Pitfalls in analytics stem from incorrect skills and tools

Enterprises across the globe have acknowledged the potential of data analytics in furthering their business performance. They also realise that capitalising on everything data has to offer in terms of business benefits mandates a re-jig of people, process and technology.

A recent global study by Infosys of large enterprises highlighted the skills shortage as a critical challenge in data analytics initiatives, according to 40% of respondents. The skills shortage is presented as a serious concern that is hampering enterprises from fully exploiting the potential of data analytics.

In order to solve this shortfall, focusing on both data scientists who build data models, and the users who consume the models, is essential.

That a data scientist’s skills need to be continuously upgraded is obvious. However, it’s equally important to enable users to easily consume data analytics outputs to ensure smooth functioning. An enterprise must ensure its users grasp basic concepts of data modelling and the outcomes these models can offer. It is essential that the user can make sense of what a data scientist is recommending and, in this way, build trust, a critical success factor. Without trust, business implementation of insights derived from data is muted.

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Ensuring your data analytics team stays relevant to the customer

There are a few ways to bridge the skills gap.

Using technology, providers can create self-service analytics platforms for end users. Here, the user doesn’t have to struggle with complicated mathematical concepts and statistics but simply plug in their input parameters, and the self-service platform churns out insights on demand. Such a platform drastically reduces the reliance on specialised data scientists who now need to get involved in specific areas only.

By carefully evaluating the skills gap problem, it becomes apparent that the solution doesn’t lie in just adding more data analysts. Today, business and IT are more closely connected, unlike in the past where they functioned in silos. Thanks to the evolution of data, the boundaries between business and IT teams are continuously blurring. The two work more closely using data analytics as a common ground and enabled by technology providers.

This context mandates a multi-skilled approach to address business as well as IT requirements. A tiered approach to problem-solving, including a consultant who understands the business side, a data scientist to conceptualise data models and a technical resource for technology solutions, works best. This tiering also diversifies the skills needed, easing the strain on lack of specialised data analysts. Over time, these resources develop a keen understanding of the related areas, including data science, business and technology, and thus form that breed of consultants who specialise in one area but know enough of related areas to be effective as an individual contributor.

A large set of the study’s respondents also found a key challenge to be that of selecting the right tools and techniques when applying data science to create business insights. For most business stakeholders, this remains a challenging task, and one with obvious ramifications. A very good way to circumvent the challenge is to adopt a fail fast methodology and ensure a POC-Pilot-Roll out phased plan. In this way, tools and techniques selected can be tested and proven without expecting a perfect choice in a dynamic world.

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Keeping your data analytics team skilled

In addition to technology-driven solutions and internal restructuring of teams, a technology provider must also look at continuously educating its team.

Much thought must go into crafting a training program that is expected to keep the data analytics team’s skills updated and customer-ready. It involves creating a curriculum customized for data scientists, with a blend of mathematics, problem-solving and some technical skills. The training cannot be termed complete unless the data scientist is tested for the concepts learned as well as the ability to apply them in a real-world situation.

Tie-ups with universities are an added incentive to a data science professional. Through such programs, they can acquire a diverse set of skills which can help them perform at a higher level.

Mature providers have also devised master track programs that enable a technology professional to become an analytics professional. By identifying a set of must-haves to become a proficient analytics professional, this encourages a person to explore beyond their usual boundaries and acquire a new set of skills. The re-skilling is very intense since the thought process to apply statistical techniques is different from the average technology professional’s work.

The founder of Fortune magazine, Henry R. Luce aptly said: “Business, more than any other occupation, is a continual dealing with the future; it is a continual calculation, an instinctive exercise in foresight.” Ensuring your business has both relevant and dynamic data analytics skills is the foundation for capitalising on all the business benefits that analytics has to offer, both now and in the future.

Written by Subhashis Nath, associate vice president, Data Analytics at Infosys
Written by Subhashis Nath, associate vice president, Data Analytics at Infosys

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