Why You Should Care About Data Driven Decision Management

Data driven decision making is one of the most powerful concepts that can be used in the business setting today. It’s not uncommon for companies to undergo organizational changes or to implement significant processes through the use of data driven decision making tools. A tool such as this will allow the company to conduct a series of activities with regard to its various operational matters. When a company makes a change to its business practices, the implementation will likely require utilizing the assistance of several different individuals, as well.

The first steps in implementing data driven decision making would be to obtain and evaluate a comprehensive set of KPI data. This data may be comprised of overall customer satisfaction, employee engagement, return on investment, and others. This analysis would provide a base for determining which strategic initiatives are currently underway, and which direction should the company go in. In a nutshell, the objective here is to get a “feel” for what the company’s current strengths and weaknesses are, so as to determine where the company should direct its focus in the future.

Once a complete data set has been assembled, data driven decision making tools can be utilized in order to generate reports and action items that will help executives make informed decisions regarding business operations. These reports and actions will then allow individuals to make adjustments to their business models, processes, and business plans in accordance with the findings of their reports and assessments. Through the use of these tools, executives will be able to take informed decisions about their company’s current strengths and weaknesses, as well as the things that need to be done in order to improve them.

While a great manager understands that he or she is required to make sometimes difficult decisions, at the same time an effective leader knows that making good decisions is paramount to the organization’s continued success. As such, a great manager should not only be skilled at analyzing data sets and data but also be skilled at communicating those insights and analyses to key members of his or her staff, other managers, stakeholders, and decision makers throughout the organization. A great manager should know how to put thoughts and ideas on paper in such a way that they can be shared with others, while also being understood and appreciated by those who will be affected by them. Thus, data driven decision making process should include not only the gathering of data and analysis, but also the communication of those insights to those who need to understand them.

It may be easy to think of data driven decision making as a matter of pulling together a bunch of disparate information and synthesizing it into a single meaningful result. However, the reality of business intelligence actually deals more with the creation of meaningful conclusions from the raw data that has been collected. Thus, it is important for a business intelligence analyst to realize that although the process does require a significant amount of data, the ultimate goal is to create meaningful insights from that data. In fact, one of the primary goals of business intelligence is to build a framework within which insights can be consistently derived.

One of the best ways to start building an intuition about a particular topic is to look at how people make decisions in similar situations. This is often referred to as behavioral science, because the goal is to identify patterns regarding behavior. For example, one set of data might show that mothers prefer to breastfeed their children. Another set of data might show that people with higher incomes tend to make better financial decisions than those with lower incomes. Combining these two sets of data could give us a fairly accurate prediction about the preferences of the general population.

One of the problems that arise from relying too heavily on quantitative evidence in making analysis is the potential for creating false generalizations. Empirical results can provide interesting insights, but they do not tell us whether the observed pattern is really a product of what would be called “emotional evidence”. It also helps to remember that no matter how robust the statistical model is, it still depends on the observations that it draws from. So combining statistical models with real data helps us learn more about the true effects of our decisions.

It’s also important to remember that no data can solve every problem. A successful enterprise will always have room for improving its decision-making process. The best way to do this is to build on the existing insights that are based on current and historical data, as well as taking advantage of new technologies that promise to speed up decision processes. Data-driven decision management thus provides one of the most useful frameworks out there: it helps us understand how we can make better decisions by using the available data.