An organization uses data analytics to implement operational and strategic decisions. This procedure helps derive meaningful insights from the data and transform knowledge into action. There are a few common steps that help in the application of data analytics within a business.
Define the objective
To perform this step, it is important to ask two important questions:
- What are they trying to obtain?
- What could the output look like?
Before you begin to look at the data, you need to decide upon your expectations from the process. By setting realistic and objective goals, you can increase productivity as you progress through the process. It will also aid in making well informed operational decisions.
Understand data sources
Understanding the source of data is another important step that will help you combine data from multiple sources or determine the type of testing needed.
The following questions are beneficial for getting more information about the data:
- What type of information is needed?
- Do I need the help of an IT resource to fetch the data or I can do it on my own?
Data preparation
Once you get a proper understanding of the business goals and the type of data, the next step would be to ask the following questions:
- Does the data require to get cleaned?
- Is it required to normalize the data?
Data preparation is comprised of several different methods, with data cleansing and normalization being the most significant. Cleansing of data addresses the information quality, whereas normalization helps in preventing redundancies. Cleansing of data is very important when information comes from several different sources. It helps in removing all of the unidentified information from different cells in a spreadsheet program.
Data normalization identifies different versions of the same data that lead to redundancies in the data. The normalization process transforms all these variations into a single format. Failing to perform these processes would result in errors in output and render the data unreliable or unusable.
Analyze Data
Once your data is free from redundancies and errors, it is now the time to analyze it. This process begins by asking a few questions such as:
- What tests can be executed on the data?
- Will I get assistance to understand results?
Data analytics tools assist in summarizing the information. In addition, seeking professional assistance from specialized consultants who will then determine what tests are best to run for data analysis.
Report Results
The results that you obtain after running data analysis tests need to be understood in order to advance action within an organization.
A few questions need to be asked:
- Will management be able to understand the output?
- Don’t overwhelm management with unnecessary information. Instead, present a summary of the information and important details as an add-on to the summary.
- Can you show the results visually?
- To help management understand the results, make good use of graphs and charts to effectively display and communicate information without lengthy explanations.
Conclusion
Each of the aforementioned steps are critical to tackling the challenges organizations may face when applying data analytics. Understanding the strategic function of data in your enterprise, along with the questions discussed, will definitely help in gaining a deeper insight into the operation of data analytics
