Summary: How do companies’ investments in data and analytics contribute to business benefits like profitability and growth? Should investments focus on talent, technology, or culture? Recent research highlights alignment between business and analytics objectives and capabilities as crucial. Even companies early in their analytics journey see value creation despite internal misalignment. However, companies at higher data maturity levels find that aligned analytics capabilities significantly enhance growth, financial, and customer KPIs.
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Business leaders are under significant pressure to rapidly enhance their company’s data and analytics capabilities. Moreover, they face the prospect of trailing behind competitors who are more adept with data. Achieving success in this area is far from simple! Previous research findings pointed that leveraging data and analytics involves establishing a robust data culture. Moreover, it involves securing commitment from senior leadership, acquiring essential data and analytics skills, and empowering employees. Each of these aspects is crucial to embark on the analytics journey effectively.
What is the definition of success here? And how could companies quantify it?
The metrics for digital transformation success have evolved. In 2018, a Gartner survey highlighted creating a data-driven culture, advanced analytics adoption, and a robust implementation strategy as crucial. By 2022, priorities shifted to creating business value in the marketplace, focusing on growth goals.
This evolution reflects a natural progression towards accountable data and analytics. Companies in digital transformations must ask: How do investments in data capabilities benefit profitability and growth? Talent, technology, and culture investments matter in achieving aligned business goals through analytics development.
How Companies Create Values with Analytics
To investigate how companies create value through analytics, an international group of professors conducted a survey encompassing 323 firms. These firms are currently undergoing data and analytics transformations. Among them, 143 were based in the United States, and 180 operated in India. The surveyed companies cover diverse economic sectors including telecom, healthcare, energy, CPG, automotive, logistics, and professional, and financial services. Notably, 43% of these companies reported annual sales exceeding $100 million. In addition, another 20% of the companies reported boasting sales surpassing the $1 billion mark.
Company-Internal Pairings
To understand the impact of alignment on business performance, two-person partnerships—an executive and a data analytics manager—were surveyed from each of the 323 companies. The selection aimed to compare strategic perspectives of senior executives with operational viewpoints of data analysts. Senior executives averaged over a decade in current roles, encompassing core business functions (12.6%), vice presidents (16.6%), and other C-suite members (16.8%).
These executives assessed their company’s data analytic capabilities and maturity level (low, medium, or high). They also evaluated their performance against competitors on eight key performance indicators (KPIs) using a five-point scale. Namely: much worse, somewhat worse, about the same, somewhat better, or much better than competitors. These KPIs included growth metrics. Specifically: market share, sales, and profit. In addition, they included financial indicators (revenue and profitability), and three customer-focused outcomes (acquisition, retention, and satisfaction).
Additionally, senior executives nominated operational data managers with expertise in data, analytics, or IT for the study. The resulting 323 managers averaged more than nine years of work experience. They also assessed their company’s data maturity stage (low, medium, or high). Importantly, senior executives were unaware of the managers’ survey questions, reducing the risk of selection bias in the study’s findings.
Main Findings
The survey question completed by both senior leaders and data managers about their company’s data capabilities (low, medium, or high maturity) was pivotal. Companies at low maturity levels engaged in manual data compilation, non-standardised reporting, and data silos. Medium maturity involved deliberate data collection and analysis, leading to informed business decisions. At high maturity, data became foundational for all decision-making processes.
The research findings revealed a startling trend advancing along the data maturity spectrum led to enhanced KPI performance. Initially, transitioning from low to medium maturity yielded significant improvements in growth (8.7%), financial (8.9%), and customer-related KPIs (9.9%). However, benefits declined beyond the middle maturity stage.
As firms reached high data maturity levels, performance across KPIs remained positive but dropped significantly from mid-level peaks. Growth KPIs decreased by 9.6%, while financial and customer KPIs dropped by 45.5% and 43.4%, respectively. These findings suggest a ceiling effect at the middle data maturity stage. Therefore, advancing beyond this stage requires more than talent and technology investments for further growth.
Further analysis underscored nuanced implications for companies undertaking data analytics transformations. Differences in data maturity alignment between senior leaders and analysts highlighted divergent performance across KPI categories.
Companies Experiencing Misalignment
Companies positioned at the lower end of data maturity experience notable improvements in business performance by investing in capabilities that elevate them to the mid-range. This progress occurs even amid internal misalignment. However, the gains achieved during this phase dissipate. Also, they can turn negative when these same companies attempt to transition from the mid-range to the high end of data maturity.
The leap from medium to high data maturity levels appears daunting. As a matter of fact, it demands more than mere investments in data and analytics talent, tools, and technology. To successfully bridge this gap, companies must not only enhance their proficiency in data analytics. Companies must also foster internal alignment regarding their true data capabilities. In addition, they must ensure consensus on the company’s data analytics competency.
Companies Demonstrating Alignment
Alignment within companies plays a crucial role in driving positive KPIs across various stages of data maturity. This impact is particularly pronounced during the transition from medium to high capabilities. Unlike misaligned counterparts, whose gains are primarily evident in the shift from low to medium maturity, companies that maintain alignment experience continuous and significant enhancements in business outcomes. This improvement becomes particularly pronounced as they progress from the mid-range to the high-end of data maturity.
Fostering Organisational Alignment
Companies at all levels of data maturity, particularly those embarking on digital transformation, may need to pause additional investments in talent and technology. Ensuring alignment between senior leaders and operational data roles is crucial. This alignment gains significance as companies progress from medium to high data maturity stages. The research findings indicate that while basic analytics suffices in low to medium data maturity, alignment becomes crucial in medium to high stages.
In this context, the researchers provided companies with a self-assessment tool and action steps to enhance internal alignment on data and analytics capabilities. The tool covers seven dimensions that impact a firm’s analytics readiness (AR) and help gauge alignment. They are: culture, analytics-strategy alignment, leadership commitment, operations and structure, employee empowerment, proactive market orientation, and skills and competences. Executives and analysts should independently answer these questions with “Yes” or “No”. The application of the tool uncovered four main findings.
Firstly, affirmative answers indicate closer alignment for superior business performance. Areas with negative responses highlight needed improvements. Secondly, contrasting the responses of a senior leader and an operational data manager facilitates pinpointing areas of (mis)alignment. This initiates informed discussions across the broader team on modifying areas of disagreement. Encouraging other team members to complete the tool can further enhance engagement and insight.
Thirdly, conducting regular checklist reviews allows senior leaders to stay updated and monitor the effects of ongoing internal changes in their organisation’s digital transformation journey. This helps in sustaining alignment within the company and consistently enhancing business performance.
Lastly, for a more reliable and cautious assessment of alignment, companies ought to involve individuals at the senior and operational levels who lack a direct reporting connection. This approach prevents potential overestimation of alignment due to close relationships among respondents.
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Across industries, the pursuit of becoming more analytics-driven and achieving data and analytics superiority is evident. The findings highlight the need for business leaders to prioritise this area. Examining how alignment interacts with data maturity levels can enhance company performance in growth, financial, and customer outcomes. Using a checklist provides leaders with visibility into their current alignment status and a roadmap for advancing to generate additional value. Finally, implementing the outlined action steps enables leaders to pinpoint weaknesses and fortify strengths in both analytic maturity and alignment. This is crucial for sustaining competitive advantage.
Source: Harvard Business Review (2024).
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