter data inclusion: the Metrics that Matter

4. Bimodal BI goes big

As trends such as big data discovery spread, companies must learn how to handle the unstructured, semi structured, incomplete, and massive amounts of data that is now available to them. That’s why I see an increase in what some analysts refers to as “bimodal BI,” which tackles both the necessity of agile, user-friendly analytics and reliability and security of data.

A bimodal BI strategy should not only facilitate traditional business operation—the classic data warehouse and continuous decision loops—but also discovery and innovation. Bimodal BI is both centralized (company-wide initiatives) and decentralized (change, innovation, and exploration). A proper bimodal business intelligence strategy is designed with flexibility and mass distribution in mind.

Learn more about bimodal BI: The modern company's new BI strategy

5. Data governance gets another gold star

The ever-increasing emphasis on self-service analytics and mass distribution is the reason data governance is a critical part of every bimodal BI strategy. In order to successfully implement a strategy that not only utilizes traditional BI, but emphasizes innovation and experimentation, security must be looked at in a new light.

The traditional way of handling data governance—centralized, strict, and secure—is still valid for enterprise multidimensional data warehouses. But it’s inefficient, riddled with unavoidable bottlenecks, and stymies experimentation. In order to promote innovation and experimentation among teams, a new way of handling data governance is needed. A de-centralized, data governance strategy is necessary for any type of ad-hoc data discovery but ideally with a centralized “supreme court” that can shut off access of any misleading data mash-up models.

From a technical perspective, BI platforms must be capable of establishing various levels of permissions and settings to ensure high data quality delivered to the right people at the right time.

Read more about improving your BI strategy with data governance: Data governance: the right balance for a bimodal strategy

6. Sandbox analytics strategies get to sit at the big kids table

As I mentioned above, data governance should solve the double-pronged need for data experimentation in a secure environment. But data experimentation isn’t right for everyone in the company. Not all data—no matter how potentially useful—should immediately be shared company- or even department-wide until the right experimentation, finessing, and cleansing for quality has been performed.

That’s why I predict the growth of “sandbox analytics.” By that I mean the creation of small, isolated groups of BI users to produce, experiment with, and share data before considering wider adoption across the company. 

Learn more about the concept of sandbox analytics: Improve data quality with sandbox analytics