People at the top of their enterprise have a growing, if sometimes unrecognized, responsibility to ensure their business’ data is managed properly. In the past, this management of information was often left to business units and even individuals. As such, leaders may not be up to speed on what they need to know.
For instance, leaders—and many articles targeting their demographic—use the phrase “data and information” as if they are synonymous. But they are not, and the distinction is quite significant. And, yes, I have been guilty of this error in syntax at times when I have not been careful.
The phrase “data and information” is misleadingly broad; the innocuous conjunction “and” may conceal more than it reveals. My view of data and information aligns with many in the data management world. In summary, data is a set of facts and information adds context to said facts. This distinction means that data is unorganized, while information brings order to that data. When placed together in context, information helps to map a bigger view of what that data means and what it is telling you.
Data and information can also be understood as alternating between these phases. All data has had a previous life as information for someone somewhere, then once its context was forgotten it reverted back into data. If this doesn't underline the need for reinforcing data management, I do not know what would.
Formats and processes change and evolve constantly, forcing data to become information in varied ways. These changes and evolutions gradually undermine the processes and practices we use to manage our data. With every passing day, they become less effective.
Usually, no one notices until a mishap comes to light. Perhaps a decision or analysis based on data slipped under management’s radar, making it unreliable or a liability in disguise. Steering clear of potential disasters is a prime responsibility of top management. This article extends that responsibility to ensuring sustainable and effective management of data, as well as the role PLM can take in support of this critical enterprise task.
The Data Challenges Ahead for Top Management
Leaders at the top of their organizations must deal with some unsettling realities, including:
- The numerous operating systems that choke task- and process-oriented data repositories.
- The various formats, task-linked processes and apps that convert data into information that is unusable by other tools.
- The amount of information that can become redundant, obsolete, or trivial.
- The large percentage of data that is stored but never looked at or processed.
- The vast majority of business information that is unstructured and difficult to search, and therefore leveraged.
- The explosion of data and information, with zettabytes stored in the Cloud.
- Artificial intelligence (AI) adding to the explosion in enterprise data.
These facts warn us that just because data is in a repository (on-premises or in the Cloud) does not mean data is under effective management. The fact that effective data management is slipping away is a compelling case for the responsibility of enterprise leadership to see to the reinforcement of sound data-handling practices.
A Digital Tool Can’t Solve Everything, Including Data Management
The reinforcement of data management extends beyond digital tools. Effective data access, use and reuse is a management responsibility, requiring solution providers, consultants and internal IT management to be held accountable for users' ongoing concerns. This means developing a hard-nosed focus on failures to address recurring data problems and identify, improve and/or remove any software or process shortfalls—regardless of the guilty party.
It also means recognizing that information management, for easy collaboration and comprehension, underlies everything the enterprise accomplishes. So that nothing will be overlooked, I recommend that reinforcement take a structured approach by applying CIMdata’s “five V’s” to address data surges in enterprise data repositories. They are:
- Volume of data should be addressed by reducing or constraining the highest inflows from engineering, production systems, smart devices and AI.
- Variety of data can be sharply reduced by focusing on needed information, instead of what may be needed.
- Velocity, defined as the rate at which data accumulates, can be slowed by blocking anything too peripheral to keep—like noisy data.
- Veracity reinforces data management by continually re-establishing the value, accuracy and completeness of all inbound data.
- Verification creates mandatory periodic check-ups on the preceding four V’s.
If these five V’s sound like data governance, this is no coincidence. But they are also designed specifically for the organization and implementation of policies, procedures, structures, roles and responsibilities that outline and enforce rules of engagement, decision rights and accountabilities.
The Role of PLM and Change Management in Data Management
For reinforcing effective management of data, an end-to-end PLM approach is unmatched. Its connections to digital twins, end-to-end connectivity and digital threads point to strategies and tactics top management can use as they prod and demand the enterprise's many business units to establish effective data management.
Reinforcing data management, with or without enabling an appropriate PLM approach, presupposes a determined commitment at the executive level to remedy past abuses, as well as current shortcomings in the everyday collaborations that require data to be readily accessible, handled, used and modified for others to use.
Other top-of-enterprise solutions fall short compared to well-implemented PLM environments in significant ways. Enterprise resource planning (ERP) focuses on bills of materials (BOMs) and the costs and revenues impacting ROI. Manufacturing execution systems (MES) are at the core of well-run production operations but manage only that information. Product data management (PDM) lives on as PLM's predecessor in small- and medium-sized organizations. Customer relationship management (CRM) focuses on pinpointing customer needs.
Effective data governance means overseeing the implementations of all new solutions to ensure that capabilities work as promised. It is at the heart of reinforcing a sound management of all forms of data. It is so important that CIMdata added a data governance practice to its strategic consulting offerings more than five years ago
A key tool for reinforcing the effective management of data is configuration management (CM). Usually implemented within data governance, CM ensures that an enterprise's products, processes, facilities, services, networks, assets and IT systems are what they are intended to be and properly optimized for their intended use.
CM tracks all forms of data and clarifies changes and modifications to it, even if unintended or undetected, throughout the product lifecycle. A comprehensive CM approach also pinpoints and fixes problems, avoiding surprises such as hidden errors and unexpected outages.
Effective data management calls for tools to have continuous access to data, regardless of changes and formats. With proper security and syntax extensions to untangle conflicts, omissions and errors, as well as formats that have become obsolete.
The Role of Top Management in Data Management
Establishing and reinforcing effective information management is not specific to information technology, engineering technology or even operational technology. It is instead tied to the executive suite’s concerns about what the enterprise can achieve, at what cost, with what products and services, and at what risk. This is why I insist that reinforcement strategies and tactics should parallel and enable business models.
With all current data challenges, only naive managers would think that their data management tools, systems and policies—even the latest and greatest—will continue to function adequately. This is why top management's reinforcement role is primarily about people. New policies, procedures and plans must be thought through, implemented and monitored.
Most important, of course, is getting key people to see their responsibilities in new ways. Everything digital in the enterprise is already being impacted. Top management should plan accordingly and brace subordinates for unexpected and even unforeseeable developments.
To sum up, reinforcing the sound management of data is primarily about how people perceive and do their jobs, rather than the digital tools and systems they use. Implementing these reinforcement practices puts a premium on persuasion, which should be a key skill of any high-level manager.