Explore five essential questions to cut across the silos that separate relevant data and employ evidence-based management in the talent space.
by Michael Chetta, Sy Islam
August 26, 2021
“I am not sure we can get access to that data.” “That team is very protective of their data.” “We do not have that data at this moment.”
These statements arise in conversations with clients on a weekly basis for many data scientists and human capital consultants. Before the work of data analysis can begin, data scientists and people analytics professionals need access to key data. While the desire for data driven insights is deep, dealing with the operational challenges of collecting or collating that data is often more than talent management professionals are prepared for.
To realize the value of one’s programs, we must be ready to cut across the silos that separate relevant data. Without a concerted effort and focus on “breaking down silos” it becomes an almost impossible task to utilize data and information in a way that allows for prediction and successful employee-facing interventions.
The first step is understanding the goals, mission and strategy of the organization. Regardless of your role, understanding a successful outcome in the eyes of the organization is key to developing a plan and moving forward.
For teams to have success in making data-driven decisions, those in talent management, training and development, leadership or the bevy of other OD and HR adjacent roles must explore several key questions:
What is your end-goal?
Carefully consider the mission, vision and goals of your organization as well as the future of work. How could you use data to bolster employee-facing, customer-facing and even holistic long-term initiatives? What would some “pie-in-the-sky,” ideal or even moderately impactful organizational findings and research look like? Understanding the organization and its lines of business will let you work backwards through this entire process. Having this information or direction allows you to interpret, align and deftly maneuver in the next step.
What are your current capabilities and who are your stakeholders?
Rather than reinvent the wheel, assess what data capabilities exist within your organization already. This involves a review of previous data analytic projects. It may also make sense to gather some preliminary information from those who may be able to share valuable insight through a “data-based decisions” inventory or questionnaire. Once you understand where you are, you can “fill-in-the-blank” between your current state and where you want to be.
Can you explain the benefits of shared workflow, data and analytics?
Since you now should have an idea of where you want to be and who to include in discussions as you form a task force, the hard work can begin. Building upon internal expertise, outside research and current best practices to tell a compelling story around your “data initiative” is the best way to get everyone on board.
How can you make this work?
Often the most nebulous step, and possibly the most time consuming, involves reflecting on whether this process is workable within your organization. There are many streams of work, data and research that must come together. At times, this is where getting additional support from organizational consultants or even IT/SAAS firms might make sense. Developing a strategy to collect data you already have in one place should go hand-in-hand with programs to gather the needed information. A data collection process may include creating consistent file names, strict guidelines for cleaning data and automating data aggregation (for example, VLOOKUPs and merging files on unique identifiers).
Are you committed to being the “guardian” of this process?
After all the brainstorming, conversations, shared insights and collaboration on tools, access and ongoing data collection, someone needs to be the “owner” of the data warehouse in one form or another. This may require resources to be set aside and a formal agreement to be put in place, but this accountability is necessary for the process to be successful over time and provide the depth of data that allows for more complex and useful analytics year-over-year.
With these above queries as an initial outline, it is possible to set your team, department or entire company down the path of evidence-based management and utilizing quality metrics for organizational decision-making.