Four Steps to Making Data-Driven Business Decisions
Date Published: Oct 21 , 2022
A crucial facet of a modern business strategy is making data-driven decisions. This means pulling metrics, key performance indicators (KPIs) and other data and analyzing it for actionable insights. Backed with solid data analysis, organizations can confidently make choices that maximize business opportunities and processes.
While most businesses are well aware of the critical nature of leveraging data for smart business decisions, many business leaders and managers struggle with data management and analysis best practices. This may arise from poor data storage and accessibility, non-reliable data sets, insufficient data visualization models, or not aligning data analysis with company goals.
Here we look at the merits of making data-driven business decisions and the four steps you can take to make it push your business forward.
The Why of Making Data-Driven Business Decisions
More organizations in recent years have adopted a data-backed decision-making approach. Relying on data to drive choices confers a number of benefits, including:
Helping to guard against biases that can undermine good decision making
Answering more questions and uncertainties
Uncovering issues not considered until revealed through data
Helping to establish measurable goals for performance
Improving processes, such as customer services based on customer feedback data
When done right, making data-driven business decisions ensures your business is run fairly, aligned with goals and focused on continual improvements.
Step 1: Know Your Vision and Objectives
Prior to making data-driven business decisions, you must appreciate to what end you are making those decisions. Know your company’s vision for the future, and executive and downstream goals. Inquire with people across the organization to understand short and long-term departmental targets.
Goals may be specific, like an increase in sales numbers from a specific sales channel, or a general aspiration like bolstering brand awareness. Keeping this in mind will inform which KPIs and other metrics to focus on and how to analyze them.
Step 2: Identify Data Sources
The next step in making data-driven business decisions is collecting data. Organizations that have a consolidated data source can easily access quality data. Conversely, companies with disconnected information silos will face difficulties in gathering inputs. In the latter case, you will need to develop an information gathering and depository system. Clean and organized data is the required foundation for meaningful and accurate data analysis.
Once you have identified where quality data is housed, or developed a system for it, you can begin pulling relevant metrics. It is advisable to prioritize data sources with the most immediate impact and low complexity, such as gross profit margins.
Step 3: Organize Data with Visualizations
To make the most effective data-backed business decisions means creating data visualizations. Visual elements such as graphs and charts make it easier to identify patterns and pain points, and to capitalize on trends.
Certain data lends itself to specific visualization modes, such as a bar chart for comparisons and a line chart for temporal data. Familiarize yourself with different tools for data visualization. Explore methods for presenting data. Being creative and well-versed in data visualization makes you more effective at communicating the story of the data.
Step 4: Find the Data’s Story
The fourth step is the nuts and bolts of making data-driven business decisions: extracting actionable insights from the data. When analyzing data, ask why? The data is telling you a story through patterns beyond the numbers and your job is to find that story.
You will likely have to pull data beyond KPIs and other standard metrics. Maybe you will need to cross-reference revenues with case studies, or check customer totals with surveys.
Always keep in mind the size, reliability and sources of the data you are reviewing. Meaningful analysis can only be had from complete, quality data. Unreliable data is close to, if not entirely, useless.
Also, consider sharing analytics tools with your entire team. Seeing the data from many perspectives may reveal fresh insights that are not possible with only a few pairs of eyes on it.
Finally, your data analysis is only as good as the questions posed. When reviewing data ask yourself the following: