Big Data Planning Stages
Four stages are part of the planning process that applies to big data. As more businesses begin to use the cloud as a way to deploy new and innovative services to customers, the role of data analysis will explode. Therefore, consider another part of your planning process and add three more stages to your data cycle.
Stage 1: Planning with data: The only way to make sure that business leaders are taking a balanced perspective on all the elements of the business is to have a clear understanding of how data sources are related. The business needs a road map for determining what data is needed to plan for new strategies and new directions.
Stage 2: Doing the analysis: Executing on big data analysis requires learning a set of new tools and new skills. Many organizations will need to hire some big data scientists who can understand how to take this massive amount of disparate data and begin to understand how all the data elements relate in the context of the business problem or opportunity.
Stage 3: Checking the results: Make sure you aren’t relying on data sources that will take you in the wrong direction. Many companies will use third-party data sources and may not take the time to vet the quality of the data, but you have to make sure that you are on a strong foundation.
Stage 4: Acting on the plan: Each time a business initiates a new strategy, it is critical to constantly create a big data business evaluation cycle. This approach of acting based on results of big data analytics and then testing the results of executing business strategy is the key to success.
Stage 5: Monitoring in real time: Big data analytics enables you to monitor data in near real time proactively. This can have a profound impact on your business. If you are a pharmaceutical company conducting a clinical trial, you may be able to adjust or cancel a trial to avoid a lawsuit.
Stage 6: Adjusting the impact: When your company has the tools to monitor continuously, it is possible to adjust processes and strategy based on data analytics. Being able to monitor quickly means that a process can be changed earlier and result in better overall quality.
Stage 7: Enabling experimentation: Combining experimentation with real-time monitoring and rapid adjustment can transform a business strategy. You have less risk with experimentation because you can change directions and outcomes more easily if you are armed with the right data.
The greatest challenge for the business is to be able to look into the future and anticipate what might change and why. Companies want to be able to make informed decisions in a faster and more efficient manner. The business wants to apply that knowledge to take action that can change business outcomes. Leaders also need to understand the nuances of the business impacts that are across product lines and their partner ecosystem. The best businesses take a holistic approach to data.