Organizations do the required amount of substitution between 'defensive' and 'offensive' uses of data and also between control and flexibility in its use. Defensive data is about minimizing downside risk. Activities include ensuring acceptance with regulations and using analytics to identify and uphold fraud. Defensive efforts are applied to ensure the integrity of data flowing through a company's internal systems. Data offense emphasizes on supporting business goals like increasing revenue, profitability, and customer satisfaction. It includes activities which produce customer insights & market data to support managerial decision making. Each strategy has its own working infrastructure. Elements of data strategy  that should be taken into account are: Data definitions, Data ownership, data access, data assessment. Read more at: https://thefinancialbrand.com/65419/data-strategy-playing-offense-defense/

According to a study, it was found that 78% enterprises agree that data strategy, collection and analysis have potential to fundamentally change the way their business operates. The sole aim of an effective data strategy is to utilize this potential . The 5 questions that one need to answer before building a data strategy are : WHAT is Data Strategy?: It is a strategy that allows you to have a comprehensive vision across the enterprise.

WHY do we need a Data Strategy? :You need a data strategy to find correlations across multiple disparate data sources, predict customer behavior, predicting product or service sales

WHEN should I start or have a Data Strategy?: Answer is NOW.

WHO in our organization should drive this Data Strategy?:Chief Data Officer

WHERE do we start with Data Strategy?:It depends on how the organization is structured , it’s recommended to start it in some business unit.

 Read more at : http://dataconomy.com/2017/01/data-strategy-part-i/

 The first block to build a winning business strategy is to create a map based on business value of the question and approximating how much time would it take to get high quality answers to that question. The idea is to break the business questions into groups that corresponds to real time data systems. It allows you to focus on a specific system at once to build a strong strategy and optimize the sequence in which each sub question needs to be answered depending upon its current business value. A pattern of actions for data strategy begins with a hypothesis and collection of relevant data followed by building models to explain the data and evaluating its credibility for future predictions. The entire process is achieved on an enterprise scale digital infrastructure using Industrialized Machine Learning (IML). This approach can have a huge impact on natural resources and healthcare industries as well.

Read more at : https://blogs.csc.com/2016/07/05/how-to-build-and-execute-a-real-data-strategy/

According to a recent research, 96% of B2B marketers say that data is regarded as “important” or “very important” within their marketing team and 90% of senior management feel the same way. A data strategy enables you to achieve business goals and a B2B data strategy provides the foundation for everything involving data, from whom you target through your marketing campaigns, to how you generate leads and increase sales. The data strategy should guide any organization and must have clear results, rather than a list of vague objectives. Suzanne Stock (the founder Directors of Marketscan) writes in her article about some steps that any organization can take to implement B2B data strategy. They are: Have clear-cut  aim, Carry out a data audit, and be consistent. Read more at: http://www.business2community.com/b2b-marketing/need-b2b-data-strategy-implement-one-01555070#kCkXyhGwW1laWRRS.97

Today, fast and accurate decisions are critical for an organization's success. But there is a risk of incorrect decisions if we rely on approximate sciences such as intuition and judgment of individual decision makers. The value of data can be realized only once a coherent ‘data strategy’ is established. A data strategy requires an organization to embed and integrate data analytics into the process of decision-making. The organization must seek and utilize data based insights that are most fruitful. Big Data demands a tighter integration of business functions and better mechanisms for integration. Evidently, various teams shall work together to understand and exploit cross-functional data. Identifying key data gaps and taking collective decisions for data gathering will ensure that specifically data with potentially useful information is collected. Read more at: http://www.bobsguide.com/guide/news/2015/Jul/13/big-data-small-data-and-fast-data-using-data-to-drive-better-decisions.html