Why Is BI and Analytics Adoption Still So Low?

Andy McCartney's picture
 By | August 11, 2017
August 11, 2017

I read a blog last week by Cindi Howson at Gartner, who was considering why business intelligence (BI) adoption is still so low at 32 percent of employees (Cindi's blog here.). Even after the deluge of visual data discovery tools and the tidal wave of analytical self-service, a large majority of people inside organizations are still not able to leverage data to make better decisions and execute smarter faster business. Ouch.

And that is just inside organizations. What about the additional stakeholders such as customers, prospects, and supply-chain partners, all of whom could easily benefit from insight related to their business relationship with you. So the percent of people in your corporate ecosystem who are executing data-driven business is likely much less than 32 percent. Not ideal in this day and age.

We have written a white paper that gets into the rationale and reasoning behind pervasiveness and adoption, and it is worth a read as our WebFOCUS platform is renowned for its ability to cater to a broad range of BI and analytical needs, which results in several learnings and recommendations that we are happy to share.

Bottom line, if you want to achieve rapid ROI and maximum value from your business intelligence and analytics environment, you need to understand all the dynamics to ensure it’s widely and successfully used. More participation means more benefits for your organization.

Here are our five recommendations to promote widespread adoption of BI and analytics across your enterprise.

#1:  Know Your Users, Support Their Varying Needs

Different users have different needs. Taking a one-size-fits-all approach to BI and analytics will alienate portions of your user base and hinder pervasiveness. So it’s important to know who your users are and what requirements they have. Your ability to satisfy the distinct needs of each type of user in your organization will directly impact your adoption rates. Executives and management love their dashboards loaded with KPIs. Your analysts and power business users have the time, skills, and inclination to leverage data discovery and visualization tools.

Your casual business users (who likely represent the majority) are better served by flexible and customizable interactive BI apps – designed precisely for their skill and proficiency levels. These apps can then be deployed standalone, or embedded into their existing applications and systems they use on a daily basis. We calls these interactive BI apps InfoApps, business-driven analytical apps that provide any decision-maker with fast, easy access to actionable information without having to understand the complexity of the underlying data and analytics.

#2: Consider the Real Merits of a Unified Platform

Some companies attempt to satisfy varying needs by deploying several different disparate solutions – one for each different class of user. While this may meet short-term requirements one-by-one, it creates larger problems that will deter user engagement in the long run. This approach risks creating siloed insights that can lead to flawed decisions. Users have their own data sets and their own ways to upload and manipulate information, so chances are they will arrive at different conclusions. Once these inconsistencies come to light, users will shy away from using those tools again in the future, reverting back to old methods of information analysis, such as Excel spreadsheets or turning to IT to create reports.

“Having to deal with different technologies and data sources in the decentralized model can have a negative impact on the users of the BI tools,” states author Richard T. Herschel. “Users have to run reports from different systems, in different business units or departments, and then integrate and validate the data to confirm its accuracy. This is a taxing process which, in many cases, will result in the loss of user adoption.” Furthermore, many of these tools do not scale easily to external stakeholders such as customers, suppliers, and partners – audiences that are critical to promoting the widespread adoption that drives true business value. Your BI strategy must embrace it all, and be powered by a unified, feature-rich platform with capabilities that satisfy the unique needs of even the most diverse user populations. By allowing users to consume and operationalize data in different ways, you can eliminate the potential for “multiple versions of the truth” by delivering a consistent view of information through dashboards and scorecards for executives, ad hoc reports and visualizations for analysts and power users, intuitive and straightforward analytics apps for casual users, and so on.

#3: Pay Attention to Performance

Some BI and analytics initiatives experience low adoption because of poor performance. Large data volumes, poorly designed data infrastructure, an increasing user base, and a high number of ad hoc or unplanned queries are just some of the factors that can negatively impact response times. Regardless of the underlying cause, users can become impatient and will seek out other ways to get the information they need. “In the age of Google, casual users expect immediate response to on-screen clicks and gestures,” analyst Wayne Eckerson says. “If users have to wait ten seconds or more on a regular basis, they grow impatient and stop using the BI tool.”

#4: Make Sure Your Data Is Trustworthy

Your users expect your BI and analytics environment to serve as a trusted means of gathering insight for planning and decision-making. However, recent surveys show that 51 percent of BI, analytics, and information management professionals consider data quality to be a barrier to BI success. If your users question the accuracy and consistency of the data being delivered to them, they’ll abandon your solution and find other ways to get the information they want. Almost every organization today has data quality problems. Close to 60 percent of companies consider their data to be unreliable, with as much as 25 percent of the information in the average database containing inaccuracies. Proactively identifying and correcting these issues will encourage users to embrace the environment as a valid and reliable source of insight, eliminating the use of disparate and disjointed reports and spreadsheets, or one-off data discovery tools.

“Casual users won’t use a BI tool if they feel it delivers inaccurate or incomplete data,” claims Wayne Eckerson. “They will force BI managers to validate or reconcile the BI data with the other sources of data before they trust it, a laborious process that can take months or years.” Choose a BI platform with integrated data quality management and master data management capabilities that will prepare and optimize data for analysis by ensuring its accuracy, completeness, timeliness, and consistency at all times. By embedding capabilities like profiling, cleansing, matching, and merging directly into your environment, you can build confidence in your data and promote greater adoption – without spending a significant amount of time manually finding and fixing bad data.

#5: Go Outside the Firewall

Nothing will expand your user base faster than opening up your BI and analytics environment to your customers, business partners, and other third-party stakeholders. This audience contains thousands, possibly tens of thousands of users who can benefit greatly from improved access to information. “Which in the grand scheme of things has a higher priority, customer-facing vs. back-office analytics?” asks Forrester’s Boris Evelson. “Well, in what Forrester calls the age of the customer (AOC), the results are in. Customer-facing priorities trump back-office priorities and business users rule.”

The benefits of customer-facing BI and analytics extend far beyond pervasiveness. Enhancing the customer experience with convenient, information-based services can boost acquisition, loyalty, and retention. Many organizations have even opened up new revenue streams by deploying fee-based information services to clients and partners. “Although developing customer-facing analytics is becoming a goal of many organizations, doing so effectively can be challenging because it requires a mindset that is different from the one required to create internal business intelligence applications,” says analyst Lyndsay Wise.

Data quality once again plays a crucial role. Customers, like employees, will come to rely on the service as a source of trusted data. If the information being shared is inaccurate, the environment that was once seen as a competitive differentiator will quickly become a liability. Information is the most valuable when many stakeholders can leverage it. That’s why high levels of user adoption are so important to achieving maximum return on your BI and analytics investment.

Thanks for your time today. The full white paper expanding upon the research and recommendations above is available here.