5 hallmarks of a successful data culture
The mark of successful tech adoption isn't the deployment of a new tool or completed upgrade projects — it's how well employees embrace new capabilities in daily workflows.
In 2021, Gartner expects data to move past a nice-to-have for companies across industries and evolve into a core business function. The future of data and analytics means offering a shared business asset that aligns with business results.
To reap the benefits of data analytics platforms or products, executives need to ensure the whole organization can access those capabilities. The task begins with a solid data culture.
A core element to shaping data culture within the organization is to let all employees use data and analytics toward creating business value, said Andrew Beers, CTO at Tableau, speaking last week at the Forbes CIO Summit. "That empowerment really has to come from the top; it has to be something that you're hearing from the executives."
What exactly does a data culture entail? For Beers, it's a set of behaviors and beliefs that shape how people approach data, and how they approach making decisions with that data.
Five elements shape a successful data culture across organization, according to Beers:
Trust: Balancing control of the data with the freedom to use it.
Commitment: People within the organization consistently returning to data as an asset that can help guide decisions.
Talent: Building data skills and capabilities across all teams. "It's not about having the set of experts that everyone can rely on, but really building [skills] across many people.
Sharing: Rather than spurring internal competition, culture encourages learning together.
Mindset: Normalizing the use of data, of analytical thought in decision-making.
"The companies that really take that to the extreme can end up seeing themselves as a data-first organization," said Beers. Data and the systems that help collect it are thought of first, even before products and services since those will be shaped by the outcome of data, said Beers.
At Northwestern Mutual, creating structure around data use means asking tough questions around the ethics of AI, according to EVP & CIO Neal Sample, speaking at the same panel.
This exercise is especially crucial for companies "when the machine intelligence is making a decision that ultimately impacts somebody's financial outcome," said Sample, speaking on the same panel.
One data-fueled application Sample points to is the use of big data instrumentation and artificial intelligence to improve its underwriting process. With data, the company can place the right customer in the right risk category, "pooling that risk appropriately, and ultimately having a better outcome."
Improving outcomes is the final step in a company's data journey. Nearly half of companies say they're working to improve their data quality and processing in the next two years, according to data from an MIT Technology Review Insights report. It takes a mature data analytics infrastructure to positively impact core company processes.
Article top image credit: Adam Berry via Getty Images