Data's unprecedented growth, both internally and externally, has made it impossible for modern organizations to rely solely on specialists to solve all their data analytics needs. 

But within most companies, you can find business users who are comfortable working with and understanding data, yet lack the deep technical skills to dive into advanced analytics. With a combination of guidance and automation, these users can find insights and collaborate with their organization's data scientists and IT to balance data and analytics.   

Call these business users your Citizen Analysts.  

The democratization of data has provided businesses greater access to data and analytics than ever before. But there's still significant room for improvement when it comes to supporting employees' skills and comfort-level with analytics. 

As a leader in such an organization, here are three steps that worked for me to deepen the breadth of analytics knowledge among employees:

Assess Existing Analytic Skills and Gaps

The Citizen Analyst is best described by a set of skills. And just as you establish goals and a business plan before introducing a new tool, you'll want to do the same when introducing a new discipline. 

Organizations must understand the needs, desired outcomes and abilities of its employees. At the same time, it should develop this discipline as a career path with methods to recruit, graduate and create new citizen analysts. 

While a marketing or sales operation department may have some people with deep expertise in analytics, it probably won’t be everyone. Other departments may solely rely on gut instincts or basic spreadsheets. They likely recognize the benefits analytics offers, but do not know how to apply it to their work. 

Assessing skill levels throughout an organization provides critical information to leadership. With this knowledge they can choose what tools will best support their employees learning and advancing their analytic skills. 

Supporting varying skill levels helps to bridge the skills gap among employees, while also removing “analysis paralysis” by taking some of the decision making away from the end user with automation. By leveling the field when it comes to analytics knowledge and making it easier to do analytics, organizations take a step in the right direction to empowering their citizen analysts. 

Enable Culture to Support Analytics

Even if your organization has the best data visualization and analysis tools on the market, that won't matter without an organization-wide, data-driven culture. 

Leaders must set the expectation that all employees develop greater analytics knowledge and apply analytics to their daily workloads, whether directly or by collaborating with others. 

Encourage employees to challenge the status quo. Push them to not just accept answers because someone said so or that are based on the loudest voice or the last interaction. Instead, ask employees to back up every decision and strategy — regardless of how small — with data and evidence.  

Organizations must also challenge biases wherever they can. This applies to people who may appear to be doing more advanced analytics, because they can give you a scatter plot with a correlation line, when the correlation may not be causal. While you can search "correlations that are not causations" to see funny examples, we’ve seen many business correlations that look causal that prove wrong and lead to the wrong decision.    

Learning Opportunities

Employees may not want to take on the extra work that comes with learning a new way of doing things without setting it up as an expectation. Set up clear expectations that all decisions should be data-backed moving forward, while at the same time creating an environment of positivity, learning and testing, enablement, persistence and acceptance to encourage employees to step out of their safety zones.

And as with all periods of change, leaders must allow for trial and error and employees to learn from any mistakes. 

One strategy to encourage the skeptics and the laggards is to create a change management team of at least two to three “champions,” who took to analytics quickly and can show what a citizen analyst is able to accomplish. Having these people find and present a few bias-busting outcomes is usually enough to get everyone on board.  

Augment Employees’ Intelligence to Support Decision Making

Employees just starting out with analytics will come across findings that conflict with their preconceived notions or that aren’t immediately clear how they apply to the business. 

In these instances, a human (often in the form of a domain expert) element is necessary for turning analytics into actionable results. While human bias should be left out of the analysis, instinct and experience are important when it comes to the application of insights. 

Automation and smarter data discovery aren’t replacing human involvement in analytics, rather, they are augmenting human intelligence. 

Citizen analysts should realize this and resist basing decisions solely on what the tool spits out — especially when the findings are vague or incomplete. Teach citizen analysts to recognize these situations so they can fix their data, whether by broadening the data set, eliminating a piece of data or supplementing company data with market data like social, weather or economic. 

Employees should also know that it is OK to start small with the data they have, as their insights will only improve as they continue to amass more data and experience working with analytic tools. 

Analytics isn’t about offloading decision making for the business, but rather augmenting human intelligence with concrete evidence for the most informed decision making. Citizen analysts who achieve this blended approach will become vital assets to any organization. 

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