The Deal With Data: A Note to Those Starting Data Companies

I will preface this article by saying that I am no expert on data businesses. Working in financial services I have had some exposure to c-suite executives in the data space, however, and I have recognized a common issue that is quite prevalent among up-and-coming data companies in the startup landscape.

Many companies do not realize that, at their core, they are data companies, and this issue can be an inhibitor to growth and revenue in the long run. This article will outline three key aspects of running a successful data operation that will allow companies to optimize the way that leverage their data.

There are many types of data companies. Some company’s primary business model revolves around the selling of data, often on a subscription basis. There are other companies, like IBM, who among other things have build their company on the management of data, now cloud storage, as opposed to a “soup to nuts” data approach.

A traditional example of a pure data company is DiscoverOrg which sells data in a B2B model for lead generation and sales augmentation. Amazon, Delta, Microsoft are also all data companies — they just choose to collect and action their data in different ways.

Even companies like McDonald’s should be considered to, at the very least, have robust data capabilities. As mentioned in Forbes earlier this year, every company is becoming a data company. Companies are operating at scales never seen before, and as they do they are collecting incredible amounts of data.

These sorts of companies have become especially popular over the past decade as data companies often scale very well — when revenues increase, that value often goes right to the bottom line (net income). There are relatively few costs associated with the business once the three central mechanisms which run data companies (I will discuss those later) are perfected and operational.

Many companies just starting out are in one of two boats. Either they realize they are, at their core, a data company, or they don’t. Either way, for those starting a company, understanding the absolute fundamentals of what makes a data company tick will be critical to its eventual success.

Earlier this year The Harvard Business Review wrote on NewVantage Partners’ 2019 Big Data and AI Executive Survey, published in February, which surveyed 64 c-suite executives from large companies such as American Express, Ford Motor, and GE.

The survey revealed that 72% of participants felt they had yet to forge a ‘data culture’ while another 52% were not competing on data or analytics. If, at the stage of these companies, you are failing to execute best practices in the data space, it will be incredibly hard to pivot towards a ‘data culture.’ If you are starting a company, it is important to get these data concepts down from the start so that you don’t end up like the participants in this survey.

In this article I hope to outline the three basic features of a data company, and how some of the biggest players in the data space are able to build on top of this foundation to create massive data conglomerates.

Any data company is comprised of three primary components: data collection, data curation, and data deployment.

Data collection refers to the methods by which a company aggregates their data. These methods must be iterative — repeatable through some sort of automation. This both removes human error from the collection process while also providing a level of consistency and reliability that can add intangible value to the data.

Speed is also an important factor to consider when building a data collection tool — often data is only as valuable as it is current and convenient, without speed the worth of data can depreciate quickly.

Data curation refers to both the hygiene of the data (discerning if there are inaccurate, incomplete, or incorrect pieces of data being entered into the database) as well as the organization and integration of the data. Data curation is essential, as the management and storage of data is crucial to its functionality.

Increasingly companies are running into the issue of their data curation methods not agreeing with the management infrastructure of other databases, making integration extremely difficult. It is important to have clean data that can sync with a number of database infrastructures.

Finally, data deployment is the means through which a company chooses to share their data with other parties. This idea includes both the logistical methods of deploying large data sets across networks as well as how that data is presented. Sometimes parties will want raw data to manipulate, but other times parties will want data that is ingestible.

This is similar to data curation in that the storage of a company’s data must allow for ease of transfer, but it also means that features such as dashboards and other means of making data more comprehensible have to exist also.

These are just the bare bones of what every data company must consider when they operate. While these three features are necessary for the successful functioning of a data company, they are by no means sufficient either.

APIs and other programmatic features can be laid over top of this data skeleton to create a more pleasant experience for the user. Whether it is an easy-to-use UI or an analytics application tied to the data platform to allow for deep-dives into the data being disgorged, companies will flesh out this data skeleton as it pertains to their specific business model.

Young companies can make mistakes at any of the three data stages. Companies can collect data that is not impactful, and subsequently is tough to action or sell. Companies can manage their data poorly such that it is hard to transfer from one database to another. And companies can deploy data to other parties in a form that is incomprehensible. Each of these can make a company vulnerable in the marketplace and significantly hurt future business.

I am a massive soccer fan. On top of my career in financial services I cover soccer matches and write long-form analysis of players and teams for fun. I even played in high school, albeit not to much success. For those wondering, I support Arsenal in the English Premier League, but have teams in Germany, Austria, and New York as well.

In soccer, the fundamentals are often stressed: passing, shooting, ball control etc. it is easy to get lost in the flair of players like Ronaldinho, or wonder goals from David Beckham, but the best soccer has always stuck to these fundamentals.

Arguably the best team to ever grace the pitch in soccer history — the 2008 Barcelona side under then coach Pep Guardiola — stuck to these fundamentals. The famous tiki-take style of play that Pep employed was based on simply passing and movement. Obviously incredible skill was needed to successfully execute this tactic, but the fundamentals always remained at the center of their game.

This same lesson applies to data companies. As more “flair” enters the marketplace in the form of new and emerging technologies and technological fads, it will be more important than ever to secure the fundamentals that make a data company strong. With strong, even proprietary data collection techniques, reliability in data hygiene, and convenient, digestible data deployment, there will be less riding on the “flair” of your business.

Using a quote from one of my favorite online personalities and entrepreneurs Gary Vaynerchuk, “You haven’t made it, so stop being fancy. You’re trying to be big time, because you think acting like it is making it.”

Don’t be fancy…yet. Secure the basics of what makes data so valuable. Starting a company is never going to be easy, but having a solid foundation of data management will certainly make it easier.