Analytics Training Isn’t Enough to Create a Data-Driven Workforce
- by 7wData
When it comes to creating a more data-and-analytics-driven workforce, many companies make the mistake of conflating analytics training with data adoption. While training is indeed critical, having an adoption plan in place is even more essential. Any good adoption plan requires online or recorded refresher sessions; mentors; online resources for questions, feedback, and new ideas; and a certification process. An analytical culture will only become more data-driven if you design strategies for encouraging employees to adopt the technology, tools, and methodologies required. Training is just a starting point.
When it comes to creating a more data-and-analytics-driven workforce, many companies make the mistake of conflating analytics training with data adoption. While training is indeed critical, having an adoption plan in place is even more essential.
Any good adoption plan should focus on continual learning. This might include online or recorded refresher sessions; mentors; online resources for questions, feedback, and new ideas; or a certification process. It might even mean rethinking your organization’s structure or core technologies. Based on my experience, here are three ways leaders can shift a company culture from a one-and-done focus on “training” employees in analytics to an “always on” focus on analytics adoption:
Form competency centers. At a high level, a competency center is a collection of domain experts who are given a goal to improve agility, foster innovation, establish best practices, provide training (and mentoring), and be a communications engine. These centers should be “owned and operated” by highly competent individual contributors with relevant expertise. Competency centers can be established by any type of focus area and require a lead, members, and a sponsor. As part of their mission, competency centers should be answering the “why” questions instead of the “what” questions. For example: Why do we analyze data? Why is data quality important? “Why” questions are about establishing a purpose and direction that will help guide focus and priorities.
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