The recent explosion of available data has created an ever-increasing need for more effective methods to help us make sense of it.
Data-driven decision-making (DDDM) is the process of collecting data based on your company’s key performance indicators (KPIs) and using that data to glean actionable insights.
Understanding the data-driven decision-making process will help you identify solutions to common challenges and highlight valuable opportunities to make better data-driven business decisions.
DDDM essentials
DDDM is an essential process for every professional to understand–even more so for those in data-oriented leadership roles.
According to a recent NewVantage Partners survey, 36% of senior executives from 57 large corporations had “advanced analytics/better decisions” as their top priority, and 69% of those had already achieved success with the objective
Effective data-driven decision-making helps businesses make strategically-guided decisions, set measurable goals, improve company operations, and maximize profits.
All modern businesses use data-supported elements in some capacity to inform the way they operate.
There are 4 essential steps for implementing an effective Data-Driven Decision-Making process.
Identify business objectives: The most common problems associated with this step center around data quality. Without clearly defined business objectives, data collection will be unfocused and unreliable.
Data quality is critical to this process since it heavily influences each following step.
Collect & organize data: Once you’ve identified the goals you’re working towards, you can identify targeted data sources and start collecting data. The most common problems associated with this phase can include challenges with storage, security, and availability.
An overwhelming majority of respondents (82%) cited confusing data governance policies as another top challenge.
A well-crafted data governance policy will establish rules and regulations for all an organization’s data-related procedures. This policy informs what data is collected, how the data is stored, and who will have access to it.
Data Analysis: In the third step, once you’ve organized your data, you can begin your data-driven analysis. This process will allow you to extract actionable insights from your data.
Common problems associated with this step in your decision-making process impact the ability of team members to glean useful insights from data and then communicate those insights to others.
76% of respondents said they found it difficult to understand their data
Investments in data literacy skills and training have proved the most effective solution to improve the ability of team members to read, write and communicate the use-case application and the resulting value of data.
one in three respondents say it is “very important” to have programs or partnerships in place to make employees more data-literate
Act on insights: The final step is to make decisions based on gathered insights. The most common challenges associated with this phase of the DDDM process arise from personal and cultural resistance to data-driven decision-making.
Many attempts to introduce data-supported initiatives often fail because team members don’t understand how or why they must change their behavior. This can create distrust and resistance to beneficial changes.
By 2023, organizations with shared ontology, semantics, governance, and stewardship processes to enable inter-enterprise data sharing will outperform those that don’t, according to current research.
Developing a data-driven culture is the key to overcoming these challenges. This collective set of beliefs and behaviors is aligned through a shared value and practice of using data to make better decisions.
The quality of the decisions made by data-driven organizations is giving them a competitive edge, especially on digital initiatives. – Patrick Long Principal Analyst
By understanding the challenges and best practices for working with growing stockpiles of data, you are better equipped to empower your organization to make the most of ever-improving tools & technology.
Building effective data-enabled operations require a structured investment in terms of time and resources. However, the return on this investment can be exponential–provided data policy, tools, and talent are aligned with strategic business goals.
