How leaders in the energy sector are accelerating their data journey
How leaders in the energy sector are accelerating their data journey
While some leaders in the sector have started to reap the benefits of their data investments, others still struggle to catch up.
- Understand why you need a data strategy
- How are other companies driving profitability with data and technology?
- Factors that will influence the success of your data strategy
- How MNP can help
Data is the most valuable asset in every organization. It’s a tool that helps in making informed and confident decisions, improve business processes, identify problems, build solutions, track trends, and measure success. Those in the energy and utilities sector rely heavily on data for day-to-day operations such as forecasting demand, increasing power grid reliability, reducing outages, and improving customer experience and engagement.
With heavy data reliance comes the need for responsible data management. A comprehensive data strategy is required to effectively organize, manage, and protect your organization’s data assets.
What is a data strategy?
The sheer volume and variety of data that organizations capture and produce at increasing velocity every day make managing data a critical responsibility for leaders. Your ability to improve trust in data is critical to the organization’s overall success, and this can be accomplished with a data strategy.
A data strategy is a tool that ensures high-quality data is readily available to solve stakeholder problems and support organizational goals. The right data strategy enables all stakeholders and business functions to extract value from data regardless of location.
In the energy sector, where data is considered the “new oil”, your organization is more likely to outmaneuver the competition if it can deploy data effectively and efficiently. This starts by building a data-driven culture and actively promoting data literacy among internal stakeholders. Power generation companies, for example, routinely capture and process massive amounts of data using tools such as meters, operational systems, and geographic information systems (GIS). When managed properly, this data translates into insights that advance your organization’s strategic and operational goals in the following ways:
Increase customer focus, personalization, and centricity
Technological shifts are empowering customers to demand better experiences. Businesses must now be customer-centric if they want to increase revenue and gain a larger market share. Retail, healthcare, finance, food services, and other industries have cracked this code and are winning, and you can too.
Leverage analytics to gain insights into your customers’ behaviors and preferences, and then use your findings to create delightful experiences, products, and service offerings. Data-driven personalization efforts increase customer engagement, satisfaction, and retention.
Improve operational efficiency
Data analytics can assist you in monitoring your operations, predicting areas that require maintenance, detecting faults, and preventing system failures. Your organization becomes proactive if you can use machine learning algorithms to predict weather conditions, determine peak times, and prevent power outages. You can take it a step further by communicating the outcomes of your predictions to customers, thereby increasing their trust, and managing their expectations.
Manage supply chain
Big data has revolutionized many industries’ supply chain. Organizations that have harnessed available data sets for supply chain management have reduced risks and inventory costs, improved their bottom line, and gained a competitive edge. Your energy company can also maximize data to monitor logistics in real time, avoid costly disruptions, and balance demand and supply.
Accelerate the actualization of strategic goals
The energy sector is currently undergoing a disruption that is altering how energy is consumed, purchased, and stored. Climate change is real, and organizations have a collective mandate to make drastic changes that reduce the harm caused to the planet. This transformation mandate is driven by four factors called the ‘four Ds’ – Decarbonization, Decentralization, Digitalization, and Democratization.
Decarbonization
Slowing the global temperature increases means bringing greenhouse gas emissions (mainly carbon dioxide) to a net zero.
Decentralization
The industry is turning to distributed generation and storage in a decentralized grid-architecture for more efficient energy production.
Digitization
Increased demands for real-time data about energy production and consumption at various points in the grid must be accessible and reliable.
Deregulation
The governments of many countries have started to turn to the private sector and are deregulating their energy markets.
The ‘four Ds’ should ideally be part of your organization’s strategic goals and decarbonization should be your primary focus if you’re keen on slowing climate change. A great first step is switching to renewable energy sources and actively developing electrification initiatives to remove carbon from the planet.
Combining your organization’s wealth of data with technological advancements such as Artificial Intelligence and the Internet of Things is a catalyst for achieving your energy transition goals faster.
Case Study – How Data and Artificial Intelligence helped our client drive profitability
How to develop your data strategy
You can prioritize the development of a business-driven data strategy now that you're aware of data's potential to transform your operations. The goal is to make data work for you, so you must be willing to invest time and energy into developing a strategy that empowers your team and adds value to your organization.
The following are the main steps to take when developing your data strategy:
Identify your strategic goals
The first step in developing a resilient data strategy is to have a clear understanding of your organization's strategic goals. In an ideal world, your data strategy would be tightly aligned with and supportive of your business goals and objectives. Meet with senior leaders in the organization to get their buy-in and to clarify pressing business objectives or problems that can be achieved or solved using data.
This enables you to map out the lines of business where you want to focus your data-driven solutions and set realistic short and long-term goals. What is more important to your organization at this time? Streamlining business processes and cutting costs? Expanding customer base and increasing revenue? Building new products and broadening service offerings? Or mitigating cyber attacks?
You may be tempted to want to hastily deliver results and get validation from management but you’re more likely to succeed if you establish a link between data and key business objectives.
Assess your current situation
Get a comprehensive view of your data environment to avoid diving into seemingly appealing use cases, overpromising results, and running into risks. Examine your organization’s data management capability – what challenges do you face? What are the potential stumbling blocks in the data journey? Does a data culture truly exist? How do stakeholders perceive data? What are your organization’s strengths and weaknesses?
At this stage, you should also evaluate your organization’s data maturity and identify areas that require an upgrade. This includes data architecture that needs to be relieved or updated, team members who need to improve their expertise, and hiring gaps that need to be filled. Review and plan to meet industry-specific regulatory and compliance requirements.
Develop your roadmap
After you’ve completed the preliminary steps, you can create a blueprint that details the changes to be made and how they will impact business processes. HR, Marketing, Legal, Engineering, and other business functions must see how your strategy will improve their performance or make their lives easier in general.
At this stage, you can demonstrate your plan to them by predicting business outcomes with simple, hypothesis-led models. Check that the model is practical, inexpensive, and capable of producing results quickly. You’d also be opening up a channel to help you capture stakeholders’ feedback and recommendations.
A data governance framework that allows you to manage the quality, usability, compliance, and security of your data should also be part of your data strategy. It also allows you the opportunity to evolve your data in response to changes within and outside your organization.
Factors that enable the successful implementation of data strategy
Your data strategy is only considered successful if it works. Start by answering the question, “what does success mean to us?” then support your goals with the following:
Executive sponsorship
The acceptance of your data strategy and a new data culture across the organization is heavily reliant on an executive sponsor – a member of the C-suite. Without an executive sponsor who is supportive, understands the vision and in a position to remove internal barriers to adoption, you may struggle to gain the buy-in of individuals and teams who are instrumental to a successful implementation.
A business and IT partnership
To secure executive sponsorship for your data strategy, you must have developed a business case and presented it as a competitive advantage. It is no longer just a data project; it is critical to your organization’s business outcomes, so you should partner with business stakeholders. Also, you’ll need to work hand in hand with the IT team to implement the strategy as it will be built on existing technology infrastructure. Bring them on early in the project so they can provide valuable perspective in mapping your data journey. They will also be responsible for deploying technologies and handling complaints when they arise.
Achieve rapid, early success
Set short-term goals and focus on quick wins. This is an efficient method for quickly convincing management of the value of your data strategy and assuring them of a return on investment. It also allows you to quickly learn what works and what doesn’t, so you can iterate, pivot or scale depending on results and feedback.
What comes next?
When generating so much data, the path forward is not always clear, even within this general framework. This is why you should establish realistic success metrics to help focus on agreed-upon goals. Why is your data strategy important? What are the key performance indicators? Most organizations in the power sector prioritize operational efficiency, customer satisfaction, and supply chain visibility. Make sure your goals are specific, measurable, and realistic.
Collaborate with business and IT units and leverage their expertise to achieve your goals. Only if these teams embrace adoption will your strategy be successful. Create a cross-functional team and involve them in all stages of implementation.
You may also need the help of expert advisors to unlock the full potential of your data strategy, get a major return on investment, and secure management approval. Ready to get started? Our proven framework will position your energy company for data success.
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