dcsimg

How to Future-Proof Your Data Lake: Six Critical Considerations

  • How to Future-Proof Your Data Lake: Six Critical Considerations-

    Make Compliance a Priority

    One of the chief business drivers behind consolidating data silos is the increasing need to comply with regulatory or other legal requirements. SEC rule 17a-4(f), Dodd-Frank and simple patent defense are examples where data owners must be able to first certify that their data has not been modified, deleted or tampered with. Also, businesses should be able to produce the data in a reasonable amount of time, generally 24 hours. This can be a huge challenge when the data is in separate silos or in systems that don't natively support compliance properties and high streaming throughput.

1 | 2 | 3 | 4 | 5 | 6 | 7 | 8

How to Future-Proof Your Data Lake: Six Critical Considerations

  • 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8
  • How to Future-Proof Your Data Lake: Six Critical Considerations-5

    Make Compliance a Priority

    One of the chief business drivers behind consolidating data silos is the increasing need to comply with regulatory or other legal requirements. SEC rule 17a-4(f), Dodd-Frank and simple patent defense are examples where data owners must be able to first certify that their data has not been modified, deleted or tampered with. Also, businesses should be able to produce the data in a reasonable amount of time, generally 24 hours. This can be a huge challenge when the data is in separate silos or in systems that don't natively support compliance properties and high streaming throughput.

We have reached an inflection point in the rate of data creation that, unless you are willing to start throwing huge quantities of it away, you simply cannot afford to continue using the same technologies and tools to store and analyze it. The existing data silos – impractical for many reasons beyond pure expense – simply must be consolidated, even if the full picture of exactly how the utility of each piece of data will be maximized is still unknown.

One potential option many businesses have chosen to pursue in the hopes of addressing current business concerns while also maximizing future possibilities and minimizing future risks is building a data lake. With that, however, comes a separate set of challenges and considerations.

Large data volumes drive the need for data lakes. In simple terms, a data lake is a repository for large quantities and varieties of data, both structured and unstructured. The data is placed in a store, where it can be saved for analysis throughout the organization. In this slideshow, Storiant, a cloud storage provider, has identified six tips on how a data lake can reconcile large volumes of data with the need to access it.