Covering Disruptive Technology Powering Business in The Digital Age

In our current digital landscape, there’s no denying that big data provides valuable business value. The recent years have seen organizations working together with the brightest minds in the digital space to constantly innovate and revolutionize the world we know today to make our lives as enjoyable and convenient as possible. With the rise of this sentiment, global businesses are embarking on digital transformations, embracing new technologies to work smarter, drive innovation, and serve customers more efficiently. That’s where big data comes in – at the center of this journey is an ever-expanding universe of knowledge available from your organization’s data.

As companies push to find the limit of innovation and move to set themselves apart from the crowd, more and more are also recognizing the importance of building a robust data analytics team to support their initiatives. Business leaders are aware that missing digital skills and shortage of IT talent is a challenge that hinders them from achieving digital transformation within their organizations. The race is on to putting digital transformation at the center of an enterprise’s corporate plan– in fact, it is predicted that by the end of 2017, two thirds of the CEOs of the G2000 enterprises will have this strategy in place. Seeing as such, here are some questions to ask when developing a Data Science team:

Who are your data scientists?

Before outsourcing for IT talent to manage data projects, organizations should first identify potential candidates within their team – chances are, they may already have internal staff working on projects scratching the surface of data analytics without noticing it. This can be done by accessing how data science is currently being used in the organization, no matter how little or widely.

In most cases, the reason for not recognizing that existing employees have the capabilities and know-how already in place might be attributed to the fact that they have been working individually across different areas, instead of together in a cohesive unit, as they should be. In addition, it pays to identify employees with aptitude and curiosity for data science work – while they may already display skills of business acumen, data access, data modeling, decision science and data visualization, a lot more training can be done to develop them into full-fledged data scientists.

How should they work with the business?

Now that you’ve identified your pool of data scientists, there are two approaches that can be taken to diagnose how they should work with the business. The first way would be to establish a centralized group that can undertake data science projects for anyone, anytime – the second approach would be to embed your data scientists within the different lines of business and product teams. Both approaches have their pros and cons: a centralized data science organization generally does a better job of managing and integrating the enterprise’s complete data ecosystem. Conversely, the challenges faced by taking the former approach include a slow project queue, a lack of domain knowledge, and an “us vs. them” dynamic between business managers and data scientists.

As such, some organizations prefer to take a blended approach. This way, a company may elect to have a central data science team, while simultaneously embedding another data science team within departments which have frequent, specific, and specialized data analysis demands. A data science team needs managers too, and it’s imperative that data scientist management be included in the process of establishing and developing the team’s data science capability and strategy. It is recommended to assign employees who have the knowledge and aptitude in the realm of data analytics beforehand to direct these projects, in order to effectively manage and navigate the tasks expected of the team. By being fluent in the language of data science, these managers would be well-poised to communicate with both internal and external clients should the situation call for it.

When should you outsource data expertise?

Keeping a lean approach in developing a centralized data science team works well in the beginning – by engaging a few business units on issues that can be addressed with data. It’s possible to cultivate the internal team slowly and deliberately, familiarizing it with the work involved, building on successes, and from there, expanding horizontally into new challenges. Once the initial groundwork has been completed it would be advised to loop in external expertise to bridge any knowledge gaps pertaining to the business of data science. This would focus on training across all internal members and evaluating knowledge gaps and figuring out how to fill them.

Traversing the data to decision journey can be a decision both exhaustive on the wallet and human capital. However, if carried out smoothly, it will do well to maneuver an organization’s path to business success in the digital transformation journey.

This article was originally published enterpriseinnovation.net and can be viewed in full here

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