Qlik customers show the allure of ordinary data - beauty is where you find it

Bring together a bunch of people who love analyzing data, and it’s guaranteed that during a conversation, someone will say: “It’s not very sexy, but this data was [informative, valuable, insert adjective here…]”. I’d like to declare an amnesty on that caveat. And I’m not talking about vibrant dashboards or pretty widgets. Meaningful, connected data is GORGEOUS.

At Qlik's recent AI Reality Tour in London, my data-loving heart was in raptures as customers were on hand to talk about their data transformation experiences. Take the example of building supplier Aggregate Industries - a member of the Holcim Group, one of the largest global suppliers of construction materials. 

According to CIO Mike Gibbons, about four years ago, there was a distinct change in business vision to prioritize sustainability, with the goal of becoming the UK’s leading supplier of sustainable construction materials. Committed to playing a significant role in enabling the UK to transition to net zero, Aggregate Industries is the first company to be certificated to BES 6001, The Framework Standard for the Responsible Sourcing of Construction Products, developed by the BRE (Building Research Establishment). 

This required new capabilities in data management and AI, moving from legacy ERP systems to an architecture that could support new applications. As part of this modernization, Aggregate Industries has introduced a variety of systems, including Salesforce for CRM and Coupa for procurement, alongside homegrown digital solutions. This integrated approach has allowed the company to centralize data across departments and introduce efficiencies in high-stakes areas like logistics. Gibbons explained that Aggregate Industries has a high volume, low margin product that’s incredibly difficult to get into the marketplace, due to the complex demands in coordinating the distribution of time-sensitive materials like asphalt and ready-mix concrete.

One of the early hurdles in making these changes was the perception of data. Aggregate Industries initially struggled to treat data as an asset rather than a cost burden. This issue was compounded by a lack of ownership over data quality. The company responded by moving data ownership away from IT and BI departments, placing it with the business units that directly benefit from it. It also took steps to create accountability for data quality. As a result, data responsibility is now part of job descriptions in order to help maintain data standards, and Aggregate Industries is actively collaborating with HR to solidify this structure. 

Transforming logistics

Aggregate Industries has started to use AI applications, with an emphasis on logistics — a key area for reducing both costs and emissions. The London market, where the company operates under the London Concrete brand, presents logistical challenges due to high traffic and urban constraints. Aggregate Industries’s AI-empowered app provides real-time data and intelligent suggestions for truck scheduling, delivery routes, and estimated arrival times.

Before this app, dispatchers managed logistics manually. The shift to automated suggestions was initially met with skepticism, especially among experienced dispatchers. Gibbons recalled: 

That didn’t go down brilliantly. But we overcame that by having them inside the design team. By getting the dispatchers involved, we created something that actually we didn't need to train anybody to use. We created something for the customer, something for the audience, something for the dispatchers. And because it was so simple to use, because the users had already been embedded in it, the orders actually increased because the customers found it so easy to order. 

The company is now in a position to expand similar logistics applications to its US operations, where the need for scalable, customer-centric logistics solutions is just as important.

AI for maintenance

While logistics has yielded immediate results, Aggregate Industries is also using AI in less visible areas, such as the maintenance of cement production facilities. Cement kilns operate continuously, and unexpected breakdowns can result in costly downtime and carbon emissions. The company has introduced an AI-powered, internal chatbot for maintenance support, enabling operators to troubleshoot issues and receive instant guidance.

The chatbot serves as an accessible knowledge base for plant operators, particularly useful during night shifts when expertise may be limited. Gibbons noted:

If a light went up on the batch panel, they can use the chatbot to suggest what to do next without referring to an out-of-date manual.

This tool enhances and contributes to both economic and environmental goals by reducing downtime and optimizing kiln performance. Reflecting on this experience, how has Gibbons been able to quantify business value from AI so far? 

It's not particularly sexy when you start talking about batch balance on cement logistics, but getting our product to market is the biggest cost we face. Just in the UK, I think we spend something like £1.3 billion in getting our product to market. So if you just took a couple of percentage points off it, you're starting to put something serious on the bottom line. Not only has it reduced the cost of our product delivery, it's increased revenue.

Proactive data strategies in social housing

Another example of the beauty of data analytics being used to achieve meaningful transformation came from Onward Homes. Chris Radford, Analytics and Insight Manager, alongside data analyst David Parkforth, shared the company’s journey from a fragmented, reactive data culture to an empowered, predictive approach. Onward Homes, which manages 36,000 properties in Northwest England had the goal of using data intelligence to improve customer satisfaction and operational efficiency. In the words of Radford:

We were very reactive. Data was in silos. Everything was Excel-based… it just didn’t work for us in terms of where we wanted to be around genuine database decision-making.

The shift to Qlik as their business intelligence platform in 2019 enabled Onward Homes to move to a cohesive, predictive system. This change was no small feat, especially in a heavily regulated industry with specific compliance demands.

Radford shared two examples of predictive models the company has implemented: a tenant churn predictor and a complaint model. The tenant churn model estimates the likelihood of customers leaving based on demographic and behavioral data, while the complaint model incorporates data from multiple sources including customer service interactions and repair data to forecast potential customer dissatisfaction. He explained that a spike in inbound contact could be indicative that a complaint is on its way:

Imagine if we could detect if a customer is angry before they even know it.

By using the predictive models to tackle practical issues related to property management, such as damp and mold, and identifying areas at high risk, the company can prioritize preventative maintenance and reduce the likelihood of future repairs. Radford described a custom algorithm he developed to classify comments as positive, neutral, or negative based on keywords. He tailored this specifically for the social housing context, including industry-specific terms such as “asbestos” and “ombudsman,” alongside more general terms indicating dissatisfaction. This has contributed to a 13% increase in customer satisfaction over the past three years. Radford elaborated:

It gives our customer service people conversation starters. They can approach a customer and say, ‘We think you might have an issue. Can we help you before it becomes a complaint?’ Colleagues feel that they’re making a difference - they’re able to engage in a way that is making a genuine difference for customers.

My take

Both of these customers talked with genuine feeling about data and AI during Qlik's event, and without hype. The value of data lies in its potential to drive change, inform decisions, or solve problems, not in how "exciting" it appears on the surface. The true worth of data is in its utility, not its superficial appeal. Often, it's the most routine, consistently collected data that leads to the most significant real-world impacts. The key is in recognizing the potential within the data and applying it creatively to solve problems or improve processes. Beauty's where you find it.