The Looming Industrialization of Data Analytics

In the data space, there is quite a bit of custom, manual work to pour through data sets and draw insights of interest to decision makers. Data scientists and data analysts spend their days discovering data sources, applying tests, and applying scripted code to build their storylines.

However, the volume and variety of data flowing into and through enterprises is growing faster than anyone could have ever imagined, thanks to initiatives such as the Internet of Things and cognitive computing. It’s getting harder to scale the work of two or three data scientists to cover all the analysis that data-driven enterprises expect. At one time, there may have been one or two reporting points that these individuals worked with – but now, there may be hundreds.

Thus, there’s a growing need to automate the analytics process – in essence, the ‘industrialization” of data. Data analytics approaches need to be part of repeatable, automated and cost-effective processes. The companies that succeed with data will increasingly be those that harness the production of data.

The power of data industrialization is recently explored in a post by Werner Vogels, CTO and vice president of Amazon.com. “In a digital economy, data are at the core of value creation, whereas physical assets are losing their significance in business models,” he observes. Two decades ago, he observes, “the most highly valued companies in the S&P 500 Index were those that made or distributed things — for example the pharmaceutical industry, trade. Today, developers of technology — for example medical technology, software — and platform operators — social media enablers, credit card companies — are at the top. Also, trade with data contributes more to global growth than trade with goods.”

Whereas companies in the past organized around producing physical products, the winners in today’s economy are those organized around the production of data. “We should ponder how we can organize the production of data in such a way so that we ultimately come out with a competitive advantage,” says Vogel. “We need mechanisms that enable the mass production of data using software and hardware capabilities. These mechanisms need to be lean, seamless and effective.” A parallel may be the lean and quality processes adopted by manufacturers in decades past – those companies that could produce product quickly, cheaply and of high quality won over those slower to the draw.

Quality becomes even more critical in the production of data, Vogels points out. “We need to ensure that quality requirements can be met,” he says. “Those are exactly the challenges that were solved for physical goods through the industrialization of manufacturing processes. A company that wants to industrialize ‘software production’ needs to find ideas on how to achieve the same kind of lean and qualitatively first-class mass production that has already occurred for industrial goods. And inevitably, the first place to look will be lean production approaches such as Kanban and Kaizen, or total quality management. In the 1980s, companies like Toyota revolutionized the production process by reengineering the entire organization and focusing the company on similar principles. Creating those conditions, both from an organizational and IT- standpoint, is one of the biggest challenges that companies face in the digital age.”

The rise of industrialized data analytics was explored by Rajeev Ronanki and a team of Deloitte analysts in The Wall Street Journal, who point out how they see companies adopting Six Sigma and Agile principles to guide their analytics ambitions. “Their goal is to identify, vet, and experiment rapidly with opportunities in a repeatable but nimble process designed to minimize work in progress and foster continuous improvement. This tactic helps create the organizational muscle memory needed to sustain industrialized analytics that can scale and evolve while driving predictable, repeatable results.”

Ronanki and his co-authors proposes several data analytics operating models to achieve the scale organizations need in today’s and tomorrow’s economy:

Centralized. “Analysts reside in one central group, serving a variety of functions and business units and working on diverse projects.”

Consulting. “Analysts work together in a central group, but are deployed against projects and initiatives that are funded and owned by business units.”

Center of excellence. “A central entity coordinates the activities of analysts across units throughout the organization and builds a community to share knowledge and best practices.”

Functional. “Analysts are located in functions such as marketing and supply chain, where most analytical activity occurs.”

Dispersed. “Analysts are scattered across the organization in different functions and business units with little coordination.”