How to build the perfect Big Data Strategy to analyze the market trends for your business

Investing in big data technologies and tools without an architectural and structural strategy can destroy time, money, and resources, in a company. Here is a method for developing a big data strategy that will prevent those unfavorable consequences.

1. Define the business goals and objectives:

To have a successful big data strategy, you must first define what business objectives you are attempting to attain. Not every company is the same, so there is no one-size-fits-all explanation here. However, you should make certain that your strategy aligns with your overall corporate business goals while also dealing with key business issues and key performance indicators.

Make certain that stakeholders -- comprising folks from your data management team, data engineers, line-of-business leaders, data scientists, and anyone else who will be using your big data stores -- are engaged right from the start and provide key input continuously.

2. Identify data sources and evaluate processes:

The next step involves recognizing the variety of your data as well as examining current business processes, technology assets, data sources, data assets, abilities, and strategies at the organization.

Once you have recognized sources of data, run an estimation on your data strategy. Make sure to deal with the business objectives you summarized in step one and work from there. For example, if a business objective of your data strategy is to enhance customer experience, then your current state examination would cover any business procedures, business models, or data assets that touch customers. When evaluating your current state, it's a good method to interview and involve all related employees and stakeholders.

3. Identify and prioritize big data use cases:

Don't boil the ocean applies here. In developing a big data strategy, start small, think big, iterate often -- and think in terms of use cases. Recognize big data use cases that fulfill your business goals outlined in step one. Use large data analytics to evaluate your large volumes of data to reveal invisible patterns, correlations, and other insights. These activities should help you build out and improve use cases.

The second step is to start prioritizing these use cases based on factors such as their business impact, fund required and resources expected. Depending on how many various departments you have indicated in the procedure, narrowing down use cases and prioritizing which ones to begin may be difficult. Remember to stay concentrated, write down use cases as they are decided, and work as a group to come up with a plan.

4. Create a roadmap for big data projects:

Once you have recognized your business goals, achieved an understanding of your data and present capability state, and recognized use cases, you can now start to plot out a big data roadmap.

This important step is often the most time-intensive step for companies. When building your big data roadmap, remember that it's only a sketch. You can proceed to iterate and modify your roadmap over time. With that in mind, picture your desired end state and work behind it, making sure the end goal is accurate, valid, and direct.

The roadmap exercise should concentrate on observing any gaps you have around data architecture, technologies and tools, processes, and skill sets. The gap examination will likely provoke a review of the use cases prioritized in step three. Again, business stakeholders will play a main role in prioritizing these initiatives based on complicatedness, funds, and expense vs. advantages.

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