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No such thing as ‘average’ for data-driven behavior

Results from a 451 Alliance survey show that a majority of organizations continue to base most of their strategic decisions on data, and that data is growing in importance over time. Some stark differences in data-driven organizational behavior are also apparent, depending on measures of self-reported data-driven maturity, such as early adoption of technology and digital transformation progress.

Common themes still exist, however, with businesses overall shifting toward more long-term objectives in their data-driven behavior and consistently acknowledging the relevance or challenges associated with data privacy and security. As businesses seek to become more data driven, tools and roles also evolve over time.

The take

Our surveys in data management and analytics over the past two years have suggested a growing divide between organizations that doubled down on data-driven investments and behavior and those that pulled back — now those that pulled back appear to be falling behind. The results from this survey cycle emphasize this growing chasm, with survey aggregates often obscuring the polarity of responses between different organizational demographics.

Most useful for analysis is often the distinction between “data driver” and “data drifter” groups: The former are organizations making nearly all or most of their strategic decisions based on data, and the latter are organizations making only some or few of their strategic decisions based on data. While nearly all organizations still strive to be more data-driven, it is suggested that advancing progress on the data-driven maturity curve is a sort of virtuous cycle. More investment and progress leads to more beneficial outcomes with data-driven measures, which often lead to further data-driven investment.

Summary of findings

Data-driven behavior is strongly correlated with an organization’s self-identified rates of data-driven decision-making, new technology adoption and digital transformation progress. Averages can be deceiving. This is especially true in data management and analytics, where there continues to be a bifurcation between businesses that double down on investment and those that pull back as a cost-cutting measure. While these patterns began to emerge at the beginning of the global pandemic, they persisted over time, and now appear to be highly engrained. In this sense, aggregate survey results often represent a melding of extremes. Clearer interpretation often requires deeper demographic analysis.

A majority of organizations make their strategic decisions based on data, and data continues to grow in importance over time. Currently, 64% of survey respondents report nearly all or most strategic decisions at their organizations are data driven. Data itself continues to grow in importance.

Nearly eight out of 10 respondents (79%) think that data will be more important to their organization’s decision-making over the next 12 months.

Only 14% assert that data will remain at the same level of importance in decision-making, and a thin margin of 4% report that data will become less important overall.

Expected benefits of being more data driven underscore long-term business resiliency rather than just quarterly performance cadence. Prior to the pandemic, it was common for enterprise survey respondents to rank lowering costs and increasing sales as top expected benefits of becoming more data driven — tangible metrics closely tied to quarterly performance. Today’s top perceived benefits are more focused on long-term business resiliency and adaptability. The top three responses now include improving/automating business processes (41%), increasing agility of decision-making (40%), and enhancing customer service and engagement (39%).

Top technical barriers associated with being more data driven boil down to data quality, data security and data privacy. There are many challenges facing organizations as they try to become more data driven, with some top influences being cultural or structural in nature, such as availability of skilled resources and talent, as well as availability of budget. When it comes to technical challenges, some common themes emerge. Data quality and consistency is the top overall reported barrier to becoming more data driven, reported by 35% of respondents; data security (30%) and data privacy (28%) follow closely behind, suggesting the complexity of the data landscape is confounding efforts at both data consistency and data control.

Emergent data-driven tools and roles see widespread adoption, but adoption patterns differ based on organizational maturity. In our survey, the majority of respondents’ organizations have adopted specific data-driven tools, roles or programs. These tools include data fabrics, self-service data catalogs, self-service data marketplaces, dedicated data engineering functions, self-service programs for data preparation, self-service programs for data visualization/analysis and programs for secure exchange of data with external parties. Yet adoption is often bifurcated. Highly data-driven organizations are often much more likely to report adoption relative to less data-driven peers. For example, 70% of respondents from data-driver organizations report a self-service data catalog currently in use, relative to only 33% of respondents from data-drifter organizations. Similarly, 60% of data drivers report using a data fabric, while only 27% of data drifters do.

Data-associated roles within the enterprise continue to evolve and mature, with the chief data officer emerging as the likely head of data-driven coordination for many organizations. Today, 39% of respondents report their organization currently employs a CDO; that number rises to 49% for data-driver organizations. Survey participants report that the CDO most commonly reports to the CEO, suggesting this is emerging as a highly strategic role. The data engineering role is also well represented. While 65% of respondents overall report their organization has a dedicated data engineering function, rates vary depending on an organization’s data-driven maturity. A total of 75% of survey respondents from data-driver organizations report having a data engineering function, versus only 46% at data-drifter organizations.

Secure exchange of data with external parties is a key concern, with a majority of organizations having mechanisms in place to facilitate such exchange. Seven out of 10 (70%) survey respondents report their organization currently has a program for secure exchange of data with external parties such as partners or suppliers. In the finance industry specifically, this rate rises to 88%. Much like other data-driven practices in the survey, secure data exchange is more likely to be indicated by data-driver organizations that identify as highly data driven in their decision-making. Programs for secure data exchange directly address some of the most common challenges reported in this survey for becoming more data driven overall, particularly data security and data privacy.

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Published by Paige Bartley

Paige is a Senior Research Analyst for the Data, AI & Analytics Channel at 451 Research, a part of S&P Global Market Intelligence, covering core data management topics. Key themes include data privacy, data governance, data integration, metadata management, data quality and master data management. She has experience covering a broad range of technologies spanning the Data, AI & Analytics stack, from databases to self-service analytics. In her current research, Paige is analyzing the need for information governance to maximize the value of enterprise data amid proliferating global regulatory requirements and rising consumer expectations for data stewardship. With data privacy and compliance as a specialty focus, Paige explores how the enterprise can align technical requirements with business strategy, enabling more profitable and compliant leverage of data. Early in her career, Paige worked on the vendor side, providing marketing and strategy for ZL Technologies, an information governance provider that specializes in the management of unstructured data for compliance, legal and archiving needs. Prior to working at 451 Research, she was a Senior Analyst at Ovum (now Omdia). Paige received her Bachelor of Science degree in psychology and neuroscience from Duke University, and a Master of Management Studies (MMS) degree from the Duke University Fuqua School of Business.