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Author's profile photo Saajan Sharma

Understanding and managing the data analytics lifecycle

87% of data science projects up till 2019 failed to reach the production stage. In a report in 2019, Gartner predicted that only 20% of analytics insights would lead to business outcomes through 2022. These numbers are pretty surprising given the widespread acceptance and integration of data analytics and data science across industries over the last decade, along with the increasing popularity of data analytics courses. One could hope that most companies will have figured a way out of connecting data with business outcomes by now.

So, what seems to be the problem?

For starters, many companies are yet to put the finger on the true purpose of data analytics in their organizations. Having a clear picture of the entire analytics lifecycle (at least knowing what you want it to look like) is essential for its success. This is as important for businesses as the analytics professionals. Let us break an analytics lifecycle into some steps and try to form a comprehensive understanding.

Identification of the problem and formulation of a business case

What problem do you have that necessitates the integration of analytics in your business processes? Find a definite answer to this question; not a generic statement like, it will enhance decision making or improve marketing. You cannot move an inch without pointing to a situation that will, hopefully, get better after implementing data analytics. Suppose you are running a retail chain and want to implement analytics for business enhancement. In that case, you should have a clear goal – it can be a model that uses demographic data to make gender-based segregation of your customer base or maybe a system to analyze your customers’ spending patterns. Business case formulation is the natural subsequent step after you have identified a problem. This is the step when analytics experts sit and have a discussion with the business owners and stakeholders. Analysts help the stakeholders understand the venture’s potential in terms of process enhancement, decision making, and, most importantly, return of investment. A well-crafted and convincing business case is likely to lead to success, whereas a weak business case sets the entire analytics lifecycle up for failure.

Designing and Developing a solution

Now that you have a problem and a business case, it is time to look for a solution. There are four aspects of the solution design and development that deserve attention.

Situation analysis

There needs to be a solid understanding of the company’s current situation and a clear vision for the changed scenario after implementing the solution. Moreover, it would help if you looked for pre-existing solutions that might be internally available to the business. You will have a choice between developing a solution and buying one. Sometimes it is easier and cheaper to buy a solution unless you need unique capabilities and wish to use in-house abilities. 

Data collection

Like the previous one, this step requires active collaboration between people with analytics skills and strong business acumen. You need to identify the kind of data required for the solution and then figure out how much of it is available and accessible. There are issues regarding data ownership to be taken care of; there are security and privacy issues that need to be monitored. Whether you build an in-house analytics capability or buy one, it would require a safe and efficient data pipeline.

Data consolidation and preparation

The data will likely be heterogeneous – it might include structured data sources like spreadsheets and unstructured data sources like reviews, or social media feeds. The variety of data will be dependent on the kind of models you are about to deploy. All the data must be consolidated, cleaned, and prepared for the descriptive and predictive models. This part is likely to take a pretty significant chunk of the analysts’ time.

Feature assignment

Features are specific properties or conditions on which pattern recognition, classification, or segmentation is based. Features depend on your business model and the kind of analytical framework you are trying to form. For instance, when Google performs search analysis to acquire insights regarding the trajectory of a spreading infection or disease, they might use features like names of medicines, locations, terms related to the symptoms of that particular disease. While developing the solution, analysts focus on features and relationships that may answer the business questions under consideration. This is followed by a series of model building, algorithm testing, calibration, and more repetitions than you can probably imagine. The solution must meet the analytical as well as the business requirements.

Implementation of the solution

The implementation process is often initiated with a solution pilot, where the analytics models are tested in a simulated environment with small data samples and little at stake. A successful pilot run leads to the full-scale implementation of the solution. A company can decide to integrate the new solution into its existing business processes.

Monitoring and assessment

Once the solution is deployed, it needs to be monitored. The results are assessed based on the assumptions made at the stage of formulating the business case. The company finally starts getting definitive answers to questions like how important the insights are, what kind of process enhancement the new model inspires, and how much return of investments one can expect.

Continuous Maintenance

Everything from the data pipelines to the algorithms supporting the models must be maintained and updated regularly and consistently. There is often a discussion among young data enthusiasts who are planning to enroll in data analytics courses about how opportune and sustainable can the profession of an analyst be if the processes involved in it are constantly automated. This is where the answer lies. A business will never run out of the need to maintain an analytics capability; hence, analysts will be in business for the foreseeable future.

Retirement of the solution

Businesses evolve along with the market, and an analytics model often becomes obsolete in that changing environment. This is when a model is retired or put out of use. The reason behind this can range from a drastic change in the market to a loss of trust in the insights generated by the model to the availability of a more efficient tool. Then, of course, the model is replaced, and a new, somewhat similar cycle begins.

Key takeaways

The importance of collaboration between business and data owners and analytics experts cannot be stressed enough; it lies at the core of any successful analytical venture. A business problem leads to a solution. Identifying the problem and building a business case around it is crucial for a data analytics project. Preparing the data will take the most of your time and energy, and there is no way around it. The market changes, the business models evolve, technology changes, and so do the customers. Change is the name of the game, and you must be willing to change the system you have spent so much time and money on when an opportunity shows itself.

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