Strategies for Optimizing Enterprise-Level Data Consumption
The capacity to handle massive amounts of data is more important than ever. Despite the rise of data-management roles and chief data officers (CDOs), most businesses are still falling behind. According to cross-industry research, less than half of an organization’s structured data is actively employed in decision-making—and fewer than 1% of its unstructured data is evaluated or used at all. More than 70% of workers have unauthorized access to data, and 80% of analysts’ effort is spent just identifying and preparing data. Data breaches are prevalent, rogue data sets are spread in silos, and firms’ data infrastructure is often unable to meet the demands placed on it.
Having a CDO and a data-management department is a good start, but neither will be entirely successful until there is a cohesive strategy for organizing, controlling, analyzing, and utilizing an organization’s information assets. Indeed, many firms struggle to safeguard and utilize their data in the absence of such strategic management—and CDO tenures are often challenging and brief (just 2.4 years on average, according to Gartner). Nallas offers a new paradigm for developing a strong data strategy that can be implemented across sectors and degrees of data maturity in this paper. The framework is based on our implementation experience as well as our research of a half-dozen other large firms where its aspects have been used. Superior data management and analytics are enabled by the approach, which a the approach enables superior data management and analytics re critical competencies that help managerial decision-making and improve financial performance. performance.
The “plumbing” components of data management aren’t as glamorous as the predictive models and eye-catching dashboards they generate, but they’re critical to high performance. As a result, they are not just the duty of the CIO and CDO; ensuring wise data management is the responsibility of all C-suite executives, beginning with the CEO.
Offense vs. Defense
Our paradigm solves two major concerns: It assists businesses in clarifying the core purpose of their data and supports them in strategic data management. Unlike previous models we’ve seen, ours necessitates firms making deliberate trade-offs between “defensive” and “offensive” data uses, as well as between control and flexibility in its use, as we detail below. Although there is a wealth of material available on business data management, most of it is technical and focuses on governance, best practices, tools, and the like. Few data-management frameworks, if any, are as business-focused as ours: It not only encourages effective data utilization and resource allocation, but it also assists businesses in designing their data-management operations to complement their entire strategy.
Different corporate goals and efforts meant to satisfy them distinguish data defense and offensive. Data protection is all about limiting risk. Among the activities include maintaining regulatory compliance (such as standards controlling data privacy and the accuracy of financial reporting), employing analytics to identify and restrict fraud, and developing systems to prevent theft. Defensive activities also maintain the integrity of data moving through a company’s internal systems by identifying, standardizing, and managing authoritative data sources in a “single source of truth,” such as core customer and supplier information or sales data. Data offensive is concerned with achieving corporate goals like as increased revenue, profitability, and customer happiness. It often involves operations that provide consumer insights (for example, data analysis and modeling) or combine disparate customer and market data to assist management decision-making through interactive dashboards, for example.
Offensive activities are often more relevant for customer-focused business tasks such as sales and marketing, and they are generally more real-time than defensive activity, which focuses on legal, financial, regulatory, and IT problems. (An exception would be data fraud detection when seconds matter and real-time analytics skills are essential.) Every business needs both attack and defense to flourish, but striking the correct balance is difficult. Every company we’ve discussed has tight competition for limited resources, financing, and personnel. As we will see, placing equal focus on the two is beneficial for certain businesses. However, for many others, it is better to choose one or the other.
Some company or environmental factors may influence the direction of data strategy: Strong regulation in an industry (financial services or health care, for example) would move the organization toward defense; strong competition for customers would shift it toward offense. The challenge for CDOs and the rest of the C-suite is to establish the appropriate trade-offs between defense and offense and to ensure the best balance in support of the company’s overall strategy.
Decisions about these trade-offs are rooted in the fundamental dichotomy between standardizing data and keeping it more flexible. The more uniform data is, the easier it becomes to execute defensive processes, such as complying with regulatory requirements and implementing data-access controls. The more flexible data is—that is, the more readily it can be transformed or interpreted to meet specific business needs—the more useful it is in the offense. Balancing offense and defense, then, requires balancing data control and flexibility, as we will describe.
Single Source, Multiple Versions
Before we explore the framework, it’s important to distinguish between information and data and to differentiate information architecture from data architecture. According to Peter Drucker, information is “data endowed with relevance and purpose.” Raw data, such as customer retention rates, sales figures, and supply costs, is of limited value until it has been integrated with other data and transformed into information that can guide decision-making. Sales figures put into a historical or a market context suddenly have meaning—they may be climbing or falling relative to benchmarks or in response to a specific strategy.
A company’s data architecture describes how data is collected, stored, transformed, distributed, and consumed. It includes the rules governing structured formats, such as databases and file systems, and the systems for connecting data with the business processes that consume it. Information architecture governs the processes and rules that convert data into useful information. For example, data architecture might feed raw daily advertising and sales data into information architecture systems, such as marketing dashboards, where it is integrated and analyzed to reveal relationships between ad spending and sales by channel and region.
Many organizations have attempted to create highly centralized, control-oriented approaches to data and information architectures. Previously known as information engineering and now as master data management, these top-down approaches are often not well suited to supporting a broad data strategy. Although they are effective for standardizing enterprise data, they can inhibit flexibility, making it harder to customize data or transform it into information that can be applied strategically. In our experience, a more flexible and realistic approach to data and information architectures involves both a single source of truth (SSOT) and multiple versions of the truth (MVOTs). The SSOT works at the data level; MVOTs support the management of information.
In the organizations we’ve studied, the concept of a single version of the truth—for example, one inviolable primary source of revenue data—is fully grasped and accepted by IT and across the business. However, the idea that a single source can feed multiple versions of the truth (such as revenue figures that differ according to users’ needs) is not well understood, commonly articulated, or, in general, properly executed.
The key innovation of our framework is this: It requires flexible data and information architectures that permit both single and multiple versions of the truth to support a defensive-offensive approach to data strategy.