Data has become a powerful driver of the Manufacturing 4.0 transformation. Your ability to effectively harness the data available to you is crucial to your success in today’s and tomorrow’s smart manufacturing environment. Forward-thinking manufacturers are moving beyond the traditional “four Vs” of data (volume, velocity, variety and veracity) to leverage a fifth “V”: value. You could be sitting on a potential data gold mine.
As with gold, the value of data in situ is one thing – extracting it is another matter entirely. In fact, Forrester estimates that between 60% and 73% of the data an enterprise generates isn’t being used1. On average, the manufacturing sector uses less than one-third of its data. At the same time, data generation is rapidly increasing – not only in the cloud but also on the edge.
These developments point to a critical strategic imperative for manufacturers and other enterprises: You must develop a laser focus on your return on data. In parallel, some companies are focusing on related sustainability issues such as reducing the greenhouse gases and the carbon footprint of data, and they are already making progress with their efforts in interesting ways. For example, collecting and storing all that data carries a cost, both financial and environmental. Microsoft, Google and Apple are all using renewable energy to power their data centers2. They are reducing their energy costs and improving the environment with this forward-looking move.
Think about it: There is a cost to storing data, but not using your data is perhaps the most expensive – yet ignored – cost.
The true value of data and data analytics is in the insights they reveal – insights that can drive business agility and innovation. The following five common challenges often create roadblocks for enterprises that are trying to accelerate the benefits from analytics:
If you’re still using legacy systems in your operations, your data is held in multiple places. Accessing, managing and integrating that data can be a complicated process.
In the pressure to get on the data analytics bandwagon, resist the temptation to implement the latest use case rather than the use case that is actually right for you.
Without a firm analytics roadmap that extends out at least a couple of years, you will probably end up adopting ad hoc use cases that don’t bring real long-term value to your business.
Disconnects between your leadership team and your implementation team can send your data management projects in the wrong direction.
A lack of qualified data scientists due to any combination of normal attrition, an unqualified talent pool, and a shortage of workers is part of the is challenge. An even bigger challenge is to retain people and unify internal teams with the goal of getting the right data to the right person at the right time so that the data can be used to enhance business value.
In your data, you can find the insights to propel your business forward. And, done right, you can improve operational efficiencies, reduce waste, and innovate in ways that improve the environment and make your community a safer and healthier place to live. There are several actions you can take to start managing your data for the greatest value. Here are four that we recommend:
Before you do anything else, clarify and get consensus on the business outcomes you’re aiming for. Having this clarity will help guide every decision you make.
As you begin strategizing, consider focusing your efforts on a strategic DataOps approach. DataOps is a series of principles and processes for effectively managing a distributed data architecture to get the greatest value from your data.
In the 4C DataOps model, you connect and integrate all types of data that would otherwise be inaccessible. Your data is then curated for relevancy and ease of use and contextualized to yield new insights about a business or operations issue. Because today’s data is derived from such varied sources, including customers, data protection and security is of paramount importance. The model prioritizes enhanced cyber-confidentiality to protect your business and your customers.
As part of the smart technology transformation, manufacturers must effectively blend the operational technology (OT) experience with the IT experience. Edge-to-cloud technologies are continuing to offer cloud transformations at lower costs and are enablers for getting more value from your data.
When migrating away from legacy systems to leverage cloud technologies, manufacturers typically take one of two approaches: either lift and shift or lift, modernize, and shift. Our recommendation is to add a third hybrid approach: Use intelligent IoT applications in conjunction with the modernization approach to extract business value.
The traditional Ishikawa 4Ms of manufacturing (man, machine, material and method) has been central to the lean journey. By leveraging the convergence of intelligent sensors, such as video in conjunction with data analytics, you can reframe the 4Ms and develop new approaches for driving infrastructure agility, data agility, and eventually, business agility – the ultimate goal of your data value maximization efforts.
Getting the maximum return on your data requires a transformation in people, processes and technology. It involves reskilling your workforce to work side by side with new AI and machine learning technologies, restyling and optimizing your operational processes for agility, and adopting cutting-edge technology that will accelerate your journey to Manufacturing 4.0. With a transformed workforce, factory and enterprise, you can improve not only your profitability but your sustainability – bringing benefits to society and the environment in the process.
With a data value maximization strategy based on DataOps, you can efficiently get the right data to the right place at the right to time to glean the insights you need to achieve business agility and data-driven innovation. By integrating data science into your operations and optimizing holistic data management through the 4C DataOps model, you can extract that important “V” – value – that will turn your data “dust” into data gold.
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