It’s rare to find an organization, especially one large and complex, in which data is tightly controlled and used flexibly. With a few exceptions, CDOs find that their best data strategy emphasizes defense and control or attack and flexibility. Paying the same attention to attack and defense is sometimes optimal, but in general it is unwise to establish a 50/50 split instead of making well-considered strategic considerations.

This allowed the company to streamline its key data systems; eliminate a large part of the supporting IT infrastructure, such as databases and servers; and reduce operational costs by automating pre-manual data consolidation. Automation alone delivered a 190% return on investment with a payback period of two years. Many companies will find that they can fund all of their data management programs, including staff salaries and technology costs, from the savings realized by consolidating data sources and dismantling legacy systems. In every organization we’ve spoken to, the two compete fiercely for finite resources, funds, and people. As we’ll see, it’s optimal for some companies to put the same emphasis on both.

For example, insurance and financial services firms typically operate in highly regulated environments, which argues for an emphasis on data defense. (That’s the case with AIG.) Retailers, operating in a less regulated environment where intense competition requires good customer analysis, may highlight the violation. Many organizations have tried to create highly centralized, control-oriented approaches to data and information architectures. Formerly known as information engineering and now as master data management, these top-down approaches are often not suitable to support a broad data strategy.

There is an increasing focus on data management and analysis as more and more organizations recognize the potential and importance of data. In contrast, several companies we studied found that data breach is best performed through decentralized data management, typically with a CDO for each business unit and most business functions. “Unit CDOs” tend to report directly to your company, but have a matrix reporting relationship with the company’s CDO. This helps prevent the development of data silos and ensures that best practices are shared and standards are followed. In general, unitary CDOs have their respective versions of the truth, while enterprise CDOs possess SSOT.

In this article, we describe a new framework for building a robust data strategy that can be applied across industries and data maturity levels. The framework is based on our implementation experience at global insurer AIG and our study of half a dozen other large companies where its elements have been applied. The strategy enables superior data management and analysis, essential capabilities that support managers’ decision-making and ultimately improve financial performance. It is important for any organization because it provides timely and accurate information to manage accountability and services. Therefore, the high quality of the data will lead to adequate information and valuable information for any organization. DCODE GROUP is an IT consulting firm that provides customized web-based systems for businesses.

Offense involves working with business leaders on tactical and strategic initiatives. Leaders may be reluctant to engage in master data management, but they like to work together to optimize spending on marketing and business promotion. A company’s data architecture alteryx training describes how data is collected, stored, transformed, distributed, and consumed. It contains the rules that apply to structured formats, such as databases and file systems, and systems for connecting data to the business processes that use them.

But even with the rise of data management functions and data managers, most companies are still far behind. Cross-sector studies show that, on average, less than half of an organization’s structured data is actively used in decision-making and less than 1% of unstructured data is analyzed or used. More than 70% of employees have access to data they shouldn’t, and 80% of analysts’ time is spent discovering and preparing data. Data breaches are common, malicious data sets spread in silos, and companies’ data technology often doesn’t meet the demands placed on it.

What is critical is that the individual sources of truth remain unique and valid, and that the multiple versions of the truth differ from the original source only in carefully controlled ways. A large industrial company we studied had more than a dozen data sources that contained similar supplier information, such as name and address. For example, a source identified a vendor as Acme; another called it Acme, Inc.; and a third named it ACME Corp. Meanwhile, various functions within the company relied on different data sources; often the functions were not even aware that alternative sources existed. People might be able to untangle such problems (although it would be labor-intensive), but traditional IT systems can’t, so the company wouldn’t really be able to understand its relationship with the vendor. Fortunately, there are AI tools available that can sift through such data chaos to put together an SSOT.