Proactive Digital Transformation: The Adoption of Data Science Tools Made Easy

Data science is predicted to be a “core business strategy” in 2025, according to the Boston Institute of Analytics, as it becomes “integrated into every part of business operations.” While this underlines the prevalent role of data science today, even the adoption of new tools can bring great value to an organization, with benefits that range from enhanced efficiency to improved predictions and cost optimization — to name just a few. However, when it comes to the actual adoption process, many organizations flounder. From the value of thoughtful implementation to the power of thorough experimentation, even workplace culture can play a role in the successful adoption of new data science tools across an organization.

Thoughtful, strategic implementation proves to be key

The successful integration of a new data science tool extends well beyond the decision to do so. In fact, one article from The Conversation by authors Rod McNaughton and Guy Bate highlights the value of the strategic implementation of digital tools, which has led some small and medium-sized businesses in Australia and New Zealand to find success, data reveals. Dubbed “digital leadership,” the article describes the term as an ability to integrate digital technologies into a business in several key aspects, such as an organization’s decision making and long-term vision. While many may find it sufficient enough to use data science tools where applicable, it’s important that they align with an organization’s values and primary mission. According to the article, hesitancy is just one reason why some businesses fall short in regard to digital adoption — when it comes to data science tools, leaders that embrace the technology can be a great way to eliminate hesitancy and build a positive, tech-forward workplace.

For some organizations, the use of digital tools may coincide with human-led innovations for a more seamless integration. For example, while digitization of request for proposal (RFP) documents results in a streamlined approach to document management, the use of templates created by industry professionals provides real elements that reflect first-hand expertise. RFPHub is just one digital tool that makes it possible to browse a variety of templates across a number of industries all in one place, from those that are geared towards data science and artificial intelligence (AI) to those that lean towards supply chain and logistics, sales, or human resources. From there, data science tools can help analyze the information for a more streamlined approach to the request for proposal process.

The discovery of true value

In order to foster widespread adoption, experimentation is necessary in order to discover the true value of data science tools due to the sheer number of options out there. Industry Wired highlights several that demonstrate potential value across a range of industries, from Tableau (used for data visualization) to KNIME, an analytics platform for visual data workflows designed by users. Jupyter Notebooks is another tool highlighted by Industry Wired. Described as an open-source application, Jupyter Notebooks is geared towards document creation and sharing in the realm of live codes, visualizations, equations, etc.. Through first-hand exploration, organizations will be able to determine which tools are suited towards their specific needs and preferences — and weed out any that simply don’t work. The time can also be used to efficiently train employees and integrate the tool into daily operations where applicable, which further underlines the value of a trial-and-error approach.

Workplace culture matters

Workplace culture can play a critical role in the widespread adoption of new data science tools throughout an organization. A LinkedIn article by Nicole L. Turner underlines the value of a company’s workplace culture and its role in digitization, and specifically points out that “A company’s values, norms, and behaviors shape its ability to adapt to technological changes.” Beyond this, however, it’s noted that key aspects of workplace culture (like collaboration) can further play into the adoption of new tools. On the flip side, Turner mentions how workplace culture can have a negative impact — for example, it’s noted that a reluctance or resistance to change that derives from a toxic workplace culture can impede positive changes like new technologies.

When the goal is to successfully adopt new digital tools across an organization, it’s imperative to consider the value of a healthy equilibrium in regard to data science tools. For starters, a healthy balance can prevent overuse (and overreliance) on digital tools. One 2020 Harvard Business Review (HBR) article cites a study by Cornell University and Qatalog, which pulled back the veil on the matter. Findings of the study revealed that 43% of workers reported too much time spent on “switching among different tools” in order to accomplish their job. This resulted in the promotion of “context switching,” and even impacted worker creativity. With a balanced approach that refrains from the reliance on too many data science tools, organizations can both heighten their efficiency and retain their core values.

The integration of data science tools can greatly benefit an organization, however, successful adoption can be a challenge. With a thoughtful approach and exploration of the various tools out there, an organization can seamlessly integrate valuable data science tools across the board — especially when the workplace culture caters toward innovation.