How to get started using analytics to improve business performance
Data Analytics has been an increasing area of focus in most businesses over the last couple of decades. The analytic process grew increasingly complex, requiring highly skilled quantitative professionals to develop analytics using complex tools. Over the last several years, the technology has evolved to allow business users to perform quantitative analytics that provides excellent insights without the need for the level of quantitative training required as little as 5 - 10 years ago. Quantitative expertise is still needed to provide oversight and develop complex analytics. However, the new software tools have allowed a broader set of users to perform very insightful analytics quicker and more focused on specific business needs.
It is costly and frustrating to spend significant time developing analytics without providing value if there is no clear goal for undertaking the process. Establishing goals does not have to be a complex process and will provide significant value.
Understanding the five levels of analytics is a very helpful way to understand where your particular area may be in the analytic process and provide insight into appropriate steps to move forward in an efficient manner. Remember that this is an evolving process that will change as the business needs change, knowledge expands, and additional techniques/technologies are added. I have always worked with the expectation that if I am still doing everything the same way for 6-12 months, then we are not expanding, improving the process, and highly likely not meeting the business needs.
Descriptive Analytics is the first step in the analytics process. It helps stakeholders understand the current outcomes of their business or, more simply, states what happened.
What are the key components of Descriptive Analytics?
Discussions with the stakeholders to determine their needs will help develop the plan for beginning the analytics process. This will improve efficiency, focus the efforts, and will ensure timely and strategic decision-making that supports positive results and growth.
These suggestions will help build a strong baseline for the ongoing development of the analytic process.
Diagnostic Analytics is the next step in the analytics process. The focus here is on the “why” or how we got here.
Predictive Analytics is the third step in the process. This leverages the results of the descriptive and diagnostic process to begin predicting future trends. It is important to have enough historical and real-time data to be relevant and useful. Here are some ways you can predict future trends.
Predictive analysis is a continuous cycle. The steps below will help you improve your process to anticipate trends and behaviors better.
Prescriptive Analytics is the fourth step in the analytics process. This will leverage what was learned in the first 3 steps to begin to make recommendations for specific actions to meet the company’s goals and objectives. What are some key considerations?
Prescriptive analytics has been the target environment for many business analytics journeys, and the continual expansion of tools capabilities has allowed businesses to begin achieving this very desirable stage of analytics! Understanding the options uncovered will suggest the best course of action given expected environmental changes that will reduce risk, improve performance or support changing direction. It is important that the first three steps are complete, including documentation, ongoing monitoring, and business acceptance. Here are a few suggestions to develop or improve your company’s prescriptive analytics process.
Artificial Intelligence (AI) is the final step of the analytics process. The focus of this area has been to automate repetitive tasks to free up time and improve productivity (e.g. general ledger entries, including assigning approvals, performing additional entries, and creating payments). This step has been growing as the technology and methodology have evolved to improve ease of use and dramatic increase in the speed and ability to access very large datasets from various sources. This has tremendous capabilities, as we have seen in recent months, and also can add risk given the potential to add bias inadvertently.
AI is having huge impacts across a number of repeatable tasks in many businesses, including customer interactions, ledgers (as mentioned above), and other items. It is a rapidly evolving process as the capabilities continue to improve. Here are some suggestions to help improve your AI Analytics journey.
For more information on how Five to Flow helps organizations accelerate change and growth, feel free to contact us. You can visit our Analytics Solutions learn more about our areas of expertise.