How to Enable Data-Driven Decision-Making at Your Organization
Data-driven decision-making is about creating a data culture within the organization so that data is trusted over individual gut feelings when making key business decisions. However, with countless articles emphasizing the benefits of data-driven decision-making, it’s difficult to find clarity on what data-driven decision-making actually is.
We already know that organizations that leverage data for decision-making have a competitive advantage. They’re able to understand customer needs better, react to market changes rapidly, enhance productivity as well as improve profitability. 46% treat data as an asset and highly value information for decision-making. With data becoming increasingly important, organizations globally spend close to $40 billion annually in data analytics technologies and services.
But is having access to good data enough to make good decisions? Unfortunately, many organizations fail to get the most out of their data. Data sits in the form of dashboards, reports, and databases—never to be used. While generating lots of reports or having dashboards is a great (and necessary) step, data-driven decision-making is about putting data at the wheel and driving decisions from it.
What is Data-Driven Decision-Making?
Data-driven decision-making (DDDM) is a business strategy where the emphasis is given on facts, metrics, and data to drive key business decisions. A decision-maker will analyze trends using historical information to make future decisions based on what worked in the past instead of making judgments based on experience, opinion, or gut feeling.
This decision-making approach is often referenced using a lot of different terms (not always that accurately), including big data analytics, business intelligence, data analysis, data modeling, and more. DDDM is gaining a lot of traction within the business sphere.
It’s quantitative nature, however, demands powerful computing systems that can analyze large data sets. Fortunately, with a reduction in the cost of computing power and cloud-based solutions, DDDM is now accessible to small and medium scale organizations.
4 Ways to Enable Data-Driven Decision-Making
While data-driven decision-making brings better business outcomes, there’s also a common belief that good data leads to better decisions, which isn’t always true. Good data is merely a starting point.
Embracing data-driven decision-making needs a much more holistic approach. It demands an organizational culture that encourages critical thinking, the right infrastructure to support the cultural shift, and a well-defined process that maximizes data usage across your organization.
1. Get the right-sized data solutions.
If you’re a startup, you don’t necessarily need to invest in expensive analytics or business intelligence solutions from the get-go just to access data. The core idea of data-driven decision-making is to ensure everyone has access to data. It means you need to centralize data in some way. It can be a simple spreadsheet that all of your team members have access to or it could be having an enterprise data warehouse with a dedicated BI team.
Once data becomes an inherent part of your organization, it’s a good idea to invest in a data solution that fits your business requirements and budget.
2. Establish the right infrastructure.
Data-driven decision-making requires skilled resources and a robust infrastructure that helps organizations turn data into insights. But not every organization can afford data engineers and scientists. Additionally, setting up a storage infrastructure to manage large data sets and providing accessibility across your organization is, in itself, a costly proposition.
Cloud offers a remedy here. Business Intelligence and analytics tools that live in the cloud require a minimum upfront investment. It also makes up for the lack of data science skills by offering ready-to-use libraries, data pipelines, and AI and ML services that your citizen developers and management teams will use to analyze data and generate valuable insights.
3. Have a clear vision.
For the successful implementation of data-driven decision-making, it’s critical for somebody in your organization to set a clear vision. Ultimately, this has to come from the top management. A data-driven leadership team will lead your organization towards a data-driven enterprise. They will lead by example and foster an environment for critical thinking.
When a data-driven leadership team is absent, decisions will most likely be dependent on the opinion and intuition of the most experienced person in the room. Prevent such a scenario. Promote data-driven decision-making through executive sponsorship. If you don’t have a leadership team, focus on building it to drive a data-driven culture across your organization.
4. Embrace the process.
Generating operational data in abundance alone won’t bring the results you’re expecting. Data-driven decision-making begins with understanding your data and what decisions it empowers. Have a deep understanding of your key performance indicators (KPIs) and how data-driven decisions will influence those KPIs.
Also, don’t forget that there are people involved in this process. Dashboards may look perfect but do they make sense to your team? Can they access tools easily to extract meaningful insights?
The ideal approach to data-driven decision-making is to start small and look for easy wins. Once there’s a substantial understanding, scale your decision-making process by integrating more data and establishing newer KPIs.
Enabling data-driven decision-making in your organization isn’t an overnight process. It’s also not about investing in the best technology to generate insights from data. Becoming a truly data-driven organization takes time and demands a culture where data is treated as a critical enterprise asset and used as a valuable resource when making decisions.
At CSG Pro, we’ve been enabling organizations to embrace data-driven cultures successfully. Reach out to find out how data-driven decision-making brings more value to your organization.