5 Reasons Enterprises Still Struggle with Data Analytics Adoption

data analytics adoption

While the field of data analytics has advanced by leaps and bounds over the last few years, many companies remain unable to capitalize on data insights in the game-changing way they’d hoped. Despite the surge in existence of advanced analytics solutions, the gulf between deployment and adoption by employees remains wide at times.

According to Harvard Business Review, more than three-fourths (77 percent) of executives considered adoption of data initiatives a major challenge in 2019 — more than the 65 percent who said so the year before. Furthermore, most of these respondents (93 percent) said “people and processes” were the primary obstacles in this department, which just goes to show it’s never as simple as rolling out analytics tools and expecting users to pick them up right away.

Here are five reasons enterprises are still struggling with data analytics adoption.

#1: Resistance from Leadership

A recent Harvard Business Review Analytic Services study sponsored by ThoughtSpot found this to be another key obstacle enterprises are encountering as they aim to become data-driven. Despite the fact that 91 percent of leaders believe supervisors and managers play a crucial role in allowing front-line workers to harness data, more than half (51 percent) admit these leaders are “not well equipped” with the know-how, software and training they need to facilitate adoption for front-line workers.

If leaders resist becoming transparently data-driven — in how they handle their own job roles and how they communicate with their direct reports — culture as a whole suffers and widespread adoption is less likely to take root.

#2: Absence of Data-Friendly Culture

The HBR survey cited in our introduction also found nearly a quarter of executives (24 percent) to consider “cultural resistance” a leading factor in these analytics adoption struggles. It’s useful to think of data culture — or lack thereof — as the pervasive environment in which data efforts can either flourish or falter. It’s about the overall attitude toward data.

Does your company encourage asking questions of data? Do decision-makers actually use available data insights to drive change? Is data-driven innovation noticed and rewarded? Do employees have the time and bandwidth to explore data, or is it viewed as an optional add-on to “regular” jobs?

#3: Lack of Accessible Tools

Perhaps the most basic consideration is the analytics software a company uses. The limitations of legacy tools are more apparent than ever — these include siloed data, non-democratized access to users across an org and long wait times for reports.

These pain points illustrate why the latest wave of embedded analytics prioritizes a straightforward user experience, interactive data visualizations, near-immediate insights and easy sharing of information aided by the ability to infuse existing workflows with analytics.

The good news is rolling out the right tech, like search-driven analytics, can help companies overcome some of their adoption struggles. The research firm Gartner predicts half of all analytics queries will use search, voice or natural language processing (NLP) by the end of 2020. In other words, when asking questions of data is about as simple as using a search engine, employees are more apt to do so regularly.

#4: Underdeveloped Data Fluency

Another important consideration is how adeptly employees can use the tools at their disposal. While power users — like data analysts and scientists — have extensive experience working with and interpreting data, many general business users often do not. This necessitates meeting teams where they currently are with role-relevant training.

Data fluency includes the ability to effectively use available tools, in addition to being able to analyze data confidently and communicate with others about results. Accomplishing this requires the creation of a universal “language of data” everyone throughout the company can use to get on the same page and collaborate around data findings.

#5: Lack of Tie-in with Business Objectives

Driving analytics initiatives with clear goals tying into business objectives tend to encourage employees to incorporate insights into their workflows. Pushing data for its own sake can actually deter adoption, not to mention lead to users losing sight of the forest for the trees. As one business intelligence expert recommends for TDWI, ensure your data strategy is aligned with your business strategy so people know “what you’re trying to achieve with it.”

While enterprises are still dealing with many formidable challenges associated with analytics adoption, concentrating on these five areas will help address common pain points — and, most importantly, get business users analyzing data for the greater good of the company.

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