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Does A Data-Driven Culture Need To Be Implemented From The Top Down?

Interview with David Gainsboro, People Data Analyst at Dropbox

7Jun

In a recent report by the Aberdeen Group, organizations classified as data-driven were found to experience a 27% year-on-year increase in revenue, as opposed to 7% for other organizations. Furthermore, 83% saw their process cycle times improve, compared with just 39% of those organizations not classified as data driven, and 12% cut their operating expenses from the prior year, compared to 1% of other organizations.

The benefits of adopting a data-driven culture are many and persuasive. For one, a focus on fact-based insights will greatly reduce the number of arguments within teams as there is less of a reliance on ‘gut instinct’ - a fairly nebulous concept that will often run contrary to someone else’s. A data-driven culture empowers everybody in the organization, regardless of their experience, to bring their ideas to the table, so long as they are supported by the data. They can also identify patterns of customer behavior and potential procedural improvements that only they may be in the position to see, driving innovation while also reducing risk. This also improves staff morale, with employees considering themselves more valued as their ideas take on a higher importance.

Changing the culture of an entire company is incredibly difficult. The importance of making decisions based on data must be constantly reinforced through various mechanisms until it is completely ingrained in the fabric of the company. It needs to be incorporated into every aspect of business strategy, based on business goals, company culture, and business intelligence landscape within an organization. This drive needs to come from both top and bottom, with everyone pushing to ensure that data is being put behind every decision. We recently sat down with David Gainsboro, leader of the Recruiting Analytics Practice at Dropbox, to discuss how best to implement a data-driven culture, as well a range of other issues around data analytics. He will also be presenting at the HR & Workforce Analytics Innovation Summit, which takes place this June 19-20 in San Francisco.

How did you get started in your career and what first sparked your interest in analytics?

I began my career in Sales. I love building relationships with customers and helping them explore and eventually solve challenges. When I realized that analytics finds the data to uncover that there even is a problem, and then measure the solution, I leapt at the chance to continue working with stakeholders and get more involved in the problem/solution lifecycle.

Do you feel HR is behind other departments when it comes to implementing data initiatives? If so, why do you feel this is the case and what can HR leaders do to rectify the situation?

Historically, there hasn't been as much data in HR, especially when compared against Web Analytics. Additionally, there hasn't been as much pressure on HR to support ideas with data and measure projects with ROI. Today, that there is an opportunity to have major business impacts, either decreasing costs or increasing efficiency, and increasing revenues.

How important is it to introduce a data-driven culture across the organization? How is it best achieved?

For folks outside of Analytics and Engineering, data literacy is one of the hardest but most important skills. Increasing literacy needs to be both a bottom-up and a top-down effort. By top-down, I mean that leadership needs to be fully educated and needs to push for all business decisions to be made with the support of data, to both validate and challenge gut instincts. By bottom-up, I mean that the analytics teams need to offer training and access to data to those individuals who are already excited about data.

What do you see as the most important metrics to look at to gauge employee satisfaction?

Reviews of managers, for now. I'm interested in exploring the extent of their network, as measured by their connections, and its impact.

What technologies do you see as having an impact in the analytics space in the near future?

Machine Learning, specifically Automated Exploratory Data Analysis will allow us to measure more variables, faster, and build more predicting models, which will allow us to uncover problems before they become significant and suggest fixes.

You can hear more from David, along with other leading experts in the field from the likes of Facebook, Airbnb, and Chevron, at the HR & Workforce Analytics Innovation Summit. View the full agenda here.

BONUS CONTENT: Tiffany Morris, VP, Talent Management & HR Business Partner, Sears Holdings discusses data-driven talent management at the HR & Workforce Analytics Innovation Summit in Chicago in November 2016.


 

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