Data and analytics (D&A) is decidedly the next frontier for innovation and productivity in business. A recent Mckinsey report says data-driven organizations experience EBITDA growth of 25% over their lagging peers. According to BCG, the top 10 most innovative companies in the world have cultures of data-driven decision making.
But achieving a sustainable competitive advantage from D&A is very challenging, with many such projects falling flat. According to Gartner, only 20% of the data and analytics solutions deliver tangible business outcomes.
While there are many reasons for these challenges, the most common is a lack of data-driven culture. Gartner’s third CDO survey lists it as the number one barrier to benefitting from D&A. An HBR report states that many enterprise organizations still don’t have a data-driven culture.
But what exactly is a data-driven culture? The collective beliefs and behaviors of the people in an organization with respect to leveraging data for improved business performance.
Fundamentally, a data-driven culture enables organizations to be more effective and efficient. According to Forrester, those that use data to derive insights for decision making are three times more likely to achieve double-digit growth. A report from MIT similarly found that a data-driven culture results in increased revenue, improved profitability and enhanced operating efficiencies.
Data-driven organizations experience EBITDA growth of 25% over their lagging peers.
So, how can an organization build a such a culture? While there are many aspects, below are four key enablers for the modern enterprise:
I. Inculcate a Service Culture
Service culture is an outlook that focuses on consistently creating value and trust with stakeholders. To provide consistent service, there must be a reliable frame of reference for each service level, and this frame comes from data. Consider these three key strategies to build a service culture in your organization:
- Data-driven culture should start at the top where the C-suite makes decisions on growth, cost, and risks using data. This can be further bolstered by being receptive to new ideas based on facts or data.
- Treat everyone who consumes the products or services in the company as a customer. A customer doesn’t have to be the one who pays for the product or services and can be an internal stakeholder who consumes it.
- Build data literacy. Strong data literacy empowers the organization to ask the right questions, derive actionable insights, and make intelligent decisions to serve stakeholders ethically and efficiently.
II. Focus on Continuous Performance Improvement
A consistent business model depends on quality data and insights to measure and improve its performance. At the core, measurement creates visibility, and visibility drives performance. Below are three key techniques required to drive performance using concrete measurements and quality data.
- Build a KPI framework that encompasses both leading and lagging metrics. Research says that the number of KPIs in the measurement framework should be 7 +/- 2.
- Set the baseline. The baseline performance is the current performance level that will be compared against future performance levels to validate and verify performance improvement.
- Find owners for KPIs. Successful change initiatives rest on accountability for addressing the gaps between expected and actual performance. Collective ownership is key.
Key Enablers
• Inculcate a Service Culture
• Focus on Continuous Performance Improvement
• Emphasis Consensus over Hierarchy
• Leverage Technology
Any enterprise transformation initiative is fundamentally about people and the culture they cultivate. Flashy new technologies often overshadow this truth.
III. Emphasize Consensus over Hierarchy
A consensus-based culture relies on collective insights driven by data, unlike the hierarchical culture where decisions are based primarily on title, position, and seniority. Below are three strategies to build a consensus-based culture:
- Include the right subject matter experts (SME) in each decision-making process. Research by Bain Consulting found that the optimal number of SMEs to make a decision is 7.
- Validate the objectives, questions, KPIs, assumptions, and ethics of the decision by having data-driven feedback mechanisms. Ensure that the problem is framed correctly and the decision is arrived at with minimum bias.
- Realize performance improvements by breaking down the objectives into smaller tasks and setting milestones based on targets, control limits, and specifications.
IV. Leverage Technology
While technology alone might not be the driver or solution to realizing a data-driven culture, it is a significant enabler. Successful business transformation programs have a strong correlation to technology due to the scale, speed, and cost benefits technology offers. Below are three strategies to leverage technology to build a data-driven culture.
- Capture critical data elements such as products, customers, assets, and more in a central system of record (SoR) for a single version of the truth.
- Deploy predictive analytics solutions to be a proactive organization and better manage business resources and cash flow by anticipating challenges.
- Manage access to data with appropriate role-based access controls (RBAC) so that the data is secure and business users consume data and insights that are relevant to their roles.
Overall, without a strong data-driven culture, organizations will miss opportunities to make faster, more informed decisions and innovate in the marketplace. Once the four building blocks above are in place, leaders can begin improving their organizations’ performance with access to real-time insights into historical, current, and future business decisions.
About the Authors
Dr. Prashanth Southekal is managing principal of DBP Institute (www.dbp-institute.com), a data and analytics consulting, research, and education firm. He is also an advisor at Astral Insights. He is a Consultant, Author, and Professor. He has consulted for over 80 organizations including P&G, GE, Shell, Apple, and SAP. Dr. Southekal is the author of two books — “Data for Business Performance” and “Analytics Best Practices” — and writes regularly on data, analytics, and machine learning in Forbes.com, FP&A Trends, and CFO.University. His second book, ANALYTICS BEST PRACTICES was ranked the #1 analytics book of all time in May 2022 by BookAuthority. Apart from his consulting pursuits, he has trained over 3,000 professionals worldwide in Data and Analytics. Dr. Southekal is also an Adjunct Professor of Data and Analytics at IE Business School (Madrid, Spain). CDO Magazine included him in the top 75 global academic data leaders of 2022. He holds a PhD. from ESC Lille (FR) and an MBA from Kellogg School of Management (U.S.). He lives in Calgary, Canada with his wife, two children, and a high-energy Goldendoodle dog. Outside work, he loves juggling and cricket.
Christopher Andrassy is an entrepreneur and managing partner at Astral Insights, focused on transforming data into sustainable business value on a global scale. He began his career at PwC in New York City, supporting the digital transformation of mature organizations struggling to innovate in a hyper-competitive world. After experiencing the shortcomings of traditional analytics practices, he decided to begin a new chapter alongside colleagues and industry veterans. His departure from New York marked the inception of Astral Insights, a Raleigh-based decision intelligence firm helping mid-market and enterprise clients transform data into profit. Chris is also an investor focused on innovative technologies including synthetic biology, sustainable energy, and artificial intelligence. Outside of work, he is an avid musician, skier, traveler, and fitness enthusiast.
