Sharing data between companies is a crucial consideration in the owner-led digital transformation model.
Data processing is important for a company's growth and success in today's business world, but realistically, it is also costly and time consuming. Converting raw data to useful analytics requires investment in technology and personnel, such as hiring and training capable employees. Additionally, as media sources are plagued with headlines featuring data breaches, it is more important than ever for organizations to collect and analyze data in a responsible, secure manner. Despite the challenges and risks, the benefits to "data sharing" outweigh the risks.
Why should utility companies share data?
When data sharing is adopted by a whole industry, like utilities, all stakeholders can benefit.
Individual pieces of data are irrelevant if they cannot be compared and contrasted with other data points, which is why some organizations have adopted the practice of data sharing. Whole industries have also adopted this practice and especially those with high barriers for entry.
For example, while e-commerce businesses have little incentive to share statistics and predictive analytics with competitors, they do share data across business units and subsidiaries. Utilities should view data sharing as an opportunity. Utility companies tend to be regional and dominant in their respective regions, naturally resulting in less competition. Additionally, the expensive infrastructure and regulations in place make it difficult for new organizations to enter the market, regardless if insightful data analyses are shared.
When data sharing is adopted by a whole industry, like utilities, all stakeholders – customers, companies, and the environment – can benefit. Here's how:
- The greater the input, the greater the output – With more collaboration, each organization benefits by having access to more data points. With more data points, advanced predictive analytics become more accurate. Consider the difference between reviewing the data generated by a single turbine versus all turbines in a region, country, or the world. The more data that is aggregated allows for both individual analysis and a broader view of overall performance, uptime, maintenance needs, and a range of measurements in which equipment operates best.
- Greater efficiency – With more data points and quality predictive analytics, utility companies may be able to better predict infrastructure and equipment failure, meaning they can proactively schedule the maintenance or replacement of the necessary components. Overall, this results in smoother and more efficient operations – fewer unplanned outages, reducing operating costs, eliminating the need to purchase power at market price, and improving customer satisfaction.
- Personalized recommendations – Collaboration between organizations can also come in the form of recommendations. For example, how to better manage relationships with customers and employees, choose the right equipment manufacturer for your site, and prepare for inclement weather. For example, a utility company operating in a wetter climate may be able to offer advice to any region suddenly experiencing significant rainfall.
- Customer satisfaction – Utility companies ultimately function to serve and satisfy their customers and data sharing done well will result in a more positive customer experience. With greater efficiency, there is opportunity for fewer unplanned outages, lowering rates, and less wasted energy.
The power of cooperation
Similar organizations that can share information, without directly competing with one another, have a major advantage in terms of conducting more thorough and informative analyses. Data sharing may also be anonymous, enabling organizations to compare operational data without revealing the asset owner. Sapere encourages harnessing the power of your data through active ownership, and we strongly believe that data sharing in certain industries is the next step forward – benefiting all participating parties, reducing overall cost, and producing greater value with a larger available data set.