Remaining competitive in a digital world

What's your data strategy?

Building a robust data strategy

To remain competitive, companies must wisely manage quantities of data. But data theft is common, flawed or duplicate data sets exist within organizatioins, and IT is often behind the curve. We help companies develop and implement a coherent data strategy that strikes the proper balance between two types of data management: defensive, such as security and governance, and offensive, such as predictive analytics.

Defense vs. Offense

Every company needs both offense and defense to succeed, but getting the balance is tricky. The two compete for finite resources, funding, and people.

The plumbing aspects of data management may not be as sexy as the predictive models and colorful; dashboards they produce, but they’re vital to high performance. Ensuring smart data management is not just the concern of the CIO or CDO; it is the responsibility of all executives.

The framework

Our framework addresses two key issues: it helps companies clarify the primary purpose of their data, and it guides them in strategic data management. Much of the existing frameworks are technical and focused on governance. Our framework is business-focused: it not only promotes the efficient use of data and allocation of resources but also helps companies design their data-management activities to support their overall strategy.

Defense vs. Offense

Data defense and offense are differentiated by distinct business objectives and the activities designed to address them. Data defense is about minimizing downside risk. It focuses on compliance, detect and limit fraud, ensuring data integrity, standardizing, and governing data sources in a single source of truth.

Data offense focuses on supporting business objectives such as increasing revenue, profitability, and customer satisfaction. It includes generating customer insights and integrating different data sources to support managerial decision making through interactive dashboards.

Single source, multiple versions

Many organizations have attempted to create highly centralized, control-oriented approaches to data and information architectures. These top-down approaches are often not well suited to supporting a broad data strategy. 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 (MVOT). The SSOT works at the data level; MVOTs support the management of information.

The key innovation of our framework is this: it requires flexible data and information architectures that permit both SSOT and MVOT to support a defensive-offensive approach to data strategy.

Get your truth in order


Building a SSOT

The SSOT is a logical, cloud-based repository that contains one authoritative copy of all crucial data, often referred to as a Dataplatform. It must have robust data provenance and governance controls to ensure the data can be relied on in defensive and offensive activities, and it must use a common language.


Developing MVOTs

MVOTs result from business-specific transformation of data into information – data fueled with relevance and purpose. Thus, various groups in the organization can transform, label, and report data, supporting their business requirements. Multiple versions of the truth, derived from a common SSOT, support superior decision making.

Striking a balance

Your data strategy should be striking the best balance between defense and offense and between control and flexibility. Together we must determine the right trade-offs while dynamically adjusting the balance by leveraging the SSOT and MVOT architectures. The best data strategy emphasizes either defense and control (robust SSOT) or offense and flexibility (MVOTs). Devoting equal attention to both is often unwise.

We can assist you in determining your current and desired position on the offense-defense spectrum, bearing in mind your overall strategy, the regulatory environment, data capabilities of your competitors, the maturity of your data-management practices, and the size of your data budget.