Companies are realizing the need to integrate analytics throughout their entire business to capture significant value. However, many struggle to achieve this goal. A survey by McKinsey Analytics identified a group of elite companies that have successfully implemented analytics at scale.   To replicate the achievements of the top 8% of companies in scaling analytics, organizations should focus on the nine key drivers of their success. These drivers can be categorized into three main areas: strategy, foundational capabilities, and activities that specifically target the integration of analytics throughout the organization.  

Driver #1: Achieving a robust and cohesive commitment from management at all levels 

Breakaway companies, compared to their peers, demonstrate stronger alignment among their leadership team regarding the vision and strategy for analytics. These companies have a clear objective of integrating analytics throughout all operations, rather than limiting it to specific business units or functions. Consequently, breakaway companies are 3.5 times more likely than their peers to utilize analytics in three or more functional areas.  Breakaway companies recognize the significance of obtaining buy-in from lower levels of the corporate hierarchy as well. In fact, 57% of the breakaway group’s middle management fully believes that becoming an analytics-driven organization is crucial for maintaining relevance and competitiveness.  

Driver #2: Enhancing investments in analytics, with a specific emphasis on the final stage of implementation and delivery  

Breakaway companies allocate a higher proportion of their budgets to analytics compared to other organizations, and they have plans to further increase these investments. These expenditures cover various aspects such as data, technology, analytics talent, and integrating analytics into business workflows. Notably, breakaway companies prioritize addressing the key challenge of maximizing analytics value, focusing specifically on the “last mile” by embedding analytics into core workflows and decision-making processes. Almost 90 percent of breakaway organizations allocate more than half of their analytics budgets to this endeavor. 

Driver #3: Establishing a well-defined data strategy accompanied by robust data governance practices  

Breakaway organizations are more likely than their peers to possess a clear data strategy and strong data governance practices for data identification and prioritization. Successful data strategies typically include four key elements: 
  • Clear data ontology based on current and future use cases. 
  • Master data model across essential domains (e.g., customer, product, location, employees) with assigned business ownership. 
  • Governance plans determine accountability for data quality and maintenance, categorizing data sets hierarchically based on importance. 
  • Technical understanding and plans for data environments, including dynamic updates and flexible data classification. 
These elements enable breakaway organizations to efficiently manage data, ensuring critical information is readily accessible while minimizing costs for non-mission-critical data. 

Driver #4: Leveraging advanced analytics methodologies  

Breakaway companies possess a well-defined methodology for developing analytics models, interpreting insights, and implementing new capabilities. These leading companies not only prioritize model development but also emphasize the ongoing maintenance and enhancement of models through a sophisticated model-management function. By continuously testing and upgrading analytics models using a challenge and test approach, breakaway companies stay at the forefront of quality and performance, consistently exploring and adopting superior alternatives.

Driver #5: Cultivating extensive analytics expertise through a customized talent strategy 

Breakaway companies possess deep functional knowledge in data science, data engineering, data architecture, and analytics transformation. They also employ over 25 data professionals per 1,000 full-time equivalents (FTEs), a ratio 2.5 times higher than other companies, particularly in certain industries.  To attract and retain top analytics talent, breakaway companies implement strategies beyond monetary compensation. They establish a clear core center of gravity for data professionals within the organization, led by executives like a chief analytics officer, fostering a sense of integration and alignment with organizational goals.  Furthermore, breakaway companies create specialized roles and career paths tailored explicitly for analytics professionals, rather than adapting existing positions. They establish innovation centers in talent-rich areas, recruit tech and analytics executives in key positions, forge career paths, and integrate professionals into crucial business areas through collaboration with managers from across the organization. 

Driver #6: Forming collaborative, cross-functional agile teams 

Breakaway companies establish collaborative cultures that drive innovation and advance analytics initiatives organization-wide. They prioritize cross-functional teams. These teams consist of dedicated business representatives, analytics translators, user-experience design experts, data engineers, and data scientists, working together in agile settings. The diverse composition of these teams helps prevent the formation of isolated silos and enables the development of impactful end-to-end analytics use cases. 

Driver #7: Giving priority to critical decision-making processes 

Breakaway companies differentiate themselves by prioritizing and mapping the decisions that yield the most value through timely data insights. This approach mirrors the business process reengineering wave of the past, reflecting an evolution in decision-making practices.  What sets breakaway companies apart is not the specific process used for decision prioritization, but rather the recognition and prioritization of key decisions. While prioritization may appear fundamental, breakaway companies are nearly twice as likely to identify and prioritize the top ten to fifteen decision-making processes for analytics integration. 

Driver #8: Defining Transparent Decision-making Authority and Accountability 

It is essential to have a clear understanding of the individuals within the organization who possess the authority to make analytics-based decisions daily. Additionally, holding business-unit leaders responsible for equipping their team members with the necessary tools is crucial. Breakaway organizations are over twice as likely as other companies to affirm this statement. 

Driver #9: Enabling frontline personnel to make data-informed decisions 

Securing management support is only the initial step; organizations must also empower frontline employees to effectively utilize data for decision-making. Breakaway companies are approximately 1.5 times more likely than other respondents to successfully achieve agile and continuously improved decision-making processes through analytics, a critical aspect of bridging the gap between insights and outcomes. 

Conclusion 

Companies across industries and sizes can enhance the breadth and significance of their analytics initiatives by incorporating lessons learned from breakaway companies in each of the nine areas mentioned. However, the most crucial insight from this research lies in the one aspect where even some breakaway companies struggle: closing the gap in the last mile.  Many companies embark on their analytics journey by focusing primarily on data, assessing their existing resources, and exploring potential applications. Yet, this approach inherently limits the impact of data. To achieve analytics at a larger scale, companies should reverse their approach. They should begin by identifying the decision-making processes that can be enhanced to generate added value in alignment with their business strategy. Subsequently, they can work backward to determine the specific data insights necessary to influence these decisions and how the company can effectively provide them. In essence, the last mile should serve as the starting point for the analytics journey.