Designing and Building a Data Driven Organization Culture – A Best Practice Case Study

Executive Summary

Creating value out of the data assets that organizations hold in order to drive positive business outcomes is now a key agenda in every industry. The continuous evolution of data analytics/data science field supported by increasingly powerful technology landscape in recent years is making this possible more than ever. Organizations have now started to realize that data is the lifeblood of their business and is their strategic and competitive asset. However, there is a general misconception in industry that if business value is created from data using data science or intelligent data driven solutions such as AI, then an organization is data driven or has a data driven culture.

Using data to create business value e.g., data driven insights generation alone does not contribute to creating a data driven culture in an organization. It requires a combination of people, process, technology and a solid and sound data management culture to build a true data driven organization culture. In this paper, we provide a brief of how an organization successfully developed a data driven culture which is regarded as industry best practice.

Driving Data Driven Culture – The Approach

The case study is about one of the fastest growing and successful general insurance companies in India, a subsidiary of one of the top 5 banks in India.

What is Data Driven Organization Culture

Performing analytics/advanced analytics and building data driven intelligent solutions (e.g., AI solutions) to drive business outcomes using data does not necessarily mean an organization is “data driven” or has a “data driven culture”.

An organization has a data driven culture if it is able to manage and govern the “lifecycle” of its data assets effectively and efficiently. This would enable the organization to organize and democratize its data assets for consumption. Democratization of data helps to drive data related activities in an acceptable manner in order to make informed decisions, create value, resolve conflicts and manage risks.

Democratization of data by ensuring that the data assets flow seamlessly and interoperate across the organization business processes and technology systems and reaches the hands of the users with minimum fuss.

Figure 1 below gives an overview of the “Lifecycle of Data” supported by Data Governance and Management functions. “Use” component of the data lifecycle is where data science and AI focus is. Close to 80% of the time is spent by data analytics professionals to prepare the data for value creation. The reason is because of poor data management practices in organizations. Data lifecycle governance and management are therefore, foundational and fundamental to an organization and should be in the organization’s data strategy.

Jim Harris, a popular blogger on data management rightly says, “Bigger isn’t better, better is better. Although big data may indeed be followed by more data that doesn’t necessarily mean we require more data management in order to prevent more data from becoming morbidly obese data. I think that we just need to exercise better data management. Whether you choose to measure it in terabytes, petabytes, exabytes, HoardaBytes, or how much reality bites, the truth is we were consuming way more than our recommended daily allowance of data long before the data management industry took a tip from McDonald’s and put the word “big” in front of its signature sandwich. More Data becomes Morbidly Obese Data only if we don’t exercise better data management practices.

Whether an organization’s goal is to achieve digital transformation, “compete on analytics,” or become “AI-first,” embracing and successfully managing the lifecycle of data in all its forms is an essential prerequisite. Critical obstacles with regard to managing data still must be overcome before organizations begin to see meaningful benefits from their big data, analytics and AI investments. 

How the Organization Created a Data Driven Culture Successfully

Following are the key strategies executed by the Organization to drive data driven culture:

• Accountability from the Top: An enterprise data strategy to drive data driven culture and value creation requires support from the top namely, CEO and his/her team and this is critical.   They should lead from the front and by example to drive cultural change. Bottom-up approach is not sustainable. The CEO and Board of the Organization took ownership of driving data culture in the Organization. This was well supported by other layers of the organization. The Board and CEO were very clear that it is not about “Sponsorship”, but “Accountability & Ownership”.  A CEO can sponsor many initiatives, but not necessarily be accountable for the outcomes. The CEO took accountability as the “Data Champion” of the Organization, by walking the talk which have been summarized in this section.

• Performance KPIs to drive data culture: Data culture related KPIs were implemented for all employees across the organization as part of their balance score card that measures their performance. This means x% of CEO and Senior leadership team’s annual bonus is tied to these KPIs. Figure 2 shows the implementation of the performance KPIs. Few examples of KPIs include Data Quality measure of critical data elements (e.g., customer data) data literacy penetration rate, data risk management, accuracy of data driven insights used for decision making, etc.

• Comprehensive Data Literacy Program: All employees of the organization (new joiners or existing) across all levels irrespective of their designation and with no exceptions had to undergo a comprehensive data literacy program to help them understand the foundations of data management and use across data lifecycle and its value to the business as part of “Employee Training Program” conducted by Human Resources function.

• Minimum Data Standards Framework: Comprehensive and clear data principles, policies, standards and procedures with supporting practical data governance framework were developed and executed as part of data strategy and ensured that they were implemented across the organization.  Any initiatives including technology and business processes have to comply with these “minimum standards”. The implementations were regularly audited for compliance.

• Data driven/led Technology Solutions: All technology solutions were built/transformed based on data strategy. A comprehensive enterprise architecture approach was used to build technology solutions and supporting processes so that changes in the future could be well managed from impact perspective. The enterprise architecture was regarded as the “Blue Print of the Organization” and it has business architecture, data architecture, integration architecture, infrastructure architecture, solution architecture and security architecture components supporting the data strategy as Data architecture was seen as the bridge/common denominator between Business and Technology. Core foundational data components were implemented on which technology solutions were developed. Today’s technology is tomorrow’s legacy, processes change and, people move on. But this is not the case with data asset. They continue to add value. Therefore, ensuring that foundational data components are implemented was critical. Some of the key components implemented include Master Data Management, Data Governance, Data Security by Design and Data Quality by Design. 

• Comprehensive Data Quality Program: Quality of data driven business outcomes or decision making is directly proportional to the quality of the data used and, data quality is a business problem and not a technology problem. The organization implemented “Data Quality by Design” culture across the organization. Following were the key initiatives:

  • Data Quality Branded: Data Quality was branded with an icon called “DeeKew” (Figure 3) who served as the mascot of organization. All data quality related initiatives (internal or external) were tied under the brand. This brand is a reflection of how serious the organization is about data quality and it is used as a reminder to all its employees about the importance of data quality when they see “DeeKew”.

• • Measure Data Quality: Data Quality was measured, validated and monitored at the point of data entry done by employees (e.g., branches and call center employees) and technology applications against a set of Data Quality dimensions. Data Quality dashboards with supporting processes were produced to regularly monitor the health of data and any initiatives required to support improvement to data quality were implemented.

  • Data Quality Rewards for Employees: Data captured/entered in technology applications byemployees were measured and were rewarded accordingly  

  • Data Quality Award: All branches capturing/entering data were measured for data quality and best performing branch for the month and its people were rewarded with trophy and individual awards. Poor and best performing branches were published in organization’s portal.

  • Annual Data Quality Award: During Organization’s annual day celebrations, the CEO recognized and presented “Data Quality Branch of the Year” award to the best performing branch for the year from data quality perspective.

  • Data Quality Discount Program for Customers: For customers providing quality data (e.g., valid email address, mobile number, address, etc.), premium discount was introduced. Capturing accurate customer data helps the organization to serve the customer better.

  • Data Quality Discount Program for Brokers: Brokers tend to hold on to their customer and do not generally provide quality customer data. For brokers providing quality customer data, better commissions were provided.

  • Communication on Data Quality: The CEO led from the front by regularly sending newsletters and email communications to all employees from his desk on the importance of data strategy and its role and the importance of data quality to the organization.

• Data Risk Management: Data is the lifeblood of anyInsurance business.  Ensuring that the risks associated with data across its lifecycle is critical and the risks include data privacy, data ethics, data security, legal and regulatory. The organization introduced Data related risk as a key KPI in the Organizations’ Risk Profile that gets Board’s attention. Data related risks that were measured include included data quality metrics, data security and privacy metrics, data access and sharing related metrics.

• Examples of Data KPIs:

• 8% of the CEO and C level leaders’ bonus was tied to data related KPIs.

• Accuracy of entry of quality data minimum threshold for branches were set at 85%

• Data literacy rate across Organization was set at 100%

• Return of mail error rate was set at no more than 5%

• Completeness of customer data related error no more than 

In summary, the comprehensive data strategy agreed by all functions across the organization and supported with a solid and sound execution plan truly drove a data driven culture with the support of well managed data across its lifecycle in the organization. This was only possible because the CEO and the Board took accountability and led from the front. One thing that the data team learnt from this initiative is that they need plenty of Patience, Perseverance and Proactiveness to drive a data strategy and importantly, the right culture. The benefits of this data strategy work are continuing to reap rewards for the organization as the organization continues to grow.  

Organizing the processes and technologies to support Data Strategy are the easy ones. But changing the mindset of people to have a “Data First” or “Data by Design” culture is the hardest and it takes time and does not happen overnight. But an important learning that came out of this program is that “Where there is a will, there is more than a way!”