Ever-Growing Intelligence for an Ever-Growing Business
With so much data available, businesses can pick and choose just about any metric to measure and guide their decision-making process. But at Dixon Technologies, we don’t want to pick and choose just any data to steer our ship; we want to be efficient and targeted in how we use data to inform our activities. Thankfully, technology has made this task so much easier.
I've been in the business of managing data for a very long time, most often working as a digital transformation leader. I have a lot of experience handling data visualizations, sifting through real-time analytics, and fine-tuning data-informed processes that help me deliver poignant insights to decision makers.
That's what I do in my current role as a transformation specialist with Dixon Technologies, which has been a leader in the electronic manufacturing services industry since 1993. We serve original equipment manufacturers and original design manufacturers with manufacturing and design solutions, and specialize in products like home appliances, televisions, and security devices. We have more than 1,900 employees spread across our 18 manufacturing facilities, and we're only projected to grow over the next few years.
I was hired to mature Dixon's data insights and help streamline the analytics process for management leaders and executives. Although our initial process had good intentions, it wasn't sustainable for such a fast-growing organization. It wasn't long before we realized we needed a new analytics platform to span the gap—and fast.
Setting Goals to Overcome Data Silos
The situation at Dixon was complex: while we had a lot of data sources, none of them were centralized or visualized for leadership teams. Key departments like Finance, Production, and Supply Chain were decentralized from the rest of the organization.
Because of this siloed system, each department needed to have multiple reporting formats and techniques depending on their niche. This meant our respective teams had to work on collating data manually for every reporting period. Our management teams were bombarded once per month with dozens of different reports, and without a consolidated view from all eight business units, the data was too dispersed to be effective.
Dispersion was only one of the challenges caused by Dixon's current infrastructure. The company's initial reporting systems also required people to manually enter information, which was extremely susceptible to human error (and stressful for employees). It was challenging to derive any insights from the data or make any informed decisions. We needed a more efficient model to generate more ideas and insights, and I began to examine some different models.
Our management team set some lofty goals for what they needed in a new platform:
- The ability to uncover a single source of truth. We shouldn’t have to toggle between multiple platforms or sort through various sources to connect the dots and form a cohesive story.
- Better visualizations and formatting. All reports should appear in the same uniform format so executives could easily decipher information and make data-informed decisions.
- Reports should appear in the right place at the right time. We need real-time data consistently delivered to a centralized location so everyone can access it, and we can strike at opportunities while the iron is hot.
We wanted to turn data into fuel for our organization, and a major propellant of better and more efficient ideas for leadership teams. And if we wanted best-in-class fuel, we would need to find a best-in-class platform to power it.
Picking the Cream of the Crop for Digital Transformation
Not just any analytics platform would meet our growing needs, because we also needed to find a specific tool with native SAP integrations. About 80% to 90% of our data comes from SAP, so our first order of business was finding a solution that came with some out-of-the-box connections.
One of these solutions was Qlik. I'd used Qlik before, so I was familiar with the company and interested to learn more about this solution. Qlik offered internal extract, transform, and load (ETL) techniques, which could help us process more data internally and meant we could avoid using separate ETL middleware to extract raw data from our different sources.
Another benefit was Qlik’s pre-set visualization bundle. We didn't have to worry about redefining our own template, and we didn't need to sift through options alone. Instead, we could load up a pre-made template for our data and define our preferred format from the beginning. Compared to Tableau and PowerBI, Qlik’s product roadmap was exciting, and included things like cloud offerings and AutoML. It's innovative spirit and simple efficiency really spoke to our needs.
With these factors in mind, we migrated to Qlik, deploying the solution over the course of five months. We’ve made some big leaps in adoption, and are rapidly expanding to include products, quality, and supply chain dashboards.
Enabling Self-Driven Business Intelligence
We initially adopted Qlik as a way to help our executive team, but it wasn't long before Qlik turned into a self-driven BI tool for teams across our workforce.
We began with a small group of 20 users, which allowed us to test the platform and identify any hiccups before rolling it out to a broader audience. However, we're quickly moving toward 200 users. The fast deployment model was a major part of this, helping my team take the finance and sales dashboards live within two months.
We also built a mobile app so audiences could access data from wherever they happened to be. Adopting Qlik has given our management teams a consolidated view of all eight business units on any device, anywhere, anytime. This provides unbeatable flexibility and is likely one of the biggest drivers of Qlik adoption in our company.
Qlik has allowed us to define KPIs in a much more meaningful way. With our 19 manufacturing plants throughout India, measuring data consistently across multiple sites is critically important to our success. So far, we've defined more than 80 KPIs, and expect to scale significantly in the coming years.
Something we want to start using in Qlik is predictive stories, which will be an AI model designed to help teams better use Qlik's reporting features. If we can answer the question, “What will happen?” we can more efficiently answer the question, “What should we do?” We also want to move into a better data integration model over the next several months by building a data warehouse, connecting satellite systems, adding an AI layer, and layering it on top of Qlik.
Leveraging Data for Process Improvements
Now that our employees have real-time information enabled, users can have more discussions about incoming data streams. We can also make quick decisions that avoid risks or take advantage of new opportunities. This is something we just didn't have before; the process was slow, complicated, and bogged down by dated information. Improved data access has opened the door to discussions with far greater immediacy, and without having to worry about whether the data in question is current or relevant.
Qlik's implementation at Dixon Technologies has also created a better process for employees. For example, the creation of data reports once took days or even weeks. Now, everything's changed. Employees already have the visualizations, so reporting simply extracts that data and transposes it to the right place. Apart from saving time and effort, this also saves heaps of frustration, since people can see the data they want in the way they want to see it.
We've also found Qlik's data is far more accurate than even our department information. Human error is part of the job, but with Qlik, we can find ways to reduce its impact. We can spot discrepancies in a dashboard and troubleshoot quickly. With the help of Qlik, our team can put all views of a plant's inventory together so managers can pick and choose the ones they want. There's no need to worry about building a one-size-fits-all report. Instead, people choose the view that makes the most sense for their plant and needs.
And because we've started seeing our WIP inventory in real time, we've started making technical and operational improvements that will increase the efficiency of our processes and people.
Fueling the Highest Level of Analytics
I see Dixon's journey with BI as a play in four acts: Reactive, Controlled, Proactive, and Predictive. Right now, we're performing acts one and two, but with Qlik, we have everything we need to reach the fourth.
This will take some time, of course. Insights and analytics are more than just a dashboard, and the analytics journey is a continuous one. There are so many emerging technologies to consider, but even then, data is the fuel for everything. At Dixon Technologies, we have created a more mature data and analytics organization that enables leaders to make quicker decisions with data that arrives in the right format and at the right time.