Top Two 2017 Priorities for Data Discovery and Visualization Vendors

B. Shimmin
B. Shimmin

Summary Bullets:

• The data discovery and visualization marketplace showed a tremendous amount of maturation in 2016 with vendors tackling major market opportunities surrounding the cloud, big data integration and collaboration.

• The coming year promises to build on this progress as vendor aim to make data discovery and visualization both widely accessible and fully trustworthy for everyday business users.

The data discovery and visualization marketplace during 2016 showed a tremendous amount of maturation as vendors tackled major market challenges. Vendors inured in on-premises software embraced the cloud as a strategic platform, not merely a loss leader. Solutions that historically operated at arm’s length from big data repositories opened up direct lines of communication with a wide array of data sources. And solutions that were once oriented toward insight dissemination began addressing insight discussion and collaboration, both within and beyond the confines of the boardroom.

In all this year solidified data discovery and visualization as a legitimate modernization of traditional business intelligence practices. And we expect these same trends to continue during 2017. However, there are many new opportunities and challenges emerging within this vibrant marketplace for major competitors such as Qlik, Tableau, Domo, Microsoft, Amazon, IBM, Oracle, TIBCO, and SAP. Can these solutions fully address data management requirements? Can they directly benefit business users day in and day out? And can IT fully trust non-data professionals to work with these tools unassisted? We believe that these questions will be answered in the affirmative during 2017. Here’s how.

Embedded Analytics over Best-of-Breed Software Packages
Already most data visualization and discovery players have made the transition to the cloud, offering SaaS licensing models covering everything from data acquisition to analysis and opening up APIs to those services. During 2016, these services helped to foster a subtle shift among enterprise buyers away from discrete analytics software and toward the application of analytics in context, that is the use of descriptive, predictive, and prescriptive analytics at the point of decision, within actual business workflows. During 2017, we expect most vendors to follow early movers such as Logi Analytics in further componentizing and then productizing services for OEM partners, enterprise developers and ISV partners. Note that this transition will take some time to play out among larger BI players, which have only just begun to address both forms of analytics consumption and must tread carefully in order to preserve existing business models.

Data Integration Front and Center
Data has been democratized. Business users now have access to the same set of analytical tools as data professionals. This includes the ability to access a myriad of data sources, ranging from data lakes to personal spreadsheets. This coupled with AI-informed analysis workflows has led to a growing distrust among business owners as to the validity of analytical decisions. In response, during 2017 vendors will prioritize the creation of self-service data integration and management tools that will retain free access to data while applying the controls necessary to engender trust among users, trust in the veracity of analyzed data and the validity of the resulting analysis of that data. The trick will be for vendors to apply these controls without forcing IT to play a more dominant role in the acquisition of data. The end result, as espoused by early entrants such as IBM’s Watson Data Platform, should be a readily scalable catalog of secured and governed data resources capable of serving executives, business professionals, application developers and data scientists.

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