Do Not Let Your Data Lakes Turn To Data Swamps: Prevent and More Prevention

While technologies like Apache Hadoop are asking their users to build data lakes, most renowned data experts are telling their clients to limit the data collection. These two sound completely contradictory to each other. However, the ulterior intention of the two is the same – to create usable data repositories, without dumping useless data along with valuable data.

How are Data Lakes Helpful?

Data lakes can categorize information very easily by breaking them down to silos. This provides a quite interesting scenario, where Petabytes of data exist in separate categories, accessible to the authorized users. The DBAs and data experts can use the same big data for marketing analysis, product development, customer service, business analytics and data mining. In fact, these bulks of actionable data from the silos also act as an important resource for machine learning and predictive analysis. Then again, that is the distant future of any raw data that is currently flowing into any database.

Where is the Problem Hiding?

The problem lies in data screening and data management. Even with experienced and trained DBAs, the problem of overcrowding these data lakes persists in reality. Reputable companies and online sellers often let their data lakes turn into data swamps simply due to the lack of better management options. Raw data that remains unusable and inaccessible to end users are notorious for turning any data lake into glorified data dumps.
Well, regular maintenance of databases can be expensive, if you are thinking of getting in-house expert help. A smart alternative is to opt for remote database management Remote database admins can manage company databases. Staying in touch with a remote DBA team will not only help you keep your actionable data up-to-date, but it will also help you with database maintenance. This includes table repairs, field addition or removal, finding specific information, screening out information and adding security layers to your database.

Smaller Companies Are in Danger of Drowning

Some tech companies, business companies, and finance companies have data lakes. Banks and insurance companies are benefiting the most from building a repository of actionable data. Leading tech companies like Google have their own data lakes. However, not every one of these companies knows what they are going to do with this huge bulk of data. As a result, they have gazillions of silos and tremendous volumes of data just sitting around in decorated databases, gathering dust. Quite honestly, there is no better way to turn any database into a data dump.

Without proper management of raw data, you are simply collecting scraps. You might as well be collecting digital junk! Except for data collection costs, a lot! It is kind of alright for big companies to spend millions after data collection without a clear perception of the future actions. Smaller companies, especially new ones, do not have that luxury. Money, time and muscle-power are limiting. If you are collecting all kinds of data, without knowing why you need to stop and think.

Take Steps to Clean Up Your Dump

To avoid drowning your company in your data lake, you need to start thinking objectively. You need to set big data goals and meet them. Start with small-term goals. Do not be a data hoarder, just because you can be. Big data has groundbreaking possibilities, only when accessible. Here is what your big data should ideally look like –

  • It should be reliable, reproducible and good quality.
  • The data should be in a form that helps the business in decision management.
  • It should be instantly accessible and actionable for all authorized users.

Accept the age of AI: Do not be afraid of automation. We know all the whispers about AIs conspiring to take over the world. Well, in reality, data miners and data experts cannot get too far without the help of automation. Embrace new technology that will help you sort through mountains of data to find whatever is relevant. Big data without machine learning and AI does not make much sense. These are developing symbiotically to make human life a lot smoother! So go ahead and indulge in some data automation.

Define your goals: When working with data, you need to define your limits. Database managers and data experts can get a bit carried away while gathering data from multiple sources. It is your responsibility to keep your feet on the ground and protect your data lake from the “data wildness.” Only collect data from reliable sources. Look for verifiable data. Always turn to automation when you need to get help with the background of a data source.

In the end, do not shy away from expert help. If you are unable to stay afloat in your data flood, always call a DBA expert to help you out. Turning a swamp back to a lake is no easy job. Start skimping on data collection today!

Author Bio: Sujain Thomas is a data expert, who has worked with Her work on database optimization for data mining and big data management for business analytics has earned her quite a few accolades and awards in the recent times.

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