By Francois Laborie
First came machines to help do things quicker and more efficiently. Then came the connectivity between machines and humans to make everything work smarter. And together, that created a new phenomenon: more data than we ever thought possible.
When we look back on the 2020s and the aftermath of the Covid-19 crisis, I believe we will see this as the era when people awoke to the value of data in heavy asset industry and started using it to adapt and improve their organizations. It will be known as the time when DataOps found its place in asset-intensive industries, with the role of the Chief Data Officer or Data Architect firmly planted at the center of the corporate org chart, on par with the likes of HR and Finance. The data leader will have earned their seat at the table, with a voice that the board and C-suite depends on to run the organization efficiently.
Heavy asset industry turns to data experts to tackle increasing complexity
The idea of DataOps has emerged as the far more collaborative cousin to DevOps, as it, by definition, orchestrates the use of data across an organization to continuously uncover value. The volume of data generation from sectors such as manufacturing, critical infrastructure and heavy asset industries is growing at exponential rates. As companies seek to compete in the digital age when everything is connected, they must learn to grapple with and understand the value of the unprecedented amounts of data at their fingertips.
Data analytics teams have become commonplace in these industries. They are characterized as the experimenters-in-chief, challenged to manage the influx of data of all types and sizes, and apply their ingenuity to extract meaningful and useful insights from it. These analytical pioneers have proven time and again the power of industrial data to improve processes, reap efficiency gains, reduce waste, improve safety, and cut costs. But on closer inspection, no matter how talented the team is, industrial data typically remains in silos, used only by a select group of experts – making it costly, complex, and hard to access.
Digital maturity means breaking down the silos that divide us
It’s this siloed and overly complex way of work that needs to be done away with if we intend to make data do more for businesses. Industrial leaders couldn’t agree more, according to Forrester research from 2020. The majority surveyed wholeheartedly concur with the fact that “Data needs to become self-explanatory to data consumers without needed subject matter expert support.” It’s this very cultural and organization shift that is at the heart of DataOps teams and efforts. By better integrating data teams into the organization’s operations as a whole, all employees are empowered to read, write and communicate data in context instead of relying on the experts alone.
Imagine an industrial operation in which all data consumers, across the organization, have instant access to contextualized data that they can use to develop the next great thing that transforms the business. For this to become the data reality in heavy-asset industries such as critical infrastructure and manufacturing, companies will need to embrace a culture shift, in which data becomes the key point of connection across the operation.
To build a successful data strategy, companies must be ready to adopt three key truths about DataOps.
1. DataOps depends on human collaboration.
Individuals and human interactions are essential to make data valuable and useful for the consumers across an organization, far beyond the power of processes and tools. It’s important to remember that DataOps is the practice of engaging and collaborating across the organization to both share and reap greater value from the data. The main investment required is that of a culture shift, teaching everyone to share and use data, rather than investing in a new tool or software to solve the data-access problem.
2. Data value depends on context and trustworthiness.
The convergence of data and analytics have made DataOps an operational necessity. However, the data requires context if it is to be used broadly by non-data experts. By automating the data process in an organization and creating one central, contextualized source of truth, the headache of data-access problems disappears and organizations can operate more efficiently. It also enables a greater degree of trust in data that stems from a single, contextualized source, verifying that this is valid input to the decision-making process.
3. Extracting the value of the data requires an agile approach
DataOps isn’t about documenting, reporting or extensive up-front design. It’s a far more agile process in which experimentation, iteration, and feedback are essential. Creating business value isn’t a one-way transaction between data scientists and departments across the organization. It’s a joint effort that will require both parties to participate, share and develop solutions that can have transformative potential.
Your data strategy starts here and now
These three tips are a good starting point for exploring how data can enable asset-intensive industries to develop solutions and data products, and to extract value across an entire business.
It’s almost impossible to imagine that not long ago, data was a protected commodity, something to be kept to oneself or even used as a bargaining chip. It was, thankfully, a short-lived narrow-mindedness, quickly killed off by the data visionaries who saw the potential data had in store. Their vision of the future has infected the rest of us, sparking the emergence of DataOps as our lifeboat in a vast sea of data.
