Data strategy in three steps: Inside Collibra’s Data Office

Creating a Data Strategy - Inside Collibra's Data Office

Got the Data Technologies, the Data Team and the Data? You better have a Data Strategy.

Inside most modern organizations, we have a data office, we have data gathering and analytics technologies, we have a Chief Data Officer, and of course, we have the data. Lots and lots of data. But what about a data strategy? 

This is a critical contingency — and to get it right, we need to identify what makes up a data strategy, how it comes together and what role it plays within the overall business strategy. At Collibra we drink our own champagne, so we’ve seen first hand the importance of a data strategy that fits into the business strategy.

What is Collibra’s strategy?

First, a word about strategy. The best description I’ve heard so far is by Harvard’s Prof. Applegate: when you observe kids playing soccer, you’ll see they all converge on the ball when the referee blows the whistle to start the game. When you observe a professional team you’ll notice they’ve literally got their eyes on the goal (what is happening in the future and where do we need to get to), they know their current position (e.g., strengths and weaknesses), and they’ll execute practiced tactics to achieve short, medium and long term goals, including learning and adapting while playing the game.

Collibra’s mission is to make data meaningful. We aim to lead the category of Data Intelligence while scaling as a world-class company with high growth targets. This is crucial business context and the data strategy needs to fit into that.

We’ve come a long way from our startup days in 2008. Four founders, a handful of developers and commercial colleagues (the numbers spiked fast), a roaring market — those were dynamic times. Racing ahead like a pirate ship worked very well for us at the time.

Now, as we are approaching 1,000 Collibrians and a plethora of emerging opportunities in new markets, we’re growing into a navy. This transition means we face similar issues as our customers’ organizations. To optimally serve our market, we must become our own best customer. 

What does Collibra’s data strategy look like?

Gartner forecasts that by 2021 “the office of the CDO will be a mission-critical function comparable to IT, business operations, HR and finance in 75% of large enterprises.” 

I agree with their position on the importance of a data office because I meet many organizations that are building or maturing one. Most teams are new to this, so they are always looking to learn and improve. 

At Collibra, we’ve broken our data strategy down into three high level objectives: the infrastructure, data products and sharing what we learn. We want to be the leading example so our data strategy can be summarized as follows: “It is 2025 – do you know what your data office looks like?”

1: The necessary cornerstone of infrastructure

Since our early days, Collibra has been a strong champion of data-driven business, so we’ve always used data across various pockets in the organization. While we don’t have the thousands of data sources our customers often face, we’ve seen fragmentation begin to appear in our data infrastructure. As our company grew, Collibrians needed metrics, dashboards and reports, and they needed to source that data from somewhere. We know this trend will continue so we decided to create a sound data infrastructure; one of our three priorities in the strategy. We needed a reliable, self-service infrastructure to keep data products running — just like we need clean, fresh water from the tap.

I believe that data management is commoditizing in the cloud as software titans like Google, Microsoft and Amazon are battling to become the de facto infrastructure for storage and computing. We are seeing the same with our customers: the architecture is flying off premise into its new home in the cloud. For our data infrastructure, we chose to build out a data lake in the cloud with important characteristics:

  • Self-service: so everyone at Collibra can easily make use of it
  • Scalable: so it works with all data sizes, big and small
  • Reliable: so our data products are always on

2: Data products to generate business value

Meet the new IT: data scientists and engineers are on the front lines, implementing and even developing tools that automate digital business practices. Corporations that are born digital —think organizations such as Uber and Zalando, with algorithms to drive the business — are blazing a radically different trail. Their products — including the operations, infrastructure and the rest of the kitchen sink — have always been data products.

Gartner sees data products as the 4th evolution for the Chief Data Officer, including managing the profit and loss associated with serving them to the market. However, most organizations today are still trying to distinguish between a defensive stance (ensure compliance and security) and an offensive posture (leverage data assets to derive value).  

Personally, I like the concept of a data product. Organizations are very familiar with products (or services) as this is a core part of their business. They know what a product looks like, how to create new ones and sunset old ones. They know how to bring them to market and how to support them. From that perspective data products are the shortest path between value and data, whether they manifest as a simple dashboard or as an automated decision-making algorithm directly touching customers.

Our goal is to continuously deliver data products either directly in the data office or by supporting our colleagues in the business functions. We’ll stay close to business value by identifying needs and opportunities across the business, for example, a corporate dashboard for Finance, automated customer scoring for Customer Success, or product analytics for our colleagues in Product. 

3: Sharing is caring

As we build out data products on our infrastructure, we’ll inevitably have to drink our own champagne while encountering various data processes. A few examples: cataloging what we ingest into the data lake, certifying metrics and reports, tracking lineage, assigning and monitoring data ownership, taking care of data privacy and protection. There are plenty of places where Collibra’s Data Intelligence platform enables us to connect the right people to the right data, algorithms and insights. 

This provides a very rich opportunity for us to be our own best customer: we’ll learn what works well and what needs to work better. We’ll identify opportunities to improve our products and our prescriptive path. 

Our Data Office isn’t an academic exercise — we learn by doing and we’re charged with passing on all learnings to the appropriate corners of our organization as well as to our market as a whole. In the era of Data Intelligence, wisdom should never be locked away in an ivory tower; it must be shared with the full nation of Data Citizens to deliver operational enhancement and business benefits. 

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