How to Achieve Data Quality?

Like any worthwhile business endeavor, improving the quality and utility of your data is a multi-step, multi-method process. Here's how:

  • Method 1: Big data scripting takes a huge volume of data and uses a scripting language that can communicate and combine with other existing languages to clean and process the data for analysis.   Errors in judgment and execution can trip up the whole process.
  • Method 2: Traditional ETL (extract, load, transform) tools integrate data from various sources and load it into a data warehouse where it's then prepped for analysis. But it usually requires a team of skilled, in-house data scientists to manually scrub the data first in order to address any incompatibilities with schema and formats. Even less convenient is that these tools often process in batches instead of in real-time. Traditional ETL requires the type of infrastructure, on-site expertise, and time commitment that few organizations want to invest in.
  • Method 3: Open source tools offer data quality services like de-duping, standardizing, enrichment, and real-time cleansing along with quick signup and a lower cost than other solutions. Support may be limited for getting the services up and running, which means organizations once again must fall back on their existing IT team to make it work.
  • Method 4: Modern data integration removes the manual work of traditional ETL tools by automatically integrating, cleaning, and transforming data before storing it in a data warehouse or data lake. The organization defines the data types and destinations and can enrich the data stream as needed with, for example, updated customer details, IP geo-location data, or other information. And because it processes data in real time, users can check the data stream and correct any errors as they're happening.

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