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6 Steps to Easily Manage a Big Data Solution

June 10, 2017

Big data is the byproduct of trying to monitor and measure virtually everything in the world; the Internet of Things. According to IDC, the amount of information created now exceeds our ability to store it, and 80 percent of that data is unstructured. That means you need a big data solution that can store, process, and manage data in all forms, structured and unstructured, and that can scale with your needs.

The mistake that many IT managers make is thinking they can address big data management by throwing more data storage at the problem. The amount of business data worldwide doubles every 1.2 years, and most companies add data storage every six to 12 months. According to Forrester Research:

  • The average organization will generate 50 percent more data in the coming year.
  • Overall, corporate data will grow by 94 percent this year.
  • Database systems will grow 97 percent.
  • Disaster recovery and server backup will increase by 89 percent.

The more data, the more you need from your big data solution. However, a big data solution is more than just data storage. An efficient big data solution requires that you: a) store, process, and manage data efficiently; b) create architectures that can scale to store large amounts of data; and c) create purpose-built systems for more efficient data processing.

Here are six steps to consider when deploying a big data solution so you can manage it more efficiently:

  1. Create unique data sets. Most data within any organization is duplicated or synthesized multiple times. For example, what happens to data in a research hospital that generates 100 terabytes of data? Data is copied by different departments for their own use, and additional processing adds another 5 terabytes of synthesized data. Soon there could be more than a petabyte of data, but only about 150 terabytes of that data is unique. Rather than trying to store and manage all that duplicate information, distill it to a single unique data set that can be easier to manage.
     
  2. Virtualize your data storage. Once you have reduced the overall big data footprint and created unique data sets, use virtualization to facilitate data access and reuse. Virtualization lets you centralize your big data solution and manage storage and access to all data sets. Using virtualization you can:
  3. Reduce the time required for applications to process data;
  4. Maintain better security since management is centralized even though the data is distributed; and
  5. Promote more accurate analytics since all the data sources are visible.
     
  6. Create a data lifecycle process. To maintain data quality, you have to determine how long that data is still viable and relevant. After all, big data insights are only as good as the quality of the data being analyzed. Some data will be considered business-critical and other data will not. And some data will age quickly while other data will have a longer shelf life. Determine what applications and analytics should get data preference and create a process to keep the data fresh.
     
  7. Simplify data backup and access. Be sure that your big data solution includes a secure data backup system as well as a means to access and recovers all stored data. This includes disaster recovery.
     
  8. Be conscious of data security. Be wary of not only physical access to data and security controls but external security concerns as well. In addition to protecting stored data from unauthorized access be aware of potential legislative concerns and even third-party agreements. You wouldn’t want to expose someone else’s sensitive data by mistake.
     
  9. Future proof your big data system. Any big data implementation is only going to get bigger. You will need more storage and more computing power as more data is fed into the system for analysis. Invest up front in the tools you need to make sure that data is accurate, up to date, and clean before you store it. Make the most of what you already have installed in the enterprise and be prepared to expand in the future, either by adding hardware and software, extending big data into the cloud, or both.
  10. When implementing a big data solution you want to be sure that your strategy aligns with that of the organization. Be sure that you understand who the stakeholders are, the potential impact big data has on corporate governance, and other factors that could affect data security and access. Use a centralized management strategy and create clean, unique data sets that are easy to monitor and control. If you choose the right big data solution, use best practices, and adopt proper safeguards from the outset, your big data strategy will be able to expand with your company’s needs.

    So what part of big data administration do you consider most critical? Is there one aspect of big data management that outweighs all others?