Reduce operating costs, boost energy efficiency, achieve sustainability goals: The energy data of a building or enterprise holds the key. But in and of itself, even a massive amount of data does not represent meaningful information. This is why building technology needs big data methods – processes to meaningfully correlate large amounts of data, like those that are being used in finance and marketing. Powerful analysis tools for energy management provide the necessary data from which suitable measures are derived.

Finding patterns and structures in the flood of big data and the systematical use of its potential – that is how value is created from big data. Decision-makers in many industries are already applying appropriate methods for reaching forward-looking decisions on the basis of transparent and interrelated key performance indicators.
One current figure underscores the extent to which this idea is taking hold: In 2015 an estimated 4.4 million people were employed in big data jobs, 1.9 million of them in the USA. Today, algorithms developed by big data analysts help design even more customer-specific marketing strategies, hone forecasts in the financial sector and customize billing systems.

Big data in enterprises and buildings

Comparatively speaking, however, big data models are not as widely used in evaluating energy streams in enterprises and buildings. A Siemens-supported marketing survey published by American industry observer GreenBiz in 2015 substantiates this fact. Only 44 percent of large companies that typically manage more than 50 individual buildings indicated that they were already using big data solutions. Yet building energy management systems (BEMS) and building management systems (BMS) already supply a wealth of data that lends itself to
systematic utilization.
When used in buildings, big data solutions always have the same goal: extract relevant information from a usually vast store of existing data and make it available to the decision-makers in facility management and to the building’s users. As operating costs account for 71 percent of the total cost of building ownership, of which 30 percent are energy costs, inefficient operation can become costly very quickly. However, only a comprehensive selection process can reveal which of the many solutions and services available on the market makes the most sense. The right combination of analytics, dashboard and consulting services as well as end-to- end efficiency project experience can deliver up to 40 percent internal ROI on energy efficiency (vs. 10-15 percent on typical business investments). But start by avoiding three mistakes that are often repeated.

Mistake 1: “There is one universal solution.”

The idea that one solution can solve every problem in every organization and every building is an illusion. Solutions need to be adapted to the individual task, company and building and assessed based on specific requirements. 99 percent of those surveyed in the GreenBiz study cited reducing operating costs as the primary goal for implementing this type of data management. 94 percent expect better energy efficiency. Furthermore, they expect to achieve individual sustainability goals without restricting the comfort of building users. They hope to
improve the availability and extend the useful life of the building technology infrastructure, ensure business continuity and, last but not least, comply with legal and regulatory specifications. And on top of that, they want building operating costs to drop.
Which of these goals can actually be achieved with the help of big data greatly depends on the organization itself. Existing knowledge of energy issues and building systems as well as available financial and personnel resources play an important role. Another key requirement for successfully implementing big data methodologies in buildings is the merger of data silos from different systems into one central system that analyzes and consolidates data and converts it into information. Like any complex IT project, big data projects in facility management require the support of the IT and finance departments.

Mistake 2: “Technology is the be-all and end-all.“

Of course a sound technical foundation and tools are important. But it is just as important for certified experts to actively support the processes. Their experience allows them to correctly interpret various situations and to implement any necessary corrective measures in a timely manner during the planning stage. Currently there is still a lack of specific expertise in the building technology field when it comes to selecting appropriate analysis tools and implementing them successfully and cost-efficiently. 43 percent of the companies surveyed indicated that they would like to use big data methods for energy management in the future, but they had not yet implemented an energy management solution. The reasons? Lack of resources to implement and support the solution (67 percent) as well as the associated costs (33 percent).
Of course a worthwhile energy management solution is much more than the software investment for selecting and implementing a system. The benefits of the solution are not evident until the data is aggregated and interpreted in a meaningful way. Only then is information available that leads the way to more efficient energy management. Improvements can be made that contribute to long-term optimization – yet monitoring by experts and ongoing investments are still needed, as changes such as new regulations, updated company sustainability targets and building and infrastructure life cycles have to be taken into account .
Energy managers of large companies, hospitals, government and educational institutions, and executives in charge of energy management in these organizations, also face the challenge of administering the daily volume of data securely and cost- efficiently. A reliable and forward-looking big data solution for energy management can generally meet this requirement as well as add value on many levels.

Mistake 3: “Data equals information.”

While incomplete or otherwise erroneous data can lead to the wrong conclusions and actions, it is also important to perform ongoing validation on reliable data and to take any weaknesses identified into account in analyses and resulting recommendations.. Good analyses require a clear understanding of the quality and quantity of the available data. For the underlying data basis of their big data projects, most companies collect key performance indicators from utility bills (96 percent of the study respondents), meters (85 percent) and building automation systems (54 percent). To a lesser extent, other sources of data, such as weather, energy market and waste management data, are also included in the analyses.
Nonetheless, only 49 percent of companies are satisfied with the quantity and quality of their collected data, and 44 percent are explicitly dissatisfied. In fact, several prerequisites must be met to ensure the collected data can be used effectively. First, a company needs to identify the data required and second, determine how to generate and consolidate it. Additionally, the data collected needs to produce a comprehensive view and be detailed enough to point to measures for improvement. Especially the latter are of central importance in conjunction with the implementation of the EU Energy Efficiency Directive (EED) and the energy audits it requires (EN 16247) or alternatively the implementation of energy management systems (ISO 50001).

A cloud-based energy management platform

Against this backdrop, a comprehensive package is recommended that includes consulting services for planning and implementation, specific expertise in the analysis of building data as well as the appropriate software tools. The key to success is a dashboard, i.e. user friendly visualization software to display data streams in condensed and, typically, graphical form.
The cloud-based energy management platform Navigator, powered by Sinalytics, from Siemens is such a dashboard solution. This customizable, scalable and user-friendly software serves as a portal for such areas as supply management, system performance and compliance reporting. It connects assets, data, processes and people and helps to deliver superior outcomes and manage progress more efficiently. The technology offers a comprehensive, enterprise-wide view of energy and operating performance, both at a high level and in detail.
The platform is suitable for larger single buildings as well as for building complexes, complete properties and even virtual networks of hundreds or thousands of locations. It supports monitoring of building system performance, energy demand and energy supply while ensuring the highest degree of transparency. The platform collects and analyzes large quantities of building data with extreme precision, providing a basis for displaying, outputting and communicating detailed trends, reports and evaluations for utility bill management, CO2 reporting, etc.
The system lays the foundation for implementing a multitude of strategic goals, such as evaluating and optimizing investments in energy efficiency measures. At the same time, the prepared data structure provides an informed basis for key performance indicators (KPIs) and future-oriented decisions. In addition to cost effective operations, companies can also use an energy management dashboard such as Navigator to reach sustainability and compliance quality targets in a controlled fashion.