4 points you should know about data-based decision-making
Data is not a factor for business decision making but a framework, encompassing a myriad of other processes. Data-based decision models empower organizations to shift their focus from obsessing over past mistakes to embracing new practices with fact-based directions.
Essential points for data-based decision-making
Most businesses today employ various mechanisms to analyze volumes of data to generate insights. Data analysis has long been used as a method for critique for answering questions like “Did the campaigns had desired reach?” or to identify key performance indicators (KPIs).
For a truly data-driven approach which can encompass decision-making as well as retrospection, it is important to generate the KPIs at the very start of the project cycles. Accurate KPIs are a foundation and cornerstone of any business initiative or a campaign.
With the ample amount of organizational data today, the business decisions should have more information and resources needed for decision-making, however, the core leadership factors still determine the effectiveness of business decisions. Transforming data-based insights into actionable plans require few proactive measures, as explained below.
Having a well-defined goal
This step is easier to state but complicated to implement. At the beginning of every business cycle, an organization should involve all the stakeholders from all the aspects in a broad discussion about the business cycle’s aim. It starts with a systematic focus on the challenges business needs to tackle. Challenges like customer churn, brand loyalty, distribution challenges, customer satisfaction need to be clarified at this stage.
Being unconventional in terms of what an organization is willing to accomplish should be actively encouraged instead of going in accordance with the prevailing decisions and habits.
Exhaustive involvement in data
After a goal is defined, it should be followed with an extensive study of the current data and the correlated past results. At this stage, an honest evaluation of the history of what data conveyed and what outcomes the business cycle led to, should be evaluated. The multitude of this finding will be in how the data-based insights have been successful in making accurate predictions. This step should be highly inclusive to democratize the data and to collaborate all the layers of the organization.
Forming right questions
This step takes a more realistic approach in organizational problem solving with venturing in the specificity of the questions required to drill down to the challenges and goals. This step, if executed with precision, can offer massive pay-offs with the possibility of discovering new insights and challenges which were previously uncovered.
Prioritization
After forming the exhaustive questions, evaluation, and questioning, prioritization should begin with the intent of forming a tangible plan of action. The clear distinction between the achievable and unrealistic goals as well as questions should be made at this stage. Formulation of high-value and meaningful goals will eventually drive the key results.
Putting data analytics at a later stage in a business process may be a common but, intractable practice. Data is not a factor for business decision making but a framework, encompassing a myriad of other processes. Data-based decision models empower organizations to shift their focus from obsessing over past mistakes to embracing new practices with fact-based directions.