Demystifying the ‘Why’ Behind Your Data Science Failure

by July 23, 2020 0 comments

Data Science

Failure of advanced analytics initiatives could not only cause time and money but also end of business transformations.

Almost every organization in today’s digital age and the highly-competitive environment is challenged to growing volumes of data. This is why they always in search of advanced analytics tools to turn that data into meaningful insights for their business success. Data Science is one such technique that involves scientific methods, processes, algorithms, and systems to excerpt actionable information companies want. Undeniably, this multi-disciplinary field has the potential to drive innovation, but a few organizations know how to continually derive business value from their data science initiative.

Most companies infuse data science and machine learning into an array of business functions to stay competitive and make an effective digital transformation journey. However, it is not as simple as it seems to apply. Implementing a typical enterprise data science project often requires the deployment of an interdisciplinary team of assembling data engineers, developers, data scientists, theme experts and individuals with specific skills and knowledge that can help execute the project.

While a company can derive much value with such talent, this talent pool is both scarce and expensive. Only a few organizations have succeeded in building an effective data science practice. Moreover, there are also various reasons why most data science projects never make it into production.

 

Lack of Talent and Resources

Deploying a data science project based on a company’s size and scope usually requires to involve a team of data engineers, a solution architect, a domain expert, a data scientist, business analysts and other resources. But due to the lack of affordability to implement adequate resources, companies often miss the opportunity to gain much value. Acquiring an experienced talent pool is also a challenge contributing to this failure. Even hiring a data science expert does not guarantee that the organization will drive profit. One of the most reasons behind this is not having visibility or an appropriate understanding of the business working environment.

So, executing effective data science projects, companies must develop a dynamic team capable of delivering comprehensive skills. It is also essential to have an advanced team that interprets business operations and objectives that ensure the project remains aligned with a company’s goals.

 

Thinking Capital Investment One Size Fits All

Without having an appropriate strategy in place, pouring massive capital into any project could not give results, causing long-term damage in both money and time. According to Chris Chapo, SVP of data and analytics at Gap, ‘One of the biggest reasons is sometimes people think, all I need to do is throw money at a problem or put a technology in, and success comes out the other end, and that just doesn’t happen.’ Analyzing how a data science team develops their hypotheses, how much do they dig in the data, how many false hypotheses are there and assessing what other actions that could cause a similar output, are very crucial in developing a data science project.

 

Cultural Change

Of course, rolling out any initiative within a company requires to address various business aspects and involvement of every team member. Similarly, in order to drive a well-organized data science project, top-level leadership must take dynamic action towards business culture. They must be aiming first and foremost at cultural change where typical business decisions are debated and made openly and collaboratively. In this way, there are several wisdom and assumptions will come out about potential risks and rewards.

In the field of analytics, cultural change has always been a red-hot topic among data analysts. Thus, business leaders must inspire and encourage data initiatives, and team members at all levels must support their recommendations with data.

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