7 Steps of Business Analytics Process

  • Neelam Tyagi
  • Apr 13, 2020
  • Business Analytics
7 Steps of Business Analytics Process title banner

“Data is the contemporary fuel”, is a notorious quote pinpointing the demanding sense of data and to flawlessly symbolize data as organic material. Data can be considered an elementary resource that is required in further processing before literally being of use.  

 

For the real-time analysis of data, organizations are employing business analytics to facilitate remarkable decision making. Real-time analysis has come up as a looming business tool that is shifting the conventional methods of doing business, for sure, you have read various Real-World Applications of Business Analytics in our previous blog.

 

What is business analytics?

 

Business analytics is the process of inspecting the gigantic and motley data sets, commonly known as “Big Data”, to divulge the varied connections, correlations, trends, partnerships, customer behavior, statistical patterns, and other meaningful interferences that aid organizations to make better business decisions.

 

These insights basically prompt novel possibilities for augmentation, formulate businesses to modify in market dynamics and locate organizations to resist troublesome new aspirants in the respective industries. 

 

"Business analytics is establishing the winners and losers in most industries. Connect cites analytics as serving a crucial character in its turnaround. Numerous companies such as Amazon, Google and Capital One have constructed their complete business model across analytics. Companies that account for this are demanding for the talent required to assimilate business analytics into their business strategy."  – Dr. Kenneth Gilbert, head of UT’s Statistics, Operations and Management Science department.

 

Components of Business Analytics

 

The components of business analytics involve;

 

  1. Data Aggregation: Before analysis, data must be accumulated, streamlined and cleaned up to escape replication, and filtered to eliminate incomplete data. Data can be aggregated from transactional data and volunteer data.

 

  1. Data Mining: In order to recognize and acknowledge prior unidentified data trends and patterns, models are designed through massive data. Data mining exploits various statistical techniques to obtain interpretation such as classification, regression technique, and clustering.  

 

  1. Text Mining: Data is also assembled in the form of textual information from social media websites, call center scripts, blog comments, etc, to drive powerful connections indexes that are used to develop new items most in-demand, promote user services and experiences, and analyze opponent performance.

 

  1. Forecasting: Future events or behaviors based on previous data can be forecasted by scrutinizing processes that take place during a particular season or period. For example, retail sales for the holiday, and energy consumption for a city in summer.

 

  1. Optimization: Organizations are continuously revealing the best possible opportunities and promising actions via designing interesting simulation techniques like to identify peak sales price and spike in-demand to scale production, major opportunities slots for sales, promotions, and new items.

 

  1. Data Visualization: Knowledge and observation extracted from data can be conferred with extremely interactive graphs to outline exploratory data analysis,  modeling results, and numerical forecasting. (read more about data visualization here)


The image is presenting the stages and components of business analytics where 3-stages are Descriptive Analytics, Predictive Analytics, Prescriptive Analytics, and 6-components are Data Aggregation, Data Mining, Text Mining, Forecasting, Optimization, Data Visualization

Stages and Components of Business Analytics 


3-stages of Business Analytics

 

Business Analytics always remains a buzz in almost every industry, an individual equipped with business analytics skills results in a sharp business decision, huge profit limits, adequate operations, and amused customers. 

 

The person once knows how and when to play with data, he can apply suitable analytical tools to extract influential insights from data and make imperative advantages.

 

Here are the three stages of business analytics:

 

  1. Descriptive Analytics: An application of a prime statistical technique that explains what a dataset contains, the main two techniques used are data aggregation and data mining that signify this method is used for understanding the elemental behavior and not to make predictions.

 

  1. Predictive Analytics: An advanced statistical application or a research method to figure out predictive variables and make predictive models determining patterns and relationships among variables.

 

  1. Prescriptive Analytics: An application of decision system, management science and operation research methodologies to conduct appropriate use of admissible resources. 

 

In addition to it, we would recommend you to read Top 10 Big Data Technologies in 2020 where you learn the latest tools highly deployed for big data analytics.

 

 

Talking About the Process of Business Analytics

 

Business Analytics techniques can be deployed in any industry where data is conquered and handy to obtain business solutions through concerned data and curve it into understanding and knowledge to make valuable decisions. Multiple BI tools are implemented that helps an organization to obtain a competing asset in the market.

 

Business Analytics in Action: 7-steps Process outlined below;

 

Step 1: Address the Business Problems

 

Initially, business problems need to be addressed, the purpose of applying analytics is sometimes designated categorically or broken into parts. So, relevant data is selected to address these business problems by business users or business analysts equipped with domain knowledge. 

 

Some examples are: keeping modeling for a postpaid subscription, fraud detection for credit cards, or customer analysis of a mortgage portfolio. Business experts define perimeters for the analytical process which is crucial for assuring general understanding of the goal.   

 

Step 2: Identify Potential Interest from Data

 

All sources of data having potential interest are required to identify. The key asset in this step is the more the data, the better it is. All the data will then be accumulated and consolidated in a data warehouse or data mart or at a spreadsheet file. Some exploratory data analysis is executed to do the computation for missing data, removing outliers, and transforming variables. 

 

For example, time-series analysis graphs are plotted to figure out some patterns or outliers, scatter plots are used to find correlation or non-linearity, OLAP system for multidimensional analysis.

 

Step 3: Inspect the data 

 

Once moving to the analytics step, an analytical model will be predicted on the prepared and transformed data using statistical analysis techniques like correlation analysis and hypothesis testing. The analyst figures out all parameters in connection with the target variable. The business expert also performs regression analysis to make simple predictions depending upon the business objective. In this step, data is also often reduced, divided, crumbled and compared with various groups to derive powerful insights from data.


Highlighting a systematic representation of the 7-step Business Analytics Process including the steps as Step 1: Address the Business Problems Step 2: Identify Potential Interest from Data Step 3: Inspect the data Step 4: Interpretation and Evaluation by Experts Step 5: Optimization of Best Possible Solution Step 6: Decision Making and Estimate conclusions Step 7: Upgrade performance system

7-step representation of Business Analytics Process


Step 4: Interpretation and Evaluation by Experts

 

Finally, after obtaining model results, business experts interpret and evaluate them. Results may be clusters, rules, relations, or trends known as analytical models derived from applying analytics. Experts use predictive techniques like decision trees, neural networks, logistics regression to reveal the patterns and insights that show the relationship and invisible indication of the most persuasive variables. 

 

Several prediction models are executed to select the best performing model on the basis of model accuracy and consequences. But yet, to explore unknown though engaging and tribal patterns are challenging that can add value to data and convert into new turnout opportunities.

 

Step 5: Optimization of Best Possible Solution 

 

Once the analytical model has been validated and approved, the analyst will apply predictive model coefficients and conclusions to drive “what-if” conditions, using the defined to optimize the best solution within the given limitations and constraints. 

 

Necessary considerations are how to serve model output in a user-friendly way, how to integrate it, how to confirm the monitoring of the analytical model accurately.  An optimal solution is chosen based on the lowest error, management objectives, and identification of model coefficients that are associated with the company’s goals.

 

Step 6: Decision Making and Estimate conclusions 

 

Analysts then would make decisions and endure action based on the conclusions derived from the model in accordance with the predefined business problems. Spam of period is accounted for the estimation of conclusion, all the favorable and opponent consequences are measured in this duration to satisfy the business needs.

 

Step 7: Upgrade performance system

 

At last, the outcome of decision, action and the conclusion conducted from the model are documented and updated into the database. This helps in changing and upgrading the performance of the existing system. 

 

Some queries are updated in the database such as “ were the decision and action impactful?” “ what was the return or investment ?”,”how was the analysis group compared with the regulating class?”. The performance-based database is continuously updated once the new insight or knowledge is extracted.

 

Conclusion

 

Business analytics can result in powerful and valuable visions that would not be feasible in the truancy of data. With implementing analytics, one can increase the operational efficiencies of any business over a range of functions. In essence, to assure the adaptation of the recommendations that are brought out from data, one can interpret organizational behavior shifts in a way that provides the forecasted benefit.

 

I hope these steps will assist you to deploy a successful business analytics process!!! Never miss a single analytical update from Analytics Steps, share this blog on Facebook, Twitter, and LinkedIn.

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Comments

  • sakethv321

    Sep 09, 2020

    Your amazing insightful information entails much to me and especially to my peers. ExcelR Business Analytics Course

    Neelam Tyagi

    Oct 15, 2020

    Thank you, Sakethv321... follow us regularly for more updates.