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Best Practices for Developing a Big Data Analytics Strategy

Best Practices for Developing a Big Data Analytics Strategy

Many companies have embarked on a big data strategy to better understand their customers and optimize management of their operations. The path to big data success is often now a smooth one and often fraught with challenges like poor data quality, resistance to adopting new data sources, and getting timely access to insights and other issues. Fortunately, there are some well-established best practices organizations can follow to help make their big data strategy a success. Here are a few approaches to consider:

  1. Start at the end - Intuitively, many companies start their big data analytics journey with exploratory analytics using the data they have available. This process can be used to identify patterns and trends but often does not progress to a point where it supports better business decision-making. For this reason, it is important to start at the end. Starting the big data journey by considering the decision points that are most important to your business allows you to work backwards to identify the types of data that are most relevant to this chosen decision-making process. This approach helps you proceed with greater confidence that your big data journey will produce meaningful benefits for your business.

  2. Build an analytics culture - Great business intelligence and insights are only valuable if they are used. Employees’ comfort with older processes and procedures can be a significant barrier to implementing a big data strategy. A key aspect of any big data strategy should be building a big data culture. Create an environment where the people working with the data understand why the data is important, how it will be used, and the benefit it will produce if used properly. With a positive big data culture, you will be able to set yourself up for big data success.

  3. Re-engineer data systems - As the name suggests, big data involves working with large volumes of data. Existing systems are often unable to cope with the demands for computing power and storage that big data requires. Extracting value from data typically requires analysis, visualization, and collaboration tools that need to be accessible in real time. Companies need to re-engineer their data systems for analytics by using distributed architectures and enabling flexible access to data sources.

  4. Focus on useful data islands - Older enterprise systems often produce data islands, discreet sets of data that were not connected to other data sets. Data sets used in manufacturing may be separate from those produced by digital marketing or supply chain processes. After recognizing these data islands, using them effectively may simply be a data visualization exercise which can provide directional insights to inform other business functions where a correlation is found. Where data islands relate to various steps in a larger process, it may be worthwhile to consider integrating the data islands to create a larger data set that provides insights and visibility into the overall process as opposed to the just the individual components represented by each data island.

  5. Emphasize business value - Business value is one of the most important aspects of big data analytics. Prioritizing big data investments and efforts based on the revenue and operational gains they can produce for the business helps ensure the success of your big data strategy. Big data, in isolation, produces insights. Big data that is analyzed to inform important business challenges or opportunities produces business value.

  6. Iterate often - Building your big data analytics model is an iterative process with each version representing a refinement of previous versions. The initial model provides preliminary results which help validate the overall initiative and serve as a basis for beginning the refinement process. Iterating often helps build a solid foundation for your analysis, improving its accuracy and reliability over time.

  7. Standardize your approach - To ensure the ongoing reliability of data feeding into your big data analytics program, and the analyses being performed, standardization is critical. The best way to do this is to set up a repeatable process for ingesting, cleaning, storing, analyzing, and presenting data. This approach will not only improve the reliability of your big data analytics program, but it will also allow you to cut down on time spent on each step. The discipline that comes from standardized processes can also minimize data and analytical errors at every step of your analytics process.

  8. Continually test your big data assumptions - It is important to approach your big data analytics program with an open mind. Long held business assumptions or processes do not always hold true when examined through the lens of new data sources or algorithms. Testing is key. Testing new assumptions and algorithms against older ones allows you to objectively determine which approach produces greater value for the business.


The key to successful big data analytics programs is a systematic and consistent approach. For information on how Trigyn can help you make your big data analytics strategy a success.