Managing industrial data (over)load
Data as the centerpiece of digitization
We are living in the fourth industrial revolution – the digital one – where data has replaced oil as the world’s most valuable resource. The skillful mining of data using AI and machine learning is how businesses can identify and achieve competitive advantages today. To enable this, organizations and enterprises are acquiring heaps of data from numerous sources, around the clock. Detailed, personalized, accurate, timely bits from shop floors, retail operations, POS centers, websites, IoT devices, user surveys, heatmaps, and more all congregate to create what we collectively refer to as ‘big data’ – ultimately helping us identify major trends that would otherwise be inaccessible to us.
Data management pitfalls
To derive useful insights and intelligence from it, data must be strategically managed. A good data governance policy is about quality vs. quantity – although – to achieve statistically meaningful insights plentiful data is helpful. An overload of data that isn’t properly processed or analyzed, nevertheless, can render the very harvesting of it useless.
In the early days of the digital transformation phenomenon, businesses scrambled to collect data any chance they got, without much consideration about how the data would be stored, safeguarded, processed, or used. Newsletter subscription website pop-ups, free e-books and downloadable guides, surveys and personality quizzes, social media profile scraping – organizations have been using every trick in the book – though not always legitimately – to get to know us and our most private needs, desires and preferences to ‘serve us better,’ or perhaps more accurately, ‘sell to us better.’
The need for improved data governance
As organizations mature digitally, the need for solid data governance policies and infrastructure cannot be overstated. The push for privacy rules like GDPR and e-privacy, and their North American equivalents underscored the need for organizations to responsibly store, process and use private consumer data, leaving little room for mishandling or intentional abuse.
We’re yet to see whether such policies will have a sizeable positive effect on institutional data governance and management. Nonetheless, adopting a legal framework to govern the direction that we want to take as a digitally evolved and responsible society is a step in the right direction.
The importance of having the right industrial data at your fingertips
Beyond simple privacy considerations, if data is merely collected and stored without being effectively mined, the effort and cost of its collection and storage would present a burden rather than an opportunity, which wouldn’t make a very good business case.
Data is as good as the insights and intelligence it can provide to organizations, informing and empowering solid decision-making. Without them, data pollution can quickly occur, leaving businesses in a haze of big data that means little to them. And, to define what type of insights businesses would like to gain from their data, they must start by defining their goals – without the ‘why’ of data acquisition, any pushes to harvest it are slated to result in a dead-end.
What this means is that, in addition to secure collection and responsible cloud management, enterprises need to carefully research, plan and implement the systems, tools and processes needed to mine the data they’re acquiring. Rather than being an afterthought, in an ideal world this would all take place before a single email address or demographic attribute is harvested. In a way GDPR-like legislation is forcing us to do all the thinking before-hand, actively managing decisions around what, why and how should be harvested.
Future trends in data mining
In the past few years artificial intelligence and machine learning have been successfully used in manufacturing information governance and compliance. The process of effectively harvesting, manipulating and analyzing data typically follows a standard pattern, which can be used as a framework for enterprises looking to integrate industrial intelligence into their decision-making processes:
- Harvesting or capturing data: the process starts with deciding what types of data need to be captured, why, and how.
- Transferring and storing data: in addition to being bulletproof, cloud information management must ensure data is being stored in the correct geographical jurisdictions, safe not only from hackers but also from prying governments and competitors.
- Data mining: data from disparate sources needs to be meaningfully combined and skillfully mined with the right set of AI tools, to derive usable and useful insights.
- Analyzing intelligence: once statistically significant patterns or trends are identified, they need to be qualified, or ‘translated’ into human language that would allow concrete, actionable conclusions to be drawn.
- Incorporating intelligence into decision-making: finally, to truly affect decision making and subsequent change, conclusions based on big data must be carefully incorporated in future strategy and plans.
Enterprises who manage to implement a smooth and timely process from steps 1 to 5, stand to benefit from the identification and implementation of opportunities for achieving a competitive advantage.
In the end, institutional reliance on data is something that takes years to build. More than a set of processes, it involves fostering a culture of being data-friendly and requiring concrete facts and figures for decision-making, however small-scale or insignificant the decision. Educating the workforce, both at the office and on the factory floor to read, manipulate and use data to support daily operations, is the key to success.
Copywriter: Ina Danova