By Alec Gardner, Teradata ANZ general manager
Smart meters, smart grids and the wider emergence of the Internet of Things (IoT) means the utilities industry must now manage unprecedented levels of data. Will it be overwhelmed, or will it find the true value in this new resource, as other industries have?
IDC’s April 2015 Market Forecast for 2020 for IoT in Asia Pacific highlighted the utilities industry as second in investing in IoT and smart grid as the leading-use case.
It’s not just smart meters. Modern network devices; retrofitted sensors; video and text logs; and multichannel customer interactions have resulted in a deluge of unstructured and structured data. Gaining a single, unified view of data that is highly-accessible is vital for any businesses in the energy sector looking to deliver business advantages from these new big data sets.
The potential in big data
Data and the insight that can be gained from analysis is dramatically transforming how the utilities industry operates. Opportunities include: analysis of advanced metering infrastructure (AMI) data for customer insights, but also as additional network telemetry; integration of customer data from all sources, from billing to correspondence to outages to social media; enhanced human resource management; and cross-functional integration of geospatial, asset, weather, demographic and other data to provide insight to network planning, maintenance regimes and regulatory reporting.
Surge of data creates challenges for the utilities sector
Organisations can, however, be overwhelmed by the amount of data coming in and will struggle to achieve these benefits.
As such, utilities companies are not as ready to exploit the opportunities as other industries. According to a Teradata survey of more than 2000 IT and operational professionals working in utilities, the surge in data has created several roadblocks including:
• too much volume for IT departments
to manage;
• time constraints that increase difficulty for business analysts;
• lack of skilled resources to extract insights from data;
• limited processing capabilities; and
• information bottlenecks caused by poor integration, high complexity and performance issues.
A unified view of data, supported by a strategic data architecture across departments can be a key enabler to remove these roadblocks to make data useful and extract meaningful insights through analytics. Managers and users will then be able to take immediate action to achieve critical outcomes in lines of business as diverse as operations, planning, people and financial management and customer interaction.
Analytic solutions might be in the form of new apps for a specific purpose such as early identification of future network failures or for fraud detection; or new interactive reports for the CFO and the financial team on unit costing or operational efficiency; or wider user access to sophisticated discovery analytics previously only available to highly skilled data scientists.
The possibilities are endless.
Five key ways utilities companies can make big data work for them
While the hype and confusion around big data can be distracting, the way companies think strategically about managing data should be no different to how the energy sector thinks strategically about asset management, human resources or customer engagement.
There are five key ways utilities companies can make big data work for them:
1. Focus on key priorities. It can be difficult to choose a few key focus areas for analysis when presented with the opportunity to tap into an overwhelming mine of information. Distracting opportunities can arise, such as a department-specific problem or a manager who needs a particular report, but it is important to stick to the primary goals of the company’s big data strategy and not get side-tracked from the overarching corporate objectives. For businesses to get the most from big data, it is vital that they focus on the strategic priorities of the business as a whole.
2. Consider technology requirements. Rather than considering preferred vendors and new technologies, organisations should keep the focus on what functionality they need from any new tools. Things like scalability, flexibility and a price appropriate to the criticality of the analysis should be the primary concerns. Simply storing huge amounts of data requires a different cost model to creating a system of record for business-critical asset analytics or regulatory submissions.
3. Follow best practices. Ensuring the way in which data is sourced and integrated is aligned with industry best practices and strategic business needs is the best approach to managing risk and promoting successful outcomes. Following best practice guidelines helps organisation integrate across existing silos and meet their strategic requirements.
4. Follow a roadmap. Analytics should become an ongoing part of the lifeblood of the business, rather than a one-off project, so it is important to clearly map out what the corporate view of the future looks like. By choosing partners who have a track record of delivering successful analytics projects, companies can avoid common pitfalls and obstacles, saving them time and unnecessary costs. Having a roadmap also enables organisations to start small, deliver value in a matter of weeks and grow their capability in line with a roadmap that ensures continued, rapid value realisation.
5. Act on the benefits and measure the results. Analytics are only useful if organisations make decisions and changes based on the insights they reveal. To really understand how and where data analytics have helped, the organisation should be disciplined in measuring the results. New return on investment, operational improvements and better regulatory decisions can all demonstrate the value of data analytics and help set the roadmap for future analytics projects.
It is clear data is transforming the energy and utilities sector. Businesses must ensure they don’t get lost in the surge of data but rather reap the benefits that can be achieved to stay ahead of competitors and provide the best service to customers.
