Data Democratization is expected to spearhead the move to providing Equipment as a Service, which requires a fundamental adoption of new ways of thinking and new paradigms.
Data democratization can be defined as providing everyone within an organization with access to the data it produces without barriers to accessing these data sets. Applying this definition to the manufacturing industry, data democratization starts with collecting manufacturing and machine data, analyzing it, and ensuring the insight it provides is available to everyone within or outside the organization.
This will create a partnership ecosystem where small, medium, and large manufacturers, alongside logistics and supply chain parties; operators and engineers; IT and OT system integrators; platform and software vendors; equipment builders; research, education, and training have access to manufacturing data. This access can then be leveraged to build a more sustainable industrial economy.
See also: Manufacturing is Dead, Long Live ‘Servitization’
Within this sustainable economy, legacy business and operational structures will transform into horizontal, flexible, and agile businesses. On the other hand, SMEs will be co-opted into this partnership and can take advantage of the data-driven insights legacy businesses have curated over time.
Making an impact at all levels of the organization
The advantages of data democratization cut across every aspect of a manufacturing operation. Manufacturers can choose to use processed data to enhance every aspect of the shop floor and beyond without limitations. To better explain the benefits, its advantages at the factory level and organizational level must be discussed.
At the factory level, data is often “siloed” and only accessible to a few various consumers. These consumers are the data analysts and data scientists who have the skills and understanding to interpret it. Unfortunately, it’s not a scalable solution for only data scientists and analysts to use data. This is because organizations need time to hire such talents, and there’s an undeniably high demand for these types of skilled employees, which may hinder a manufacturer’s efforts to become data-driven.
One way of alleviating this talent bottleneck is through data democratization. Organizations are increasingly realizing that data needs to be used constructively and appropriately enterprise-wide if they want to catapult their business to new heights. This means all employees – even non-technical end users – can easily access and understand data to make informed decisions quickly and uncover opportunities for the business to improve.
At the organizational level, data democratization ensures stakeholders are on the same page with respect to making decisions that affect productivity and revenue generation. One example is the use of machine data to determine the cause of downtime. In situations where a shortfall in operator staffing is identified as the cause of low machine utilization, management can then choose to hire more operators to man the machines.
Data democratization also facilitates an ecosystem of out of the box use cases, custom applications, and integrations. As a result, the time to value and need for DIY IoT is greatly reduced, empowering companies to rapidly drive new process optimizations across their organizations. By democratizing data in this way, more employees with diverse expertise will be empowered to make data-driven decisions in every process, which indirectly helps improve business agility.
To optimize the utilization of equipment, it’s not only the manufacturer who needs data democratization. Everyone who partakes in the operation of a machine; from machine builders and distributors to service and parts providers, from systems and system integrators to consultants and financial firms have something to gain from data democratization.
Extending data democratization advantages to OEMs
This means original equipment manufacturers also benefit from the data democratization process in diverse ways. One example is delivering remote services to customers using these machines. OEMs can drive remote service in times, such as during a pandemic, when factory maintenance visits are almost impossible.
Data democratization has an important role to play in bringing the Equipment as a Service concept to fruition. The availability of data provides OEMs with the information needed to advise customers about usage and utilization options based on the manufacturer’s unique processes. These recommendations may include the identification of optimal changeover sequences, maintenance schedules, as well as other value-added services.
Data democratization use cases
Track Machine Utilization and Reduce Downtime
With the right data collection infrastructure in place, manufacturers have deep visibility into their operational performance, allowing them to know when machines go down or if they are being underutilized.
Industry and Benchmark Analysis
The only way to know if you have improved is by setting up benchmarks based on accurate data. As you keep tabs on your primary KPIs, you’ll be able to accurately pinpoint how well the operation is performing against benchmarks.
Supply Chain Visibility
The more elements of an operation that are connected, the greater ability to spot bottlenecks and ensure efficiency. Data can provide manufacturers with visibility into the supply chain, allowing them to adapt to increased demand or disruptions to their usual supply process.
Equipment as a Service
In this business model, an OEM essentially rents out equipment to a manufacturer. The opportunities for data exchange are vast. Not only can the OEM collect the customers’ data for understanding product usage, but they can also offer services based on equipment performance. On the other hand, the manufacturer gets affordable access to expensive machines, coupled with all the data needed to optimize its use.
Concerns About Data Democratization
The proponents of data democratization believe unrestricted access to manufacturing data leads to shared responsibility and accountability across the manufacturing industry. One example is the use of benchmark machine data to determine how well individual machines function. Others believe data democratization will ensure individual manufacturers focus on what they do best, thus optimizing the quality of the products they manufacture.
The antagonists or realists see the democratization of data as a process that comes with numerous bottlenecks. They believe the misinterpretation of data by employees can lead to unintended consequences. These consequences include teams from an organization working on similar projects, thereby duplicating manufacturing operations or businesses comparing themselves against unrealistic benchmarks.
One of the more important consequences is related to cybersecurity and data loss. Many believe that unrestricted access to data could lead to cybersecurity incidents, which negate the benefits that come without restrictions. While these challenges are important considerations, the type of data being shared must be taken into context.
For example, the sharing of equipment or machine data without reference to the end-user producing the data reduces the majority of these concerns.
Driving New Use Cases and Greater Value in the Future
Data democratization is expected to spearhead the move to providing Equipment as a Service, which requires a fundamental adoption of new ways of thinking and new paradigms for both OEMs and manufacturing businesses to drive new revenue streams and increase innovation. These changes will link OEMs and manufacturers in new ways, opening both to greater possibilities for new business.
Data democratization is a tremendous challenge. However, if and when achieved, the opportunities for continuous improvement for all manufacturing lifecycle members will be far greater than ever before.
