Data science created a buzz in the corporate world with the evolution of smart technologies that produced data at every nanosecond. While the organizations want to hire skilled data science professionals (read certified), the dearth of skilled professionals creates a barrier that is difficult to bridge.
But why do businesses require data science professionals or data science leaders?
Data is everywhere. It is present in every imaginable form – raw, structured, and unstructured. Organizations across the world are dealing with data daily.
To understand why data science leaders have become important for businesses, it is imperative to know the functional significance of data science for corporates.
Data Science - The Value Addition to Businesses!
Data science is a field that brings the expertise of various domains including mathematics, programming, and statistics to offer insights and further to make sense of available data.
But why are organizations emphasizing the importance of data science? The simple answer to the question is that over the years the value of data has soared heights.
Picture this – The renowned Southwest Airlines saved about $100 million by leveraging their data. Result: They were able to reduce the idle time of their waiting planes at the tarmac and were able to drive a change in using their resources.
To put it simply, businesses cannot court success if they are not data driven!
Advantage: Data helps in making critical and sharper decisions that further aids in organizations to streamline their business and stay ahead of their competitors.
Besides this, here are some top ways data science adds value to your business.
Despite all this know-how, organizations world-over find it difficult to adopt data science in their business processes. Remember – in business, knowledge is the authority, and data is the fuel that builds this authority.
According to the experts, the data volume reached about 44 trillion gigabytes in 2020. Data Science helps in harnessing the power of this data using scientific processes, methods, systems, and algorithms to extract insights from data and leverage this data to make important decisions. A significant and a strategic practice for any organization.
Data Science - Five Stages of Adoption
However, before a business or an organization can harness the power of data science, they need to adopt data science and incorporate them in their daily business practices. There are essentially five stages of data science adoption in any organization.
Each of these stages plays an important role in deciding how data driven your organization is or can become one.
Data Science Adoption: Challenges and Implementation
Talking about the current scenario, mid-sized and small businesses, which constitutes about 99% of the entire businesses in the US, lag in adopting the right platforms which will enable data leveraging.
According to a report by Gartner in 2019, it was revealed that about 87% of the businesses were categorized as businesses with low analytics maturity and business intelligence (BI).
As per the further indications, it has been found that organizations struggle to manage implementation projects for boards, which will allow data analysis even to a small extent.
A report by NewVantage revealed that about a handful – 27% – of businesses in 2020 thought their work environments were data driven. Further 73% of businesses revealed that big data management proved to be an operational challenge. Result: A lack of significant progress in the last two years.
No doubt, the pandemic conditions of 2020 forced numerous businesses to enhance their digitization efforts. However, most of the efforts were concentrated toward IT management, communication tools, and cybersecurity. There were no concerted efforts to leverage their data.
Consider this – despite decision makers and business leaders willing to adopt data analytics, the number of obstacles remain high. To successfully implement data science and data analytics in their organization, here are some measures business leaders can take –
These are the steps every data science leader should practice and follow for a successful implementation of data science.
With these nine steps a data science leader will be able to successfully implement the data science process in the organization for a resounding growth of the business.