18 May The Seven Critical Data Quality Challenges in Enterprise Asset Management
Due to the complexity of Enterprise Asset Management (EAM), data quality challenges are inherent in its practice. Such prevalence, however, prevents the asset managers from reflecting upon the substantive state of organizational productivity, performance, and prospective growth.
For instance, some organizations’ assets are not accurately defined, so their real value is unknown or hidden. Likewise, some organizations have inconsistent descriptions or identifiers for assets which can lead to inconsistencies when used in reports and analysis.
Not only this, at times, there are gaps between what an asset looks like (actual) and what it should look like (ideal) — disparity of the highest order as far as the business decisions are concerned.
The thing is that the quality of decisions for asset management depends on the quality of data available. If this data is incorrect, the organization might never achieve its objective of successful EAM and, in turn, might never be able to cater ideally to its customers.
And while software solutions have been developed to help enterprises with asset management, a host of situations still reflect upon the data for asset management being dispersed, inconsistent, and inaccurate.
The Seven Biggest Data Quality Challenges that Hinder EAM
Lin et al., through a profound case study in 2006, investigated the data quality issues pertaining to asset management. As a result, they were able to outline the major factors that influenced data quality — from interoperability standards to cleansing techniques and organizational structure/culture to control systems. Personnel competency was also a factor listed out amongst others, which didn’t come as a surprise.
After all, the quality of data depends on the combination of technology, organization, and people. To that end, here are the most critical data quality challenges that make the lives of modern-day asset managers difficult.
Measurement of success in EAM is based on two aspects.
- First, the success in meeting customers’ expectations for time and cost.
- Second, the success in meeting regulatory requirements such as privacy and cybersecurity.
In order to suffice these two facets, enterprise asset managers must continually measure and analyze the activities and attributes of their assets.
However, a lack of quality data in EAM can distort the measurements. It can render incorrect data inefficient (frankly worthless) for decision-making and planning. Of course, the two measures of success are then not an authentic and absolute reflection of the enterprise’s performance.
Lack of Uniformity
The data gathered for EAM may lack uniformity in its composition and arrangement. The non-uniformity stems from the falsified aggregation process. In other words, the data quality might suffer from the aggregation of disparate data sources. If the data is not aggregated well, it can obscure the business processes that led to the original collection of it. The results and the associated actions based on such data are flawed.
When different systems and methods are used to collect the data, rigorous data cleansing is required. In addition, these systems may not present themselves as win-win models for data sharing and data standardization.
Every so often, data may be tagged to the wrong asset or not tagged at all. The tagging of data refers to the tagging of an asset or entity with metadata that makes it more convenient, usable, and available for managing that asset or entity.
If the asset or entity to which a data element belongs is missing or incorrect, the data tagging itself can be inaccurate. As a result, the EAM systems may lack the ability to be shared and standardized. When no shared standard for defined metadata exists, the enterprise asset managers must create one. The EAM system must then be changed to produce reports in that new format, which will require training for the users.
Lack of Standard-Authority
There is no common standard or authority defining the identity of assets and their components, values, fields, descriptions, elements (or attributes), etc. This results in discrepancies between the data elements collected by each of the stakeholders (regulators, service companies, IT vendors, asset owners/operators).
For instance, the stakeholders may be using different systems and methods to collect the data, thus, paving the way for inconsistencies in the data quality.
Notably, the data management process occurs at the lowest level of the enterprise and is affected by each stakeholder’s peculiarities. These may include discrepancies in terminology, rules, and business practices. And with no central authority to look up to, the discrepancies habitually work their way into jeopardizing the data consistency.
By now, it’s indisputable that data obscurity stems from the lack of data quality in EAM. Before any action is taken, the accuracy of the data needs to be tested and validated. However, the insufficiently validated data (as is usually the case) may require that decisions are deferred or not made at all.
This way, the enterprise asset managers lose the opportunity to act on time. And when these decisions are not made, they are challenged by the organizational stakeholders and auditors monitoring the enterprise’s performance.
In concrete terms, data gathered without a consistent collection method (acquisition-testing-validation) will not be comparable or transferable. Again, data that is not adequate for comparison is not fit for accurate decision-making.
Multiple Data Sets
At times, enterprises might need to use data from multiple sources; however, such a requirement can prove to be extremely challenging. The problem with multiple data sets is that the information from these different sources may contain conflicting data.
Such information is likely to make it tricky to eliminate or decrease the risk involved in certain sophisticated business decisions.
That being said, a number of different challenges source from this position.
- Related work is one of these problems, and it includes challenges of data quality as well as a combination of multiple data sets.
- Historical records are another problem when an asset is sold or leased. Historical records might not be shared with other stakeholders; even if they are shared, they may not be accurate or consistent.
At the helm of every data quality issue in EAM is the most basic or perhaps the most commonly-faced challenge – acquiring valuable data.
Employees and external sources of information like vendors or customers don’t always understand what kinds of information are needed for their projects, what is necessary, and when it should be collected.
As a result, there may be a lot of unnecessary effort put into EAM, leading to underperformance and cost escalation.
Opportunities for Using Data to Manage Organizational Assets Conveniently
Undoubtedly, the availability and employment of data are paramount for effective decision-making. But what to do when all the challenges mentioned above loom large?
Adhere to modern-day data management services such as MRO Data Cleansing, Material Master Management, Onsite Data Sourcing, etc. Leveraging such solutions not only makes quality data available but also removes the inconsistencies that result from incongruent operations.