Using Data-Driven Decision-Making to Drive Business Growth
- Posted by GM, Digital Solutions
- On April 1, 2021
- Artificial Intelligence, Business Goals, Data, Data-Driven, Digital Business, Machine Learning
An Overview of the Data-Driven Process
Thanks to recent advances in cloud computing technology, modern enterprises can gather, store, and process more data than ever before. With this wealth of information comes the tremendous potential to optimize processes, increase productivity, enhance customer experiences, reduce costs, and much more. In short, data-driven decision-making (DDDM) underpins the trajectory of business growth.
So, what exactly is data-driven decision-making? Data-driven decision-making is when organizations use insights derived from data to inform future actions, investments, and strategic planning.
Data is the powerful resource that can significantly de-risk projects, uncover new growth opportunities, and empower leaders to align business activities with overarching goals.
In a previous post, we defined a data-driven organization as one that builds a culture around using data to drive nearly every decision. Consequently, data-driven decision making is more than something a few people practice on a few occasions – it’s all encompassing. Once it’s fully integrated into a company’s culture, data-driven decision making can truly transform organizations.
Overall, DDDM is growing more prevalent. However some enterprises are making more progress than others. According to a 2019 survey from NewVantage Partners, only 28% of 64 C-level executives reported that their organizations had built a data culture, and 69% reported that their organizations were not data-driven. It’s clear that today’s leaders still need support in this area.
Below, we explain how Daitan views the data-driven decision-making process at a high level and share best practices around how you can start using data across your business today. Our hope is that this post will put you one step closer to achieving data maturity.
The Data-Driven Decision-Making Process
For the purposes of this discussion, we’ll assume three things:
- You have access to high-quality internal and external data
- You have people who understand how to interpret, manipulate, and use data
- You have a supportive executive team that believes in the value of data
So long as this foundation is in place, you can start implementing data-driven decision-making best practices immediately in the following order.
Clarify Your Objectives
One of the first things that needs to happen once you have good data is that your leadership team has to clarify the organization’s primary objectives. Doing so enables analysts to hone in on specific datasets and data points, rather than parse through everything within reach.
On a related note, you should make a habit of gathering feedback early from teams that actually use data. Ask individuals what they already have access to, as well as what data sources they wish were available across the organization.
Use this feedback in combination with clarifying your objectives to ensure that teams are aligned around target outcomes and that the people who will leverage data do, in fact, have what they need. Otherwise, your analyses and decision-making process may start off in the wrong direction.
Collect and Prepare Data
At this stage, you can start collecting and preparing data for processing. It’s critical that the data you obtain is complete, accurate, and meaningful with respect to your organization’s goals. The data you use must also comply with regulatory requirements and privacy laws. You must be able to trust your data entirely, especially if it will contribute to major decisions with potential bottom-line impact.
If your business is still in the process of building a strong data-driven culture, this step is crucial. A bad experience here will make it hard to win executive sponsors for future projects.
Produce Insightful and Intuitive Visuals
Next, you would create rich, easy-to-understand data visualizations that you can share with people all across the organization. Producing quality visualizations is useful for exploring data further, identifying trends, and telling cohesive stories to executive sponsors so that they can make the best possible decisions.
Furthermore, well-designed visualizations may bring forth patterns or insights in the data that you wouldn’t have noticed otherwise by looking at spreadsheets and tables. Even a simple bar chart or line graph could lead to valuable decisions.
Analyze Your Data for Actionable Insights
Data visualizations aren’t useful in and of themselves. When you are studying dashboards, charts, and graphs, you should be asking yourself the following questions:
- How do these trends and patterns affect our business?
- What do we do with this information?
- How should things change based on these findings?
At the end of the day, we analyze data to pull out actionable insights, which (hopefully) lead us to decisions that generate meaningful returns on our investments.
Share Findings and Act Accordingly
At this point, your team is ready to disseminate findings and empower others to execute.
Be prepared to answer questions around your data sources and analyses, as others will want to validate that they can trust your recommendations. Also, keep thorough records of your processes in case you need to revisit previous datasets or research questions.
Incorporate Data-Driven Best Practices Today
As we’ve said previously, the only way to become a data-driven organization is to get complete buy-in from everyone – IT leaders, business analysts, executive sponsors, operations personnel.
If you don’t establish a strong data culture, the process outlined above won’t stick. And worse, you won’t be able to take full advantage of data-driven decision-making for the benefit of your enterprise.
For more guidance on the importance of data-driven decision-making, download our eBook today. You can also check out a recent case study in which Daitan helped a leading software company enhance its customer journey based on data-driven insights that led to significant business growth outcomes.