The significance of data cleaning is becoming increasingly apparent. Companies and industries must manage data more efficiently and effectively as it grows in size and number. Dirty data can lead to a lot of wasted time and money. Companies must cleanse data in order to be as cost-effective and efficient as possible.
As data is the lifeblood of Machine Learning (ML) and Artificial Intelligence (AI), enterprises must ensure that data is of high quality. While data marketplaces and other data providers can aid enterprises in obtaining structured and clean data, these platforms do not allow companies to ensure data quality for their own data. As a result, organizations must understand the critical steps involved in data cleansing and eliminate issues in data sets.
Most companies feel 29 percent of their data is flawed, according to Experian’s 2019 Global Data Management Research report. Furthermore, the quality of enterprise data sets can degrade at an alarming rate. If businesses are absorbing a lot of data from many sources, some of it likely will be “dirty.” Unclean data can also come from a structured source like a relational database. Out-of-date, duplicated, corrupted, missing, or erroneous data can skew the findings of analysis and reporting processes substantially. It might also have a negative impact on a company’s bottom line.
The financial consequences of dirty data are astounding, yet with the right approach, a company can avoid a messy situation. Reliable and clean data improves the agility and responsiveness of the company while reducing the load on data scientists and knowledge workers. Here are a few steps that organizations can follow to address dirty data.
Building agile processes
Instead of erecting new hurdles to entry, organizations should evaluate how the tool will transform processes toward an iterative, agile approach. If people can see the impact of their data preparation, they will be more motivated to prepare and understand it.
The notion of enhanced transparency and visibility is also included in the implementation of an agile approach. It’s all about planning and tracking based on real-time data and progress, which is why traceability is so critical in agile organizations.
Creating a data dictionary is a big job. Continuous iteration is required of data stewards and subject matter experts, who must check in as requirements change. A dictionary that is out of date can really be detrimental to a company’s data strategy. To define where the glossary should live and how often it should be updated and enhanced, communication and ownership should be embedded into the process from the start.
Companies can include strategies like standardizing data by altering it to adhere to standards uniformly. This can be done by using a standardized file to match and merge records. They can find duplicate and missing data using a filtering mechanism.
Encourage collaboration and empower data experts
Self-service data preparation requires users to learn the ins and outs of the data before it can be implemented across an organization. Because this information has traditionally been reserved for IT and data engineering jobs, analysts must take the time to learn about the intricacies inside the data, such as the granularity and any transformations that have been applied to the data set. Engineers can share the most up-to-date approach to query and deal with valid data by scheduling regular check-ins or a defined protocol for inquiries, helping analysts to prepare data faster and with confidence.