How to go from data paradox to data productivity with a business culture transformation

Did you get the memo? Updating your technology isn't enough. You need to make certain cultural changes too.

By Kirk Borne, chief science officer, DataPrime Inc.

I frequently say that it’s unhelpful for a business leader to declare, “We need data [and analytics] to move at the speed of our business.” It is better for them to recognize that they need business to move at the speed of data. (As a physicist, I am required to tell you that data is not moving at a constant speed, but that the speed of data is accelerating.)

That’s quite the challenge given that over the next five years, humans and machines will generate at least 175 Zettabytes of data from connected devices, autonomous vehicles, businesses and private citizens. To put that into context, a Zettabyte is equivalent to 1 trillion gigabytes. That’s 250 billion DVDs worth of storage—a stack about 93,000 miles high.

The gathering speed of such a colossal amount of data is foreboding for many businesses, who are already struggling to keep up. The Data Paradox study, conducted by Forrester Consulting on behalf of Dell Technologies, reveals that for many firms, data is a burden rather than an advantage.

It also uncovers that the main driver toward productivity is cultural rather than technical. Yet many firms lack a data-ready culture.

Moreover, Forrester asserts that data science is a team sport. That’s true if you’re referring to “your entire organization” as your team, not just a few brilliant data scientists working as an isolated unit.

Straining under a deluge of data

The Data Paradox study shows that businesses are straining under an immense amount of data, and yet they want more of it. They’re unsated because so much of their data is going to waste. They’re only analyzing a small percentage of what they’re generating/capturing (circa 10% according to some studies).

So, how can businesses flip the switch and wield data to their advantage?

1. Learn the language

I’ve written a lot about data fluency in the past. Succinctly, it means having abilities to recognize data in its many forms, to identify the great value that diverse data brings, to manipulate and thoroughly explore data, to identify patterns and trends, to infer insights from those patterns, and to effectively communicate data-driven recommendations, decisions and/or actions. These skills are far more sophisticated than data literacy (which is now just a baseline for existing in the data era). To thrive, your workforce needs to be data fluent.

2. Develop a vibrant data co-curricular

Data initiatives shouldn’t be extra-curricular but co-curricular. They should give rise to a vibrant data culture that encourages both data sharing and data-driven decisions and actions. Its fruit will be curiosity and experimentation.

A core principle of this co-curricular should be data democratization, which incorporates acceptance, accountability and reward systems that encourage and empower all people in the organization (who have legitimate access to data) to explore, learn from, and innovate with the data assets.

3. Embed a mission-led data strategy

Too often, businesses focus more on the data than the mission. A data strategy should be deliberate, purposeful, mission-oriented and intentional. Probe and answer the following questions: What are the data sets that we need to collect? For what purpose? To be used by whom? With what goal in mind? How often must the data sets be refreshed? Is the data static (collected once) or streaming (a dynamic influx with the aforementioned rapid growth)? How will we measure data utility, productivity, ROI and impact across the organization?

Consider your data as an invaluable asset, to be equipped accordingly. Dedicate your strategy to unearthing insights in the seams: for example, anomaly data that reveals something unusual and surprising about your business’s processes, products, services, client base or market.

The data volume isn’t the problem; it’s the people

In doing all the above, you will encounter resistance and obstacles from people.

I ascribe some of the inhibitors of data success to the three F’s: fear, friction and fragility.

  1. Fear of missing out can drive phantom analytics projects—busy work projects using data with no clear business purpose or useful outcome, other than to “look good” on paper or in business conversations (i.e., “See how we are doing stuff with our data.”).
  2. Friction in starting new data initiatives and getting things moving can be caused by people, culture or technical debt. For instance, people who generally don’t like change may tacitly oppose a data initiative until the 11th hour, and then only provide lackluster cooperation. Without enthusiasm on the ground, the company might find itself diluting its hero AI proposal until it’s running a continuation of its traditional BI activities, under the guise of something innovative.
  3. Fragility in analytics and data science results, because talent is scarce, hard to train and retain.

The best medicines for these inhibitors are quick wins, short sprints, and agile development and deployments of analytics products and services that produce real proof of value for the business. These small victories build confidence and advocacy across the entire organization for bigger projects, deeper investment (in both human capital and financial capital), and more impactful victories.

From paradox to productivity

In summary, data doesn’t have to be a double-edged sword. It can be an unequivocal advantage if you put it to work and deploy data as a route to AI and a more collaborative relationship with technology.

Data has gravity and inertia. It can be hard to get moving, but it can also become a flywheel of productivity once the processes of insights discovery, data product innovation, and data-driven decision support are put into motion.

To read the full Data Paradox study, visit

Dr. Kirk Borne is chief science officer at AI startup DataPrime, and he is the founder and owner of Data Leadership Group LLC. He provides thought leadership, global speaking, content creation, mentoring, training, and strategy consulting in data science, machine learning, and AI across multiple disciplines. He has been a worldwide influencer on social media in those areas since 2013, promoting analytics and data literacies for all.