Data has been described as the next gold rush for organizations in various industries, markets, and verticals. That sentiment can be interpreted in many different ways.
Externally, data can be used to better market to customers, keep track of their spending habits, understand their pain points, and gain insight into their purchasing decisions. Internally, data can be used to improve processes, cut through overhead, spur productivity, and make decisions.
It all comes down to business intelligence, which encompasses all of the things that leaders take into account in order to make decisions about their organizations. However, data has allowed leaders greater insight into the reality behind these decisions, where for many years these leaders relied on smaller data sets and gut feelings.
Digital transformation has become a favorite phrase in the lexicon of business strategy over the past several years. At the heart of digital transformation is data and analytics. Gathering data about various aspects of the organization, analyzing that data to find trends, and understanding those trends to create business intelligence to help make decisions.
How do technology managers realize the benefits of data and analytics? On the whole, by implementing sensors and software that gathers information, and utilizing an analytics platform to parse through that information to recognize trends. All of this is available to companies right now. However, different businesses and different environments will require different solutions. There is no one-size-fits-all solution to data and analytics. Instead, building an analytics strategy is about figuring out what’s possible and customizing it to your organization.
Let’s look at what’s possible, and what others are doing as part of their digital transformation strategies in terms of data and analytics.
The meeting space has become a great starting point for organizations implementing a strategy around data and analytics. Meeting rooms are isolated, relatively uniform, and a huge part of the productivity of an organization’s staff. It makes sense that organizations want to use data and analytics to streamline the meeting space as much as possible.
Room occupancy sensors can be installed to understand when and how many people participate in different meetings. This information can be gathered to provide an overhead view of how meeting spaces are being utilized in your office. Perhaps the meeting space on the third floor is seldom used. The meeting space on the second floor is used often, but only at 50% capacity. Huddle spaces on the other hand are being utilized throughout the day.
The specific technology within the meeting rooms can be connected to control platforms to gather data as well. How often are screens used? What about audio equipment? Are people actually utilizing the touch capabilities of the screen? What kind of devices are being brought into the room? How often are different products breaking? How much time does the IT team spend fixing these problems?
Air quality sensors also bring in information. Are meeting spaces too hot or too cold? Is there too much carbon monoxide in the room, making employees unhealthy?
You begin to make decisions. That third-floor space can be converted into huddle spaces. The second-floor space can be divided into two meeting rooms. People love the touchscreens, so we should bring in more of those. People also like to use their own devices, so we should enable them to easily connect to displays in the room. This product from this manufacturer breaks down too often – let’s not use them anymore.
The secondary benefits in meeting rooms are great as well. If you learn that most meeting rooms aren’t being utilized after 4:00, you can automate those rooms to shut down hours earlier than before. This saves on utility costs. If a room is too hot or too cold you can regulate that and make the office more comfortable for employees.
None of this information is possible to gather without data and analytics – unless you want to hire full time employees to sit in meeting rooms and write everything down.
Being in the pharmaceutical industry, data is extremely important in both ensuring that the yield is at maximum capacity (and therefore maximum profit), and that the process and yield meet strict FDA requirements. Analytics have allowed both companies to better monitor and better create manufactured products.
In 2016, TechDecisions was able to speak at length with Gloria Gadea-Lopez about how Shire has utilized Dell Statistica in its process. Gloria works as the Director of Manufacturing Systems in Internal manufacturing at a facility that manufactures only about 30 batches of its product per year, so every batch is extremely important to the overall profitability of the company. More importantly, these batches will be used to combat real life diseases, so the pressure to make sure that there are no anomalies and all of the batches are delivered is serious.
“My joke is, we don’t have big data, we have wide data,” says Robert Dimitri, Associate Director Manufacturing Systems at Shire. “Meaning we’re lucky if we manufacture 30 batches in a year. Our processes run for weeks and months. So we have to collect an enormous number of variables in order to monitor our process. We need to enable the end users on the data side with us much data as possible because that’s all we have. With the length of time that our processes run we have to be aware of what’s going on and able to react to anything that’s happening during the manufacturing process. Having that visibility is absolutely critical.”
There was once a time when scientists at the facility would hand-record data samples and enter them into spreadsheets for analysis. This took time, as data could only be obtained by checking sensors manually, and in many cases by manually taking samples and recording the data. By adding sensors and tying them into a data collection platform, Shire was able to save time and effort, and collect data sets at a much faster rate – days and weeks which used to take weeks and months.
Statistica then analyzes those data sets to provide information about how the manufacturing processes are performing. This analysis can happen for processes that are daily as well as those that take months to run their course. Data scientists within the manufacturing facility can then take the analysis and use the information to better run their processes. So, for example, analysis might recognize that yield is greatest when a batch has a certain pH level at a certain temperature. Scientists can then monitor the pH level and temperature to ensure greatest yield. And this happens in almost real-time, so that scientists don’t have to wait until the batch is finished to understand how well the batch will come out. They can monitor as it goes in order to create the best yield for every batch.
Statistica also allows Shire to scale and validate data. “Things happen in an environment of constant change. So maintaining compliance while operating in constant change is challenging,” says Gadea-Lopez. “So we need to have the right application that allows us to manage change. At the same time, continuing the expansion of these applications, either by onboarding new products and processes, or by expanding the use of the core application to new areas of use.
“One area that’s very interesting right now is that the facility in Lexington utilizes disposable technology. Traditionally, we make the product using stainless steel [reusable technology]. Single-use systems are a completely different way of manufacturing. We have another plant that makes the same product on the traditional stainless-steel technology. So we can monitor performance, get approval for the facility, and demonstrate parallels. That’s where these applications are priceless – we can monitor and use all of the data from the separate systems at the same time.”
This same process can be applied to any manufacturing facility – implement sensors, gather data in real time, and ensure that manufacturing is at peak performance.
Shipping and Receiving
The transportation industry has also seen huge benefits from implementing data and analytics in order to better track and prepare for shipping and receiving. This involves GPS sensors in vehicles moving products and materials around, and scanning those shipments as they arrive and depart.
This allows organizations real-time understanding of potential delays in shipments. When an accident causes a truck to sit still for two hours, the organization is aware of this delay. They can reach out to customers to warn them – some platforms even have the capability to automate this and let customers know about delays quickly so those customers can better prepare for a delayed shipment.
As this data is gathered, organizations can find patterns for better efficiency. Perhaps, with all of the miles racked up, it is better to switch to electric vehicles and save on gasoline. Maybe there are shipments that can be slightly delayed in order to place them on the same vehicle. Perhaps certain drivers aren’t performing as well as others. Maybe certain vehicles are breaking down more often than others.
Data and analytics gives you this insight before it becomes a problem. It offers real-time updates to shipping and receiving. The faster you get shipments where they are going, the more revenue you can generate. The more efficiently you can get shipments to where they are going, the more money you save.
Retail environments have also become great entryways into data and analytics strategies.
Again, people counting sensors can give you an idea of how many people are in stores and where they congregate. Facial recognition software can give you an idea of where people of specific genders or ages move throughout the store. This can help organizations better set up the store, to ensure the right products are in the right spots.
Inventory management is also a huge opportunity for data and analytics. The faster organizations understand what products are flying off the shelves, and what products are gathering dust, the faster they can update their strategies. Cancel a shipment of the underperforming products and use that money to purchase more of the best-selling product.
Data and analytics in a retail environment can help organizations better understand how customers shop – which allows organizations to better suit their stores toward customer habits.
Again, data and analytics aren’t one-size-fits-all. You’ll need to tailor your strategy toward your information.
The most important parts are the sensors that gather the data you need, and the analytics platform that digests that data and provides charts, graphs, and spreadsheets for your leaders to easily interpret.
Keep that in mind, and you’ll be well on the way to digital transformation.