Living a data-driven culture

How Data-Driven Culture makes your business stand out and become a front runner?

Image Credits. Featured Image: Pixabay

This article will consider what a data-driven culture is, how you can make your employees and business live such a culture? and how it helps your business become the front-runner? The latter part details the five pillars of such a culture. Furthermore, we conclude with the data monetization so that you can contextualize it for your business once you hop on to a data-driven culture endeavor. 

Introduction: 

Being data-driven is critical to succeeding in today’s world. When an organization exercises a “data-driven” approach, it makes strategic choices based on data analysis and interpretation. Such an approach facilitates companies to experiment and organize their data with the goal of better serving their customers, employees, and improving their operations. Using data to drive its actions, an organization can contextualize and personalize its messaging to its prospects and customers for a more customer-centric strategy.

However, organizations face many challenges in becoming a data-driven organization and building and retaining a data-driven culture. Some of those challenges include their inability to emphasize long term objectives, lack of shared vision, focus on short term RoI gains and ignoring the Return on Value and opportunities that data brings, skills gap, and lack of having the full picture or true understanding of what it would mean for them if they become a DD organization. 

Enterprise success in a data-driven context depends upon complete access to data and instantaneous action, among other factors. To carry data to every decision, leaders need not only to tear down silos; rather, they have to manage and work with data where it lives strategically. Such a strategy should entail at least the following:

  • Identify the technological and cultural obstacles to realizing the full potential of data.
  • Leverage data organization while reducing friction.
  • Align their approach to data with overall business strategy.
  • Brainstorm and plan for data monetization opportunities

“Data-drivenness is about building tools, abilities, and, most crucially, a culture that acts on data.” Carl Anderson

Attributes of a data-driven business: 

A Data-driven organization must display the following attributes:

  1. All Decision making based on the data
  2. Not being victims of their past success
  3. Redesigning their strategy based on collaborative intelligence
  4. Build a data culture
  5. Data monetization

Without data, you’re just another person with an opinion.” William Edwards Deming

1. All Decision making based on the data

The organization shifts its focus from gut-feelings to data-driven decision making for every decision, every opportunity they are presented with. Instead of relying on their cumulative experience, the legacy of historical ups and downs, individual smartness, and data-driven organizations move towards data and insights-based decision-making and risk-taking. This move from human experience based on data, an AI-based approach to run the businesses offers a competitive advantage and makes the company Business 4.0. 

2. Not being victims of their past success

When trying to innovate and transform with data, companies’ biggest obstacle is a culture of “we’ve always done it this way.” Gabie Boko

The businesses that get rid of this “past legacy of doing things in a specific manner” approach and their market standing, leverage data and AI in their operations, create new opportunities, decision-making they can harness and grow a data culture. AI and data have changed the business models, enabling users to have more options to choose from. They will choose the company that serves and suits them best in terms of convenience, cost, service. In a data-driven organization, leaders start to inspire through their actions and base their decisions on not rather than their intuition. 

3. Redesigning their strategy based on collaborative intelligence

The ones who redesign and revamp their strategy to leverage both humans and machines’ powers as the future is based on collaborative intelligence. There are somethings where humans are better and some other things where machines are better.

 

4. Build a data culture

To thrive in the data and AI era, you need to establish the missing link that will enable your business to fully transform and capitalize on the potential offered by data and AI. That missing link is to create, promote, and evangelize the data culture throughout your organization. Make data as a first-class citizen in every business setting. Businesses invest huge amounts of money in becoming more data-driven, but only a small percentage can successfully achieve the intended goal [1]. 

To get the value out of your data, just technology is not sufficient- it also requires changing the mindsets, attitude, and approach of individuals, which then arrives at embedding data into your organization for everything you and your employees do. 

Building a data culture means empowering every employee to achieve their targets by leveraging the data at their fingertips. It centers around challenging ideas, asking questions, and requesting and providing data to support your thoughts. People come together, align on a common mission to develop the organization with data. 

In a data culture, everyone benefits when more people can ask questions and get answers; an organization’s entire effectiveness can elevate [2].

To successfully build and live a data culture, you need to establish the following pillars across your business. 

a. Trust

To establish a baseline of trust in your organization, you need to empower your employees, set explicit expectations for data usage, encourage sharing data and transparency, and build trust in data. To make that happen, you need to have the right governance in place supporting well-governed, protected, and widespread access to data and establishing confidence in data. Create a single source of truth and break down the silos across teams to build a high degree of collaboration and trust within your enterprise. Those, as mentioned earlier, would result in having the insights shared across your organization. 

b. Commitment

Your business needs to treat data as a strategic asset and a priority. The leadership needs to model the data behavior and not just to sponsor it. The executive leadership must exhibit a commitment to benefit from the data and insights by collecting the data and leveraging it to improve the business. And this commitment should be evident in the organizational structure as well as in daily operations. As a business, you must assign an executive (a CDO- Chief Data officer, for example) to be responsible for using organization’s data and mapping the analytics projects to business objectives. 

In short, to succeed with your data and AI efforts, it is very important to build and maintain a data-centric culture as outlined below by IBM SVP Rob Thomas emphasizing the importance of creating a data culture [3]. 

“Real success in using AI comes down to an organization’s ability to adopt a culture that is centered on data.”

c. Mindset

Change the Mindset across the organization that encourages data over intuition, rank, experience. Put efforts into ensuring that everyone across the enterprise shares this Mindset; this would yield an environment where open discussion, healthy debate, and ideas would give birth to innovation and growth. People should challenge ideas with data and go for experimentation and failing fast. Data should be seen as an embodiment of personal growth and development, as it makes people ready to challenge or be challenged by others on the assumptions based on the data.

d. Talent

Prioritize the data and AI skills in your recruitment, development, and retention of talent. Ensure that all of your employees have a basic understanding of AI and data, irrespective of their job function. Even though you can deploy the best technology and processes, it would be of no use if your employees don’t know how to work with the data; people need to be aware of and use the data and insights. Different strategies might be needed depending on your in-house expertise and business, but do consider reskilling your employees, among others. 

e. Sharing

Once people share the purpose, it is easy to share the rest of the many things. In a data culture, solving problems involves multiple teams and different business units requiring data from different systems, domain expertise from various teams. Once your teams start to share the purpose- using data to make their organization better, they can amplify their success with the data. Thus sharing in a data culture plays a huge role; teams can share best practices, develop a sense of community, learn from each other’s experiences, and realize other use cases. 

In addition to these pillars, if you want to look for steps to build such a data culture, have a look at an HBR article which details ten steps to build a data driven culture [4]. 

5. Data monetization 

The one which transforms data into a monetization opportunity. It is the process of using data to increase your business’s revenue and may come in many shapes. It is about producing measurable commercial advantages from your data and analytics endeavors. The top-performing and fast-growing companies have adopted data monetization and are increasingly making it a significant part of their strategy [5]. With the right data monetization strategies implemented, you are fully outfitted to hone your competitive edge. And in the absence of those, you risk dropping critical insights that could advance your business. 

a. Direct monetization. 

It includes selling direct access to your data to third parties- You can sell it in raw form or sell the insights. Common examples may be contact lists of potential business prospects or findings that impact buyers’ industries and businesses. 

b. Indirect monetization

i. Data-based optimization 

It involves analyzing data to expose insights that can increase an organization’s performance. Data can strategize how to approach customers and understand their responses to push your sales. Data can also highlight where and how to save costs, avoid risk and streamline processes.

ii. Data-driven business models. 

It focuses on utilizing data to uncover new business possibilities. You can install analytics and AI into your products or services, presenting advantages for you and your customers. Customers profit from direct access to usage statistics and other data generated by their product usage. It can be offered as a value add-on or as a new service, fostering customer loyalty. Ultimately, you get greater insights into how your products are being used. 

Image Credits. Photo by Dennis Kummer on Unsplash
References:
  1. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/breaking-away-the-secrets-to-scaling-analytics#
  2. https://blogs.microsoft.com/blog/2014/04/15/a-data-culture-for-everyone/
  3. https://www.ibm.com/blogs/think/2017/11/a-culture-of-data/
  4. https://hbr.org/2020/02/10-steps-to-creating-a-data-driven-culture
  5. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/fueling-growth-through-data-monetization

Editorial Associate

Chan Naseeb is an author, a keynote speaker  and a technical leader in Data Science and AI with a demonstrated history of working in the information technology and services industry.

He is an established Thought Leader in Artificial Intelligence, Data Science, Quantum Computing, Blockchain, IoT, Digital Transformation and helps organisations to become data driven.

About Chan Naseeb

Chan Naseeb is an author, a keynote speaker  and a technical leader in Data Science and AI with a demonstrated history of working in the information technology and services industry.

He is an established Thought Leader in Artificial Intelligence, Data Science, Quantum Computing, Blockchain, IoT, Digital Transformation and helps organisations to become data driven.

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