Clean Up (But Don’t Drain!) Your Data Swamp
Organizations want data lakes. But too often, they end up instead with data swamps that are of little use for transforming data into value. If you’re struggling to turn your data swamp into a clear data lake, keep reading for data lake organization and storage best practices.
In case it’s not clear, the term data swamp is something of a play on words. It’s not a term that professional data scientists frequently use.
Data scientists do, however, talk about data lakes. A data lake is a body of data that you transform, modify or analyze to gain valuable insights.
The Problem with Data Swamps
Ideally, your data lake will be clear, smooth and actionable. But if it’s not, your data lake looks more like a swamp – a murky, burdensome, difficult-to-maintain body of data that is difficult to turn into value.
Data swamps arise when you face challenges like the following:
By definition, data lakes can contain multiple types and structures of data. But that doesn’t mean you can simply throw data into a data lake in a willy-nilly fashion. You need a data governance framework to ensure that your data does not become so complex and unruly that it is difficult to analyze.
Data Transformation Challenges
To transform your data into value, you need to be able to convert data to formats that your analytics tools support. Data conversion is the process that connects your data lake to your data analytics operation.
This can be challenging to do when conversion tools don’t support the types of data in your data lake. It’s a common problem when your data lake includes data from legacy infrastructure, such as mainframes, and you attempt to analyze it using modern tools.
Data Offloading Difficulty
Getting data into a data lake quickly can also be a problem, especially when dealing with legacy environments and tools. To go back to the mainframe example, your legacy mainframe infrastructure may lack the ability (without the help of third-party tools) to offload data in real time into a data lake that lives in the cloud.
Poor Data Quality
Even the best governed, most transformable data is of little use if crucial information is missing. For example, a data lake composed of machine data that is missing information from certain devices won’t give you a reliably clear idea of what is in your infrastructure. Nor will a database of customer details that has missing or inaccurate address information.
Historical data often has value. But real-time or near-real time data usually has much more value. Data lakes filled with data that is dated can easily become more trouble than they’re worth.
Cleaning Up the Data Swamp
The easiest way to keep your data lake clean and clear is to avoid ever letting it slip into a swampy state in the first place.
You should have strong data governance from the start. You should use data transformation and offloading tools to keep your data agile and relevant. You should adopt data quality best practices to keep your data consistent and reliable.
But if you already have a data swamp, fear not. You can clean it up and turn it back into a healthy data lake using the following four techniques:
1. Modernize your data operations. Your data sources may be old in many cases. The techniques you use to store and analyze your data don’t have to be.
2. Automate, automate, automate. Manual workflows breed errors, inconsistencies, and delays, which translate to poor data quality and governance. For this reason, adopt tools to automate as many data storage, transformation and analytics processes as possible.
3. Educate your team. Not everyone in your organization needs to have a Ph.D. in data science, but everyone should have a working understanding of how the roles they fill relate to the organization’s data operations. (Read: How Do You Get Data into Your Company DNA?)
4. Strive for real time. Wherever possible, use data extraction and transformation tools to speed up your data operations and enable real-time results. This prevents your data lake from growing stale.
Keep in mind that your goal is not to drain the swamp. That would mean eliminating your data entirely, which would deprive your organization of the foundation it needs to operate.
Instead, your task is to clean up your data swamp so that your data is easier to work with and leverage. You want your data lake to remain flexible in terms of the types of data it can store and the volumes it can accommodate, but you also want it to be clear and easy to access.