Straight Talk on Data Driven Culture

Ten years ago, research cited in a?Harvard Business Review?article concluded that:

“Managers need to wake up to the fact that their data investments are providing limited returns because their organization is underinvested in understanding the information.”

Despite technology advances over the past decade, including the Cloud, I’d say this applies to most organizations even today.

I can count on two hands the number of technology companies that have made “data driven culture” the centerpiece of marketing at some point over the past ten years, which itself says a lot about the number of organizations successfully making this shift in thinking.

Just about any company that sells something data or analytics related has attempted to persuade their buyers in this manner. After all, research shows that adoption and business value of data driven practices begins with culture. It’s difficult to sell additional analytic technology products to an organization struggling to achieve a return on their current investments.

Yet after more than a decade of seeing these messages, most business leaders and their technical counterparts continue to struggle. A lucky few have figured it out and continue to pull away from the pack.

Unlike anything disruptive before, the Pandemic highlighted this dynamic across industries and reignited the importance of data driven culture to digital transformation success. If the definition of insanity is doing the same thing repeatedly and expecting a different outcome, what else can be done that hasn’t already been tried?

In the ideal case, organizations create a strategy for analytics, based on use cases that reflect business priorities and rationalized with existing technology and skills. This can be exceptionally challenging since it requires a forensic analysis of decisions made over many years by different stakeholders. The situation leads to quick fixes often rooted in technology searching for a problem to solve.

Making it all a bit easier today is the Cloud, which McKinsey describes in a March 26, 2022 article titled,?How the cloud has moved advanced analytics from exclusive to accessible. The title says it all — Cloud democratizes access to advanced analytics such as AI by eliminating barriers to adoption and value, like the necessity to deploy software in your data center. However, ultimate success still hinges on a cultural evolution, and so McKinsey recommends “communicating a change story”:

“Insights lie within the data, but the ability to see those insights requires an organizational culture that believes in the importance of data, encourages curiosity, and uses data in all decision making. This includes communicating a change story of new decision making within the organization, being a living example of new data-based decision making, and close monitoring of the adoption of new solutions, including mitigating measures in cases of nonadoption.”

Contrasting this quote with the one ten years ago in HBR illustrates how little progress has been made.

Before you can scale, you need to start with a baseline from which you hope to improve and a plan to communicate progress to stakeholders, especially the C-suite. This is a step missing in many organizations struggling to realize business improvements from data and analytics.

Executives running the business on a set of old or new metrics can’t sponsor data driven improvement if they don’t have a lens into how analytics unfold and create value across the business in support of operational and strategic decision making.

At Alteryx I recently developed a?template?to help anyone identify their best opportunities for business improvement through Analytics Automation — a low or no code visual approach to analytics development accessible to workers of all skills that embraces automation to do more with less.

To realize the most value from Analytics Automation, it should be applied to as many use cases as creates meaningful value to the business. The lowest hanging fruit are often operational decisions suffering from a lack of detail or timeliness. Analytics Automation closes this gap by eliminating barriers to data, detail, and speed, while opening up time for analysts to explore more advanced (and valuable) analytic methods like machine learning.

Alteryx for many years has been the choice of analysts and other data workers to accelerate and automate the hard work behind operational and strategic decisions. Today the Alteryx application known as Designer forms part of the Alteryx Analytics Cloud, which accounts for other important analytic processes such as data engineering, self service insights, and automated machine learning.

Using a framework to also document the current state is a good idea to begin creating a change story for analytics improvement in your organization like McKinsey describes. However you do it, knowing where you’ve been is the best way to ensure you move forward.

This post was adapted from Data driven culture starts here