When businesses collect more and more data, the end aim is to turn these details into information that will help them maximize company efficiency.
The questions are posed about the data are infinite, but the time and money to find the responses are not available, a person needs to plan, how to test the data for useful insights. However, when it comes to analytics, some of the questions may be ill-defined, inconsiderable, or even incorrect. Contributing to expensive, time-consuming procedures to process data that do not produce any actionable insights results in wasting precious time.
Using resources on useful data:
An analyst, Brent Dykes, after spending 15 years in consultation service, has built a framework based on the basic assumption that the issues that need to be addressed are guided by the viewer. For example, what the finance team cares about will be different from the interests or needs of the marketing or human resources teams. Hence, to produce impactful feedback, one needs to either consider the desires of the community or to explain the customer’s desire.
Brent Dykes, in his latest novel, “Effective Data Storytelling”, presented the 4D Audience Model, which focuses on four intertwined dimensions (4D)— problem, outcome, actions, and measures. Those four dimensions hold a balance in your details and give your queries a clearer focus. The system underlines the significance of improving the public’s sense and transparency on their main issues, difficulties, and goals. All the following dimensions contribute to enriching the research experience and allows a researcher to pose the right questions about the data:
Problem: A main problem or question that the community needs to address. A company may try to make one of the company elements more successful or productive than it is at present. For instance, the issue may be that the marketing department is failing to produce a sufficient amount of potential leads. The more it is able to grasp the problem and the implications, the more it will be able to discover possible triggers and remedies. If the problem is obvious, the company is less likely to run aimlessly through the details.
Outcome: “A business target or the intended end product that the viewer needs to accomplish”. When the problem is the present situation, the result is the potential or the ideal condition. If the intended result is clearly specified, one can realize how much of the difference there is and what needs to be done. For instance, the marketing department might have set the target of growing the number of leads by 60% over the next year. In case the result has not been decided by the group, one might need to set a fair one on their behalf.
Actions: “Main events and policy strategies the group has or should be adopted to solve an issue or accomplish a result”. That shows the investment, time, and energy that will be important and of the utmost value to the audience. For example, if the marketing department concentrates on increasing interactive marketing events or improving the digital marketing activities to generate further leads, any insights discovered on these activities or initiatives will be of great interest to the audience.
Measures: “Primary indicators and other data used to illustrate the problem, measure the success of the solutions, and identify the achievement of the desired result”. Not all data will be relevant or important to answer key questions. Knowing through metrics and measurements apply, as well as how to perceive what they imply, would be necessary to make a sense of the statistics.
GPS analogy and the 4D Audience Framework:
To show how these different dimensions come together and can help a researcher move strategically through the data, Brent Dykes has used a GPS analogy.
One can begin by identifying the point of initiation (their question or current state) and then discover what their expected endpoint is (their ideal result or potential state). Then, one can determine the route (actions or activities) and then assess towards their target (measures or primary metrics). This basic method means that one doesn’t get stuck in the data abundance and places the correct questions to ask about the results.
