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Offensive and Defensive Data Strategies

Offensive and Defensive Data Strategies

Every organization must plan and execute a variety of strategies to be successful.  All organizations need a data strategy, with offensive and defensive components

There is no debate about the importance of data to organizations. Data is at the forefront of almost every discussion; it is connected with analytics, which depends on the data to derive accurate, realistic, and appropriate insights for business decision making.

Nonetheless, there is much division about what are effective data strategies. There are many questions pertaining to what the best ways are to formulate a data strategy, how to assess the organization’s existing strategies, and the most appropriate approach to transformative initiatives while also ensuring business continuity, achieving best results, and achieving maximum benefits. It is important to demystify this massive effort and find a productive and sensible approach to solving the problems associated with data strategy development and implementation.

What are Offensive and Defensive Data Strategies?

Avoid troubleBenefit from data
Minimize riskMaximize value
Compliance with regulationsCompetitive advantage
Financial integrityIncrease revenue/Profitability
Fraud detectionCustomer insights
Single source of truthInnovation and new product development
Legal, financial, compliance, and riskBetter decision-making

Figure 1. Defensive and Offensive Data Strategy Comparisons (Gupta, Cannon, 2020)

Defensive strategies – in essence, these are strategies established with compliance and risk minimization in mind. These strategies are a means to an end to fulfill internal and external requirements with a focus on having a single source of truth. It is a simple concept, but it inevitably imposes tremendous challenges to companies attempting to adhere to it, since data is usually presented differently and viewed as well as used differently by different people, as it should be. Companies, especially large ones, which are also the usual recipients of regulatory effort, often face the challenges with siloed data among various business components. With differing views and use of the same data, having a consensus in regulatory and financial reporting, therefore, becomes a common challenge, and can be addressed by defensive data strategies.

Offensive strategies – playing offense fulfills the desire and need for companies to stay competitive. These strategies stress deriving business values, fulfilling the visions and missions, as well as finding competitive advantages from internal as well as external data. Devising such strategies requires a thorough understanding of the underlying nature and structure of the business, knowing competitors, and following the industry trend in the micro-and macro-economic environment, etc. Offensive strategies require contemplating short-term necessities balanced against long-term visions. Effective offensive strategies allow companies to stay viable, relevant, thrive, and gain sufficient advantages against competitors through innovation and continued evolution.

Current state

While an effective and cohesive approach is important in both types of strategies, many companies view them as separate efforts. Defensive strategies are often simply viewed as an inconvenience, or a chore imposed upon them, while offensive strategies are favored in comparison due to their ability and potential to derive better performance, higher profitability, more customer satisfaction, etc. Although a case can be made for the need to keep them separate, within certain components, these two types of strategies do not need to be completely or mutually exclusive. Instead, with changes in perception and approach, looking at these two strategy types together can help companies leverage them effectively.

Balancing Offensive and Defensive Data Strategies

While fulfilling the required regulatory requirements, if some thoughts and considerations can be given to the underlying organizational challenges that prevent finding and keeping a single source of truth, companies can gain a better understanding of their data and make more informed business decisions to drive growth more effectively.

Ultimately, although it is acceptable to have multiple versions of the truth, finding a single source shouldn’t be a challenge. Otherwise, how can companies be sure of the fidelity of data used for business decision-making? Although defensive strategies are viewed as an inconvenience, they do uncover fundamental challenges and inconsistencies in how data is collected, aggregated, reported, moved throughout the ecosystems, and ultimately analyzed and used. Uncovering these deficiencies and subsequently resolving them can help organizations utilize these observations in the formulation of offensive strategies, which also need to have consistent reporting and validating of data qualities to enable better decision making. Finding answers to difficult questions that require substantial effort benefits companies in the long-term. Since extensive effort is already invested in the defensive strategies to meet the requirements, why not build upon it and leverage the process and outcome in other areas as well?

Both the financial reporting data and the data used for quantitative forecasting are subject to regulatory validation and acceptance. If companies cannot substantiate the appropriateness, accuracy, and consistency of data before establishing usage, then it is a tell-tale sign of the disconnect among different business components.

The question remains, how would the quality of data used in an offensive strategy be proven appropriate? If the answer is not a resounding approval with clear and well-documented proof, then companies risk making important business decisions using imperfect data. Or, at the minimum, the decision-making process doesn’t fully consider all needed components to arrive at a result. Rather, it reflects a less than holistic view of the business’s current state. Therefore, the strategic decisions are incomplete at best, with the potential risk of making ill-informed decisions. It is a mistake that companies cannot afford to make.

The challenge in meeting regulatory requirements calls for defensive data strategies. Rather than viewing such requirements as mere inconveniences, companies would be best served to integrate the approach in the overall data strategy to reap maximum benefits. With the consideration of reasons behind such requirements, it could also help companies view different perspectives and appropriately tailor future strategy formulation and decision making to serve not just the immediate stakeholders but also public interests, to build a transparent, effective, and efficient data framework that would serve both short- and long-term interest. It is only then that companies will be fully exploiting the benefits from data.


Defensive strategies – have-to-do
Offensive strategies – need-to-do

The best of both worlds would be addressing some of the need-to-do initiatives while doing the have-to-do. As a result, maximum efficiency can be achieved without having two distinct efforts. Then and, only then, these two strategies can fully and successfully leverage and build upon each other.


Uma Gupta, San Cannon (2020), A Practitioner’s Guide to Data Governance.


Wenbo Wendy Zhang

Wenbo (Wendy) Zhang is a Data & Analytics leader with extensive experience in both the financial sector and the US Federal Government. She has extensive experience in leading compelling and successful projects, driving strategic business outcomes leveraging data, and using predictive analytics. Wendy earned a BA in Economics from the University of California, Santa Cruz, and holds dual Master’s degrees in Accounting and Business Analytics from Ohio State and George Washington University, respectively.

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