The Two Fundamental Building Blocks to Data Driven Business Value

For the past few years, members of Evanta, a Gartner Company's CDAO communities have been asking their peers to share advice and lessons learned on how to better drive business value at their organizations. Dr. Ansar Kassim, Head of Analytics & Insights at Verizon and Community Member of the New York CDAO Community has over 20 years of experience leveraging data and technology to drive business outcomes. Dr. Kassim’s doctoral research was in the area of driving business value with data, and he has won numerous awards globally, all of them have been around driving business value. In this Executive Blog, he shares two key aspects on what he thinks is foundational to drive business value with data.
 

Every CDAO wants to drive business value, but CDAOs neither have responsibility for business functions nor have ownership of systems or processes that are engines to drive business value. Yet, some CDAOs drive disproportionate business value. As part of my research projects and industry engagements, I have been fortunate to learn from some of the best data leaders who have driven significant business value. I have also been able to implement some of those learnings and see how they accelerate business value creation. This is an attempt to highlight the two major lessons I learnt from these successful data leaders and my experiences while implementing those learnings. In a nutshell, those lessons are:

1. Business is best understood by the teams who run the business on a day-to-day basis. Empower business teams with data without barriers.
2. Data is best understood by the teams who deal with data on a day-to-day basis. Empower data teams with business knowledge.

In other words, take data to business knowledge or take business knowledge to data. Ideally both should be true. When both happen together, business value creation moves towards the steeper region of the exponential curve. But as simple as it sounds, this has been such a complex undertaking, and it took us time and energy to make either happen, let alone both.
 

Pillar 1: Empower Business with Data

What we say as data empowerment is often referenced as data democratization. However, we call it data empowerment, because we believe data democratization is only one part of data empowerment. It was also important to draw the distinction between “enablement” vs “empowerment.” We just didn’t want to enable business with reports and dashboards, but instead enable them with access to a lot of data, not just information, that was required in order to generate business insights. We didn’t want to just enable them with data that we think is best useful to them, but empower them with the possibility of interacting with the data to address the current business problem.

A few points to emphasize in this context. Firstly, it is important to realize that the “current” business problem will be different from time to time. Secondly, data empowerment is not just limited to providing business teams with the ability to interact and interrogate data, but also the ability to seamlessly work with the assets and products built on top of data including advanced models that leverage artificial intelligence. With this foundation, the data, data driven assets and the insights process are made available to the people who understand how to run the business using data and make a difference.

Enabling business with data, in our mind, has an inbuilt assumption that the business needs tend to be static and does not change often, and when business needs change, data teams can quickly react to them. With data empowerment, business is empowered to react and respond to changes as they happen. Enabling business with data also has another inbuilt assumption that business knows what question to ask, which may not be true all the time. During interactions with business partners, I have often heard that the next question depends on the answer to the previous question. But why should my business partner even ask me a question if they are empowered to answer it themselves?
 

What are the steps to empower the business?

A starting point is to democratize data at scale. Instead of focusing on generating the insights by a few people, let the data teams work on democratizing data through a suitable platform for many people, and focus on adoption and usage. Instead of asking how we can help to drive business value, ask the business how they can drive business value with the assets we have empowered them with. 

In our journey, we focused our efforts on making data an asset, not a liability. We spent almost two years focused on empowering business through various data empowerment platforms, and users were attracted to it. User communities started talking to each other about these platforms as our initial user base grew from just 500 active business users to approximately 20,000. This was our first priority, because we knew that to take the game to the next level we need to learn from the business teams about how the business works from the inside, and it is important to give first before we ask for something in return.
 

Blind side to watch for:

Embarking to directly measure business value may end up as a dead end, as business users often do not credit the value of good decisions backed by data to data. The credit for good business decisions will be associated with good business acumen. On the contrary, bad decisions could get blamed on data. It is not something data leaders should be worried about; it is just how the world works. But it is important to know how the world works to set expectations accordingly. However, you have several indirect measures, many of which revolve around adoption of what you build.
 

Pillar 2: Empower Data Teams with Business knowledge

This is typically the harder part, where many data and analytics teams fall short. Most data professionals do not have an innate understanding of their business, and there is no established training program in most organizations that will teach them how the business works.

Business knowledge is often trapped within specific functions, making it challenging for data leaders and their teams to extract that knowledge. Most are doing the best they can within their control, reskilling and upskilling their technical expertise and gaining certifications in areas such as AI, machine learning, and data engineering to name a few. These are all critical areas, but without business knowledge, we often don’t understand the opportunities as we would if we knew how the business works.  

Data and analytics leaders need to look at widening the skill spectrum of their teams. One of the several findings from my doctoral research was how companies that hired for business skill sets in data talent were more successful than the firms that looked solely at data skills like python and artificial intelligence.
 

What are the steps to empower data teams with business knowledge?

It is a tall order to drive broad business knowledge, because of the sheer breadth involved. Business knowledge spans across multiple business functions. So one needs to ask where to start from. Especially for a data team, you can go into many directions including marketing, operations, sales, finance etc. The good news is that a lot of this knowledge is interlinked, however, you should start in an area where your proximity to make an impact would set the momentum on your journey to build your team's business knowledge. My personal advice would be to start with the function where your team has the strongest relationships. 

We leveraged both training as well as recruiting as a vehicle to improve our business knowledge, and both should go hand in hand, but the sequence matters. When recruiting precedes training, and training takes importance, the engine starts to deliver value. Without recruiting talent from the business you don’t have trainers who can train, which is why recruiting precedes training.

Blind side to watch for:

How you position your business hire is extremely important. One of the common paths we tend to take is to hire business talent, train them on data and put them to work, which is a fine path. But, it doesn't lead to an engine that generates perpetual incremental business value. The business value engine starts to work better, when they play roles that allow them to bridge data with business value. Business talent with leadership skills tend to perform very well in the data teams because of this reason. The combination of their skills, relationships and their ability to exploit data drives business value as well as their careers.
 

Dr. Ansar Kassim is a community member of the New York CDAO community, and he was a featured speaker during the New York CDAO Executive Summit in June 2023. Join your local CDAO community to connect with like-minded data and analytics leaders on mission critical topics, such as this, at one of our upcoming summits or programs.