As Data Consultant, I work on behalf of Mobilee in the change team of O&S (Maintenance & Faults) at grid operator Alliander. Within our team we supervise complex changes in the maintenance chain. My role? To determine how data can contribute content to these changes and how we can make impact truly measurable.
For me, successful transformations revolve around the right balance between people, process, technology and data. In this article I take you through the practice of data-driven work.
The gap between will and ability
When you ask within a large organization if people want to work data-driven, the answer is almost always a resounding yes. A data scan I conducted at the start of my assignment showed that willingness is high. At the same time, people are also still working hard to develop the skills to turn data into insights. This leads to a number of promising developments, which we would like to add to in the coming period:
Data gets plenty of attention: Within change teams, attitude, behavior and developing new processes play a major role. Systems and necessary data are increasingly taking a central place in the conversation. Colleagues are actively discovering the way to relevant data and dashboards, making the use of data broader and more accessible.
Strategy inspires execution: Organizations are working with the OGSM model (Objectives, Goals, Strategies, Measures) and chain plans. We are increasingly seeing strategic goals guide measurable KPIs on the shop floor. This motivates teams to more sharply articulate the link between initiatives and impact.
Data ownership is growing within the business: Data ownership is evolving from IT to business responsibility. Data stewards gain clear insight into their role and the data that falls within their domain, which will encourage collaboration and ownership.
Data quality becomes transparent and open for discussion: The quality of data, for example regarding absence registration of mechanics for operational planning, receives structural attention. Teams define together what 'good enough' means and set clear standards, so that trust in data grows.
The road to structure, cooperation and measurability
To make the transition to data-driven work, you have to look beyond technology. Although hard work has been done in the background on a modern data mesh architecture and a central Snowflake platform, our solution lies primarily in building the bridge between the business and the data specialists.
1.The 'Success is measurable' template
To force change consultants to think about data in a structured way, we now work with a 'Success is measurable' template. This format asks targeted questions per change initiative (Epic): What key KPI are we trying to influence? What relationship does this have to our strategic OGSM? What data do we need to do a baseline measurement and monitor our progress? This simple format ensures that teams formulate functional needs clearly before an Epic is picked up. You very deliberately create a wheel and gauges to navigate when embarking on the path of change. Without this tool, fact-based decision making is difficult.
2. The connection between business and data (Yin-Yang)
We actively link the change consultants to the internal data team, the Reporting House. During so-called pre-PI (Program Increment) planning, change consultants and information analysts now sit together at the table. The business explains the operational problems, and the analysts translate this into available data products. This collaboration creates tremendous mutual understanding.
3.Agile prioritization of data needs
Because the demand for data insights grew rapidly from three to 30 initiatives, we need to manage capacity. We integrated this into our Agile/SAFe way of working. Data requests are now given a T-shirt size estimate of expected effort. Then the product manager weighs this effort against the expected business impact. In this way, we make objective, transparent choices in what we do and do not pick up each quarter.
4.Operationalize data quality
To increase confidence in data, we will make data quality measurable using a specific use case: operational planning. Specifically, we will measure in dimensions such as timeliness and reliability. For example, if a mechanic's absence is in the system timely and correctly 95% of the time, we accept that 5% margin as a business risk. Thus, the discussion shifts from gut feeling to actual acceptance. With this use case, we also undercover the bumps we still need to overcome. How do we get the SoR (System of Record) data accessible? Who in the business determines the quality standard? How do we shape the escalation process for data quality issues?
Start at the functional need
From this journey, I draw some valuable lessons for portfolio managers, team leaders and data experts who want to make their organizations more data driven:
Data-driven work is not a call for data
The core of data-driven work is not, "Give me access to all data." It always starts with the impact you want to make on your target audience. Start with the functional need. What problem are you trying to solve? What insight is needed to adjust? First define the form in which you need insight, only then follow the technology.
Start small and prove the value
Don't immediately design a complete master plan of what the entire data organization should look like. Choose specific, manageable use cases. Build an analysis or dashboard for a concrete process, as we do with the breakdown service, and show the results. Once the business sees that data actually provides guidance, enthusiasm naturally follows and you can scale up further.
Invest in mutual understanding
You can set up systems perfectly, but if people don't understand each other, you won't get anywhere. Make sure data specialists understand how operations work (like getting complex checkmarks for a mechanic's training) and make sure the business understands what a data product is. That cross-fertilization is the absolute key to successful transformations.
From underserved child to insight and direction
Data-driven work is, in my experience, not just a technological challenge, but primarily a matter of conscious choices, structured collaboration and continuous learning from practice.
Those who dare to start small, connect data to concrete business goals and invest in mutual understanding can actually make impact visible and measurable. This is the only way to turn strategic ambition into demonstrable results. And that is where the value of data comes into its own.
This blog post is a contribution from Mobilee, a consulting firm for issues of digital transformation, strategy execution and team development, for Data Expo readers. Find more inspiration at www.mobilee.nl or visit Mobilee during Data Expo.