Often, organizational strategies evolve around entrenched perceptions; trends identified through sampling and organizational aspirations. Past performances and future prospects play a key role in this process. Organizations consider macro-economic environments, market trends, industry growth, competitive actions, core capabilities and adjacencies, and finally shareholder expectations in building their business strategy. In the past, such an approach has been able to keep pace with the evolution of various forces that have an impact on an organization's performance. Today, the rate of innovation, interventions, and insights around their current and future impact outpaces many other factors that impact the long-term or even medium-term sustainability of an organization's growth and profitability. Businesses need agility to respond to such challenges. Big companies sometimes get pulled back by their size and momentum; smaller companies sometimes lack the resources and reach to invest in response.

Building strategies around big data analytics is about finding value in data, and it is about enabling value through data. The following are seven simple steps that will help you do this:

Throughout this chapter, I'll take you through each stage to ensure that your big data strategy is closely tied to your organization's strategy, which in turn will ensure that you are able to get the most from your data.

However, before we proceed further, it's a good idea to compile a list of various sources of revenue or various areas of key operations for your organization in a business catalog. For each of the revenue sources or operation areas, briefly capture how the company makes money from them and what the fundamental components of their operation are. This acts as a good compass for you to put some of the upcoming discussions in this and future chapters into context. It could be in a very simple manner as follows: