Data Analytics Lifecycle: How Your Business Benefits

Data analytics describes examining raw data to make conclusions, recognise trends, and solve problems. Data analytics has been a growing industry in India as increasing numbers of the population have started using the internet. IIIM Skills estimates 900 million more Indians will use the internet by 2025, with the data analytics market growing by more than 26 per cent annually [1]. 

This growing field is essential, not just for technology, but for businesses across all industries. Businesses, organisations, and professionals can use the insights gained from data to make sound decisions and keep pace with the evolving landscape. To understand how it can benefit your business, learning about the various phases of the data analytics lifecycle is important.

Data analytics lifecycle phases

The data analytics lifecycle consists of a set of phases that can vary slightly depending on the user:

Identify the goal or problem.

The first step of the data analytics lifecycle involves identifying a goal or problem that’s specific and actionable. Goals or problems could involve the following:

• Inefficient inventory methods

• Low employee retention

• Reduced marketing ROI

• Rising employee theft

Collect, prepare, and process data.

The next step involves finding data to help achieve the goal or fix the problem. A business working on this step should check to see if it’s worked on similar issues in the past and make use of any information already gathered. Various ways to locate this data include checking company databases or requesting information from the files or personnel of specific departments within the company. You can also gather data from outside sources.

Then you’ll take the gathered data to process and organise it so it is relevant and usable for the particular objective. You might resolve, encode, or transform it to convert the data to a more comprehensive form. At this time, you’ll scrub the data, inspecting it for errors, extreme deviations, or missing or duplicate information.  

Design a model.

In the model design phase, you’ll examine the prepared and processed data further to determine how to apply it to a model properly. This phase also involves deciding which modelling methods work best for your data (some examples include factor analysis, linear regressions, and time series analysis). 

Build a model.

Model building involves using the information gathered in the model design phase to develop data sets and build a model for testing. 

Interpret results.

After testing the model, it's time to interpret the results. The hope is that using the model will lead business stakeholders to take action or decide based on the goal or problem identified in the first phase of the data analytics lifecycle. Some examples of decisions or actions might include the following:

• Changing the font and background colours on your website

• Improving the quality of a particular product

• Keeping the price of your services where they are

• Updating your employee leave policy

Communicate

In the communication phase of the data analytics lifecycle, a report explains the entire process, from identification to interpretation. You may present the information orally and in writing, and the business stakeholders often discuss the central findings to determine if they offer value. 

5 ways data analytics can benefit your business 

In addition to helping your business stakeholders make decisions like whether to change prices or policies, data analytics can offer several other benefits to your business. Here are five:

1. Enhance efficiency.

Data gathering and analysis can help improve efficiency in your business in many ways. Examples include:

• Identifying whether certain suppliers can deliver the product you need for your busiest selling season

• Identifying whether an automatic payment reminder system would be a good investment in your accounts receivable department

• Identifying your top-performing employees and using employee development plans to help others match their success   

2. Reduce risks.

Data analytics can help you identify and understand certain risks that can affect your business, which allows you to take precautions. For instance, a statistical model using data about local crime might be helpful to predict whether your business is at risk for theft or fraud. After interpreting the results, you may decide to beef up your security. 

By running analytics software that helps identify pattern deviations, you can also use data analytics to predict and prevent cyber attacks.  

3. Improve staff morale and raise retention rates.

Gathering and analysing data can help keep your employees happy and increase retention rates. For instance, analytical models can help you make better staff-related decisions by:

• Determining if you need better training programmes

• Detecting attrition patterns among employees

• Identifying the success of certain types of recruiting campaigns

• Assessing whether an extra 15 minutes for lunch will boost employee satisfaction

• Analysing whether you need to hire more staff at certain times of the year 

4. Enrich the customer experience.

Data analytics can help boost customer service. According to Emeritus, using data analytics may help India move ahead of the United States to become the second-largest market in the world for retail and e-commerce [2]. 

You can use engagement data from social media, email subscription data, and sale data from e-commerce and a physical store to create individual customer profiles. This can help you target customers with personalised advertising and predict what types of products they might buy in the future.  

5. Maximises profits.

Business success always comes down to the bottom line. Data analytics can help you maximise profits in a variety of ways: 

• Analysis of browsing and shopping behaviours can help you promote and stock products your customers are likely to buy.

• You can use analytical models to assess which areas of your city might have more potential customers than others.

• You can gather and analyse data to predict whether demand for a product or service rises at a particular time of the year.

• Analytics can help you determine which products and services are profitable and which aren't. 

Next steps

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