What Do Employees Need to Confidently Harness Data Analytics?

Despite many organizations’ best efforts, many companies are falling short of how data-driven they want to be. This often stems from neglecting a certain component of data analytics adoption, whether inadvertently or purposely.

Here’s more on all the different areas that must come together to allow — and encourage — employees to confidently harness data analytics.

Self-Service Tools

We’re experiencing firsthand the ever-increasing importance of data democratization, which entails breaking down data silos that formerly withheld data from users and cultivating company cultures supporting the widespread usage of self-service analytics.

Search analytics empower employees to ask questions and receive answers in the form of interactive data visualizations with drill-down capabilities. This is a vast improvement over legacy systems which required data teams to act as go-betweens, pulling insights and compiling static reports on behalf of employees. Artificial intelligence-driven analytics use algorithms to mine vast stores of data for potentially useful insights that’d otherwise take human analysts hours upon hours to uncover.

These self-service tools shorten the time to insight and present the information in a straightforward way accessible to power users and regular business users alike.

Data Fluency Training

Employees need to draw upon a shared language surrounding data analysis and usage, which is where data fluency comes into play. Data fluency also encompasses the training users need to feel comfortable using analytics tools, analyzing insights, drawing conclusions and communicating their findings with colleagues in a meaningful way.

Without efforts raising data fluency throughout an organization, even users with analytics may lack the proficiency to avoid mistakes like:

-Manipulating data incorrectly
-Taking into account just part of the story
-Making faulty decisions as a result of information gaps
-Asking irrelevant or misleading questions to start
-Failing to ask the right questions when it counts
-Inserting biases into questions or interpretations
-Misapplying data insights to the real business world

Data fluency training, tailored to be as relevant as possible for each user, reduces the likelihood of falling into any of these traps. It also helps boost confidence when it comes to forging ahead with data-driven decision-making.

Data Strategy Alignment

It’s difficult, if not impossible, for employees to take full advantage of data analytics without alignment of data strategy and business goals, tech, people and culture. An overarching data strategy must address and drive these various areas of interest to create an environment in which employees can thrive while harnessing data analytics.

According to the Society for Human Resource Management, the following elements must become aligned in order for companies to avoid “the risk of poor or limited results” in their data endeavors:

-Quality and accessibility of data
-A consistent, companywide data perspective
-Buy-in from senior leadership to adopt and evangelize data
-Business targets tied to data insights
-Employees with the skills to harness data, from specialized roles to “rank-and-file” employees

This need for alignment speaks to the big picture. Tools are only as effective as the culture surrounding them, which, in turn is fueled by leadership and tie-in to business objectives. A single version of the truth everyone can count on depends on the architecture supporting the tools, as well as the quality and usability of data.

Think of these interlocking components as pillars that must all work together to hold up a structure. If one crumbles, the organization becomes weaker if not completely unviable.

Company Culture

As we mentioned above, culture plays a huge role in either promoting or holding back employee data analytics in their decision-making processes. It’s useful to think of culture as the operating environment in which employees use data.

It’s worth asking: How do people talk about data, publicly and in smaller groups? How do they feel about data? How are leaders discussing data? Even more importantly, how are leaders using it? How closely united are business goals and data usage? What cultural barriers are hindering people from taking advantage of information?

Long story short: Employees need access, confidence and data-supportive culture to reap the benefits of data today.