From data paradox to productivity, through transformation of business culture

[msttasnuvanava]

It is not enough to update technology. Certain cultural changes must also be made.

I often say that it’s not helpful for a business leader to say, “We need data and analytics to move at the speed of our business.” It’s better for them to recognize that they need the business to move at the speed of data.

It's quite a challenge, as 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 this into context, a Zettabyte is equivalent to 1 trillion gigabytes. That's 250 billion DVDs of storage, a column about 93,000 kilometres high.

You may also like: How to prepare for the world of ZettaBytes

The speed at which such a colossal amount of data is accumulating is worrying for many how to get uk whatsapp number companies, which are already struggling to keep up. The Data Paradox study , conducted by Forrester Consulting on behalf of Dell Technologies, reveals that for many companies, data is a burden rather than an asset.

It also finds that the primary driver of productivity is cultural , not technical. Yet many companies lack a data-ready culture.

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

Stress under a bombardment of data
The Data Paradox study shows that companies are under an immense amount of data, yet they want more. They are dissatisfied because much of their data is wasted. They only analyze a small percentage of what they generate/capture (around 10% according to some studies).

So how can businesses turn the corner and use data to their advantage?

1. Learn the language
I’ve written a lot about data fluency in the past. Simply put, it means having the ability to recognize data in its many forms, identify the great value that diverse data brings, deeply manipulate and explore the data, identify patterns and trends, infer insights from those patterns, and 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 age). To thrive, your workforce needs to be data fluent.

2. Develop a dynamic data co-curriculum
Data initiatives should not be extracurricular, but co-curricular. They should lead to a dynamic data culture that encourages both data sharing and data-driven decisions and actions. The fruit of this will be curiosity and experimentation.

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

3. Incorporate a mission-driven data strategy
Too often, companies focus more on data than mission. A data strategy needs to be deliberate, purposeful, mission-driven, and intentional. Ask and answer the following questions: What are the data sets we need to collect? For what purpose? Who will use them? For what purpose? How often do the data sets need to be updated? Is the data static (collected once) or in flux (a dynamic influx with the aforementioned rapid growth)? How will we measure data utility, productivity, ROI, and impact across the organization?

Consider data as an invaluable asset, which must be equipped accordingly. Dedicate your strategy to unearthing insights at the seams – for example, anomalous data that reveals something unusual and surprising about your company’s processes, products, services, customer base or market.

Data volume is not the problem; it's the people
In doing all of the above, you will encounter resistance and obstacles from people.

I attribute some of the inhibitors to data success to: fear, friction, and fragility.

Fear of missing out can drive phantom analytics projects – projects that use data without a clear business objective or useful outcome, other than to “look good” on paper or in business conversations.

Friction in launching new data initiatives can be due to people, culture, or technical debt. For example, people who generally dislike change may tacitly oppose a data initiative, and then offer only mediocre cooperation. Without enthusiasm on the ground, the company could find itself watering down its heroic AI proposition until it is a continuation of its traditional BI activities, under the guise of something innovative.

The fragility in analytics and data science is that talent is scarce and difficult to train and retain.

The best medicines for these inhibitors are quick wins , short sprints, and agile development and deployment of analytical products and services that produce real proof of value to the business. These small wins build trust and support across the organization for larger projects, greater investment (both in human and financial capital), and more impactful wins.

From paradox to productivity
In short, data doesn’t have to be a double-edged sword. It can be a clear 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 difficult to get up and running, but it can also become a productivity flywheel once the processes of insight discovery, data product innovation, and data-driven decision support are in place.