Analytical Process and Life Cycle
Every data-driven decision ever made, every data-driven problem solved correctly undergoes a different kind of cycle that is particularly prepared to handle situations in ways that are different from the process adopted to solve normal problems. Data is efficient enough to provide us analytical insights, but do we understand the actual steps of “how to formulate the process of understanding whether how can we find the best possible solution to data-driven problems”. Analytics as a whole has a vast nature and a step-by-step process to solve problems with an analytical life cycle that can be efficient to tackle issues that take time to give insightful information. The analytical problem-solving process has six steps that form the main five phases of the analytical life cycle.
The first step is ‘Ask’. It means to define the problem in detail which leads to the first phase of the life cycle determining the ‘Identification of the problem’ which further leads to proper details towards what the problem matter is all about and gives the analyst a brief idea on understanding the future requirements of the problem. To ask and identify the problem is the first step to judge the basic needs of the problem and what insights are expected out of it, which also gives a predictive understanding to analysts towards what level of brief understanding is required and what part can make the project complex and what can be the one that can correlate with the complexity and that is where analysts know how to make the insight more efficient yet effective and where to spend time and on which part of the problem.
The second step is to ‘Prepare’. It means to prepare the further steps and the necessary resources for solving the problem, it mainly refers to the second phase of the life cycle ‘designing the data requirements’ which further leads to designing the needs and resources for the analytical project that is going to take place. It gives the analyst a brief insight into what is necessary to complete the project efficiently without causing any sort of change in tracks and losing sight of the actual solution that can be derived from the project. By that, it can also mean to choose the right and the only necessary data that is required to complete the analysis in the right and broad manner.
The third step here refers to ‘Process’. It means to make the data undergo certain changes to adopt the scenario and to derive the requirements for future steps to derive solutions and to enhance the performance which leads to the third phase of the life cycle ‘Pre-Processing Data’ which then leads to performing actions on the raw data that has been gathered and generates only the necessary data that is required for the project and removes the one that cannot be used or might not dwell properly to execute the project. This mainly helps in enhancing the performance of the data and helps in providing proper visualizations and insights for analytical solutions.
The fourth and the main step is to ‘Analyze’. This is the step where every ounce of the data generated comes into action and can make sure that the analyst generates proper insights towards the project and refers to the fourth phase of the life cycle ‘Performing Data Analysis’ which then leads to deriving solutions that are necessary to solve every sort of complex problems that hold solutions in the data that was pre-processed and improved. Analysis can contain formulating questions that are still not answered in some contexts or it can be deriving solutions to previous questions that were left unanswered. The best part here is that it is wide in concept and can be used in any area, department, or field of information.
The fifth step that also leads to communicating the insights is to ‘Share’. This leads to the fifth phase of the life cycle i.e. ‘Visualizing Data’ which then leads to studying the insights derived from data and communicating it in the form of business storytelling. This action spreads information to the remaining public and shares the insights to take further actions towards deciding the next plan of action or the next decision that needs to be taken towards a growth approach for which the particular analysis was done. The communicated information can lead to a decision for either profit at a particular stage in an organization or a cost-cutting approach for an operations department. Visualizing the data and sharing the idea behind the data analysis can create positive changes if the methods and steps involved are used systematically.
The last stage refers to ‘Act’. The process where the shared, communicated and visualized data upon which the plan of action was decided needs to be executed and use the analytical insights for a profitable approach.
To conclude, these six steps strongly support the analytics life cycle and if followed with the right approach can lead to an efficient insight which will further lead to a better and proper approach towards the growth of an organization or a business. The application of ideas may look different but Analytics stands everywhere and in every strategic decision taken by anyone anywhere. The steps remain the same, the only thing that matters is whether we systematically follow them or not.