Using data analytics to improve efficiency and lower costs

Your business generates a lot of data from both internal and external sources. Unfortunately, most of this data is noise if it is not analysed into insights to inform your leadership decisions. Big Data Technologies, advanced analytical models and computational methods have helped many organisations uncover inefficiencies and internal bottlenecks to growth.

Take a case of Umeme, one of the key players in Uganda’s Electricity Supply Industry.

Any electricity transmission and distribution company across the continent, can save money, reduce risk, and improve the reliability of critical assets while reducing both technical and operational losses through leveraging on enterprise data that is generated by the minute.

Figure 1: Untapped enterprise data

As a critical production input resource, the price of electricity affects economic growth and household incomes. For that reason, the focus of the ESI players is to reduce the unit cost of electricity supply. But this calls for lower costs of generation, distribution, and last-mile connection to the final consumers.

Most electric utility companies face regulatory and public pressure to make overdue upgrades while holding down costs. In Uganda, Umeme’s Yaka rates to customers are said to be billed at a higher rate compared to the traditional electricity meters. Yet the Yaka innovation is supposed to reduce defaults, pre-finance Umeme and therefore reduce the interest rate expenses which Umeme would have needed to invest in operations and other working capital needs. In simple terms, Yaka customers would be subsidised as they deliver long term success to Umeme. But that is not what happens.

Umeme and any other utility provider must contend with financial constraints and regulatory requirements and thus must explore opportunities to cut costs through streamlining their processes and outsourcing support activities. An ongoing analysis of the business performance and specifically the financial impact of investment decisions must be made in real-time, so that right calls are made.

To improve decision making calls for the adoption of advanced technologies to remotely monitor business performance, highlight bottlenecks and predict suspicious behaviours across the enterprise.

A case for business analytics at a Utility company, like Umeme

UMEME can leverage from the opportunities of business analytics to make their asset-management programs more productive. Asset management in terms of repairs and maintenance accounts for about 30% of a utilities company’s operating expenses and a big part of its CAPEX. Analytics tools and robust predictive algorithms can be connected to real-time performance data to improve decision making.

For management and the board, advanced analytics would allow visibility and control over operational risks and asset-management practices and thus gain insights to have more constructive conversations with regulators and other strategic partners.

An analytics-powered approach to managing assets and operations would help bring down costs by more than 20%, improve customer satisfaction by more than 30% and increase the reliability of service at UMEME.

Experience vs analytics

Asset management has had its drawbacks, especially with management who have consistently found it much more difficult comparing the effectiveness of adopted measures and allocating resources efficiently.

For instance, a business unit responsible for power lines might use sophisticated measures of asset criticality and high voltage units to decide the timing of the maintenance and routine. In contrast, support systems and other high voltage units have no asset management mechanisms at all.

While most utility companies have opted to put engineers and maintenance works, rather than executives, in charge of planning asset management, it has caused one priority to override others, that is, maximising the availability of assets. Such an approach has been deemed wasteful, leading workers to decommission useful assets years before they manifest any problems. It has also obliged staff to perform inspections and maintenance procedures, even when assets are working well to fulfil performance targets.

Data Analytics help utility companies to retire the old model of relying on specialists to set maintenance schedules based on their experience in favour of a flexible. It is a rigorous approach that collects data from systems in real-time, feeds it into predictive models to guide asset-management decisions in real-time. As an asset manager, you want real-time visibility into your network to predict asset failure and potential impact of the failure on your network in terms of downtime hours.

Business Analytics enables companies to improve productivity in several ways;

  1. Lower costs through identifying day to day procedures that can improve useful asset life
  2. Increase reliability of asset service through planning additional maintenance work for assets that can disable UMEME’s networks if they fail.

Through robust analytical models, Summit Consulting limited has highlighted three essential elements of an asset management system powered by business analytics;

  1. Measures of asset health, indicating the probability that assets might fail
  2. Measures of asset criticality, gauging the importance of those assets to clients, employees, regulators, and other organisations that rely on them
  3. Asset management decision models combining these two elements with executives’ priorities to recommend a comprehensive plan for asset maintenance and replacement.

Improve operational efficiency and reduce fraud.


For operational excellence and fraud prevention, business intelligence and data analytics as a service could help detect non-technical losses including fraud detection, among others. By analysing all the Yaka meters or smart meter information, into the system, it is possible to monitor usage patterns and payments. It is possible to detect billing errors or frauds, consumption patterns among different people in different areas and the comparative pricing between pre-paid electricity (Yaka) and post-paid electricity patterns to inform pricing. This kind of analytics could save the electricity sector industry a fortunate by pointing to the potential end-user habits and possible losses due to billing or otherwise.


Copyright Summit Consulting Ltd, 2020. All rights reserved.

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