Governed analytics and discovery

Balancing business user and IT needs

It’s no secret that subject-matter experts in LOBs, departments and corporate management have become frustrated waiting for IT to deploy business intelligence (BI) tools – especially when the applications they receive are primarily for managed query and reporting. Not the flexible, ad hoc analysis they crave.

Because of this, easier-to-use, self-service tools for BI, analytics and data discovery are spreading by popular demand, often outside of IT control. This has created tension between IT and business users – some political, but much of it valid – as the democratization of BI and analytics does have the potential to introduce data chaos.

The TDWI Checklist Report, “Gaining Business Value from Governed Analytics and Discovery,” suggests seven steps your organization can follow to balance governance needs with the expanding use of self-service visual analytics and discovery tools.

Download the TDWI report

As business users move from spreadsheets to powerful visual analytics and discovery tools, governance issues must be addressed to ensure accurate insights – things like data quality, proper access credentials, reuse of analytical models and dashboard standards. Here are some highlights from the TDWI report.

Step 1: Address people and process challenges

Visual analytics and discovery technologies are often chosen by LOB units or departments seeking to solve specific business problems – and the technologies are obtained without IT involvement. When business users succeed in their discovery endeavors, they want to share their work with colleagues who then want to apply the same technologies. This is where IT and BI teams should get involved so projects are properly governed. But they need to do so as enablers not obstacles. TDWI offers two best practices that organizations should consider adopting.

Step 2: Make solid and balanced governance a priority

Striking a good balance between user freedom and governance is critical, and organizations should avoid imposing unnecessary rules just for the sake of control. TDWI strongly recommends governance committees because they can facilitate productive discussions between business users and IT so that everyone is on the same page.

Step 3: Improve data preparation processes for governed analytic discovery

Long waits for IT to prepare data defeat the purpose of fast, self-service analytics and discovery. TDWI offers several tips for improving data preparation, including investigating new self-service data preparation technologies as well as improving traditional ETL routines. Well-managed data preparation is truly critical to analytical success.

Step 4: Use governance to guide users' interactions with integrated data

Business users often need to look at trends, find correlations and pursue other insights by looking beyond just one source. Organizations should evaluate new approaches that give users access to data views built from broader, varied data sets and the ability to access and analyze multiple data sources. Data integration is crucial for users of visual analytics and discovery tools. Governance processes ensure users are viewing high-quality, relevant data and can also help protect sensitive data from wrongful exposure.

Step 5: Increase agility by governing projects to deliver faster value

Increased agility is one of the main reasons organizations want to move from traditional BI and data warehouse environments to systems that allow for fast-paced adjustments to changing markets, modifications in customer preferences and emerging new competition. Because many visual analytics and discovery tools are so self-service, they’re perfectly suited to faster, more agile decision making. Governance approaches must adjust to fit this quickened pace.

Step 6: Train users to take advantage of data visualization and storytelling

While dashboards are often the first foray into the world of data visualization, users need better ways of consuming data that will inform their data-driven decisions. Visual data interaction, the ability to drill down to deeper layers, geospatial analysis and data storytelling are all on the list of popular data visualization technologies that enable subject-matter experts to work more effectively with data – and increase collaboration with colleagues.

Step 7: Use governed analytics to produce smarter operational decisions

Improving operational efficiency and effectiveness has always been one of the top reasons for deploying BI solutions, and it remains a key driver today. Users are implementing self-service analytics tools so they can better understand their data and goals – and they want more than just spreadsheets to analyze operational performance. TDWI recommends three areas of focus to help users improve operations using governed analytics and discovery, including setting user expectations about the quality of near- or real-time data to which data quality processes may not have
been applied.