Building Data Strategy
This post is the part of blog post series 'Becoming Data-Driven', kindly refer the pilot post to get the table of content & links to related posts topic-wise.
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Continuing from the last post, where we discussed about being 'data-driven', lets understand data strategy and explore how we can build it.
Data Strategy, What & Why?
"Strategy is a high level plan to achieve one or more goals under conditions of uncertainty." ~ Wikipedia
In a business, when an organization builds a high level plan to achieve its goals and to get or stay ahead of the competitors, its called Business Strategy. As data has become a strategic asset in this information age, every business is going to be a data business. Hence when we integrate the insights gathered from available data into our business strategy, we call it Data Strategy.
Data strategy & business strategy has started complimenting each other, while business strategy can steer data strategy, data strategy can drive business strategy as well in innovative ways.
In a holistic way, as is any business strategy, a data strategy should be actionable, relevant, evolutionary & integrated.
So, why do we require a data strategy? Data strategy provides centralized vision & foundation for data-related capabilities, be it identifying analytics opportunities, resolving data problems or applying data management. As data has become a strategic asset, there has to be a data strategy to fully exploit its business value to stay relevant & competitive in today's evolving business ecosystem.
How to build Data Strategy?
Depending on the type of business it is in, type of operations it performs or type of data it has, different data strategy can be built for different organizations. In general, these are the areas can be focused upon:
- Quick-wins: For any business, it really important to know or realize the ROI asap. So first priority can be to identify areas of smaller impact and turn-around. Based on the results, business would be more comfortable and eager to invest for longer terms.
- Improving business decisions: Identifying how business decisions are being taken right now and how available data can help business to make these efficient more efficient and quick can be other area to explore.
- Improving operations: As more and more operations using technology, enough data is available to know which are the parts of operations taking long to execute and how those can be optimized.
- Monetizing data: For some organizations, data itself can become a product and they need to identify and evaluate the ways to monetize it.
Above it not an exhaustive list of the steps can be taken to build data strategy as its subjective to the kind of business an organization is in, kind of problems it is facing and kind of opportunities it can identify with available data. Based on these factors, above aspects of data strategy can be prioritized as well, i.e. for some business monetizing data can deliver more value than optimizing operations.
In the next post we will be discussing about 'Exploiting Emerging Technologies', please stay tuned for upcoming posts in this blog post series.
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