Today’s rapidly evolving workplace provides a context where our decision makers must be able to leverage their data to make optimal judgments. This is not limited to managers and organizational leaders. On the contrary, we are (or soon will be) all data-driven decision makers in our organizations.
The issue at hand is identifying and building an organizational framework that enables people, especially non-technical staff, to use data to become better at their work, to provide more meaningful outputs, and to boost their own sense of personal worth by giving them the ability to measure and analyze their (and their team’s) efforts. The way to accomplish this is through DataOps-IST.
In our previous post, we looked at the importance of understanding your data and how it’s used in your organization. Acquiring that knowledge is a critical first step in developing a data-centered environment. This knowledge underpins the next pillar of DataOps, and that is the social aspect.
Developing the social dimension of an analytics culture is indispensable. Without it, an organization will be left with regrettable and failed technology initiatives, resulting in sunk costs in software, equipment, and other tools. The social dimension is THE forgotten piece of the puzzle. The one that all managers pay lip service to, yet fail to truly develop or understand.
Perhaps the biggest obstacle that stands before an organization that hopes to build a successful analytics and data-oriented culture is the lack of Data Literacy, which begs the question: How do you overcome the lack of data literacy
While there is certainly a multitude of ideas in the tech sphere regarding how we can improve the ability of our people to use data. What’s usually missing from those prescriptions is an understanding of the psychology and social aspect of getting people to engage with their data. Here’s how Pluralsight goes about fostering a sense of data-ownership from everyone:
Make your data available to everyone in your organization
Allow your people to see the data that affects their work. Allow them to engage with the granularity that comprises the larger processes they participate in. Doing so will make them more informed, more skilled, and more enthusiastic about the ways they can improve the organization.
Make analytical tools available to everyone
Data doesn’t belong to IT. It doesn’t belong to management. It doesn’t belong to your analysts. It belongs to everyone! Thus, you need to provide tools that promote your data. Those tools need to let your people create their own analyses and visualizations, and then allow them to freely share their data discoveries amongst their colleagues. (For this, Pluralsight uses Tableau and Logentries.)
Remove the barriers between technical and non-technical staff
It is one thing to figuratively do this, but at Pluralsight, we literally remove physical barriers. This fosters an environment of increased communication and collaboration.
Whenever possible, remove technical jargon from discussions, meetings, training, and documentation
Non-technical staff are reticent and discouraged from engaging in their data because they, literally, do not know how to talk about it. Question: What is the percentage of people in your organization that know what a data warehouse is? Or an ETL job? Or Big Data? Or SQL? Or Data Science? Or even analytics, itself? On the contrary, what is the percentage of people in your organization who are affected by these ideas and products? The latter number is obviously much higher than the former. These terms are ubiquitous is the tech realm. Yet, they are not nearly so in the rest of the business world. If we truly want our people to take ownership of their data, to analyze it, and to make decisions based on it, then we need to be able to speak differently to each other about it.
Encourage all staff to engage in opportunities to develop their data skills
You must continually assess and improve the data skills of your employees. Does your staff know the basics in data manipulation, statistics, and data visualization? In order to achieve a viable ROI on analytics tools (such as Tableau), users must be able to successfully leverage the features that a tool provides. For example, we routinely provide training (live and archived video) to all staff. We then provide shared data sources that everyone in the company can access and analyze. Transparency of data goes hand-in-hand with the development of a strong data-centered culture.
Recognize and reinforce any and all data curiosity
Every time an employee says something like, “I wonder how many times X happened before Y occurred,” we recognize this as an opportunity for data exploration and analysis, as well as an opportunity to encourage subsequent and ongoing research.
Do not rely on an “Intuitive UI” to engage your people
System-based enhancements and optimizations are prerequisites for DataOps and data literacy, not a panacea.
Leadership buy-in and fluency
From the CEO to our front-line managers, our leadership uses data to support and validate their decision making processes. When there is data to be had, they believe in discovering the truth, not creating it based on their intuition or hunches.
The so-called analytics skills gap is a very real thing. We have endless data, and not nearly enough skilled people to create value from it. Employing DataOps strategies fills this gap by fostering an environment of social engagement with data. The solution to the skills gap problem will not be found in traditional education, standard BI strategies, or from systems themselves. Instead, it will be found when we encourage the data-curious among us to take ownership of their data, improve their own skills, and become analysts themselves.
This post was originally published on blog.logentries.com in May 2015.