Becoming an Active Intelligence Organization

Business intelligence and analytics, an area I’ve been researching and consulting on for several decades, has always had several frustrating elements. First, it’s been too hard to use. Only a small percentage of most companies’ employees know how to find, analyze, and interpret data. And, because it requires specialized skills, we have ended up with centralized business intelligence and analytics groups that are often unresponsive to business users.

Another problem is that preparing data for analysis has always taken too long and required too much human intervention. In most firms at least 80% of analyst and data scientist effort goes into data manipulation when it should be going into insight generation. An additional issue has been that making data-driven decisions and acting on them are voluntary acts by managers. Many executives may not employ analytics, even when they are readily available, simply because of their habits or preferences for intuitive decisions.

I am pleased to report that there is, at last, a new way of working with data that holds the solution to these time-honored constraints: Active Intelligence. Qlik is helping organizations democratize data and gain in-the-moment awareness about every aspect of their business. Suddenly, in a digital economy where agility is essential, data has become an asset that can be freed, accessed, understood, and acted upon throughout a business — not just to support it but to transform it.

A great example is Greene Tweed & Co., a privately held manufacturing firm that is making extensive use of data extraction and automation capabilities to help it achieve Active Intelligence. The company makes high-performance thermoplastics, composites, and engineered materials for applications that demand high reliability, such as brake seals for commercial aircraft. In such businesses, high quality data and high quality products go hand in hand, so the company has recently embarked on a data-driven transformation.

Greene Tweed has built an analytics data pipeline with Qlik to extract data from SAP and Internet of Things (IoT) devices, then store it in the cloud, enabling analysis of important product quality data in real time. It has also adopted predictive analytics to forecast demand and revenue. It is achieving predictive analytics forecasts for up to 90 days with more than 95% accuracy, and is now exploring predictive maintenance algorithms for factory machines. David Hufnagle, Greene Tweed’s Manager of Enterprise Data and Analytics, says the company’s data latency has been dramatically reduced, and the culture and behaviors of employees are rapidly becoming more data driven.

Greene Tweed achieved predictive analytics forecasts for up to 90 days with more than 95% accuracy.

CSC ServiceWorks is also undergoing a digital transformation. The company, which was established in 1946, is the largest provider of common laundry services in the US and owns more than a million laundry devices. CSC’s business, once standalone and analog, is now highly interconnected and digital. Brad Paine, CSC’s Chief Digital Officer, says the company has a holistic approach to integrated data and datasets: “We run data across the entire enterprise, integrating IoT data with transactional systems – our customer data, customer service tools, and service operations.”

Qlik is at the core of a wide variety of analytics use cases at CSC that have transformed the business. Data on machine collections is used to optimize the routing of collectors, and data on machine failures is used to route and manage service technicians. This has enabled the company to eliminate 15% of service truck maintenance visits because they were not needed, saving between $2 and $3 million a year. Decisions about where to invest capital in new laundry machines are optimized with data, and CSC can now perform real-time revenue analysis from its connected machines, when there was previously a 30-day lag. The company can also see consumer analytics and engagement data, such as users per machine, users per property, and average spend per user.

“We run data across the entire enterprise, integrating IoT data with transactional systems, saving between $2 and $3 million a year.”
-Brad Paine, Chief Digital Officer, CSC

CSC is even experimenting with automated decision-making. One application involves dynamic pricing decisions for laundry services based on water temperature, size of load, cost of electricity by time of day, etc. That type of data-driven, real-time decision couldn’t possibly be made quickly enough by humans. I expect to see many more automated decisions within companies in the future, particularly repetitive, tactical decisions like pricing. In almost every case I have observed of automated and dynamic pricing decisions in companies, margins have improved substantially.

Both CSC and Greene Tweed, as well as many other companies, are increasingly using data integration and analytics tools together to empower ‘citizen data scientists’ in their businesses. Across its operations and engineering teams, Greene Tweed has a motivated and capable group using Qlik. CSC had, until recently, the classic centralized team that created standard reports, as Paine notes: “You had to submit a request, then get back a report weeks or months later. It wasn’t dynamic, and if you wanted to further analyze the data you had to load it into Excel.” That slow and unresponsive process won’t work in CSC’s current culture, says Paine: “We want teams to find new insights to change the business, and a culture of people who explore the data and act on the insights. We can’t be constrained by a central group.”

It would be difficult to overestimate the impact that real-time data from the IoT and connected machines – and the ability to easily store and analyze that data – are having on business. Active Intelligence is much more than a technology change, it is driving operational transformation and changing business models in companies like Greene Tweed, CSC, and many others I have encountered. Companies are adopting new technologies and changing their operations and cultures simultaneously to get real-time data insights. The cycle time of businesses is faster; repetitive decisions are being automated and improved; and business users are controlling their own destinies. Companies and their employees are now truly moving into a new data-driven world.