The following is a contributed article by David Price, chief technology officer, Black & Veatch Management Consulting.
After 2017 chalked up more than $68 billion in utility mergers and acquisitions, Bloomberg attributed the hot M&A market to cost-cutting pressures prompted by "stagnant or declining electricity sales" and the need to "replace aging infrastructure." Merger activity continued in 2018, and it is still with us in 2019, as seen with the recent acquisition of South Carolina's SCANA by Dominion Energy.
A fundamental driver for mergers and acquisitions is achieving economies through scale and consolidation, particularly in business operations. But if utilities don't immediately adopt a strategy for consolidating the work and asset-related data supporting the current business and M&A target, they'll likely be unable to achieve all the desired financial outcome.
A good indicator of future success can be found in a cursory comparison of the two organizations' data. Specifically, the data used in the underlying enterprise asset and work management systems that support day-to-day management of the utility's physical infrastructure, the people that manage and maintain them, and the supporting business processes the latter execute to do so.
A key source of failure to achieve the optimum level of economies of scale results from a lack of standardization in usage of the enabling IT solutions and the critical operational data models within them.
Thus, even if both parties to the merger have state of the art solutions, this does not automatically mean that the smooth integration of a future acquisition is achievable, or that the intended levels of efficiency and scale will be achieved. Even if the two utilities have the same software, they almost certainly configured it differently and implemented markedly different physical data model content.
The issue of data alignment is often obscured by the immediate appearance of operational and organizational consolidation and process re-engineering. The more chronic, but no less damaging issues do not surface until later in the post-merger period.
The first symptom usually reveals itself through a continued rise in the cost of IT operations due to the multiple integration designs, and software configurations that were not addressed in the initial merger process.
Additionally, in this new and rapidly growing world of machine learning, data analytics and robot-based solutions, this chronic condition can be seen in the greatly inflated costs to adopt and implement these technologies due to the underlying poor condition (both in terms of design as well as quality) of the data available in production systems that these new technologies require to enable them to deliver on their promise.
As a result, hoped-for long-term cost savings and operational flexibility from consolidation may never materialize.
However, by taking some key organizational and supporting technological steps, there is a way to unlock this elusive potential. In short, it calls for the establishment of Enterprise Operations Data Model Governance and the creation of a utility's "Asset Golden Record" as the embodiment of their drive for excellence in operations and technology adoption.
Through this process and its supporting organizational philosophy and technology solutions, utilities can create and then drive the transition to a universal data language used to define, track and report on the characteristics and condition of their critical assets, define and track the work needed to support and maintain them, and monitor the performance of the office and field resources that run the company on a day-to-day basis.
This language, in turn, enables more accurate asset analysis, leading to more effective spending that results in the ability to realize the economies of scale that come as a result.
Data really matters
If you can't align on data, it's difficult for everyone to use the same systems, the same processes, the same platforms and the same practices. In turn, it then becomes equally difficult to leverage enterprise IT solutions such as Enterprise Asset Managment, and Spatial and Operational Management to enable the organizational alignment and standardization that, in turn, delivers new levels of efficiency and value.
The Work and Asset Management Solutions in place at a utility can enable enterprise-level visibility through a single lens of consolidated data. However, newly merged businesses struggle to create consolidated views of the business due to various systems used, levels of integration between them and, most importantly, the philosophy and approach to their definition, deployment and day-to-day management of the critical data used to run the business.
In general, these platforms are there to enable utility managers to drive efficiencies and enact strategies that lower costs or improve effectiveness in numerous ways — by scheduling operational activities such as maintenance and inspections efficiently, as well as monitoring asset usage and performance to catch issues before they become serious problems.
These solutions are especially important in realizing value from mergers because they are the systems used to run the consolidated utility's day-to-day work processes.
These systems represent the critical meeting point where data flows across the company — from the field into engineering and records; from real-time management systems such as a Distribution Management System back through GIS solutions to network planning; from customer operations systems out to the field and back again; from supply chain through materials management out to the field, and then back through financial and plant account systems, to cite some of the more critical ones.
Most important to recognize is that the scope of data critical to the delivery economies of scale through effective operations is much broader than a singular focus on asset-related data. There are several key categories to encompass in one's thinking:
- Asset Types – the means by which all physical assets are classified and that drive all subsequent data, processing and operational data requirements. (e.g. valve, pole or transformer).
- Symbology – the generic term used to define the graphical representation of a specific asset type when viewed in a spatial system.
- Asset Attributes – the set of data associated with a specific asset type that has been identified as required for the various stakeholders in operating the business.
- Defects (or Faults) – the set of defined possible issues that could arise with a given asset type that require corrective action.
- Critical Observational Data – the set of possible data for an asset type that has been defined as vital to support the wider business needs of the company in monitoring and managing their assets.
- Required Work Result Data – the set of data that, either due to regulation or corporate standards, must be collected when performing work on or around a given asset type.
- Field Work Definition Data – the set of data used to describe a given unit of work, its skill requirements, and standardized estimated time for performance.
In the case of mergers, to ensure that these platforms can steer the two newly merged enterprises in the right direction, you need an aligned critical data management strategy and platform to govern the consolidation process and to manage it on a day-to-day basis, starting from day one of any merger.
The journey to achieve consolidation comprises two discrete but interrelated paths; the technological and the organizational. The most challenging path is undoubtedly organizational. It involves bringing organizations to a common view and approach to their data models.
Data is a digital manifestation of an organization's philosophy regarding their existence and, in this particular case, their operating model, processes, asset and risk management strategies.
Thus, it is a source of great contention at the beginning of the merger consolidation process. This is one of the major reasons why companies shy away from truly driving standardization due to the friction and issues that result. The underlying cause is often due to the fact that the acquiring business does not have a clear philosophy and model of their own that has been universally rolled out across their own business.
Therefore, a practical consideration for companies looking to acquire and integrate future businesses is for them to have already subjected themselves to such a process of standardization and philosophical renewal.
Renewal refers to the need to examine the purpose and value of every piece of data the organization currently collects, and to explore the gaps and opportunities presented by new types of data that can be collected and employed effectively. Having gone through this process themselves, it becomes far more straightforward to bring other organizations into the fold, perhaps making slight modifications and improvements to the model based on the best practices and learning gained from bringing in more businesses with different views and philosophies of their own.
Perhaps the biggest challenge in aligning businesses' operations-related models lies in the use and design of spatial symbology — the critical representation of asset, work, and other critical geographic and operational data used by staff across the organization to monitor, control, manage and work on their complex network of interconnected assets safely. These are critical to both operations and field staff in quickly identifying the right asset, its current state, relationship to other assets and understanding of the overall network as a whole.
Symbology represents a most difficult problem to address as, like a country's flag, it is steeped in its company's history, philosophy and traditions. Of all types of data, it is most critical to human beings rather than computer programs.
Thus, even if one is successful in aligning an organization's data, without aligning on symbology, there remains a profound barrier to achieving true operational consolidation, particularly in safe field operations.
Therefore, symbology must be considered first, to provide an understanding of the size and nature of the gap between the two organizations being merged.
The process of identifying, selecting and agreeing on textual data models is no less straightforward, though consensus or alignment is more easily achievable, due to the greater room for compromise and inclusion when determining the attribution of critical data entities.
Once a model has been created and agreed to, gaps identified, and consensus or compliance secured, the technological process can move forward.
Much of the heavy lifting during the design, gap analysis and implementation planning can be supported through the use of Asset Information Management platforms, essentially highly specialized data brokers that use reference models and other specific rules to analyze then govern the movement of data across the company's systems on a day-to-day basis. These platforms perform the overarching role of data governance at the system transaction level.
However, the implementation of the target "Asset Golden Record" is not without its own complications.
A major challenge lies in the fact that to achieve the new standard without very costly field surveys executed outside of normal operations, the solutions and models must accommodate different business rules governing the data due to the fact that existing assets will not have the full complement of data defined for them by the new model.
Thus, the overall solution must accommodate the day-to-day accretion of attribution on existing assets through one set of rules, while enforcing a different, more rigid set of rules when processing data about new assets.
Pulling it all together
Ideally, the Enterprise Operations Data Model Governance and Consolidation process should start before the merger takes place. If possible, one should perform a comparison between the target or future acquisition data model and the current data models in place to determine what it will take to bring them into alignment.
In nearly every case, consolidation should start with a careful and realistic appraisal of the data that already exists.
There is a tendency to start with processes and map those out. When it comes to implementing Work and Asset Management systems and Geographic Information Systems (GIS), one should start with the physical asset, work and reporting data itself that workers are using on a day-to-day basis in their production systems.
That data will give the clearest set of measurable differences between both organizations, including their relative levels of sophistication compared to where your own company is as a business. This is because the pursuit of the data leads to a deeper understanding of the level of fragmentation, sophistication and adoption of technology in the day-to-day operations of the business being assessed.
In addition, early into the process, one should define and implement a robust and joint Enterprise Data Governance Program.
One of the challenges in achieving and keeping accurate operational network models is that several systems feed into them. These systems include Supervisory control and data acquisition (SCADA), GIS and advanced metering infrastructure as well as the emerging next-generation solutions to enable distributed energy management and operations.
The data models needed to run the business are, therefore, distributed across these multiple systems in what is increasingly a federated approach, due to the inherent difficulty in adopting a simple ownership model of the individual data elements themselves.
Consequently, an enterprise level approach to the definition and governance of data critical to operate, manage and analyze the business needs to be elevated above any one individual part of the business and its supporting suite of systems. This will enable a wider understanding of and adherence to standardization in data and can enable the elusive quest for the "Asset Golden Record".
Naturally, this needs individuals drawn from every key organization across the utility. The adoption of the governance model can be a critical element in the pre- and post-merger process, as it represents a very empirical solution to the identification of differences across the business.
Most other approaches require intense and protracted meetings involving large Visio diagrams and process discussions to uncover differences in philosophy and day-to-day practice.
A data governance program and the supporting Asset Information Management platforms that enable it to represent a more rapid and cost-effective way to identify and define differences and opportunities between companies, allowing the teams to create more effective and targeted merger activities to address them once they are identified.
What helps along the way
It is critical that organizations recognize that there is no one industry standard data model design that addresses the full spectrum of the requirements of a utility in this day and age. The scope and diversity of new technology solutions and capabilities coupled with the ever more rapid introduction of new types of assets that generate and control the flow of energy have made that impossible to accomplish.
So today it is vital that organizations adopt their own enterprise asset information strategy and supporting platform to help them manage and execute their unique journey.
When you undertake standardizing the overall enterprise asset data model between two merged utilities, this becomes even more critical. The enterprise approach to data governance for operations also allows you to be more discerning in bringing in the right outside assistance.
The right blend of outside consulting knowledge is critical in helping companies design their interpretation of the Asset Golden Record. There are also new technologies and platforms that can greatly assist and accelerate the analytical and operational ends of the data governance process as well.
Also, one of the key missions of a data governance effort is to ensure that there is a rationale for every field defined for every entity. For example, if one cannot find a part of the organization that uses a piece of data currently being collected by the field, one must revisit the need to keep doing so.
Finally, enterprise operations data consolidation and maintenance are not static activities. They are processes that must continue to grow and change as an organization's goals and requirements change.
The establishment of an operations data governance body and management process and, ideally, the adoption of new technologies to manage and drive data excellence are critical to long term success. They can also play a critical role in the evaluation, due diligence and planning of future mergers and acquisitions once fully embedded within the organization.
A final thought
The benefits of scale and standardization are definitely there for those companies who intend to grow through acquisition. However, these companies must be ready to adopt a less collegiate approach to the integration of their targets.
This, in turn, calls on these companies to subject themselves to a process of internal introspection, renewal and digital discipline to be ready to integrate other utilities in the future. And this calls on those companies to have a logical, thought out data and IT governance philosophy and process that aligns with the company's business strategy; and that can readily absorb future acquisition targets while also learning and incorporating sound concepts and ideas from them along the way.
In a future article, we will explore the empirical ways that data can be used to derive a specific measure of organizational convergence and, in turn, drive the creation of enterprise data standardization indices with which to govern, monitor and direct the underlying efforts that enable the next level of utility business operations performance.