“Four years after the worst electric power blackout in United States history, the nation’s electric companies can now be punished if it happens again. Reliability standards for the nation’s power system quietly got teeth Monday, marking a major shift in how the U.S. electric industry is regulated. Utilities now face fines of up $1 million a day if they fail to meet any of 83 standards intended to keep the nation’s power grid healthy. But compliance was voluntary. The industry largely endorsed the changes, despite the risk of fines, because the nation’s transmission grid is so interdependent: As happened in 2003, one utility’s mistakes can hurt another utility down the wires.”
The 83 standards and compliance only means one thing – the electric utilities now have to identify, collect, review, analyze and manage their information collection, which in turn means putting to use better business processes management coupled with emergency preparedness and disaster recovery – all this powered by improved data governance across the enterprise – if they want to continue to stay in business.
Historically, the electric utility industry has acted responsibly by voluntarily complying with consumer expectations, some of which have now basically become industry ‘standards’. Electric utilities are in the limelight, now more than ever, owing to devastating hurricanes, public awareness of the energy crisis, rising natural gas prices and efforts, or the lack thereof, to reduce our dependence on foreign or domestic oil and shrinking economy. Consumers not only want ways to manage their energy use, they also want the industry to be held to definite ‘standards’ that will ensure energy availability at a reasonable price with lesser impact on environment All of these factors, make the grid the key spoke in the wheel of the business process management. Electric utilities have to be on the top of their grid!
The key to getting on the top of the grid and staying there, in other words managing the grid, and managing it well, is really linked to better emergency preparedness, and more importantly business continuity post-emergency. All this starts with the basics, identifying and tracking key contributing factors to business like the interdependencies with other utilities’ infrastructure and the activities required for providing continuously available power within utilities’ own infrastructure. Monitoring these and other factors that drive grid operations provides insight into how the grid can be made more efficient and, more importantly, reliable. There may still be that power line that is down and needs to be repaired. But realizing and limiting the impact of that downed power line can only be accomplished through a holistic management information view of the grid that helps identify the impacts, suggests actions to limit the impact, rescheduling/re-routing the power to the customers and continuously monitors the ‘weak links’, provides up to date information to the field personnel, provides better customer service function and at the end a well informed, satisfied end-customer.
There are several potential technologies that promise to revolutionize data collection capabilities that range anywhere from broadband over power line to self-identifying intelligent devices, web services that can communicate with intelligent devices to send specific instructions globally, through wireless technologies, if required. While there is cost associated with all of these, what these technologies promise to provide is the ability to gather and track vast amounts and types of data from any point within the grid, some real time, some near-real time, such as voltage delivered, flow usage, outage, meter readings in any intervals, substation status, substation load, transformer load, energized, de-energized, power delivery source and amount, power quality, transmission loss, type of meter, light type in case of streetlights, temperature and distribution loss. Basically the data that not only tells you the “Light is On”, but also gives you information like for example, “How to keep that light ON”, “What else does this network serve”, etc.
A question remains as to whether any or all of this data is sufficient enough to gauge for example, the annual or monthly or even daily load requirements at a given electric utility should be prepared for. The answer will vary depending on who you talk to. The states have implemented varying protocols for deregulation in each of their territories. In some states the answer lies with the retail provider, in others it lies with a distribution provider and in others with the co-op or municipality that is not deregulated entirely. Add to that, with the advent of AMI/AMR and the associated information explosion and availability, the customers can increase and decrease their usage based on their own needs and more importantly price. Given all these factors, data collected by different entities may vary based on the state regulations – but that calls for more collaborative effort on the part of not only the utilities but also the laws governing the deregulated utilities to have an operational plan that serves the customer better. The surprising fact is the electric utilities may already have this type of data available to them from their existing deployed systems in one shape or the other. Some of this may also already be stored in one format or the other in the enterprise and may even have been backed up, thanks to SOX implementations, but may be buried deep.
Now the question is – Is the data available is at the right level for optimization or efficiency evaluation – and if it is at such a level, is it available for integration with the rest of the data collected from across the grid? Can the data be processed in a way to enable performing functions like network optimization and disaster planning; is the quality of such data that is available at a level that it can be used to drive operational planning? The basic idea is the key data performance measures, if not already established, need to be established, revised, re-assessed and standardized and applied across the grid and the data in a uniform manner. The performance measures can then be primary indicators to how the data collected needs to be integrated and organized to support meaningful information retrieval. There has to be the basic recognition within the business and corporate IT, that this adds value to the bottom-line by using the existing investment in technology to improve customer satisfaction and regulatory compliance, along with reliable operations delivery and shareholder value. Yes, it ultimately boils down to ROI and plain and simple customer and market service. But customer service in a way that has not been done before in the utility industry – by pro-actively monitoring and managing the supply lines, centrally and remotely, to provide the continuous service that is expected of the utility ‘suppliers’ even in the wake of hurricanes.
A ‘utilities’ way of looking at the data is to think of it as a collection of information that really keeps track of a “continuous” supply, bringing “goods”, in this case power, to the “customer”. There are hence additional factors to be monitored and measured – the demographics, population growth, real estate developments, customer preferences, appliance usage, usage times, weather changes, customer preferences with changing weather to name a few – these, in addition to the electric grid information, become key indicators in forecasting demand/load, load management, scheduling, outage management, criticality and operational needs in the grid. This then plays a big part in creating a predictive model, and the ensuing analysis on how the network can be optimized using the projected demand changes, line-down situation, hurricane strike path projections, power availability for transfers, tree-trimming progression, etc.
While finding the data within the enterprise is the first step, the time is definitely ripe for utility companies to adapt to the rapidly evolving data integration, master data management standards, and start embracing the culture of data management organization into their enterprise. Applying data management techniques within the entire organization, identifying and separating gold data from the not so gold data, establishing master data management hierarchy, etc. – the core principles of overall data management will definitely get these companies out of exploring the disintegrated data in the darkness.
A mature level of data management/ data governance methodology and organizational practice, coupled with investment in intelligent and advanced data collection, centralized meter data management, AMI systems, intelligent metering devices, intelligent field devices that can transmit load information through wireless enablement will definitely put these companies on the right path towards compliance, but also help them manage the grid better without unplanned transmission losses.
As the organizations mature through the data governance maturity matrix, there are several benefits they can reap through such practices. Some of these benefits are listed below.
- Eliminate data redundancy
- Improve data quality
- Improve data delivery technology and methods
- Improve data availability
- Reduce inefficient data storage/archival practices
- Provide avenues to improve data security across the enterprise
- Improve business “view” of the data landscape
- Improve organizations’ response to customer and market regulations
- Prepare organizations towards making better judgment on technology and infrastructure projects
- Prepare organizations towards business process definition, confirmation and potential orchestration and automation
- Helps business rise above the daily data issues and focus on core business processes
Enterprise-wide data governance is not limited to governing the data’s quality, uniqueness and management, but arranging the data in such a manner that it is available for taking business action, performing business analysis for not only using it as a indicator of business performance, relying upon it to give new direction to the business. In the case of utilities, the case is clear, it is time to govern your data or perish! Data and the information it provides will define the future business processes that will enable the organizations to survive. Organizations that are “data-fit” will see themselves evolve into leaders in the industry that can define policy for every participant.
Several organizations outside the Utilities vertical now have their own data governance maturity model and each has a different approach to data governance. It can be cumbersome to choose which one to go with, there is a maturity model coming up every other day. The first thing to do is to perform preliminary, high-level self-assessment to document issues like for example, how many times the reports are having to run and rerun because of bad data, how many steps is it taking to generate a particular view of data that the business is needing daily or weekly or monthly, what data views are critical for the operational efficiency, is that data available, does it have the right meaning, how and where is this data being pulled, what transformations are taking place, are the transformations accurate, are the transformations taking too long, is the method being used to transform the right method for the kind of data, is this the “gold data”, is there better, fresh data available, and there are many such questions someone like a information architect could very easily come up with and answer to capture the ‘as-is’ state.
Define a ‘to-be’ state. This does not have to be big exercise – compile at a high level, the results from the ‘as-is’ assessment and determine which problems the business stakeholders like to see go away so their operational efficiencies/readiness can be achieved.
Start with maybe two or three maturity models out there in the market and see which one actually defines the ‘as-is’ that you captured in their maturity levels, along with the ‘to-be’ that you think you should at as one of their levels. Remember to choose a model that outlines the detail steps that can take you from whatever level your ‘as-is’ is to the ‘to-be’ level without taking you down a diversion (read solving problems extraneous and unrelated to the data issues at-hand) – there may be some that you may have to do when you determine you may have to split some apps and house them in separate places, etc. but try and cross that bridge when you have clearly established the data governance roles as job functions in the organization, architected the solutions and taken the baby steps in that direction.
Again, it is very critical to implement the right technology that gives you capability of gathering up to required data. That data also needs to be made available to the right people at the right time in the right fashion with the right accuracy and quality through an organization that is data-conscious and data driven, so you can make the right business and operational decisions that impact the customer.
At the end, the utilities business plan needs to include emergency and post-emergency operational planning, the lack of which could really impact business continuity. Such business planning cannot happen overnight, but it can definitely happen with the right information available at the enterprise level. Your grid has all the answers; it is just a matter of getting on top of it.
