Integrating data into decision-making from the ground up
Organisations today collect more data than ever before, and whether or not they use that data to drive business decisions can be the difference that makes or break them in a fast-paced environment.
In order to implement a change that can be sustained, organisations need to find a way to embed it into their culture.
Tableau shares four steps organisations can take to build a data-driven culture from the ground up.
Step One: Build consensus on the value of data and insights
Getting people to buy into a data-driven approach is critical to embracing a data-centric culture in your organisation.
Employees must understand that data is fundamental to the company's value and success and that organisations that are better equipped to make sense of their data will do better than those who are not.
Where there is resistance to using data to make decisions, there will be barriers to new technologies that aid analysis.
Organisations can start by focusing on making data widely available across the company.
Make analytics capabilities available at every level and reinforce the importance of making every decision data-driven.
Reinforce the behaviour by bringing data and analytics directly into decision-making meetings and answer questions in real time.
Measure how data is used.
Understand its impact.
Finally, build a community that evangelises it, including with executive support to reinforce its importance.
Step Two: Demystify smart analytics
Often people will avoid what they don’t understand, and they hate to look foolish by not understanding something.
We need to help people realise that most of us don’t really have a grasp on smart analytics.
It is a relatively new field and we’re all still learning.
Education and transparency are key to wider trust.
As algorithms and models become more sophisticated, it’s critical they don’t become incomprehensible.
The concept of “explainable AI” is a powerful one—I should be able to understand the operations and logic that were applied to come up with an answer.
This helps build my conviction that the answer is right.
AI techniques need to expose their inner workings, while at the same time helping us acknowledge and avoid the biases that humans tend to introduce to analytics.
This combination will help leverage the best of both worlds—human and machine.
Step Three: Help people see smart analytics can help them, not replace them
People will not trust something if they believe it endangers their livelihood.
However, people should view smart analytics as a way to help them perform better, instead of a threat to replace them.
We collectively need to quell misconceptions like “AI is going to replace my job” and help people understand how machines learn from data—not experiences.
Smart analytics can help employees make better decisions to increase efficiency, automate, personalise the customer experience, differentiate versus competitors, and more.
Step Four: Promote data literacy
Tools and technology are certainly important parts of the greater movement, but employees must also learn to think critically about data.
They need to understand when it’s useful and when it’s not.
Acting on the wrong data—or wrong recommendations from a “smart” machine—will lead to bad decisions and wasted resources.
This is where data literacy, critical thinking, and people development come in.
An impactful data education requires both practical and creative skills.
Introducing smart analytics into business processes will require trust in these technologies alongside good judgment from the workforce.
Even more experienced data scientists may have hesitations—why, if they have tried and true experience, should they trust a machine?
Less experienced users will need to learn how to interact with and validate smart technology recommendations, or to interject human knowledge to correct course.