BI Trends

Seeing the Forest through the Trees: The 2016 BI Trends you should Actually Care About

By Mark Lockwood
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At this time last year, we were saying that the BI industry was at an inflection point. And if you look at everything that has happened in the past year, we definitely saw major shifts in the market. Traditional BI all but went away, as modern approaches pressed ahead. Data was a huge focus, with more organizations leveraging big data sources, as well as data prep and wrangling tools.

However, as you look ahead to 2016 the trends are much more practical than they have been in the past. You may see some touting “the next big thing” will arrive this year, but the reality is that those technologies are just not quite ready to go mainstream.

That being said, there are plenty of BI trends that organizations should be paying attention to this year that will ultimately help them prepare for that next big thing.

So without further ado, here are the seven trends that we think organizations should actually pay attention to this year.

1. Bimodal BI
This first trend should come as no surprise. Bimodal BI was the keynote subject of Gartner’s BI & Analytics summit last year and continues to gain traction as more and more companies try to rationalize their traditional data warehouse and BI stacks with growing data discovery adoption and departmental independence.

Organizations today want centralized data – for governance and security reasons, but also want to be able to leverage a decentralized mode of operation that uses capabilities like data discovery to find new insights in a self-service fashion without increasing the workload of the IT team.

2. Data Prep
As self-service analytics gains a stronger foothold in organizations, business users are becoming more sophisticated, they have a better understanding of the data, and actually want to participate in more of the analytic process.

Enter data preparation. Data prep is all about getting the data ready for analysis, including cleansing, filtering, and shaping the data in some way that makes analysis of the data easier or more understandable for the end user. And this is trend is positive for everybody involved. After all, analytics is a virtuous cycle that requires the business and IT to work together.

3. Social Curation
As BI adoptions becomes more pervasive across the organization, we believe that we will see a rise in social curation, as it’s one of the most efficient and effective mechanisms of determining relevancy across massive data sets.

Social curation in the context of analytics is really born out of a necessity of business today. With the rise of SMBs, more and more organizations have informational needs, but may not have a large, data-savvy team. In order for business and operational users to draw insights from data held across these systems, organizations much take a collaborative approach.

4. Big Data Acceleration
Big data is changing how people approach analytics, and this change is accelerating. Valuable business data no longer comes just from business applications managed by IT. Now you have multiple applications in the cloud, and other social media and video sources that are pushing the data volumes and velocity at much greater scale.

There is also a heightened expectation that our business applications will provide as much utility as consumer applications provide in our personal lives. It’s got to be about driving business value for all the data you get your hands on.

What’s the big deal about big data? Listen to this on-demand webinar to learn how to execute more accurately on your big data analyses to gain a competitive edge.

5. Embedding Analytics
In order to improve analytics adoption, users need to be able to bring analytics everywhere, which includes the applications they use every day. And not just inside the applications you use, but inside the application workflows themselves.

Many business applications are about transacting, capturing data, and retrieving records. And many applications have a reports tab or reporting module. But if you want to pull up a report, and want to act on that information, you usually have to go somewhere else to do something about it, or maybe even to another application altogether. And there is friction created in the process of jumping around different places.

Insights must be intertwined with the transactional functionality of an application. It is this deep integration of analytics within applications that improves the user experience, raises adoption of BI, and can differentiate your product.

6. Advanced Analytics
Advanced analytics has been the “next hot thing” for a while now, yet it is still primarily the domain of analysts who are mining data, creating models, and testing their hypotheses.

However, this space has been advancing, and may soon make its way out of the hands of R&D of larger companies, and into the hands of operational departments. For example, marketers could predict the effectiveness of campaigns, finance departments could understand the likelihood customers will pay, and customer support teams could predict churn and renewals.

7. Chief Analytics Officer
The Chief Data Officer was the new role of 2015 – now the Chief Analytics Officer is starting to make its way into organizations. While the Chief Data Officer focuses on the tactical management of data, the Chief Analytics Officer focuses on the strategic deployment of analytics. As analytics are embedded throughout organizations, there is a greater need for executive-level position to convey that strategic emphasis.

Although the CAO role is in its infancy, there’s plenty of upside in adopting a more strategic approach to analytics. Edmunds, the consumer auto website, reached an inflection point when they realized they had a half-dozen analytics team reporting to different parts of the organization.  As a result there was a lot of duplicative work and no cohesive strategy. By consolidating those teams under a Chief Analytics Officer, Edmunds has already seen improvements – the analytics capability has become more of a partnership than a job shop and resulting insights are deeper because the data isn’t siloed within one business unit.

Listen to our full presentation of these trends with additional insights in this on-demand webinar.


Originally published January 15, 2016; updated on August 9th, 2017

About the Author

Mark is the Director of Customer Account Management at Logi Analytics, where he is responsible for customer success, market development, sales enablement and thought leadership. Prior to joining Logi, Mark was a Lead Strategy Associate at the management consulting firm Booz & Company, where he helped create the firm’s first Big Data service offering. Mark earned a dual degree in Industrial Engineering and Economics from Northwestern University and holds an MBA from Harvard Business School.