7 important steps in the Business analytics process
The traditional manner in which businesses conduct their operations is being disrupted by a new type of business tool known as real-time analysis. Business analytics is being utilized by an increasing number of companies in today's world to facilitate proactive decision-making. To put it another way, many companies are shifting their focus from responding to events to proactively preparing for them.
Because it can be used in any sector where data can be collected and accessed, business analytics has been increasingly popular as a tool in recent years. This is one of the primary reasons for this trend. This data may be put to use for a variety of purposes, including but not limited to enhancing customer service, enhancing the organization's capability to forecast fraud, providing important insights on online and digital information, and so on.
In today's article, we will understand business analytics in detail and the 7 steps in the business analytics process. Let's get started.
Business Analytics: An Introduction
Business analytics is the process of analyzing massive and diverse data sets, more commonly referred to as "Big Data", to reveal the myriad of connections, correlations, trends, partnerships, customer behavior, statistical patterns, and other meaningful interferences that assist businesses in making more informed business decisions.
These insights fundamentally motivate innovative opportunities for expansion, form firms to change in response to shifting market dynamics, and locate organizations to withstand challenging new entrants in their particular industries.
The 7 Important Steps in the Business Analytics Process
We understood what business analytics is. Now, we will focus on the business analytics process which includes 7 very important steps. Here they are:
Step 1. Determining the requirements of the company
The first step in the process of business analytics is to gain an awareness of the issues that the company wants to have resolved or the areas in which it wishes to see improvements. On occasion, the aim is subdivided into several more manageable objectives.
The business stakeholders, business users with domain knowledge, and the business analyst collaborate to decide on the relevant data that is required to solve these business goals. At this point, it is necessary to provide responses to fundamental inquiries such as "what data is available," "how can we use it," and "do we have adequate data."
Step 2: Explore and Refine the data
At this point, the data will be cleaned, computations will be made to account for missing data, outliers will be removed, and new variables will be formed by altering combinations of existing variables. Plotting time series graphs is done because these graphs can point out any patterns or outliers in the data. If they are permitted to remain in the data set, outliers frequently affect the accuracy of the model; therefore, it is a highly significant task to remove outliers from the dataset. Outliers often affect the accuracy of the model. In other words, if you put garbage in, you'll get garbage out (GIGO).
After the data has been thoroughly cleansed, the analyst will work to derive as much meaning as possible from the information. The data will be plotted by the analyst utilizing scatter plots (to identify possible correlation or non-linearity). He will visually verify all available slices of data and then summarize the information using appropriate visualization and descriptive statistics (such as mean, standard deviation, range, mode, and median) that will assist in providing a fundamental comprehension of the data.
At this point in the process, the analyst is already looking for broad trends and specific insights that may be put to use to achieve the business objective.
Step 3: Analyze the data
At this point, the analyst will uncover all of the components that are associated with the objective variable by employing statistical analysis methods such as correlation analysis and hypothesis testing. The analyst will also carry out a straightforward regression study to determine the viability of making straightforward forecasts.
In addition, the various groups are compared by making a variety of assumptions, which are then put to the test through the process of hypothesis testing. To gain insights that can be put into practice from the data that has been collected, it is common practice to chop, slice, and dice the data at this point in the process. Additionally, various comparisons may be made.
Step 4. Determine what outcomes are most likely to occur
In business analytics, taking a proactive approach to decision-making is essential. At this point, the analyst will model the data by employing prediction methods such as decision trees, neural networks, and logistic regression, amongst others. These methods will reveal insights and patterns that emphasize linkages and "hidden evidence" of the variables that have the biggest impact.
The analyst will next determine the predictive errors by comparing the predicted values to the actual values and computing the difference. Typically, several different predictive models are evaluated, and the model that proves to be the most successful is chosen based on the accuracy of the model and the results.
Step 5. Optimize (find the best solution)
At this point, the analyst will use the predictive model coefficients and outcomes to run "what-if" scenarios. These scenarios will use the targets that managers have established to identify the optimal solution, taking into account the constraints and limitations that have been provided. The analyst will choose the best possible solution and model by considering factors such as the amount of error, the goals set by management, and his own instinctive awareness of the model coefficients that are most closely related to the organization's overarching strategic objective.
Step 6: Decide what you want to do and evaluate the results.
Following this, the analyst would make decisions and take action based on the insights that were gained from the model as well as the objectives of the organization. After an adequate amount of time has passed, during which this action has been carried out, the results of the action will then be evaluated.
Step 7: Integrate the outcomes of the choice into the relevant parts of the system.
In the final step, the outcomes of the choice and action, together with any new insights that were gleaned from the model, are documented and then updated in the database. Data is entered into the database with questions such as "was the choice and action effective?", "How did the treatment group compare with the control group?", and "what was the return on investment?"
The end result is a dynamic database that is constantly updated with fresh information and understanding as soon as it is gleaned from the process.
Final Words
So, this was about today's article on the seven important steps in the business analytics process. To summarize them, first, we must understand the business needs to get relevant data. Second, we need to explore and refine the data to only those which are relevant to the business requirements. Third, we must analyze the data. Fourth, we need to predict the probable outcome. Fifth, we need to optimize the approach to get the best solution. Sixth, we must plan out what needs to be done and evaluate the results. Finally, the seventh step includes combining the outcome into the relevant parts of the framework.
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