It’s the Time to Start Being Data-Driven

It’s one thing to know what the data-driven decision-making process is and the benefits that it can offer, it’s another thing to implement it. Now it’s the time to start being data-driven.

With so many companies adopting a data-driven decision-making approach, experts in data analytics and business insights have been able to uncover what works and what doesn’t, the best practices, and smart strategies for the process’ implementation.One of the best ways for a company to approach this endeavor is by assessing to what extent their decisions are currently being driven by data. This is not complicated to suss out. Organizations that use a data-driven decision-making strategy have characteristics that differentiate them from ones that don’t:

  • A c-suite that is passionate about asking questions, leading innovation in their industry, and values more than just traditional KPIs
  • A data infrastructure that provides easy access to valuable data to every member of the workforce, not just management
  • A company that continuously trains its workforce on how to collect, analyze, and utilize data in their daily tasks
  • A business that has automated processes surrounding data collection, data analysis, and its incorporation into systems and processes.
  • A data-driven culture that expects to utilize and harness data in all of their processes

While this is what a data-driven organization looks like, it is not necessarily a checklist for achieving the data-driven decision-making process. Companies need to take foundational steps in order for these characteristics to become fully integrated across their entire organization.

How To Promote Being Data-Driven In Your Organization

The pathway to becoming a data-driven organization is not an easy one, but it can be done with time, consistency, and leadership. The following steps will help you develop a road-map for data-driven decision making in your field:

1. You need to identify a data champion within the organization, who critical to understands the value of data-driven decision making and is willing to dedicate time and effort toward getting the workforce on board. Typically, this champion is a c-suite executive who has budget allocation and authority to implement change.

2. This data champion should identify key groups within the organization who are leveraging their data effectively or identify particular project successes that hinged on data analysis. Then, highlight these projects and teams internally, through webinars, newsletters, and other means. These types of successes are crucial to getting buy-in from leadership and developing a mind shift amongst the staff.

3. Once there is more interest in becoming data-driven and leveraging big data, the company should offer internal trainings to train up existing staff on these data techniques. By offering continuous trainings across teams, your workforce will develop a common data vocabulary and feel empowered to explore more data on their own.

4. Additional ways to speed up adoption include developing a community of practice, scheduling monthly lunch-and-learns from data experts or data colleagues, and highlighting successes frequently.

While you may feel an urgency to adopt a data-driven decision-making process, there is no overnight fix. Don’t take shortcuts or provide inconsistent support – remember, if you fail to plan, you plan to fail. Executives should work towards a holistic plan that builds up data usage and knowledge over time in order to get the highest returns on investment. Instinct. Intuition. Experience. Data. Those are the four ingredients you need to make data-driven decisions and drive your company forward into the data revolution.

Merav Yuravlivker, CEO

Merav has over a decade of experience in the education field, from classroom teaching to instructional design and building online learning platforms. Through Data Society, she has helped companies save millions of dollars with their pedagogically-sound and effective data science training programs. Merav is passionate about automating tedious tasks and providing the data science skills that professionals need to solve their most challenging problems.

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