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1 The opportunity is also the challenge
At Bellrock Technology, we are fortunate to work with some of the world’s most forward-looking utilities. Many wish to use data analytics to improve support for engineers. Through discussion, they have shared their experiences with us. This has helped us to assess the opportunities and challenges involved.

Bellrock Technology recently spent time assessing power utilities’ needs for data analytics and their experiences of how these can improve decision support for engineers. This included discussion and exploration with leading utilities in Europe, the Far East and North America.

All of the companies wish to undertake digital transformation. They have all arrived at similar conclusions about this journey:

  • They can and should do more with their data. This will make a greater impact on their bottom line. For instance, they could reduce outages, move to predictive maintenance, maximise revenues and minimise operating costs. They could also secure the knowledge of key staff.
  • Enterprise-wide data management products and solutions do not deliver value immediately. The uncertainty of what they will provide makes them difficult to justify.
  • Most vendors sell products with lengthy installation times and large upfront costs. The use of analytics is not addressed until after this initial phase. Their value only follows much later, if at all.

Perhaps the greatest realisation is that traditional waterfall project management doesn’t work. Many vendors and consultants are advising that companies need to be more agile. But utilities’ infrastructures do not lend themselves to this. Outsourcing to consultants loses ownership. And partners with these skills and domain knowledge are hard to find.

We have learned how to give our partners the control and agility they need. We combine experience of building advanced decision support tools with patented technology. Our software lets partners build in stages. With each stage, they can add specific, justifiable value without significant upfront investment.

We want to share some of our learning in this white paper. It explains how we approach data analytics projects to deliver value early and ensure success.

Approaching Analytics with Agility

If you’re introducing data analytics support, you may find that the biggest challenge is getting started. How can you prove value before investing large amounts of time and money? We find that the nine principles below help to show benefits quickly and prioritise efforts. By following them, you can develop and evolve your digital strategy in stages. These will be more manageable and each will deliver measurable value.

2 Identify specific pains or opportunities
Define a problem or opportunity that improved use of data would solve or take advantage of. Predict the financial value of any potential improvements. Then plan a short, focused piece of work, otherwise known as a sprint, to deliver a basic but fully functional system. Use this to prove the concept. Test your prediction of the financial benefit by giving users access to the system and gathering their feedback.

3 Be quick and cost-effective
Be prepared to start small with opportunities that are well understood. Look for the low hanging fruit and quick wins. Work in timescales of weeks and months, not quarters and years. Run multiple sprints in parallel using different teams so that in a short space of time you gain an understanding of wide-scale opportunities.

4 Be flexible
When needed, break more complex problems down into a short series of sprints. Focus on the decision support that is needed first and build a prototype for user feedback. Then add analytics to interpret the data. Only integrate the data you need to prove the value of the concept you are testing. This can be scaled later.

5 Scale your wins
Once you have proven a concept, look to improve your results. Plan extra sprints to extend the data analysis and decision support capabilities and continue to gather feedback. Build your systems in stages to add specific value with each sprint. And make results operational as soon as possible to begin making returns on your investment. New features and capabilities can be added over time whilst systems are in use. If a concept can’t be proven, don’t spend further time and resource on it; try another.

6 Work with users
Your digital strategy is not about the IT department or innovation teams, it is about giving decision makers the ability to improve your bottom line. So your users should not be an afterthought. They must be involved. Don’t be scared to do this from day one. They are the customers for what you are building and their buy-in for change is needed. They also have the domain expertise to explain how data can be interpreted and analytics developed. Do not expect to succeed with big data techniques alone.

7 Deliver in a common way
Don’t overcomplicate things by writing a brand new system for every sprint. Be smart. Use platforms and advances in software technology to minimise the work you do each time. This keeps your speed of delivery up and your costs down. A common platform will be more likely to engage your users since they only need to learn one system. It also makes it easier to combine results later since everything is in one place.

8 Every analytic innovation makes a difference
Make use of expertise from all potential sources of data science – internal teams, third party developers, universities and any others. Make sure to choose a platform that can deliver analytics written in any modern data science language (e.g. MATLAB, Python, R, etc). And preferably choose one where these can be deployed without extensive IS and IT support. This will allow developers to use the tools they feel most comfortable with and ensure your sprints proceed at pace.

9 Partner with experts
You may have the skills and resource to deliver your digital strategy in-house. But if you look for external support make sure to choose an appropriate partner. Look for those with an understanding of industry challenges. They should have expertise in interpreting complex operational data and a track record of delivering useable decision support. You are likely to need a partner, not a consultant or standalone developer. Do not underestimate the importance of your partner’s domain knowledge.

10 Build your corporate digital strategy from the ground up
Before starting this process, set high-level goals for your corporate digital strategy. But do not try to define exactly how these will be achieved from the start. Keep your vision at the centre of each sprint but allow yourself the flexibility to try new ideas and approaches. By doing so, you will be able to adapt quickly to changes in markets, regulation and technology. This will greatly increase your chances of overall success.

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