With the rise of self-service data discovery tools, many organizations are scrambling to figure out what types of tools best fit their organization’s use cases: data discovery or enterprise business intelligence solutions?

Truthfully, the rise of data discovery tools came from the idea that business intelligence could fulfill the self-service promise made many years ago -- and vendors strived for that. Amidst the discussion of what tool to employ, the case should be that both approaches to BI should be considered for the varying use cases across the enterprise.

We are all very aware of the powerful capabilities of an enterprise BI tool such as employing governance over data to deliver sophisticated reports to knowledge workers and customers. However, data discovery tools aim to empower users with the ability to accomplish complex analytical tasks all on their own. By providing users with an intuitive user-interface focused on self-service, data discovery tools provide an end-to-end tool for users to run on-the-fly analysis with in-depth analytical functionality.

It now comes down to the enterprise to figure out what are the key differences and how they match the various use cases within their organization.

Approaches to governance

This is the holy-grail question of BI. Enterprise BI tools have historically delivered on the governance front; however, had limited self-service capabilities.

On the other hand, data discovery tools have launched a liberalizing platform for users to connect, prepare, and analyze data all on their own but with little governance capabilities. Instead, they rely on storytelling, social media features, and annotations for report creators to share their analyses with background knowledge on how they arrived to their interpretations of the data.

While both tools differ in terms of level of governance, it is important to consider their core purpose. Discovery tools are meant for users to conduct data mashups and independently find or create insights while enterprise BI is used for information delivery and monitoring.

Time to deployment

Enterprise BI tools provide strong governance, thus demanding a lot of collaboration between IT and business departments, which is partly the reason for longer deployment times. They deliver in-depth functionality such as management tools for users, monitoring and alerting, scalability, and many other characteristics that fit the profile for governed reporting across the enterprise.

Naturally, setting up these tools will be laborious whereas any user can jump onto a data discovery tool and begin analysis. This is definitely a pro for discovery tools considering users can quickly begin crafting rich reports and data visualizations within minutes. Usability as a key aspect for user adoption

Data discovery is set to lead in the usability arena. With self-service capabilities in mind, it’s no wonder discovery tools vendors have focused their resources on improving user experience (UX) to complement functionality. These tools provide an intuitive experience for users to complete whatever analytical tasks their hearts desire.

On the other hand, many enterprise BI tools also provide great UX even in their ad hoc features. The determining factor here is that user experience for discovery tools is often better considering the relationship between usability and analytical capabilities. Discovery tools simply deliver better user experience in the self-serve space. But, this is not to imply that enterprise BI solutions suffer from user experience issues.

Enterprise level capabilities

Enterprise BI tools that cater to large enterprises have the necessary features for large-scale deployments for multiple types of users. These features include options for a metadata layer, administrative tools for management, focus on scalability, and applications focused on casual users and developers.

Since data discovery tools are aimed at serving end users, they deliver features for the user rather than the enterprise. There is a demand to expand data discovery platforms to the enterprise level, but there has been little movement on this front to match enterprise BI tools. Gartner exquisitely defined this problem with customers of data discovery:

“Gartner inquiries and survey data suggest that, increasingly, companies would like to expand their use of, and even standardize on, data discovery platforms for their larger enterprise BI deployments, but find that in many cases these platforms lack the necessary enterprise features in relation to governance, administration and scalability (among other things).” -Magic Quadrant for Business Intelligence and Analytics Platforms, 2015

Moving towards a complete BI solution

To this end, enterprise BI solutions excel at reporting, business analysis, and monitoring and alert tasks, but lack in-depth self-service capabilities. Discovery tools fill in the gap for on-demand analysis, prototyping, and data mashups, but leave organizations much to desire for enterprise features.

Riding on Gartner’s bimodal IT recommendations, businesses must analyze their needs in both spaces-- for governed business intelligence as well as rapid, one-off analyses. This will allow organizations to balance governance and user empowerment, allowing departments to have the in-depth analytical richness of a discovery tool while ensuring operational analytics are governed and monitored via enterprise BI tools.

To date, no vendor provides an all-in-one solution that fits the needs of both BI styles. Enterprises must define their requirements and deploy the tools that best fit their needs whether it is enterprise BI, data discovery or both.

With the knowledge of these key differences, IT can begin their search for the best BI approach to enrich the work of their users.

(About the author: Leo Zhao has been a senior business intelligence consultant at Jinfonet for over 10 years. Prior to working at Jinfonet, Leo was involved with the systems integration and business intelligence industry for almost a decade. He can be reached at lzhao@jinfonet.com)

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