Asset Data Quality - Challenges and Opportunities for Transmission and Distribution Utilities

By Prasanth Kumar
Intelligent Utility
March 23, 2015

Modern day Transmission and Distribution utilities are rapidly moving towards a smarter world and have the requirement to manage and maintain millions of physical asset attributes and characteristics in order to effectively manage and monitor the infrastructure network. Traditionally, asset data management has always been a prime issue for these asset intensive utilities. The data coming from the field had various quality issues and there were no effective means to verify and validate the asset data that was flowing to the back office applications from the field devices. With stricter price control reviews and regulatory pressures governing most of the transmission and distribution utilities, the financial repercussions of a poorly managed asset data repository has become more treacherous. For example, inaccurate data in regulatory reporting submissions can lead to tighter scrutiny by the regulatory agency on underlying business processes and procedures and can even lead to the cancellation of distributor licenses. On the qualitative side, the brand image of the organization can go down in the social media. Providing useful, accurate and relevant information for enhanced decision making is also in the best interest of a utility organization for enhanced performance, productivity and protection of stakeholder interests.

The asset data in transmission and distribution utilities cuts across management of assets ranging right from the substation transformers, pumping stations and compressors to pipelines and conductors. An effectively managed asset data repository should be able to provide the asset managers with various insights into aspects such as asset conditions, failures, locations, spatial co-ordinates, qualifications and skills required to maintain and operate these assets, asset connectivity information and their innumerous physical characteristics. The data should be easy to be retrieved and should come in the form powerful consolidated views to enhance decision making.

Fig 1: Asset Data Quality dimensions

Asset Data Quality Challenges

The key challenges facing the transmission and distribution utilities in managing the physical infrastructure asset data are as follows:

1.Lack of accountability and responsibility from management and operational teams

2.Flooding of data from smart devices and sensors

3.Aging field teams has most of the knowledge in their brain and has very less confidence on the data in the IT systems

4.Lack of understanding on the part of the back office administrators on way network is constructed and operated

5.and finally the Product issues in managing and maintaining the data in a complex manner

Fig 2: Key Challenges

Lack of accountability and responsibility from management and operational teams

Most of the organizations do not define the ownerships for data. When nobody owns the data, there is lack of clear accountability and responsibility in maintaining the data. In this scenario, the data quality trends lower and people loss confidence. On the other extreme, when there are clear ownerships for all the data that resides in the IT systems, proper steps are taken by the relevant data owners to define appropriate business processes and procedures as they own proper accountability and responsibility for the state of the data.

Flooding of data from Smart devices and Sensors

The emergence of smart metering and smart grid regime has thrown up big challenges to the utilities in handling the real time data coming out from these devices. The smart meters fitted to the asset infrastructure network keep on transferring millions of asset measurements and readings from these field devices to the back applications. If the data quality is not managed properly for this incoming data, it can lead to huge issues.

“Data in the Brain” - Aging field teams

Most of the field teams that are on the field maintaining and operating the assets are set to retire in the coming 10 years and this has opened up a wide gap in terms of the knowledge these teams hold with their long experience. Most of the times, they don’t rely on the data shown by the systems in maintaining the assets in the field but it will be with their own back filled knowledge that helps them in effectively maintaining these assets.

Lack of understanding on the part of the back office administrators

Many a times, the people sitting in the office rooms have no or peripheral understanding on what happens in the field. They are no properly trained on the ways in which the field teams work and make use of this data and hence cannot think from the field team’s shoes. This gives rise to lot of issues in maintaining and managing the data.

Product Issues

Enterprise IT products implemented some time cause issues and are responsible for poor data quality. User unfriendly UIs and lack of consolidated views causes mis-interpretations and confusion in the usage of data from these repositories.

How is asset data managed currently?

Many organizations classify the available data according to its relevance, usefulness, importance, criticality and fitness for intended use. This classification helps these utility organizations to deal with business critical issues in an effective manner and deal with data issues.

Fig 3: Legacy Data Management Techniques

But only the classifications of the data elements into various buckets does not help these organizations resolve the issues associated with the monitoring and tracking of quality. The data quality team will not be able to effectively monitor whether the quality of the data elements trending upwards or downwards over a period of time?

There is no proper way to measure and monitor the data quality improvement or one cannot make out whether the data quality has improved or decreased over a period of time. The measurement and monitoring of the asset data quality index can provide important insights into the way in which the business processes needs to be improved in the organization.

How can it be managed better?

A comprehensive framework for data quality management and monitoring is definitely and improvement going into the future. “One cannot improve something which cannot be measured and tracked” is an apt phrase over here. Unless we qualify our data quality health index and the factors impacting the quality we will not be able to change our processes to improve the data quality index.

Fig 4: Future Data Quality Management Framework

A comprehensive data quality index based framework with data quality dashboards which display trends over a defined period of time is a definite measure to improve the quality of data.

Business Rules for Asset Data Quality Evaluation

As part of the creation of asset data quality framework, various business rules can be identified and created as part of the rules repository framework. These rules can be scheduled to run on top of the asset data repository at various points in time and the outputs provided by these runs can be converted into a data quality score.

Some examples of these business rules that can be used to evaluate the asset data quality score can be as mentioned below:

1.Transformers of a particular make and manufacturer which has Installation date before a certain date

2.Pipes of a certain material and diameter shown with an inaccurate thickness value

3.Values not available for certain characteristics

4.Inaccurate location characteristics for linear and point assets

5.Wrong lengths and units of measurements for linear assets

6.Asset “Status” values are not populated correctly

7.Asset financial figures such as maintenance costs, depreciation, regulatory asset values are not adding up correctly to reflect the year to date changes

8.Failure details of assets are not getting recorded consistently and associated data mismatches

Fig 5: Business Rules Library

When a comprehensive set of business rules are setup in the rule library and run at different time intervals an accurate view of the quality of the data available in the repository can be assessed.

Asset Data Quality Measurement & Monitoring

Standardized set of commercial of the shelf tools and products are available in the market to automate the asset data quality measurement and monitoring activity. Some of these data analysis tools come with a variety of standard out of the features and functionalities that can be readily made use of in effectively measuring and monitoring the quality of asset data in the repository. A monthly and quarterly monitoring of the data quality score is a healthy way of assessing the robustness of underlying business processes and procedures that organization has implemented. A deteriorating data quality score over a period of time indicates that there is something drastically wrong with the way the underlying business process and procedures are implemented and executed.

The outputs available from the business rules library can be plotted in a time interval scale, a trend chart can be produced to see whether the asset data quality score is improving over a period of time or not. Some of the graphs and charts that can be produced to provide a quality score view can illustrated as shown in the diagram below:

Fig 6: Data Quality Monitoring Dashboards and Trend Charts

Benefits of effectively managing and monitoring asset data

The key benefits of effectively managing and monitoring asset data are as follows:

•- Improved and accurate decision making

•- Cost savings in Asset Investment Planning, Asset Replacement

•- Better Engineering and Construction decisions

•- Optimized asset reinforcement framework

•- Lower CAPEX and OPEX for physical infrastructure asset life cycle management


Currently a majority of the utility organizations are unaware of the fact that where they stand in terms of Asset Data Quality. The organizations don’t have a standardized way to monitor and track the quality score and a means to find whether over a period of time the quality score is improving of deteriorating in the asset repository. This scenario is set to undergo a drastic change. In future, continuous improvement in asset data quality management will become a routine activity that everyone in the utility organizations will be involved in and customized algorithms will be in place for managing the quality of asset data. Overall the utility organizations of the future are expected to be much smarter in defining and delivering the network improvement programmes.

A structured way of monitoring and managing the quality of asset network data and their attributes can be a definite differentiator for utility organizations that want to traverse through the asset management maturity journey.