How to Approach Data Consolidation in a Data Lake

Posted on May 26 2018 - 7:51am by Matt Holtzen

Data lakes are in quite common nowadays, especially for businesses and enterprises seeking to transform their data into something that can give them valuable insight to help their operations grow. However, just as with any huge business moves, there are some important considerations to make in order to truly utilize your data lake effectively. Neglecting these will not only affect the kind of insight you will be getting but could also land your organization in hot water, especially when it comes to data breaches and regulation compliance.

Data Consolidation

With that said, here are five critical considerations you need to make when approaching data consolidation in an enterprise data lake.

  1. Make sure that you can accommodate multiple analytics platforms. A data lake’s strongest point is that big data analytics platforms can glean more interesting and detailed results from them, compared to other data repository styles. This is because the data is preserved in its original form when placed in a data lake. However, the results you get from a specific analytics platform may be different from using another – which makes relying on just one platformsomewhat lacking when it comes to maximizing your data lake’s potential for business intelligence. As such, when consolidating data for your data lake, ensure that it’s capable of handling different analytics platforms rather than just one.
  2. Eliminate redundancies where you can. Consolidating your data in a data lake can result into multiple copies of the same data existing at the same time. This can create a lot of issues for you and your business, especially when it comes to big data analytics being run on your data lake. One of the bigger issues include the risk of out-of-date information being processed, which can then create false positives or negatives in your results. By removing these redundant copies and ensuring that only one copy exists, then not only will your data lake run better and be more efficient in terms of data access and storage capacity, but it will also help your business make the right decisions.
  3. Overestimate how fast your data lake will grow and how much storage it will need. Data lakes are superior over other data consolidation styles, in that you can easily scale up its storage infrastructure compared to its more complicated and structured counterparts. However, this doesn’t exclude it from the fact that your data lake will grow exponentially and quickly, what with technology becoming ever more central to people’s lives and livelihoods. Expect your most ambitious predictions when it comes to the infrastructure you’ll need to accommodate your data lake to be quickly exceeded, as well as the demand for more staff to adequately keep things running.
  4. Don’t forget about cybersecurity and compliance. When it comes to wrangling your big data into something that can help your company operations grow, it’s tempting to forget about cyberattacks and other threats that plague businesses every day. Don’t make this mistake and join the many companies that become data breach victims! Ensure that your data lake is secured with the latest cybersecurity measures, with all attack vectors protected from external and internal infiltration. This not only protects both you and your customers but also ensures that your company complies with global data protection regulations.
  5. Increase the scope of your data lake. Your data lake, upon its inception, may only have data that your company deals with in its day-to-day operations, and even then only the information generated by your main office. Something to consider is to gradually increase your data lake’s scope to encompass all the information generated by your company, including those coming from your satellite offices and branches. This will allow big data analytics to have a richer pool to study and gain actionable insights from, especially when it comes to insights and decisions that involve a specific location or department.

By keeping these suggestions in mind as you approach data consolidation in your data lake, you stand to gain much more insight and information from this particular data storage style, as well as make your data-reliant business operations that much more efficient.