Business Analytics is a very prevalent term in the 21st century across various sectors. It corresponds to a set of methodologies and tools that change the way of how organizations approach decision-making. Since the impact of Business Analytics is very heavy, organizations have defined a business analytics lifecycle to make sure not to commit mistakes or miss out on any crucial information. This process is termed the Business Analytics process. The steps of the process may vary from organization to organization as a lot of factors, viz. the industry, the type of product, the size of the company, etc., play major roles in determining them. However, broadly, you can classify the entire Business Analytics process into six steps.
In this blog, we will discuss the Business Analytics process and its six steps in the following sections:
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Introduction to Business Analytics
Business Analytics is a term that took industries by storm in the 21st century. All businesses around the world were looking to make more and more profits, and the only way they could do that was by finding out gaps and filling them. The Business Analytics process initially came as a problem-solving approach to many organizations where data was being captured and accessed. This data was then used for multiple purposes, ranging from improving customer services to predicting fraud. Due to its vast success, people realized quickly that Business Analytics can not only solve pre-existing visible problems but also can notify them about illusive problems that do not seem to be existing.
Once the world started noticing the impact of Business Analytics, organizations soon realized that its potential is not related to just problem-solving, but they can also use it to predict, plan, improvise, and overcome various obstacles that they may find.
Business Analytics is a discipline where you use pre-existing data to find out key insights that can help you solve a business problem. To find the said insights, you have to apply a lot of statistical models, as well as manipulate the data to fit such models.
In today’s world, Business Analytics is so important that almost every organization has a Business Analytics team and well-defined business analytics process steps. Since there are problems and gaps in all forms of business, Business Analytics is a viable approach across all industries. From the food industry to the IT sector, everyone is employing Business Analytics to find out the optimum ways to do business.
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Moreover, almost every organization of the day follows well-defined Business Analytics process steps. These process steps differ from organization to organization. However, some key steps remain the same for almost everyone. Let’s discuss them in this blog.
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The Business Analytics Process
The Business Analytics process involves asking questions, looking at data, and manipulating it to find the required answers. Now, every organization has different ways to execute this process as all of these organizations work in different sectors and value different metrics more than others based on their specific business model.
Since the approach to business is different for different organizations, their solutions and their ways to reach the solutions are also different. Nonetheless, all of the actions that they do can be classified and generalized to understand their approach. The image given below demonstrates the steps in the Business Analytics process of a firm:
The above image just covers the overview of the Business Analytics process. Now, let’s convert it into the actual steps that are involved in solving problems.
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6 Steps in the Business Analytics Process
Step 1: Identifying the Problem
The first step of the process is identifying the business problem. The problem could be an actual crisis; it could be something related to recognizing business needs or optimizing current processes. This is a crucial stage in Business Analytics as it is important to clearly understand what the expected outcome should be. When the desired outcome is determined, it is further broken down into smaller goals. Then, business stakeholders decide on the relevant data required to solve the problem. Some important questions must be answered in this stage, such as: What kind of data is available? Is there sufficient data? And so on.
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Step 2: Exploring Data
Once the problem statement is defined, the next step is to gather data (if required) and, more importantly, cleanse the data—most organizations would have plenty of data, but not all data points would be accurate or useful. Organizations collect huge amounts of data through different methods, but at times, junk data or empty data points would be present in the dataset. These faulty pieces of data can hamper the analysis. Hence, it is very important to clean the data that has to be analyzed.
TO do this, you must do computations for the missing data, remove outliers, and find new variables as a combination of other variables. You may also need to plot time series graphs as they generally indicate patterns and outliers. It is very important to remove outliers as they can have a heavy impact on the accuracy of the model that you create. Moreover, cleaning the data helps you get a better sense of the dataset.
Step 3: Analysis
Once the data is ready, the next thing to do is analyze it. Now to execute the same, there are various kinds of statistical methods (such as hypothesis testing, correlation, etc.) involved to find out the insights that you are looking for. You can use all of the methods for which you have the data.
The prime way of analyzing is pivoting around the target variable, so you need to take into account whatever factors that affect the target variable. In addition to that, a lot of assumptions are also considered to find out what the outcomes can be. Generally, at this step, the data is sliced, and the comparisons are made. Through these methods, you are looking to get actionable insights.
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Step 4: Prediction and Optimization
Gone are the days when analytics was used to react. In today’s era, Business Analytics is all about being proactive. In this step, you will use prediction techniques, such as neural networks or decision trees, to model the data. These prediction techniques will help you find out hidden insights and relationships between variables, which will further help you uncover patterns on the most important metrics. By principle, a lot of models are used simultaneously, and the models with the most accuracy are chosen. In this stage, a lot of conditions are also checked as parameters, and answers to a lot of ‘what if…?’ questions are provided.
Step 5: Making a Decision and Evaluating the Outcome
From the insights that you receive from your model built on target variables, a viable plan of action will be established in this step to meet the organization’s goals and expectations. The said plan of action is then put to work, and the waiting period begins. You will have to wait to see the actual outcomes of your predictions and find out how successful you were in your endeavors. Once you get the outcomes, you will have to measure and evaluate them.
Step 6: Optimizing and Updating
Post the implementation of the solution, the outcomes are measured as mentioned above. If you find some methods through which the plan of action can be optimized, then those can be implemented. If that is not the case, then you can move on with registering the outcomes of the entire process. This step is crucial for any analytics in the future because you will have an ever-improving database. Through this database, you can get closer and closer to maximum optimization. In this step, it is also important to evaluate the ROI (return on investment). Take a look at the diagram below of the life cycle of business analytics.
If Business Analytics is something that excites you, then you must consider a career in the field as there are always new challenges in it, and the demand is never-ending. You can check out our blog to Learn Business Analytics and excel in your career. Happy learning!