Traditionally the challenge with BI has always been not having access to right data at the right time and difficult-to-use reporting and analysis tools. In today’s information driven market, business users prefer access to data discovery tools that enables them to quickly query and understand data.

Good Data vs. Bad Data: “Trusted” Data Discovery

Data Discovery – Filling the gap created by traditional BI

  • Business driven
  • Provides Ease of Use and Flexibility in the hands of the business user
  • Enables sophisticated and Interactive Visualizations

“Data Silos” – Challenge introduced by Data Discovery Tools

Although data discovery has largely eliminated the traditional challenges related to BI adoption, it has also reintroduced the age old issue associated with “data silos” created by departmental implementations of these new age data discovery tools Each department creates their own dataset thus resulting in inconsistent dashboards in the hands of the senior management. This “multiple versions of truth” makes business question the validity of the analysis and avoid making critical business decisions based on this data.

Flexibility and self-service is key but so is availability of “trusted” data that is eventually essential for the successful adoption of these tools to ensure that the data used by business users for their decision making is accurate and not flawed.

Good Data vs. Bad Data: “Trusted” Data Discovery

Traditional data governance processes revolve around data integration, master data management, data quality  and security which are cumbersome and time  consuming and might not work well with respect to data  discovery tools especially while using data that lies  outside the enterprise network.

Governed Data Discovery –Gartner’s Focus

Data discovery tools in the market today are however lacking the necessary features with respect to data governance and security. In Gartner’s 2014 Magic Quadrant for BI and Analytics platforms, the focus is on this critical market need and as the report writes

“The race is on to fill the gap in governed data discovery”
“Next year, Completeness of Vision positions will in part be determined by which vendors achieve
success in addressing this critical market requirement”

Data Discovery and Governance – What can we do

In the process of getting power to analyze in form of business driven self-service BI, enterprises are not acquiring data through enterprise data sources by passing enterprise security protocols or hierarchical roll up of information that are typically required. While we wait for data discovery tools to mature in this space without compromising on the ease and speed provided,” here are some things that companies can consider in terms of data governance as we continue to use these tools to analyze data

Good Data vs. Bad Data: “Trusted” Data Discovery

Allow visualization or findings to be easily shared among end users For instance, SAP Lumira desktop Tool helps build storyboards and infographics that can be shared easily via Lumira Cloud or the existing SAP platform

Data in the Cloud: Using the data discovery tool to access data in the cloud as opposed to local user systems will enable users to access the same dataset

  • Data governance dashboard: As long as access to metadata is not an issue, data governance dashboards can be built to help address questions related to the data lineage providing visibility into the data sources and data flows between them. Qlikview governance dashboard (Qlikview product) is one such example that allows its users to identify inconsistencies and better manage/govern data
  • “Certifying” the data:< Implementing a rigorous “data promotion” process will help in data governance along with the departments retaining decentralized access to data for their decision making. This process will help validate and promote this data from being individual user generated mashups to enterprise wide system of record

Data governance is integral to any business process and as gartner suggests, data discovery tools need to fast bridge this gap in their future releases providing capabilities to better govern data and increase “trust” in the data discovery process.

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