The difference between defensive and offensive data strategies and when to use them to support operation and expansion.
Is data consistently used to support the critical decisions that are made throughout your organization every day?
Does your organization have a unified data strategy that guides how everyone utilizes data to drive and measure growth? Is it communicated throughout the organization?
The abundance of data that businesses generate and collect today makes it more important than ever to identify a data strategy based on a foundation of strategic principles that support business goals. Failing to establish order and governance over the use of this data leads to chaos and poor business decisions and places businesses at a severe disadvantage in today’s data-driven world.
Establishing a data strategy is no longer the sole responsibility of the CIO or CDO. It impacts every member of every department, from entry-level analysts to the CEO. How data is used affects the entire organization, and it’s critical that all C-suite executives are involved in the process.
Data Strategy: Defensive vs. Offensive
According to a recent article published in Harvard Business Review, a data strategy helps organizations “clarify the primary purpose of their data and guides them in strategic data management.”
A “defensive” data strategy focuses on mitigating downside risks by ensuring compliance, limiting fraud, preventing theft, and understanding departmental performance. It also involves data governance to ensure the integrity of data flowing through a company’s internal systems.
An “offensive” data strategy is designed to support business objectives such as increasing revenue, profitability, and customer satisfaction. Decisions are often made by combining customer insights with market data with a focus on predicting trends and driving growth.
The two data strategies should never be at odds with each other, but should be combined to utilize data in different ways to support operation and expansion.
For instance, defensive analytics are critical in helping companies understand their business performance, while offensive analytics build on these insights to inform forward-thinking decisions.
Different industries can benefit from different approaches when devising their data strategy. For example, organizations in heavily regulated industries such as healthcare or financial services may lean towards using defensive data for reporting and analytics, supporting budget management, tracking website statistics, enforcing regulatory compliance, and ensuring data integrity.
On the other hand, companies that adopt an offensive data strategy tend to be in competitive environments where the focus needs to be placed on growth activities such as net promoter score, lead conversion rates, return on investment of marketing campaigns, performance forecast, and predictive analytics.
A Successful Data Strategy Starts with Trust In Data
One of the biggest challenges faced by organizations is maintaining data integrity. After all, employees won’t act on the information if they don’t trust the source and accuracy of the data. A recent study across industry sectors indicated that only 3% of data quality scores can be rated as acceptable. In fact, knowledge workers are wasting 50% of their time finding and correcting all that bad data.
In order to optimize data usage, you need a data management tool that meets the needs of your data strategy while maintaining the quality of your data. This tool must be able to address the data discovery, access and agility needs of those doing the work AND provide IT/administrators with governance over who uses data and how use it AND it must provide decision-makers with the ability to drill down into the process and answer follow-up questions.
We no longer live in a world where all of our co-workers sit in the same building. It’s not always possible to just walk over to someone’s cube to get clarification on their reports or findings. In an ever-growing world of remote employees and global corporations, consolidating data, processes and insights in one platform is an absolute must for minimizing the opportunity for discrepancy with data.
The ideal integrated data preparation and collaboration solution will provide you with the ability to efficiently and effortlessly extract analytical insight for both defensive and offensive applications. It should have the flexibility to allow for collaboration across the organization, the creation of a centralized data marketplace, and the use of socialization to foster a culture of data-driven decision-making.
The data management solution should also offer the level of control for enforcing data governance through credential setting, data lineage tracking, activity logs, and digital fingerprints to help ensure the quality and accuracy of information used in the implementation of your data strategy.
Datawatch Monarch Swarm is a data preparation, management, and collaboration tool that combines flexibility with control, allowing the implementation of rigorous data governance without sacrificing agility for end-users.
Request a demo today to see how you can take your data strategy to the next level.