Although still in its infancy, big data analytics is already impacting on the resources of many early adopters. Sean McGowan explores this further with Scott Horsnell, team leader with BUENO, Airmaster’s analytics partner.
What has changed in the big data space since we last visited the topic last Year?
The big data space has continued to progress at an increasingly rapid pace since 2015, enabling its growth across the breadth of building systems beyond Building Management Systems (BMS) into areas that were almost purely theoretical a few years ago.
Root cause analytics, machine learning and data-driven maintenance based methods have evolved from being bleeding-edge features to now being incorporated into various business-as-usual type of applications and are capable of providing earlier and much more effective and valuable outputs than ever anticipated in the past.
What does big data analytics look like today? How would you describe it, and its influence?
Within the building services market, there is a growing awareness and push for building system data collection, but often without a clearly defined purpose.
This endemic problem, as we have observed, leads to each building generating thousands of data points every second that are going nowhere and achieving nothing. Through big data analytics, we believe these data points can be used to tell us precisely when a sensor is broken, if ductwork is unbalanced or if there is an opportunity for efficiency tuning. Through a data analytics led approach we are confident that genuine actionable information will be created from day one and will continue to provide insights at the frequency, resolution or accuracy that is not possible with typical manual point in time reviews of these building systems.
Where it has really evolved is in the applications of the technology. Rather than analytics historically being considered as a “bolt on” to building operations, more and more we are seeing analytics being adopted into the core way that the building operations function is delivered.
How is it transforming the industry, and is this consistent with predictions?
The transparency that analytics is bringing into an industry previously cloaked under proprietary systems has been a significant transformation. The value it is unlocking is gaining more traction with clients who no longer have the desire to have companies that provide and maintain these control platforms being the only ones that can report on the performance of them.
Who is adopting big data analytics and why? Is it more likely to be found in some sectors than others?
We have customers from diverse range of property types from petrol stations, supermarkets all the way through to some the largest retail, entertainment and commercial portfolios in Australia.
The drivers are generally common to any of our clients, and include:
Leveraging portfolio scale
Working with existing platforms
Maximising the value of a defects liability period on projects or new developments
Easing reporting burdens
Delivering performance transparency
Producing OpEx savings
Ensuring correct CapEx focus
Delivering system optimisations
Maximising labour productivity
Measuring success across energy, comfort and experience for all types of buildings and systems.
As has been the case in the past, customers that are obliged to meet certain performance outcomes through either corporate commitments or via programs such as NABERS are often the most receptive to data analytics.
What are the challenges that big data analytics has created in respect to people and resources?
With any new system or change in business-as-usual practices, the hardest part is culture change and the engagement with clients and technicians completing the work needs to be inclusive to find any traction. If analytics are the only part of the solution you will not be able to close items off and make improvements. Analytics partnered with engaging and intuitive tools to raise, track and measure any action taken is a requirement for success.
I would also say that big data analytics is creating opportunities, particularly in many sectors that have or are being increasingly affected by an aging workforce and experiencing a drain on technical expertise, experience and knowledge. Big data analytics is enabling people to perform at high levels of performance without the need for extensive experience or technical expertise in many ways.
How often is a large amount of actionable data found, but there are not the resources or time to action it and make it valuable?
The thing to remember about analytics is that they provide information, and that the information is of no value unless action is taken from it.
Analytics can identify hundreds of faults or optimisation opportunities at any given building, but if the tools aren’t used prudently then the humans delivering the interventions can easily be overwhelmed. Identifying a million issues and fixing none is less valuable than identifying one issue and closing it out. So having a good handle on the operational workflow for a site/portfolio/business and integrating your tools into this workflow is much more important than the latest and greatest machine learning algorithm that you can implement into your platform.
We have adopted various techniques from other management frameworks like Agile (a software development philosophy) and do things like managing the active “work in progress” for a site.
How do users or practitioners of big data analytics prioritise the actionable data available to them, to ensure they can realise its value?
In practice, this means that you have to be ruthless in how you prioritise the information that you deliver to the technical resources managing the services.
We manage this by using an approach that prioritises the opportunities based on criticality and value, and work closely with our customers to ensure that their resource planning aligns with their current and future state requirements.
We look at the typical hard services touch points within the operational workflow and focus on getting more value out of each “touch” on an asset rather than bombarding and bamboozling the technicians and operations managers with the technology that sits behind the back end of our platform.
We have developed an interface we call ‘Bonfire’ which provides a dynamic triaged list of issues to our customers (and any interested party) and the ability to review and program the action to them. The Bonfire system assists with creating a seamless interface between BUENO’s analytics platform and end users. This approach ensures transparency and ease to monitor and measure issues as they progress through the workflow, access is available through any mobile device and via standard internet browsers.
What lessons can be learned in this regard?
The days of static reporting on a monthly basis are dwindling if not over. Customers expect high quality and reliable information at all times, as this enables them in many ways to move from preventative maintenance programs into the data driven space. Data driven maintenance provides significant benefits in areas such as lifecycle, cost and productivity over traditional approaches. The fastest way to a return on investment is to use the analytics platforms to supercharge how operators are doing their business-as-usual right now, rather than trying to force the cultural change required to fully leverage the new technologies.
This is an excerpt from the article ‘We have the data. Now what?’ that appeared in AIRAH’s Ecolibrium. To further explore big data analytics and its impact download the full article here.