In 2021, McKinsey published the results of one of their surveys: 71% of customers want personalization. And 76% of customers are frustrated because they are recommended irrelevant products or offers. Businesses today collect so much data about their customers (often long before they become customers) in spite of growing criticism and newer regulations around data privacy. Yet, how much of this data is actually harnessed to smoothen out the customer’s journey or deliver a better experience? 

If you feel you are doing well in this regard, you are part of the minority - studies indicate that over 70% of companies don’t know what to do with the data they collect. In many cases, this is due to technical constraints - they don’t have a proper mechanism for analyzing data, or for deriving insights that can be used to drive business decisions. 

But in just as many cases, the problem is at a more fundamental level: the belief that only some data is useful, that only some people need to worry about data, that only some decisions need data. When your organization as a whole develops a data mindset, everyone will know that this isn’t true. 

Data mindset is an attitude that uses data to inform decision making and drive outcomes. It is demonstrated through a way of working in which people seek information and analyze it to improve their understanding of the situation. 

At the individual level, it may mean having conversations with subject matter experts in their domain, getting access to relevant data sources, gaining a deep understanding of utility and limitations of the collected data, and processing that data to come up with insights. This can be used to drive, delay or stop action. 

At a team level, the same facts can lead to varying interpretations. Collective wisdom gets enriched when more hypotheses can get built and quickly. When the team moves from anecdotal evidence to empirical evidence, several biases get reduced. It can lead to a robust decision making process. 

At a business level, there is a plethora of data to be consumed, and often the data is imperfect. Businesses with a data mindset have the ability to deal with ambiguity and use data optimally to make decisions.

When a data mindset manifests itself at all the levels in a firm, it becomes easy to take smart risks for improving business outcomes. Which is why, building a data culture with a supportive data environment is absolutely essential.

Find out: How deep is your data culture?

Here’s a questionnaire you can use to assess your organization’s fundamental ability to leverage data effectively.

If you’ve mostly agreed with these statements, congratulations! Your organization seems to be working with an active data mindset. Read on to see how that intent can be translated effectively into execution. If you’ve mostly disagreed or remained neutral, this blog will hopefully convince you to move in the right direction.

Why a data mindset matters

Why is data a more important buzzword for organizations today than, say, ‘instinct’ or ‘experience’?  For one, the world is evolving rapidly—too quickly for yesterday’s experience to hold true in every situation. You can go from the drawing table to the customer in a day’s time, what with no-code app development, 3D printing, digital delivery, and so on. Experience now is more valuable as a way of thinking than the outcome itself. 

For another, we are all humans, and humans are notoriously prone to biases. At this point of time, studies have identified over 180(!!) cognitive biases. At best, we are merely unconscious of our biases when we make decisions that are driven by instinct or intuition. At worst, there is an agenda that makes our decisions for us. The impact of these biases increases exponentially with every human stakeholder involved in decision making. 

Consider the case of a furniture seller whose new product - an aggressively-priced ash-gray chenille sectional sofa - is seeing a lot of demand. But what is actually driving the interest among customers? Is it the color (ash-gray), the fabric (chenille), the configuration (sectional) or the price? The wrong decision, made purely on the basis of instinct or experience, can result in inventory worth lakhs of rupees gathering dust in the warehouse. 

Businesses that can separate biases from their decisions are more likely to achieve better outcomes more consistently.

A Three-Tiered Organizational Shift

To build a data mindset in your organization, you need to think of the shift happening at three levels.

Be mindful of one of the major dangers while developing a data mindset in your organization: analysis paralysis. Just the sheer amount of data that’s available on tap can lead to endless rounds of analysis and discussions that never result in actionable decisions. 

A good way to guard against this risk would be to set up very clear problem statements and time-bound expectations for all the decision makers right from the outset. Where the stakes are higher (such as tying up a significant portion of your cash flow in inventory or capex), periodic review milestones can be built into the plan so that the agility of the business to respond to other opportunities or crises is not compromised.

Strategies for cultivating a data mindset

The evolution of an organization from being one that merely uses data to one that is driven by data can be rapid, but only if it is supported through the following:

1. Enabling Continuous Learning

The field of data analytics is ever-evolving, with new tools, techniques, and best practices emerging regularly. To stay ahead, businesses must encourage their teams to embrace a mindset of perpetual learning and facilitate this to address current skill gaps and prepare the team for future challenges.

How can you do this?

• Give access to existing learning resources and sponsor the development of new ones to keep pace with new tools and techniques. 
• Promote diversification of skills by encouraging team members to explore areas beyond their immediate roles. E.g. cross-training programs, rotations or shadowing opportunities, allowing team members to see how their work integrates into the broader organizational context.
• Incorporate learning objectives and OKRs into performance reviews. This will drive home the importance of data literacy as a measurable competency and boosts personal motivation for investing effort in mastering it. 

2. Promoting Analytical Thinking

This Isn't as technical as it sounds. Fundamentally this involves instilling an enquiring mindset in your team members—asking questions, seeking evidence, and being able to distinguish between instinctive and data backed ideas/suggestions.

How can you do this?

• Host Insight hackathons where you give the team access to organizational data sets and challenge them to derive meaningful insights.
• For every project, from marketing campaigns to product feature discussions, encourage the team to form hypotheses and test them before making decisions. 
• Introduce a monthly analytical thinking challenge. Pose a problem or scenario that requires employees to analyze information, identify patterns, and propose solutions.

3. Cross functional collaboration

It is not enough for just your data team to have a data mindset. This thinking has to extend to teams across functions and levels. By fostering cross functional collaboration, you ensure that data teams can work hand in hand with marketing, sales, customer service, finance and every other team.

How can you do this?

• For every strategic project, form problem solving teams that have members from different functions working together to arrive at a solution.
• Regularly host peer learning sessions where members from different teams can share their data driven approach to problems within their function or area. These can be informal brown bag sessions, virtual sessions, or a part of your all hands meetings.
• Create an organizational knowledge hub where learnings from different functions and areas are uploaded in a form that is easy for other teams to access and understand. They must also be able to reach out to people involved in a particular project and seek specific insights.

4. Integrating Business Objectives

Your team might be proficient in data analytics - but they also need to understand how their work contributes to the overall success of the organization. This integration ensures that data-driven insights are not isolated but directly inform strategic decision-making.

How can you do this?

• Clearly define short-, mid- and long-term strategic goals and explicitly connect each data project to these goals. When initiating a new project, communicate how the outcomes of the project will contribute to achieving business objectives. 
• Involve the data analytics team in strategic planning sessions. This allows them to contribute their expertise and ensures that data considerations are integrated into the overall strategic decision-making process.
• Foster a culture of open communication where team members feel comfortable asking questions about decisions in the broader business context. The more approachable the leadership, the more likely it is that team members will scale up faster to an organization-level thinking instead of restricting themselves to my-role-my-deliverables.
• Regularly share success stories and achievements of the organization with the entire team. Highlight instances where data analytics played a crucial role in decision-making that led to positive outcomes. Even ‘failure stories’ can be turned into learning opportunities if they are presented in a way that highlights what went wrong and what could have been done better.
• Create feedback loops. When key decisions have been made with data, the cycle should not end with implementation. Instead, the outcome should be analyzed and used to develop a deeper understanding of what aspects of data, its analysis and insight generation worked, and what needs to be improved upon. 

Some practical ideas/suggestions

• OKR planning: Do a quarterly exercise with the team asking them to develop their OKRs and tying this to business outcome every quarter. 
• Expert talks: Invite analytics leaders and business leaders to address the team and have AMAs. This will expand their mental canvas and rethink their approaches.
• Retrospectives: As each project gets completed, share the learnings with the team while also answering the question, “If I had to re-do this project, areas I would tackle differently/better are…” This will get the team to think in both 'zoom in' and 'zoom out' modes.
• Storytelling: Teach your team how to communicate complex data insights in an interesting and effective way using storytelling techniques. This will make it easier for non-technical stakeholders to understand and appreciate the value of data.
• Real-world use cases: In every learning program, share real-world use cases, not just theoretical or hypothetical situations. Introduce gamification elements (quizzes, challenges, simulations) to make learning about data enjoyable and interactive. 
• Data in everyday work: Integrate data into your daily workflows, making it a natural part of the decision-making process. For example, using data to support arguments, validate assumptions, and inform choices in regular team meetings.
• Publicizing data: Make room in your communication channels (newsletters, Slack, etc.) to share data-related learning resources and success stories of how data aligns with the business’ strategic goals.

If you are in the early days of some of these strategic shifts, you might encounter skepticism or even resistance. Be patient—don’t expect a cultural shift overnight. Build a team of data evangelists—not just leadership but folks from different teams—who believe in the power of a data culture. Make sure that there is a feedback loop in place so that you can iterate and improve some of these initiatives. And lastly, don't be overly ambitious and try to implement all of these in one shot. As with any project, have clear goals, proceed in phases, and be consistent with your efforts.