Why Now Is the Time to Leverage Data Science_Blog_Inteliment.com

Why Now Is the Time to Leverage Data Science

If the role of the data scientist was hot, it just got hotter.

The COVID-19 pandemic has severely impacted the global economy leaving it in strands. Businesses have been hampered. People have been locked down. The physical world, as we know it has slowed down drastically. Thankfully, we have made enough technological progress over the past few years that has allowed us to continue the business, albeit digitally.

The world is now slowly inching back to normalcy. As the world opens up, we realise that this pandemic has caused us to pause, relook, and rethink how the world of work has been operating. With new rules of engagement in place and an economic crisis, moving back into the road to profitability demands deeper insights, more planning, and foolproof execution.

If the world wasn’t focused on data science enough, now is the time to do so. Here are a few good reasons why.


Reliable decision-making

The first and the most obvious advantage of leveraging data-science is access to reliable information that can fuel powerful decision-making. While most organisations have jumped on the big data bandwagon, to unlock its power and realise value, they need data science.

Data science helps organisations communicate the value of the institution’s data, which facilitates good decisions. By measuring, tracking, and recording performance metrics and associated information, data science takes out ‘gut-feel’ from the decision-making vocabulary.

However, data science is something that has to be employed across the organisation now. Organisations have to democratise data science and ensure that all business units and all business-users too lean towards data science to take business decisions.


Faster collaboration

The need for collaboration is only going to increase in the post COVID world. If the world was becoming small by becoming more connected, it is now going to get even smaller. Distributed teams and remote working have become a reality of the enterprise.

While data is valuable for an organisation, it only becomes an asset when business users can understand the information and insights and translate those into workable solutions. By encouraging participation and empowering business users to leverage data, organisations can enable in-person collaboration. This becomes especially more relevant as collaboration is not restricted to projects and documentation alone.

Collaboration across teams and business units is now essential to boost innovation and increase competitiveness. It is only with data science that organisations get in-depth information that helps them identify the impediments to collaboration, evaluate the variables at play to assess market dynamics, understand the influence of business drivers, etc. boost their collaborative capabilities.


New business problems need new solutions

A 2019 study on the data science landscape revealed that this market is expected to grow to USD 140.9 billion by 2024.

Some of the key growth factors include a growing interest in data science to ‘extract in-depth insights from voluminous data to gain a competitive advantage’. Given the competitive time we are in, business strategies have to become data-backed to create opportunities.

This is all the more relevant because organisations now need to become more proactive in their decision-making approach. With real-time and deeper insights from data science, organisations can better understand the market shifts.

Given the changes in the business environment owing to COVID-19, it is also likely that we shall be witnessing some new, unforeseen, never-experienced-before kind of business problems. And in these problems will lie opportunities. Only with the help of data science, we can uncover these problems, the underlying opportunities, and unlock business value.

Increase agility

As uncertainty becomes a constant in a post COVID world, organisations need to become more flexible and agile in the face of these shifts. Therefore, the lines that diverge business and data have to blur. Organisations can no longer work in silos if they want to remain agile and relevant.

Organisations have to work towards removing data silos and ensure that data can flow seamlessly across the organisation to enable impactful collaboration. With every stakeholder getting a clear picture of all variables at play with insights derived from data science, it becomes easier to enable reliable decision-making, respond to change faster, and become a truly agile organisation.



Increase organisational efficiency and productivity

The coming months look challenging for all organisations as they go back to the ‘business as usual’ mode. However, to increase profitability, organisations have to pay a close look at all their processes and resources to ensure that everything is operating to maximum efficiency and productivity.

The ability to create efficiency is one of the highest values of data science and helps an organisation become more responsive. Combining real-time reporting with historical data can provide relevant insights into the organisations’ resources and process pool. It also helps in identifying new business opportunities, new ways of engaging with the customer and weeding out old processes that are no longer self-serving in the post COVID world. Organisations can only do so successfully when they depend on robust data rather than on their collective gut feeling.

“Change is constant. Satisfaction is relative. Innovation is continuous…” The world ahead of us looks something like this. But, to be able to navigate this, organisations have to also ensure that the reigns of data and data science do not remain in the hands of the select few (the data scientists). The time is now to look at tools, technology solutions, and platforms that democratise data science and enable everyday business users to use data to make the best decisions.

“Measure twice, cut once” is a prudent plan to follow in this new age we are entering. We just need to make sure that we measure right. And that can only happen with data science.

Related blogs

Scroll to Top