6 Strategies to Improve Business Analytics Adoption

Businesses today look to extract more value from their BI applications by focusing on a user-first approach to speed up user adoption. Planning for changes across the enterprise and ways to improve user experience is critical when it comes to the success of a new BI implementation. Every BI application is created with the partnership of users, where their detailed requirements are structured, artifacts are designed, and the required functionalities are converted to features. But with every incremental product feature rollout, user adoption never meets the target because users are not accustomed to change.

In such situations, it is important to reduce the gap between the BI objectives and the usage of the application in the post-rollout phase to enable continuous engagement and eventual effective BI landscape evolvement. Also, after any release, continuous assessment is needed to baseline adoption, the impact on the overall BI objectives and re-assess user awareness or design complexities.

Any BI application, website, or product gains popularity when it has minimum complexities, and it follows a simplistic design to help users achieve goals faster and with more efficiency. There is a reason successful organizations like Google and Apple follow a simplistic design approach. BI applications that need minimal user training have a better chance of success. One should also keep in mind that the userbase of such applications would be a wide range of people from across the organization with varied skills, knowledge and expertise, and the solution should resonate with all.

We have worked with several customers across industries in their data adoption and democratization journey. Based on our experience, here are the top things to keep in mind while designing BI programs to enable higher adoption.

1. Drive standardization of data and insights: Invest in creating standardized reports, dashboards and define a data structure that helps organizations quickly reply to user questions, improve productivity and user experience. With the increasing popularity of BI tools and their usage across business teams, there are now many complex and elaborative visualizations available under each tool. Storytelling with data is a powerful method to secure stakeholder buy-in. The key here would be to start with simpler visualizations standardized by analysis type and use complex ones only where they are really needed. Something as simple as a bar graph or a pie chart can provide the right information and does not need much user training. A good example would be standardizing trend reports which give key metrics by analyzing week-over-week (WoW), month-over-month (MoM), and year-over-year (YoY) data using trend charts.
2. Assess product requirements and acceptability: A sluggish application will never be accepted by users. In this era of big data, users must deal with the vast ocean of data to solve problems and leverage product innovations. Implementing solutions that leverage the modern data architecture greatly enhances your ability to rapidly disseminate data and deliver only the essential data subsets to recipients. Faster data retrieval helps improve performance, and hence organizations need to plan an assessment of the digital landscape and how it affects the product or application. Also, applications have different visual requirements and achieving all can be a challenge, hence it is important to have realistic expectations set with the users and all stakeholders. A good example of this is, if end users do not need the entire data, aggregated datasets should be provided. Certain data limits can also be put in place over a larger dataset to allow efficient retrieval for maximum use cases. This should be agreed upon upfront with the stakeholders.
3. Facilitate AI-Enabled Self-service: With increasing user expectations, customization and personalization play a vital role in setting any product or application apart from others. Leveraging AI in business analytics significantly enhances user adoption by providing advanced features such as search-based queries for self-service, enabling users to generate insights quickly without extensive technical knowledge. Generative AI can further enhance the experience by providing personalized responses, tailoring the analysis to specific user queries and preferences. This empowers users to explore data and discover new possibilities, fostering deeper engagement with the data. Moreover, AI can streamline the preparation of datasets, making them more user-friendly and ensuring that users receive relevant insights. This seamless integration of AI in business analytics not only boosts adoption and creates a more intuitive and efficient user experience, driving greater satisfaction and productivity.
4. Define data dictionary for seamless data management: Larger enterprises have many divisions and sub-organizations which in turn have different taxonomies and terminologies for the same data elements. It is important to have a data dictionary accessible from the application which would easily explain different elements used in any report. This saves a good amount of time and reduces basic questions about applications or any of the associated processes. Tools like MicroStrategy and Tableau provide features like attribute description and field description respectively which can be used to enter business definitions of every field. This is available for any dashboard, report, or self-service report which uses the same object thereby creating an in-sync environment.
5. Evangelize application and train users: Internal marketing should be part of every BI application’s overall planning. Creating awareness across teams helps increase adoption, cross-functional learning and promotes internal systems. It is also equally important to plan and roll-out a well-defined training strategy by working closely with training and other relevant teams. Key user training material should be made easily available which helps users tackle frequent questions and queries. Ensure your training strategy includes the following:
   1. Training videos for how to use a report or dashboard
   2. Training gifs for features like how to change a filter, move or add columns
   3. User manuals available for quick download
   4. Help section for Q&A requests
   5. Help desk set-up for users to come in/log tickets to clarify queries if any
6. Analyze usage and publish performance metrics: To gauge if insights delivered are really helping the users, it is important to have certain usage metrics tracked for analyzing performance. Analyzing valuable metrics which show usage and user analysis is critical to track adoption and its trends. These metrics help us analyze and improve the application by removing unwanted objects, starting proactive maintenance, and improving the overall health of any business application. Below are a few common examples of metrics we have implemented for our customers to track usage. These are typically monthly, and variance compared with previous months
   1. Users added every month
   2. Total new users
   3. Active and inactive users
   4. User activity analysis
   5. Time spent per activity/execution
   6. Total reports accessed
   7. Usage of collaboration features

In Summary, an ideal application that guarantees user adoption would be the one that requires minimal user training, is simplistic in design, and answers the maximum user queries with ease. Focusing on excelling in these areas as part of the product planning ensures return on investment for initiatives across BI landscape and the overall digital ecosystem. It reduces the overhead on business stakeholders like IT teams, Support, Finance, and others, who spend nearly 10% of their bandwidth in catering to basic questions coming from various users of such applications.

To know more, read our e-book: Improving User Adoption: Bridging the gap between data and users on how Infocepts can help you transform your BI application portfolio. Talk to us to know more.