Best practices for implementing big data Learn what you want with big data. Encourage collaboration between business and technology teams. Analyze only what you can use. Big Data can easily get out of control and become a monster that consumes you, rather than the other way around.
Here are some of the best Big Data practices to avoid that mess. The truth is that the concept of “big data best practices” is evolving as the field of data analysis itself evolves rapidly. Even so, companies must compete with the best possible strategies. That's why we've summarized some of the best practices in the hope that you can avoid being overwhelmed with petabytes of worthless data and ending up drowning in your data lake.
It should involve the owner of the data, which could be a line of business or a department, and possibly an outsider, whether a provider that provides big data technology to the company or a consultant, to attract an external view to the organization and assess its current situation. Harnessing big data is no easy task, and many data scientists are faced with the challenge of adopting effective data management practices. This is less of a problem with the small, routine and regular levels of data used in enterprise databases. Over time, you'll discover new uses for big data sets as your analytics team develops, business needs change, and technology evolves.
For example, many companies tell suppliers and consultants that they want to perform real-time data analysis. On the one hand, cloud platform providers price data storage as a basic product, which typically makes it much cheaper than buying their own local storage devices. The promise of big data is that, if organizations can learn to take advantage of this huge, unstructured and voluminous data, they can gain a significant competitive advantage by using predictive analytics not only to predict consumer behavior, but also to actively drive behavior in their favor. There is an inherent conflict between data owners who want to restrict access to protect data and data scientists who want to access and review as much data as possible.
Understanding business needs helps data scientists identify the right data sources and elements that will be needed to address the business question. Care must be taken when using the cloud, since usage is measured and Big Data means that a lot of data has to be processed. Given these discovery patterns, your big data strategy should include the right data visualization tools, along with relevant training for both analysts and business users. By its nature, big data must be managed on a large scale, but we must also recognize that they are very diverse.
Big data doesn't necessarily mean abundant data sources, but could simply indicate many endpoints that generate voluminous data. Whether you need to address general data security and privacy legislation, such as the European Union's GDPR, or vertical regulations, such as HIPAA, for healthcare information in the U.S. UU.