The Dutch utility market is facing multiple disruptive challenges. To successfully face these challenges, we must be able to rely on high-quality asset data. However, such data is not always available. The solution? Treating your data as an asset. Or, in other words: asset data management.
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The Dutch utilities market is facing multiple disruptive challenges: an aging network infrastructure, stricter regulation, more demanding customers and the upcoming energy transition. To successfully navigate and leverage these challenges, utilities must be able to rely on high quality asset data for decision making and action taking.
The increasing need for high-quality asset data
So what are the challenges and issues that the Dutch Utilities market is currently facing?
An aging network infrastructure
First of all, an aging network infrastructure. As a Dutch Utility, you are most likely dealing with an aging network infrastructure, seeing that a great part of the Dutch energy networks have been built several decades ago. A big challenge is enabling a digital grid while maintaining safety and reliability for society and environment. It requires increased levels of capital investments for replacement and upgrades. However, at the same time budgets are expected to decrease the coming years. So, how do you determine where to most effectively spend available funds?
Secondly, regulators, such as the Autoriteit Consument & Markt, are becoming more prescriptive. They increasingly demand compliance with standards and emphasize transparency. As Dutch Utilities are for the most part paid for by Dutch citizens, there is increasing pressure to demonstrate that costs to society are minimized. As more and more network infrastructure is put underground, it becomes increasingly important to share details of the network with third-parties and to coordinate efforts to minimize damage during construction for example. That’s easier said than done. The question is, how do you show regulators you are in control?
More demanding customers
Thirdly, customers are becoming more demanding, interacting through social media, expecting rapid responses. They are getting this experience from other organizations they are doing business with, so why not from their Utility? If there is an outage, people start twittering right away. How fast can Utilities respond with a satisfactory answer? At the same time, with the digitization of the grid, more services (e.g. in-home displays showing detailed energy consumption) are becoming available to customers. What do you need to provide customers with the optimal service they are demanding?
The upcoming energy transition
Lastly, the upcoming energy transition will change local load patterns due to a growing number of electric vehicles and local energy production amongst others. As a response to these developments, Utilities are rapidly digitizing their grids. These smart-grids will allow better balancing of the network, reduction of peak-loads, earlier fault detection, self-healing capabilities and more... But how do you enable and leverage these developments given the increased complexity of (operating) the network?
No matter what way you choose to respond to the challenges facing the industry, there are two common themes:
- More demands are placed on the quality and availability of data regarding the assets in the network;
- More data is becoming available through technological developments in IT and OT.
In order to make effective business decisions, you have to rely on key insights obtained through trusted information, which in turn is generated from the raw material: high-quality asset data.
How available are high-quality asset data really?
High quality data, however, is not always available. Perhaps, the cause for poor data quality lies in the past, like unfortunate data migrations or changes in systems and processes. Or maybe, the problem is caused by issues that are happening today, such as a mismatch between the data currently required by the organization and the data currently created. Human errors might even be a contributing factor in labor-intensive processes. Finally, the organization might simply lack the tools and processes to capture high quality asset data effectively.
It goes without saying that there are many factors to consider when you want to turn your low quality data into high quality data, let alone create an environment in which the quality of data can be effectively sustained. The solution? Applying the same urgency and rigor to managing your asset data as you are applying to the management of your network assets. Asset Data Management provides a solid foundation from which to continuously drive value and cope effectively with the industry challenges.
Asset data management: how does it work?
Asset data management is the business function that allows you to develop and execute plans, policies, practices and projects that sustain the value of data and information. As the definition suggests, it is a vast domain with a very broad scope ranging from data governance to data models, encompassing people, processes and technology. A holistic data management framework allows you to understand the many topics within the data management domain. Also, it provides guidance towards developing this business function.
Key components of a holistic data management framework are:
- the development of a data strategy;
- the implementation of data management;
- and ensuring data sustainment.
What makes a clear and practical data strategy?
First up: the development of a data strategy. Solely implementing a data management framework is not enough. Obviously, you also need a strategy. A clear data strategy entails the development of the vision, scope, business case and road map to transform the management of data to meet both the business and technical needs of an organization. When applied effectively, a data strategy provides direction, prioritizes efforts and demonstrates the business value of data management and data sustainment.
The data strategy is closely linked to the overall strategy of your organization. For example: when the overall strategy emphasizes deployment of smart-grid technology, you might prioritize the development of logical data models. Or, you might focus on the quality of data to be incorporated in new SCADA-systems. Furthermore, if there is no clear governance structure in place to support decision making regarding asset data, the road map might start with a project to install an Asset data board and to look for people to take ownership of specific data domains.
6 steps to implement effective asset data management
Effective asset data management means controlling, protecting and facilitating access to data so that (data) consumers have timely access to the data they need.
These are 6 steps to achieve effective asset data management:
- In order to improve data quality, you first need to understand the high level data domains.
What do you want to know about your assets? Most likely what the key characteristics are of the asset itself: the asset-domain. You might also benefit from grouping data into an asset-condition-domain that describes the state of the asset, like inspection reports or information on outages. In this step you gain a clear understanding of the different data-area’s that are important to the organization.
- Next, you need to develop insight into the organizations most important data at the right level of detail.
Data creation is costly. Therefore it is key to understand what the critical data of an asset is exactly within each high-level data domain. For example: the critical data of a gas pipe might be its location or its material-type, while its manufacturer might be of less importance.
- The third step is to perform risk assessments to understand the impact of poor data quality on the organization.
It is impossible to achieve perfect data quality. Therefore it is important to have an idea where to draw the line. What levels of risk, due to imperfect quality, is the organization willing to accept? Linking this level of acceptable risk to data quality levels, provides you with a means to set quality goals and prioritize data quality improvement efforts.
- Create measurements to enable insight into data quality levels for different data quality dimensions.
The easiest quality dimension to measure is completeness: for each data field you simply assess whether meaningful values have been ascribed. For example: you could look at the placement dates of all transformers in the network and verify that meaningful dates are registered. Dates in the future or too far in the past would be considered as incomplete data. In this step you produce a detailed report on quality levels for all important data you have identified earlier and comparing them with the required quality levels.
- The next step is to prioritize data quality issues and manage improvements through a data management program.
By using the data quality measurements and the insight into risk-levels of data obtained in step 3 and 4, you can start to prioritize the proposed projects to improve data quality. Combine this with the road map from the data strategy and you’re enabled to initiate and coordinate a portfolio of projects through a data management program.
- The last step is to execute data quality improvement projects in order to solve the data quality issues you identified earlier in the process.
In this last step you typically address two things: cleaning up the existing data and preventing the inflow of poor quality data in the future. Within a project, you might define certain business rules based on statistical analysis to enrich or complete corrupt data. Furthermore you can evaluate the entire data supply chain in order to identify and eliminate the root cause of corrupt data.
How to sustain long-term value and cost-effective data anagement
Once your data management framework has been set up, you want to make sure it lasts. To sustain high quality data, you need to continuously drive, develop and enhance value from data capabilities enabled by the people, processes and technology of the organization.
To make sure that is happening, ask yourself the following questions:
From the technology-perspective: does the organization have multiple sources of truth regarding asset data or a single source? Are systems ready to receive exponentially increasing amounts of data? And s your organization aware of the latest technological developments and the opportunities they may bring towards creating and managing quality data?
In terms of processes, you may critically evaluate the way data is created in the organization. Ask yourself: can you standardize processes? Can you automate data creation? And Are you optimally organized to absorb peaks in demand for data entry, yet flexible enough to deal with a drop in demand?
In terms of people you can discuss if the organization has the right set of skills on board: for example do you have enough analytically strong people to develop data cleansing solutions? Are they up-to-date on the latest tools? Furthermore, do you have people who can bridge the worlds of IT and business and make complex data-issues understandable? Are people available who can effectively plan, structure and coordinate efforts to improve the data quality?
Data sustainment ensures data will be a source of long-term value and the different stages of the data lifecycle are managed cost-effectively.
A yes to all of the questions above means you’ve got a solid data management framework set up.
What key challenges are you facing in improving data quality? And how is your organization ensuring the availability of high-quality data? If you would like to know how we can help you to create, improve and sustain high quality asset data, please get in touch.