Value series: Measuring and adding value go hand in hand with modern analytics

Editor's note: This is the fourth post in our series about the value of analytics for transformation. We invite you to revisit the previous posts about technology evaluation, agile analytics deployment, and measuring value.

In the last post of this series, we discussed holistically measuring the value of your analytics investment to prove ROI. These methods for measuring value are consistent with the activities your analytics program needs to be successful. So let’s look at a few important factors for building activities into your modern analytics program that encourage stronger returns on your investment. With these approaches ingrained in your analytics strategy, you’ll add measurable value to your users and deployment faster.

You won’t see much value by throwing a modern analytics solution at your workforce (or rapidly expanding its use) without thoughtful change management. For people to feel empowered, you have to meet them where they are—which can be through training and enablement, whether free or paid, internal or external, or by introducing the platform in a more relaxed context, like having them practice with or explore fun datasets that aren’t related to their day-to-day. We often hear from our community that people who regularly use Tableau for passion projects often learn new problem-solving techniques to bring back to their work setting.

You should also consider a formal process for helping users try more advanced analytics capabilities. Some organizations create their own internal certification programs to signal when they are ready to uplevel their data access or responsibilities—for example, a Viewer might be ready to upgrade to an Explorer after passing a certain test. A formal process for adjusting licenses as people increase their literacy also serves as a great indicator of the growth and success of your analytics program.

Companies don’t often think of tracking how much of their data is actually accessible for analysis. Of the billions of records and tables you have stored in various places, what percentage of it is available for people to actually ask questions? And can employees with a reasonable level of technical understanding connect to and explore a high percentage of your data? It’s certainly worth measuring as your deployment matures. Consider providing broad access to data while leveraging the ability to certify content (data sources, analytics, and dashboards) that is accredited for business decisions. This way, all users can explore data, but also see which analytics should be trusted and which should be scrutinized.

It’s also important to recognize that not all data will require the same processes and effort to prep and promote before users can see value. Companies are increasingly investing in unstructured data, allowing them to store more data from the vast array of sources. However, this creates challenges in how users will be able to identify and connect to data. Some technologies (Spark on Hadoop being one example) are built to structure data only at the time it is needed for analysis. Another way you can accelerate value with data lakes or the like is with self-service data prep. Users with the right context and skills can prep the data and IT can later determine if there is value in establishing a more formal process to prepare the data for wider consumption.

Developing and scaling your analytics program is not a linear path. Rather, there should be several areas of concurrent development that will help facilitate stronger growth. Think of it this way: If you start building up an internal community that’s passionate about using data, but don’t have a robust enough deployment to support them, they’ll be met with disappointment.

Monitoring things like who has data access, the frequency of logins, and content engagement contribute to a good baseline for ensuring your investment is being used. But this also helps you get more insight on user behaviors, and ultimately, trends in your adoption metrics can help predict future growth. Understanding how a certain team uses published and embedded data sources to populate their dashboards can drive server scalability and sizing decisions—which in turn inform hardware and licensing requirements, budget planning, and procurement requests.

At Tableau, we’ve worked with hundreds of customers to develop a proven methodology for becoming data-driven—regardless of where you are in your modern analytics journey. Tableau Blueprint offers best practices and actionable steps so you can develop critical capabilities and spread a culture of data across your organization.

Learn more about creating and measuring value with our webinar series. We’re excited to have our customer Dropbox, partner Slalom, and market research analyst Forrester—we hope you’ll join us!