Key Takeaways:
In every meeting, strategy discussion, and boardroom, individuals strive to be data-driven, but in most instances, they fail to take data to action. What is data to action? More importantly, what is it not? Whether it’s the newest artificial intelligence tool, a new dashboard, or a data warehouse, all too often we start with a solution already in mind instead of first asking the right question to frame the dilemma.
Starting the data to action process with the solution is among the most often occurring errors. Whether it’s “we need AI” or “we need a new app,” the emphasis on technology sometimes comes before a thorough understanding of the current problem. This solutions-first approach can lead to wasted resources, frustration, and failure to reach the intended results.
Consider how frequently you have heard someone say, “We need a dashboard so we can click through a bunch of stuff,” only to find the dashboard hardly used once it is produced. Alternatively, companies will spend a tremendous amount of time and money on a data warehouse to store their data, and never look at it again. These illustrations show the risks of rushing directly to the action without first fully appreciating the context or problem.
The right information to act upon begins not with a solution but with a dilemma. A dilemma is really the issue or question that has to be answered. It forms the basis of the whole process and prepares everything that comes after.
The starting point of the data to action process creates a dilemma. Ask yourself, instead of assuming things, what is the actual pain point? Who’s your audience? What possibilities and constraints exist? If someone remarks, “We need AI,” for instance, the answer should be, “Why do we need AI? What problem are we trying to solve?”
Focusing on the difficulty helps you pinpoint the actual problems that require attention and create well defined objectives and expected results. This phase also entails spotting organizational champions with a taste for creativity who can move the process along.
Data discovery comes after the pain point is revealed. This involves compiling measurable data and insights that accurately represent the situation. Data discovery is the study, cleaning, and extracting of useful insights from the accessible data. Though it should be enough to guide decision-making, it’s important to keep in mind that the data might not always present a flawless picture of reality.
In addition to numbers, data discovery includes other reality captures such as conversations, experiences, art, and other information. This all-encompassing approach helps the business have a more complete understanding of the circumstances before moving to the next step.
You should enter the action phase only after careful investigation of the problem and the facts. Here you present your story—a narrative linking the insights to actionable next steps. The narrative might develop as an email, a chat, a presentation, or perhaps a dashboard. Whatever the style, the narrative should succinctly highlight the problem, offer the evidence-based analysis, and outline doable objectives.
Remember that the narrative is a conversation starter; it is not the end. Good data to action strategies result in dialogues that lead to decisions, plans, and actual action. It’s an iterative process whereby comments on first steps could take you back to re-examining the facts or honing the original question.
Beginning with a solution sometimes results in confirmation bias, where the pre-determined answer is justified using the stages of problem and data discovery. As the real issue was never correctly identified or handled, this gut-driven approach can provide mismatched results.
If you conclude, for example, that artificial intelligence is the answer before knowing the problem, you could find yourself using a technology that either aggravates the actual problem or generates new ones. The same is true of other technologies or initiatives; without a clear awareness of the problem, the solution may not be appropriate.
Building trust and buy-in is vital for the data to action process. In the dilemma stage especially, this is crucial. People must believe their opinions are acknowledged and that their involvement counts. Involving stakeholders in the definition of the problem and data exploration increases their likelihood of supporting the end results.
The data to action process is iterative rather than linear. You might find that your current data is insufficient, or that fresh data reveals a different kind of problem. This adaptability of the process is a strength rather than a weakness. It enables you to change and improve your strategy, producing better, more powerful actions.
A good data to action system depends on asking the correct questions on the front end. Starting with the problem and carefully going over the facts can help you to build a narrative that calls for meaningful action. This method guarantees that your activities are based on truth and have more chances to provide the intended results.
The objective is to methodically advance from knowledge of the problem, through data discovery, to actionable insights that propel actual transformation rather than rushing to answers. You can make data meaningful by concentrating on narratives that lead to decisions, discussions, and plans.
In a time where data drives decisions, a data analytics service can help take your organization to the next level. If you want to turn your company’s data into actionable results, a data analytics team of experts can make a significant difference. Investing in a data insights solution today can yield dividends in the long run.
If your business is interested in unlocking the full potential of your data, get in touch with us today.
Learn more about LBMC’s Data Analytics Services here.
Content provided by Charlie Apigian, Lead Data and AI Strategist for LBMC. He can be reached at charlie.apigian@lbmc.com.
