Paigo empowers clients with intuitive data-driven decision making, powered by Tableau

We first started using data analytics more than 20 years ago, in order to predict future returns more accurately and to steer and improve internal workflows. Fast forward to the present day and it plays an intrinsic role in all aspects of our operations. We have over 30 full-time data and business analysts collecting and working with data every day, to streamline our own business processes and provide important insights to clients that they can use to improve their own products.

Until recently, most of our advanced data analytics activity was available internally only. However, there was a strong desire amongst the senior leadership team to make data and reporting services available to our customers as well, via a new analytics self-service solution. Not only would this help clients identify key insights much faster, but it would also put data-driven decision making at the heart of their operations by helping them to ask and answer their own questions.

In mid of 2019, we started building a potential solution for our customers on MicroStrategy - our primary analytics platform at the time - but quickly realised it wouldn’t respond to our needs for fast delivery, professional visuals and deep, intuitive self-service analytics. Knowing the benefits of Tableau, we demonstrated its capabilities to the senior leadership team. In just a couple of days we developed a series of Tableau dashboards based on our requirements that would have taken two to three weeks to produce in MicroStrategy. The decision was quickly made to go with Tableau and within three months we’d built a complete professional solution and given access to the first wave of customers.

Feedback has been extremely positive. Most clients were used to static Excel reports, but suddenly they had the flexibility to analyse data on their own, whenever they wanted. All the dashboards were automated on our end too, meaning clients could self-serve their analytics needs in confidence that the data was always up-to-date, secure and accurate. As a result, demand has soared and we’ve scaled up access significantly.

Initially, we delivered everything using HTML with embedded analytics. However, we soon switched to Curator from Interworks, a portal solution which made it easier to create customised user experiences that were both professional looking and highly interactive.

Based on the positive results from our first externally-facing self-service analytics solutions, we started to work on a new offering for companies in the mobility sector, where ticket evasion is a key concern. We created a product that helps our customers use data to identify specific locations and times where ticket evasion is most likely to be occurring on their services. This product uses historical data on successful convictions for fare evasion and creates a heatmap in Tableau that highlights where and when the majority of these offences took place. Companies can then use the information to ensure ticket inspectors are present on the most ‘high risk’ services, helping minimise lost revenues.

Our original adoption of Tableau was driven by the desire to extend our external analytics services and offerings. However, after seeing the positive impact it was having on clients, we quickly realised how beneficial it would be internally. We began migrating at the end of 2019 and now all of our own analytics systems are based in Tableau as well.

Simply put, Tableau is a much more engaging platform to use. Dashboards can be developed faster and customised more easily, meaning time to insight is significantly shorter. In the past we’d have 10 separate views in a dashboard with diverse parameters, making analysis tricky and time-consuming. Now the same data is viewed and analysed in a single platform, which can be quickly filtered based on individual questions and needs. Colleagues regularly tell me that Tableau is genuinely fun to use too, which encourages them to go in and just experiment with data. This has led to a much stronger internal data culture developing, where data is more transparent and people aren’t afraid to conduct their own analytics every day.