Launching a New Data Science Initiative? You Could Face These Hurdles

 In Blog, Our Thinking

Adopting new technology is never a cake-walk. Irrespective of the size of the company, with every process transformation, there’s always anxiousness and struggle. With a relatively newer technology such as data science where huge amounts of investments are at stake, organizations are willing to go the extra mile to get everything right. 

Implementing new initiatives in data science is like a transformation in itself. However, it is not limited to the understanding of tech-tools, hiring data scientists, analysts, etc. alone. It actually goes way beyond the obvious. 

Implementing new initiatives in data science is like a transformation in itself. However, it is not limited to the understanding of tech-tools, hiring data scientists, analysts etc. alone. It actually goes way beyond the obvious. 

Read on to know what can be some of the not-so-obvious challenges that you need to know and in knowing them could help you avert huge losses while launching a new data science initiative!

What does a data science initiative entail?

Data science is all about giving your data a purpose. It helps you transform all the acquired data into a certain value – to bring improved revenue, enhanced customer experience, business model innovation, reduced costs, and agile business solutions – to serve the customers better. 

A data science initiative that is based on clarity about how and where the data is going to be used, inevitably, brings better results. But it is also imperative that the goals of data science be embedded within the business teams and also be aligned with its objectives to produce outstanding results.

With a clear objective, any organization can optimize data in the right way. For any data science initiative, the company needs to have experts such as data analysts, data scientists, data evangelists who can extract maximum value out of data and also bring that data to actual use. But more than anything, it is the mindset of the people and the processes in the organization that determine the success or failure of any data science initiative.

The Not-So-Obvious Hurdles

While many companies might be doing well in data science, there are still several others that struggle with their first data science initiative. 

The absence of data science culture might be the real challenge that needs to be addressed in all data science initiatives. This is the huge precursor for having a healthy ‘data culture’. The people and processes of the company must be prepared to embrace the data-driven transformation.

Experts say that the need for this data culture should be imbibed in all the teams involved in the initiative.  It is a crucial step in order to build feasible frameworks and sustainable infrastructure that supports data analytics. Companies need a culture that transforms the way people perceive data in their decision-making. 

Hiring data scientists and analysts might be the easier part. However, integrating the new talent into a data-based culture or re-configuring the entire corporate culture to make it more data-driven is the real challenging part for any company that wants to succeed in its data science initiatives. 

Stakeholders of the company play a pivotal role in setting the right tone and ground for an all-inclusive data culture for the organization’s success in the initiative. It is important for the company leadership to lead the data-driven culture and practice the same. 

What defines a data team in an organization is also very important for success. Is it a mixed bag of data-handlers? Does it comprise of a group of hand-picked data scientists or analysts? It is also important that the business folks, data scientists, and experts are marching to the same drumbeat and that all are on the same page. 

To inherently incorporate the knowledge and better understanding of markets, customers, and their needs that are based on data can pave the way to a successful data initiative. People of the organization should consider data as a high-value component and data science as a serious business. Clearly, for building a culture like that, it should be drawn from the source and simply, just imposing such as a data-driven culture will almost always fail. The executive and major influencers should set an example by demonstrating a serious data mindset with clear objectives by placing data over intuition.

The Other Obvious Hurdles

Poorly defined needs

If your project needs are not well-defined from the very beginning, it could become a huge challenge in the future. If your project falls short of clearly defined objectives, there are more chances that the project might fail in the longer run. You need to clearly define where will the data come from, how it will be processed, who will use it for what kind of decision-making, and how do you measure its success. 

Unmatched objectives

Data science is useful only when the data can be used effectively to bring about a change. If the data analytics and implementation don’t match the users work-flow or needs, such data is of no value at all. You need to clearly define what you aim to achieve through the initiatives, and only then will you know how to measure its success.

Lack of a data analytics translator

Business folks are an integral part of a data science project. However, it is important that a data analytics translator is also included to bring synchronicity within the team to make sure that all the people who are involved understand the technical language of data science. 

Conclusion

While launching any data science project, it is important to go to the basics of data science, analytics, its needs, and the objectives. While one needs to keep an eye on the obvious challenges that are more technical and tangible, keeping in mind the other, not-so-obvious challenges can guarantee better success for the data science project.

Turning around the mindset of people of any organization is not an easy task. While it is the stakeholders of a company who are the torchbearers, it is also more of an individual-ownership-job to know the true value of data!

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