- Business Intelligence & Decision Support
- Energy Volume Reconciliations
- Data Quality
- Estimation Accuracy
E&R organisations do not consistently make best use of the information and data they hold to inform both day-to-day operations and key tactical and strategic decisions. Management may lack the ability to ask “what if” questions or model different scenarios being constrained by inflexible legacy systems or absence of analytical support capability for corporate ERP solutions with outsourced or offshore support.
The appropriate use of analytic techniques and business intelligence tools can bring great benefit to support management in making better informed and hence more accurate business decisions. The extent of change within the E&R sector known or expected in the near future (for example electricity market reform, introduction of competition in water, changing regulation and risk focus for deep-water extraction) make this a hot topic for the E&R sector in particular.
Why is it an issue?
- Organisations too often rely on business as usual processes and reporting activities to understand their commercial activities. Their wealth of data held is rarely looked at from new, innovative perspectives – and in some cases the people and tools are not available to enable analysis of changing circumstances in a cost effective way.
- Decisions are often influenced by opinion that has become embedded within a organisation’s culture as facts – without regular challenge based on genuine data the organisation can become ‘stuck in a rut’.
- Organisations which handle a large volume of transactions often operate controls at aggregated, summarised or average levels too often missing opportunities to add value by focusing on issues and errors within specific elements of the population.
- Business management can often struggle to have their important questions answered due to inflexible systems, challenges in obtaining and analysing large or complex datasets and lack of accessible analytical support.
- For critical decisions management may want independent analysis and review of key data to provide additional perspective or to confirm internal findings.
Our team has extensive experience of identifying solutions to challenging business problems and can support through:
- Point in time data analytics to support key decisions/projects or assess the impact of planned or expected business/regulatory change.
- Short-term support to bolster internal teams if resource is constrained at periods of significant change.
- Working with clients to implement and configure a structure, team and tools to provide the internal capability to enhance business intelligence provision on an ongoing basis including the use of selected self-service capabilities for users.
For UK gas and electricity utilities, differences in volume, allocated by industry settlement arrangements and volumes billed to customers (“imbalances”) can represent a significant cost to energy suppliers, with the financial impact greatly magnified by the significant price risks over the last few years. Whilst the resolution of some differences requires changes in market design many differences are preventable, correctable or avoidable.
Why is it an issue?
- It is a challenge to understand the make-up and recoverability of ‘unbilled’ volumes on balance sheet (where imbalance is typically masked) and differentiating between genuine unbilled versus unbillable volumes
- The availability of detailed settlement volume information, particularly for domestic customers, is limited
- There is a disconnect in operational processes between billing and settlement processes, which introduces a wide range of potential exceptions
- Internal processes often do not consider, or are even unaware of the imbalance impact. Internal ownership may be limited and imbalance resolution is typically an ad hoc, silo-based process rather than underpinned by controls in all relevant processes
- A range of restrictions exist for recovery/corrective action, many of which also have a significant lag between action and benefit. In an era of customers rapidly switching between suppliers, this benefit may flow to a competitor organisation.
- Bottom-up reconciliation of imbalance at meter point and bill level, comparing billed and (where available) unbilled volumes to recalculated industry settlement
- Quantification and ageing of unbilled volumes, distinguishing timing unbilled and genuine imbalance
- Identification of root causes for long-term remediation through process change or improvement
- Analysis can be run on a one-off or regular basis to provide management with ongoing quantification and exception identification
- We can also review existing management approaches to measuring imbalance and benchmark against our approach.
Data is at the heart of any modern business. High quality data is an asset that supports the business’ goals and objectives, whereas poor quality data leads to process failure, increased costs, higher risk, poor decision making and more. Data Quality is often at the core of broader business issues, yet many organisations have no clear definition of what constitutes ‘good’ data quality or any way of assessing and monitoring the quality of their data on an ongoing basis.
Why is it an issue?
- Data underpins almost every process and operation, and poor data quality can ultimately lead to high levels of manual intervention or off-system workarounds (including management by spreadsheet rather than using core systems)
- There is increasing focus across the E&R sector by regulators on the quality of data reported coupled with increasing requirements to report across a broader range of business operations due to new sustainability initiatives for example. Poor data quality can lead to regulatory sanctions which can cause direct loss due to fines, additional costs and burden due to subsequent regulatory focus as well as reputation damage
- The potential for competition within the water sector in England and Wales brings specific focus to this issue – variable data quality has long been recognised as a major issue in the water industry. The quality, reliability and integrity of data in the water industry is impaired by inconsistencies between multiple applications and disparate data sources
- Poor data quality can often be a barrier to realisation of benefits from new systems or processes, with data quality often not considered fully or at all within the implementation project and requiring significant rework and remediation activity downstream
- Another key aspect to data quality is the consistency of data across systems – without processes to measure and maintain this, important business intelligence can be missed or lost e.g. the ability to identify safety risks before they are realised by comparing information from across multiple embedded systems.
We can provide a range of services to support clients in this area including:
- Data quality assessment – using industry leading tools to quickly profile and assess business data, identifying areas of strength and weakness and making recommendations for remediation
- Tactical data quality eemediation projects – resolving data quality issues using business logic and rules to minimise manual cleansing efforts, leveraging information from across the organisation as well as relevant external databases if required (e.g. postcode matching, bereavement register)
- Providing advisory or implementation support for the creation of internal governance processes and technologies to provide ongoing monitoring and management of data quality.
Poor billing estimation accuracy by energy suppliers is a major source of dissatisfaction for consumers and an avoidable cost for the suppliers themselves, issued from Ofgem, consumer groups and the Energy Supply Ombudsman. It has also been the focus of commentary from both Ofgem and the Energy Supply Ombudsman. With the increased level of metered supply in the water sector this is likely to be an emerging problem for water utilities.
Whilst smart metering will eventually largely eliminate the use of estimation, it will remain a key tool for utilities for the next seven - eight years.
Why is it an issue?
- Delayed cash receipts while bills are queried by customers
- Costs of additional consumer contract and cancel/re-bill activities
- Costs of complaint handling and resolution
- Debt problems where long-term estimation has masked actual consumption levels
- Incorrect calculation of regular payment scheme levels such as direct debit increases debt risk.
- Incorrect revenue recognition for both billed and unbilled volumes.
- Measurement of current estimation performance through the large volume comparison of billing estimation compared to measured consumption
- Analysis of root causes of good and bad estimation including segmentation and clustering analysis
- Scenario analysis of potential changes to estimation, to identify improvements available and support a business case for change.