Common Characteristics of Successful Enterprise Data Platforms

Several years ago, I was walking through the streets of New York City and wandered into Times Square. I had seen it on TV many times but seeing it in person was mesmerizing. As I stared in amazement at the many messages flashing before my eyes my senses were overwhelmed by the many colors, visuals and messages. One single ad drew my attention, and it wasn’t the brightest or the biggest or the flashiest but that one ad stood out. It was an all-black background with a single word that I picked out of the sea of opportunities because I was searching for something at that moment. I picked it out because I was looking to “cut the cord” with my cable provider and researching Netflix, HBO and a host of other streamers and there was this simple billboard with a single word (AppleTV+) on a black background that provided clear focus and timely meaning to me.

Almost every second of our waking lives, we are inundated by messages to buy or consume some product, but we ignore most of it. Just like Apple was able to make a singular impact on me in that cacophony of light, sound and noise, how do successful data and insights-enabled companies adapt their data platforms to identify their customer’s needs? How do they enable personalized touchpoints with their customers in context at the right time, the right touchpoint and with the right message resulting in an exceptional customer experience?

We are going to examine some common characteristics that successful data and insights-enabled companies exhibit fundamental to their data platforms including their focus, culture, and ability to adapt quickly.

Platforms are business-driven

As data geeks, we love to opine on Kafka vs. Pulsar, SQL vs. NoSQL, data meshes, knowledge graphs, etc. and this is all well and good in the appropriate context, but our primary focus should be on enabling sustained business value. We must develop the relationships and methods to elicit from our business counterparts their goals, challenges and aspirations for creating business value and delivering robust capabilities to the marketplace. This is a key characteristic across the board for data and insights-enabled organizations and is evidenced by the fact that all successful data platforms have been built from the ground up, focused on enabling business needs.

For example, Netflix is widely known as a data and insights-enabled company with a powerful data platform and in 2020 was valued at over $234 billion, surpassing Disney as the most valued media company in the world. Their success can be attributed to their impressively high customer retention rate and low churn rate which is rated one of the best in the industry. However, it’s not just their ability to retain their 203+ million subscribers that has made them successful. One of the ingredients to their success is that they dedicate an entire business-focused analytics team with the following charter:

“Our …work involves diving into large, complex data to answer ambiguous business questions. We work cross-functionally across business domains to discover and assess new opportunities, create new business metrics to measure success…”

This team’s entire goal is to provide valuable insight into every aspect of Netflix’s business including, internally, its partners and its members’ rich customer experience.

Platforms reflect data literacy

In 2017, The Economist published a groundbreaking article creating a new focus on a data and insights-enabled enterprise that not only collects and manages raw data but treats it as a valuable enterprise asset that can be enriched, refined, mined and monetized. These enterprises understand the need to apply data to every business use case, to observe customer behavior in real-time with historical context, to analyze demographics and behaviors and to leverage external data to enhance its view and increase its value.

A data and insights-enabled enterprise has a culture where data is respected and valued; people are data literate and think holistically about data, free of silos and are allowed to add input and shape its use and tie data-driven insights to a set of business outcomes.

Sometimes I’ll ask clients for their latest data strategy, and they will provide me with some PowerPoint deliverables that are usually very comprehensive and well thought out. I will peruse the roadmap section and ask them where they are on this roadmap and most will look at it with a blank stare, shake their head in puzzlement and treat it like a stroll down memory lane.

Fortunately, there are a set of prescriptive steps to get to a data and insights-enabled focus:

  • Complete a comprehensive and detailed data strategy driven by business outcomes sponsored by the C-Suite.
  • Incrementally but methodically implement the roadmap initiatives; remove it from PowerPoint and into working, successful applications, transitioning it from a stale document to a living, breathing ecosystem. Dr. Rolf Hichert, president of the IBCS Association said it best, “In the PowerPoint culture, information is sacrificed in favor of decoration.”
  • Drive and develop the essential competencies required to support those initiatives. Define clear roles, objectives, and scope, define competency and organizational gaps and implement a detailed plan for re-skilling and hiring to close those gaps quickly.
  • What does this mean for the Chief Data Officer? They own that data strategy and instead of monitoring the status of the nightly batch reporting, they become responsible for a key part of their company’s success. Their role should be clearly defined and established as the enterprise data champion and their metrics should be defined and rewarded based on progress.

A data and insights-enabled enterprise is characterized by a data literate culture where everybody in the entire company understands data’s place, has bought into the overall data strategy, is constantly questioning, learning and growing, knows the business outcomes that will result from a data emphasis and focus every day on incrementally working towards that optimal target state.

Platforms reflect business imperatives

Using a three-legged stool analogy, we’ve learned that successful data platforms are 1) business-focused and 2) are part of a data literate culture, but to stabilize that stool, 3) these platforms must reflect the business imperatives in an agile, flexible, scalable manner so that business goals can be realized quickly. To illustrate, let’s look at a couple of real-world examples.

LinkedIn has a well-defined process where they match and align business goals to KPIs. Once the key business success factors are defined and appropriate data and product features are identified, LinkedIn has a platform where this new application can be deployed, and extensive controlled experiments are executed in fast iterations to determine production feasibility. Their platform can run hundreds of experiments at a time providing feasibility in a much faster timeframe so that important business imperatives can go from ideation to production much quicker than in the past.

Uber is known for using its real-time platform to deliver real-time decisioning and insights around forecasting and surge demands. Their entire business model is built on the premise that they can transport you from Point A to Point B faster, better, and sometimes cheaper than any other similar mode of operation. Uber can do this because their flexible and scalable platforms leverage data and machine learning in real-time to anticipate and meet demand.

Other examples of real-time insights are Adidas collects and aggregates the right data at the right time to invite consumers to purchase products that match their profile. Amazon uses real-time data to recommend products and provide an overall superior customer experience. They keep detailed data for every interaction so they can adapt and extend their platform features quickly as business imperatives change.

What’s next?

We’ve examined and described some of the common characteristics that successful data platforms exhibit including their relentless business focus, their data literate culture, and their ability to quickly adapt to meet fast-changing business imperatives. This is supported by a recent IDC report stating that data-driven organizations are rewarded with better business outcomes including increased profits and higher employee satisfaction. In this report they identified five traits that are characteristic of data and insights-enabled enterprises:

  1. Data talent – The workforce has excellent data-related skills,
  2. Trust in data – Data is governed with appropriate controls allowing employees access to do their jobs and them trusting that data,
  3. Data-focused mindset – Encourage data exploration and curiosity,
  4. Priority on collaboration – Application silos are eliminated and collaboration emphasized,
  5. Data is valued – Committed to realizing and leveraging the value of data

So, how do successful data and insights-enabled companies adapt their data platforms to identify their customers’ needs? They can quickly adapt their platforms because they are nimble, flexible, and scalable, and they had the foresight to collect customer data and interactions over time.

How do they enable personalized touchpoints with their customers in context at the right time, the right touchpoint and with the right message resulting in an exceptional customer experience? A platform designed to provide useful insights in real-time based on contextual, personalized, clean, and historical data.

Remember the next time you are on Amazon buying a product or on Netflix perusing recommendations or doing a Google search and noticing the very fitting prompts you receive during the query process or trying to get to a very important client meeting with no taxi in sight and Uber shows up in less than five minutes; be thankful for these companies, their dedication to data reflected in their platforms and how much these robust data platforms have improved our everyday lives.

To learn more about our data and analytics services, contact us.

Dan Sutherland

Senior Director
Enterprise Data and Analytics

Mark Carson

Managing Director
Enterprise Data and Analytics

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