How To Build a Payments Data Team
By: Dwayne Gefferie
Tags: Analytics, Data Science, Payments, Artificial Intelligence, Startup

Paying with Apple Pay on a Square Device

In the last couple of years, we have seen a growing number of acquisitions in the Payments Industry. From large investors buying established global companies, like San Francisco Partners -> Verifone, to strategic acquisitions for capabilities, geo-expansion or consolidation purposes, like ING -> PayVision, WorldFirst -> Wyre or Worldline -> SIX. However, the companies that don’t seem to be bought up just yet, are the ones who have made leveraging their Data a key strategic objective. In this blog, I share how Payments (applies to other FinTech’s and Tech as well) companies, can build a Data Team and outperform their competitors.

Necessity vs. Luxury

To be fair, the payments industry has changed drastically in the last ten years, and like all changes this wasn’t because of the large enterprise companies, but because of lean startups innovating and finding new ways to include payments in their product and process. The enterprise “startups”, we look up to now, including Facebook, Spotify, Uber and Netflix, have been extremely crucial in defining new E-Commerce categories, that didn’t exist ten+ years ago. The major thing these new enterprises bring, is their data-driven way of working.

Instead of focussing on how much revenue was being processed, these companies focused more on the efficiency of their overall platform. From conversion rates to authorisation rates. Using their data-driven way of working, they influenced a lot of the “new” payments companies, to provide as much data as possible. As these new companies are overtaking some the older companies, Payments companies both old and new have to adapt and continuously provide data.

Building a Payments Data Team

Payments is a technical product, which means that especially in the beginning of building a Gateway or Acquirer, the Development team calls the shot. If I were to build a new payments company today, that wouldn’t be very different. However, by keeping in mind that at one point Business Users are going to start asking questions, it is best to start with an infrastructure that will be able to retrieve the data and insights necessary to answer those questions.

1 . Hire a Manager of Data (Head of Data, VP of Data, etc.), preferably with experience in Payments.

Because the business is going to have to transition from Development focus to Business focus, you need a Manager of Data, who understands both sides, knows how to communicate with both and is able to assemble a team to execute on the strategy and vision. The beginning period of a Manager of Data, is spend with both management and business users. Understanding the business and what management wants to achieve, while also having conversations with the business users, to understand what they need to do their job. Having gathered all the requirements, the Manager should develop a Data Strategy, which should be the vision for the business to build on.

2. Hire a Data Architect

Too often companies try to run before they can walk. The reason you hire a Data Architect, is because the infrastructure that is in place, has been build for the Development side of the business. Payments has for a long time, been very structured in it’s data, however due to E-Commerce, M-Commerce and IoT-Commerce, an increasing size of data is becoming more unstructured. That is why if you want to retrieve data and derive insights, you need to build a separate Data Infrastructure. A Data Architect, will be able to review the current infrastructure, leverage existing parts, and design an infrastructure that does not interfere with the existing infrastructure, while at the same time give the Data Team the resources necessary to support the organisation.

I often get the question, why can’t I just let my Data Engineer design the infrastructure, to which I reply, if you are building a house would you hire an architect first or a builder. The Architect makes the plan, takes into consideration all the pro’s and con’s, uses his experience (mostly as a Data Engineer), to design an infrastructure that works in the short-term and can be build upon in the long-term.

3. Hire a Data Engineer

A great Data Engineer, is up-to-date with the latest Cloud-Technologies, and at least a master in SQL and Python. Most Data Engineers will prefer working in AWS, as this is the most technology driven platform, which has been around the longest. However, with the increase in more Modern Cloud Technologies like Matillion & Fivetran for ETL’ing (Extract, Transform & Loading) and Snowflake and Google BigQuery for Data-Warehousing-as-a-Service, a lot of infrastructures are becoming less about complexity but more about speed, agility and quickness. With the rapid change of data in its volume, velocity and variety, Data Engineers who know how to find the right tool for the job, get more done and help get your team the right results.

4. Hire a Data Analyst and or BI Analyst

After some time designing and building, the next hire should be a Data Analyst, who preferably has some business background, but is far more advanced in using Databases and querying hard to retrieve data. Using programming languages like R or Python, to do more complex Data Analysis than Excel is able to do. Another reason you want a Data Analyst with at least some programming skills, is that in most cases the data could use some cleaning and transformation, before it becomes the type of data that can be analysed by either the Data Analyst or Business User.

As the capabilities of this team will continue to grow and the number of similar query requests increases, the Data Analyst will be key in transforming his queries into Dashboards, which can be distributed throughout the organisation. By translating the language of the business into a Modelling Layer and using a Data Platform like Looker, the role of the Data Team will start to shift from controller to liberator of the data, by giving all Business Users the ability to ask as many questions as they want and the freedom to explore the data, how they see fit.

5. Hire a Data Scientist

Only when the previous four hires have been successful in developing a Self-Serve Data Platform, is when the Data Scientist can come in and help the organisation become truly Data-Driven. A great Data Scientist, can focus on working with teams to develop Data-Driven Applications. In the Payments Industry, that could mean developing Predictive Applications like Acquiring Routing (based on historical data and weights, select the optimal route), Dynamic Authorisation (inputting or removing data, before submitting a transaction) or building a Fraud Engine, that is able to predict which transactions are fraudulent and which are not.

Have you build your Data Team yet?

If you are a business owner or data falls under your responsibilities, you might be wondering, if this is actually worth it. If we look at companies like Facebook, Uber, Netflix and Spotify, we can see that being in control of your data, doesn’t just lead to better decisions and improved operations, but it can actually make you more money. For the payments industry that is no different, leaders in the space include Stripe, Square, Transferwise and Plaid, the common denominator, they use Data better than all their competitors.

So, have you build your Data Team yet?

Thanks for reading ;) , if you enjoyed it, hit the applause button below, it would mean a lot to me and it would help others to see the story. Let me know what you think by reaching out on Twitter, Linkedin or at DataBright. Or follow me to read my posts on Data Science, Payments and Product Management.