How to Sell Data Analytics to Non-Data Scientists
While data scientists know the value of their work, it can take some convincing to prove that value.
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Most data scientists are well aware of the positive impacts their work can have on organizations. However, it can be challenging to convince people who don't work in data science of those benefits.
Some of them resist doing things differently because they dislike changes. Others balk at the perceived high upfront costs of data analytics technology and the time required to develop a related strategy. Here are some useful things to mention when trying to sell these individuals on the need for data analytics.
Show How Data-Backed Intelligence Drives Competitiveness
One key selling point is mentioning how using data to make decisions is a proven way to put a company ahead of its competitors. Achieving such results is especially important as the business landscape becomes increasingly demanding and crowded.
A 2020 Collibra survey showed that companies using data for intelligence had a 173% advantage in their compliance with requirements and regulations. Moreover, they were 81% more likely to grow their revenue and 58% more likely to meet revenue-related goals than organizations that did not use data intelligence.
Those statistics highlight how data analytics can help companies succeed more than they otherwise would. Using data analytics in an organization takes time, effort, and money. However, outcomes like those mentioned here make such resource allocations pay off.
Discuss How Data Analytics Can Benefit the Whole Organization
Not so long ago, IT departments primarily owned and managed an organization’s data. However, enterprises are now more likely to use cross-functional and democratized information across teams to meet business objectives. Then, everyone in the organization can notice the associated advantages, making data analytics good for the company.
Even data collected from one department could inform other areas of the business. Consider a case where a company gathers information about customer service interactions to feed into a data analytics platform. Understanding people’s problems, how long it takes to resolve issues, and what customers think the company is doing well can influence changes that don’t directly involve customer service.
A 2022 survey from Tableau also indicated that employees appreciate getting opportunities to build their data-related skills. About 80% were more likely to stay at organizations that helped them develop such capabilities. However, the study showed only 39% of companies polled currently make data-related training available to all workers, suggesting there’s room for improvement.
Remind Them That Data Analytics Use Cases Are Virtually Endless
Another compelling reason to use data analytics is that people can use it for almost any need an organization has. Some companies utilize it to detect potential fraud, saving significant time. Others rely on data analytics to anticipate customer trends or strengthen cyberattack defenses. Thus, there’s plenty of evidence to the contrary if the data analytics hesitation relates to a belief that it won’t fit business needs.
Plus, if decision-makers wait too long to deploy data analytics within an organization, they’ll risk falling behind peers and struggling to catch up later. A 2022 survey from NewVantage Partners revealed that 97% of companies polled were investing in data-related initiatives. Plus, more than 92% of respondents said they got measurable business results from those decisions.
Those outcomes suggest that companies prioritizing data analytics have obvious advantages. However, associated representatives should take time to think about the biggest challenges or weaknesses currently facing the business. They can then consider how data analytics could enable overcoming those aspects.
Multiple Discussions Are Often Necessary
It’s usually unrealistic to hope that someone reluctant to use data analytics in an organization will become fully supportive of it after just one conversation. People typically need time to reflect on a discussion’s content and realize that they might need to broaden their viewpoints before changing their initial opinions.
That means data scientists should not feel discouraged if they don’t get anywhere after their first attempts to sell the worth of data analytics to non-data scientists. People are more likely to have mutually beneficial conversations if they show patience and a willingness to hear about the other person’s doubts or hesitations. That makes reaching a shared understanding easier, increasing the likelihood of using data analytics in an organization.
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