Widespread digital transformation in the utilities sector has significantly increased the amount of operational and transactional data available to organizations. However, utilities often struggle to create value by taking advantage of data gathered from the increasing number of connected devices and IoT sensor technologies deployed in the field. Data management has been challenging for utilities that are unable to effectively store, categorize, and analyze information from deployed assets such as smart meters. —46% of business leaders believe they do not have the technology in place to utilize operational data for decision making.

This is due in part, to a lack of understanding of how data is stored and used. Data hubs, data lakes, and data warehouses are terms often used interchangeably despite their unique natures. Here is an explanation of how these types of data storage differ from one another and how your utility can integrate various data storage options to create a comprehensive and cohesive data management solution.

What are data hubs, data lakes, and data warehouses?

Data hubs, data lakes, and data warehouses each have unique characteristics and are optimized to store specific types of data. As information is converted from unstructured raw data into structured data, it moves between different storage facilities. This is why a combination of data storage types is critical for a cohesive data management strategy.

Data lakes store raw data. They act as a starting point for all data generated by a business, regardless of state or relevance. Data lakes often exist individually within operational silos and contain information that can be difficult to integrate in its unstructured form. This data is then organized and structured before being moved into data warehouses.

Data warehouses store information that is already processed and has been given a specific reason for existing in its processed form.

Data hubs are effectively integrated data lakes. Data hubs are extensions of data lakes. They have an additional integration layer in the form of an iPaaS (integration Platform as a Service) solution. The iPaaS allows several distributed data lakes to be combined creating a data mesh. Structured and unstructured data streams derived from the data lakes can then be analyzed to provide domain-specific real-time insights. They can also be combined to create an aggregate view of the domain business.

How can you choose the right data integration method for your business needs?

For utilities, data integration is critical to offering teams operational insights and a holistic view to help reduce operational costs and improve efficiency and customer services. Top performing organizations report extremely high levels of data integration as an enabler for data driven decision making and personalized services. Utilities often use one of two main data integration techniques, ETL (extract, transfer, and load) and iPaaS (integration Platform as a Service). Understanding the differences between these integration methods can help you choose the technique that best fits your unique business needs.

The difference between ETL and iPaaS

ETL

ETL is a method of data integration that has been around for decades and is a favorite of business leaders who must accommodate and integrate data from legacy systems. This method comprises three main steps that can be automated using a software-based ETL tool.

• Extract: Collecting first and third-party data from the original information sources

• Transform: Converting unstructured data into a standardized format that is optimized for storage in data warehouses

• Load: Transferring processed data into data warehouses for analysis and storage

ETL offers business leaders a much more efficient way to integrate data without spending too many resources on manual integration. Processes to store and analyze data within enterprise data warehouses can be automated to generate relevant insights on a regular basis. The prevalence of this method of integration makes running ETL tools predictable for business leaders who must consider complex data laws and changing regulatory requirements.

Despite all the benefits of ETL tools, there are important drawbacks that modern utilities must be aware of. First, ETL tools are unable to integrate data in real time. Since the ETL method moves data in batches, often during predetermined time periods, real-time data generated by intelligent smart meters is often wasted. Additionally, ETL processes are only able to integrate data and not the applications that generate the data. With smart meters and IoT devices often coming with proprietary applications and software that have to be integrated with complex technology stacks, ETL tools may be of limited use to modern utilities.

iPaaS

Utilities operating in 2021 have adopted a wide range of connected technologies that improve their ability to deliver a consistent and reliable service. For these organizations, particularly those thatdo not already have ETL tools, iPaaS offers an advanced integration technique that is more suited for cloud-native or hybrid cloud environments. iPaaS offers utilities real-time integration of information across the enterprise. This is especially important for utility providers that already generate and analyze real-time data using AI and advanced analytics. Utilities that require multiple endpoints to be integrated in a single map can also reliably use iPaaS to integrate data from various proprietary systems. This advanced method of integration is also able to run on the cloud, therefore requiring less from enterprise servers that businesses might already own.

Utilihive from Greenbird is a cloud-native, big data integration platform, purpose-built for the digital data driven utility. It is offered as a managed service in the cloud, hybrid cloud, and on-premise deployments. Getting started with Utilihive is straightforward with minimum disruption . Ultimately, advanced and cohesive data storage systems can only be effective when data is allowed to flow seamlessly between integrated connected technologies, smart meters, and storage facilities. With preconfigured Utilihive Connectors, utilities can implement an architecture fit for the future and effective in managing the tsunami of data the modern utility now has to deal with.

Related insights

Our CEO Thorsten Heller explains the main difference between the concepts and shares why the combination of different data organization & processing methods will help you get the most out of data.

About Greenbird:

Greenbird is an international solution and technology company with roots in Norway. We simplify the complexity of Big Data Integration to help organizations unlock the value of their data and mission critical applications. Our flagship innovation, Utilihive, is a cloud-native platform combining enterprise integration capabilities with a data lake optimized for energy use cases. We founded Greenbird in 2010 with a mission to revolutionize how the energy industry thinks about enterprise system integration. Today, Utilihive is used by utilities across Europe, the Middle East, and Asia — serving more than 50 million consumers.

Greenbird is headquartered in Oslo, with approximately 50 employees, comprising primarily senior developers and consultants specializing in technology development and customer onboarding of the Utilihive platform.