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5 Tips for Avoiding Data Analytics Disappointment

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Businesses often plunge headfirst into data analytics efforts — enthusiastically envisioning the instant, game-changing results they’ll start to see. But many organizations, despite their best intentions, end up experiencing a letdown. A recent survey of 200 business leaders found more than half believe their “advanced analytics initiatives” have fallen short in terms of the benefits they expected to gain, as Information Week cites.

This may take several forms. Among them are lower-than-expected return on investment (ROI) and a lack of measurable business outcomes driven by data. In fact, after the ceremonial “ribbon-cutting” ceremony on data initiatives, companies may observe things operating pretty much the same as before — almost as if the data rollout never happened.

It doesn’t have to be this way, though. There’s much organizations can do to optimize their chances of maximizing the value they’re getting from data. Here are five tips meant to help enterprises avoid this so-called data analytics disappointment.

Focus on Actionable Insights Tied to Outcomes

It’s exciting to learn how much data companies can capture, store and analyze nowadays. Businesses may even find themselves bogged down by an overwhelming quantity of data, something some experts call “analysis paralysis.” It’s not at all uncommon for organizations to inadvertently start tracking metrics and creating reports with weak — or non-existent — ties to business objectives. This eats up valuable time and hampers measurable ROI.

Focusing on deriving actionable insights tied to specific business outcomes is very important. Use data first and foremost to address current pain points and work toward concrete performance goals outlined by a data strategy. 

Bridge the Gap Between IT & Business Users

In the past, many companies harnessed legacy analytics systems in which IT teams acted as the overseers of data — while “average” employees had to make reporting requests, lacking the ability to interface with data themselves. However, the latest wave of data analytics platforms democratize data, allowing business users and power users to derive insights.

Still, it’s important to bridge the gap between IT specialists and business users. Both parties bring something different to the table: business users understand which insights drive desired outcomes, while IT experts facilitate the technical solutions. As one expert writes for CIO, it’s important to “build the proper feedback loops” by opening the lines of communication between these teams.

Lead from the Top

To put it plainly, analytics projects often fall short of expectations without buy-in and active participation from executive-level users. Ensure leaders are championing analytics at every turn, not to mention demonstrably using these tools in their own decision-making processes.

Help All Users Become Data Fluent

It’s important to remember IT and data specialists have years of training most other business users lack. According to a Harvard Business Review research report sponsored by ThoughtSpot, a leading barrier to data-driven decision making is frontline employees “lack skills to make appropriate use of technology-enabled insights.” In fact, nearly one-third of executives (31 percent) see this as a barrier.

Employees need to know how to use the tools at their disposal as well as how to interpret insights and use the information they glean. This highlights the importance of bolstering data fluency across the entire workforce with relevant training.

Support a Single Version of the Truth

Multiple versions of the truth can undermine even the best-intentioned data efforts, as it allows different stakeholders to operate under different assumptions — and can even undermine employee trust in data as these conflicting reports come to light.

Implementing a business intelligence architecture capable of bringing together many disparate data sources to furnish one universal version of the truth is imperative to establishing clarity, transparency and trustworthiness in the eyes of users. As one expert notes for CIOReview, multiple versions of the truth downstream indicate the problem starts farther upstream — with architecture. And this breeds potential stakeholder misalignment.

When companies invest time, money and human power into data analytics efforts, they want to see measurable results. These tips can help stave off many common sources of post-deployment disappointment.