By Ron Chapek
As convenience stores continue to evolve to adapt to changing customer demands and infrastructure and facility requirements, operators are under increasing pressure to gain operational efficiencies. Of growing importance in this effort are the intelligent applications that allow operators to effectively use the data gathered by building management systems (BMS) and environmental monitoring systems (EMS).
There are a number of macro and technology trends impacting convenience stores; a few in particular are making it easier for operators to gather even more data and take advantage of building management system (BMS) insight. These include the proliferation of new dashboards and user interface tools, the explosion of new analytic tools and the growth of IP-enabled devices /the internet of things (IoT).
This shouldn’t surprise anyone. You can’t pick up an industry publication or go to an industry news website without reading something about big data, analytics and data-driven decision making. As consumers, we all have come to expect information on-demand, with our smart phones putting information at our fingertips. This expectation and ability is bleeding over into the business world, where we need real-time data to make important operational decisions.
In the execution of building these intelligent applications, the challenge is to effectively convert rapidly expanding and disparate data sources into visually insightful, prescriptive, actionable and value-adding graphical interfaces across multiple stakeholder departments with a diverse range of usage and persona types. This is one of the reasons why it is so important that you have the right application for your facility and your internal business processes.
Historically, the static application was very linear with high latency. Data was not real-time, it was gathered and delivered a week or even a month later. One example of this is utility bill data where you’re looking at data that is at least three months old and trying to determine an action to take that will save energy and lower costs.
In contrast, intelligent applications are now cloud-based, infinitely scalable, high-volume and near real-time. Rather than making decisions on historical data, you have the ability to make decisions and take actions faster on more current data. Therefore, you may be able to save time and money almost immediately, rather letting inefficiency remain in place for weeks as you wait for the historical data.
Before you can take advantage of these new intelligent applications, there are four building blocks you must first consider putting in place.
- Modern Data Architecture that delivers access to a wide variety of data at high velocity and scale. It supports data access for all applications whenever any data is needed, and it compliments existing data architecture by including a Data Lake and Hybrid Cloud strategies that can carry the heavy data load and deliver the information in a timely manner.
- Advanced Analytics, which is the science of using a wide variety of data to understand factors that impact customer experience. This is happening at two levels. First, at the machine level pushing analytics to the edge (what information can we put inside the machine to send vital data upstream). Second, big data downstream where we look at trends and the data to enable automated decisions and actions.
- Smart Devices that are all gathering data and sending it through the architecture. Operators need to expand the business by establishing an IoT business model with the right set of devices, connectivity, security and cloud adoption – all packaged in a deployable and profitable solution.
- Real-Time Business that is making decisions in real-time. This happens when data in motion from IoT devices is combined with other enterprise and cloud ambient data and injected with embedded machine learning to personalize customer engagement, improve field workforce impact and create new products and services.
At the end of the day, the intelligent application you put in place must meet some business need. Is it customer comfort? Fresher food? Customized experiences? Otherwise it is not a good use of your time and resources; and could actually negatively impact your operations. So before you take that first step, think about the business value. Only then will you be in a position to effectively use the data to increase operational efficiencies.
Ron Chapek is the director of product management, retail solutions for Emerson Commercial and Residential Solutions. For more information, visit Emerson.com