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Analytics Adoption: Why Analytics on Analytics Matters
Category: Analytics

Analytics Adoption: Why Analytics on Analytics Matters

One of the primary challenges that most companies experience with data is how to get more employees to embrace their analytics and business intelligence (BI) tools. After investing heavily in these platforms, many organizations continue to only see limited adoption rates around 20-30%.

As analytics and BI teams struggle to engage more business users, many factors have contributed to the low analytics adoption rates. For example, without an involved and committed executive sponsor, organizations won’t have anyone who can provide the necessary influence, support, and prioritization to drive their data analytics adoption. Data literacy is another familiar adoption barrier. Unless your business users have gained basic data skills and are comfortable working with data, the only ones who will benefit from your analytics and business intelligence tools will be your analytics staff.

A Hidden Roadblock to Analytics Adoption

confident business man walking

Having worked in the analytics space for nearly 18 years, one common roadblock that isn’t mentioned as much is something I call the Shoemaker’s Shoes Syndrome. It’s based on a mid-sixteenth century saying that a shoemaker always wears the worst shoes (in some versions, it’s his kids’ shoes). Essentially, the tradesman is so focused on his customer needs that he fails to take care of his own (and those of his family).

This same problem occurs in the analytics industry whenever we see a lack of analytics-on-analytics, which is the measurement and analysis of an analytics tool’s performance and usage. Today, most large companies have dedicated analytics and BI teams generating all kinds of reports and dashboards for key stakeholders and internal teams. However, when you ask them who is accessing these reports and how frequently they’re being viewed, there’s a good chance you’ll be met with blank stares.

On many occasions, I’ve encountered organizations that had simply no clue how often their dashboards, reports, and analytics tools were being used. For example, analytics teams are often shocked to discover none of their leaders are actively using the executive dashboard they built and maintain. In these cases, they are so fixated on the delivery of clean, accurate, and timely information that nobody is even paying attention whether it is actually being used.

In other situations, organizations may only have weak or limited analytics capabilities in their tools, making it difficult for them to really know how effective their data analytics adoption strategy is. As a result, like the shoemaker with shabby shoes, many analytics teams are not applying their data expertise to comprehend how they can expand tool usage and optimize user engagement.

Improving Analytics Adoption is Impossible Without Measurement

man taking notes on analytics adoption

If your organization’s goal is to become more data-driven, having a clear understanding of the current analytics usage and adoption will be critical. Quality control expert H. James Harrington stated, “Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.” Without an accurate picture of how analytics is being used at your organization, it’s going to be very hard to manage adoption, track your progress, and improve it over time.

In my experience, analytics-on-analytics is a great barometer for determining a company’s level of data maturity. I’ve found organizations that are performing analytics on their own analytics platform are more likely to be making progress towards establishing a data-driven culture than those that aren’t. They understand using analytics on their analytics tools can yield some of the following types of insights:

  • Content refresh: Reveal which content is being actively used and which content may need an update to become more relevant or useful to end users
  • Feature usage: Identify which features and functionality are being used or overlooked by users, which can inform future training activities or feature enhancements.
  • Team engagement: Highlight which business teams are avid users of the platform and which teams may need additional training or support to fully embrace it
  • Key advocates: Pinpoint potential power users who can be recruited to help with support and training efforts
  • Data quality: Alert the analytics team to potentially broken data pipelines or potential errors in the datasets that could impact the overall tool experience
  • Business case: Quantify how important analytics is to the business and justify additional analytics headcount to support ongoing usage

All of these usage and performance insights can help inform organizations’ data analytics adoption strategies and drive greater returns on their analytics investments. If we truly believe in the power of data, why would we limit analytics to just the business and not the analytics platform as well? The more people who are using the analytics tools and finding success with them, the more data-driven your culture naturally becomes. While currently underappreciated by most companies, analytics-on-analytics will be another way in which data-savvy firms will outsmart and outperform their competitors.

Analytics-on-analytics will be another way in which data-savvy firms will outsmart and outperform their competitors.

Make Analytics-On-Analytics a Key Business Priority

As you explore your analytics-on-analytics practices, you may want to re-examine your vendor’s current analytics capabilities. Too many analytics solutions view this functionality as a trivial checkbox feature with little development focus and support. Surprisingly, if a vendor provided better analytics on its own platform, it could not only empower its customers with valuable insights into how they can expand user engagement and adoption — but also benefit from its clients’ efforts by expanding its user base and generating more renewals. Look for analytics vendors that understand the importance of analytics-on-analytics and want to help you get the best possible return on your data investments.

Brent Dykes
About the Author

Brent Dykes is the Senior Director of Insights and Data Storytelling at Blast Analytics. He is also the author of Effective Data Storytelling: How to Drive Change with Data, Narrative, and Visuals. Brent has more than 15 years of enterprise analytics experience at Omniture, Adobe, and Domo. His passion for data strategy and data storytelling comes from consulting with many industry leaders including Nike, Microsoft, Sony, and Comcast. He is a regular Forbes contributor and has written more than 35 articles on different data-related topics. In 2016, Brent received the Most Influential Industry Contributor Award from the Digital Analytics Association (DAA). He is a popular speaker at conferences such as Strata, Web Summit, Shop.org, Adtech, Pubcon, RISE, Crunch, and Adobe Summit. Brent holds an MBA from Brigham Young University and a BBA in marketing from Simon Fraser University.

Connect with Brent on LinkedIn. Brent Dykes has written on the Blast Digital Customer Experience and Analytics Blog.

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