Realizing the Promise of Data Analytics
Article by Steve Tadeo and Allen Hillery, Photo by Stein Liland
Today for many organizations the business benefit of data analytics is still just a promise. Everyone agrees that analytics present limitless possibilities of business impact and competitive advantage but realizing these benefits have been a challenge. Facebook, Amazon, Netflix, and Google are leaders in this space, and they are far and away the best in class. The FANG successes highlight the scope and scale of rewards, but most organizations are still on a data journey.
Because of the incredible potential, companies are eager to invest in learning and development. Data analysis is a skillset, and it makes perfect sense to invest in the development of that skillset. This investment is perceived as "safe" even with the uncertainties of ROI and payback period. Focusing on talent development is a logical first step However, the emphasis on learning alone can produce capabilities that doesn't always translate into tangible business results. I describe this as an abundance of “I understand” but a scarcity of “I can do”. A wealth of knowledge yet to be translated into application.
As a learning and development professional, I applaud all efforts to re-skill or up-skill, but to fulfill that promise it takes more than learning. Fulfilling the promise will require changes not only in talent development but also changes in Mindset and Practice. Companies must find ways to transform how they utilize data.
Below are three areas of focus that will help companies make that transition.
- Mindset – seeing data as a corporate asset to be leveraged for business benefit.
Changing to a Data Analytics Mindset increases the scope and depth of application by creating an environment that fosters and encourages analytics. It creates rewards for best practices and once a critical mass of commitment is reached, data driven decisions become noticeable only by their absence.
Activities to drive Mindset and Culture change:
- Communication - Conduct company wide events with internal and external speakers. A company wide event sends the message that leadership is committed to a data driven company. There is and will be resistance and so this visible leadership endorsement can begin to address this resistance. External speakers provide perspective and credibility.
- Celebrate - Recognition programs, certifications, achievements, best practices, and awards all promote the commitment and value of Data Literacy. Recognition programs have been proven to drive performance and create value and business benefit. A Data Recognition program can be a cost-effective way to change the perception of the business impact of data decisions.
- Data Brand - Focus on building a data brand. Highlight data activities and successes both internally and externally. Tell the world on LinkedIn, Twitter, Instagram, and other social media sites that your organization uses data as an operational differentiator. A strong data brand creates an internal and external identity for data analytics.
- Talent –talent development to ensure analytics knowledge is available to meet business demands.
Realizing the promise, will take more than just “the data” team. Data is the language of business and companies will need to develop data talent across all disciplines. Data practitioners must stay current with new techniques and technologies but in addition non data roles must re-skill to be able to see data opportunities and work collaboratively with the data centric disciplines. Data is being generated at exponential rates, and fully utilizing the data the companies generate will require all job roles and organizations working in concert utilizing a common business language of data.
Activities to develop analytics talent:
- Full Spectrum - Ensure that your learning offers are developing talent across the full analytics spectrum, from descriptive to prescriptive. This continuum of talent development provides skills that can match a variety of business needs and will also serve as a talent progression. The next Data Scientist is already part of the organization.
- Blended Learning – Connect knowledge transfer and application though domain specific exercises. Exercises will reinforce learning and allow students to apply what they have learned in a safe environment where feedback will enhance their learning experience. A domain specific context will make the learning relevant and improve content retention. The goal is to integrate learning and application to create a learning with a purpose environment.
- Collaboration – 70% of what we learn is through others and so learning offers should be delivered in teams. These teams can be co-located or virtual, as long communication between students is actively promoted. These learning relationships are not time bound and once learning networks are established, they can be leveraged long after the learning program has ended.
- Practice – operationalizing analytics talent – turning “I know” into “I can do”
Operationalize talent is how value is created. The amount of talent and the speed at which it’s applied will determine the ROI of the learning investment. Speed is particularly critical because talent is not a secure asset. Companies must create a sustainable engine of data analytics impact. The key is to create a fast transition path from knowledge and skill development to real world business application. A business setting where skills can be applied, mistakes made, learning can continue, and progress can be achieved.
Activities to operationalize analytics talent:
- Placement Focus on application environments. Data is a team sport and it’s important to create the team. With a virtual workforce there are lots of options. Temporary assignments and job rotations. Place people wanting to develop their skills around other people who have developed the same skills. A temporary job assignment or a structured job rotation schedule. Students in analytics programs are put in a rotation where mentors can coach them. There are economies here - a regular commitment is repeatable. A one off is burdensome.
- Path and Fast Tracks Connect learning and development to a job. Create a career development path where the goal is a job placement. The end game is motivating and while having a “pull through” effect. This accelerates the ROI cycle and allows benefit to be created before talent is lost to market forces.
- Mentors and Coaches. Give back program Data Mentors – invest in talent that invests in talent development. There is a shortage of Data talent, but you must invest. Formally create Data Analytics instructors whose job is to develop talent. This could be a job rotation. This could be message board where data concerns and questions are monitored. Recognition program for participants
Summary
It’s critical that organizations make a commitment to all three components. They work collaboratively and each enhances the effectiveness of the other two. Balance is important as well. Each component creates value and opportunities for advancement but being out of balance will create under leveraged benefits.
Realizing the promise of data analytics is the goal but it’s important to note that it’s also a race. Every company is competing to operationalize more data talent and continue to move up the analytics value chain. The cycle time between discovery, insights, application, and competitive advantage is diminishing. Advancements will continue and the analytics gap between competitors will begin to expand at an accelerated rate, so to stay competitive, it’s critical that organizations commit to changing the way they “Think”, what they “Know” and what they “Do” with data.
Talent + Practice without Mindset – Limits the scope and speed of adoption of analytics.
Practice + Mindset without Talent – Demands results beyond capabilities
Mindset + Talent without Practice – Cannot overcome the barriers of application