Clean and lean data is your best bet
7 MINS READ
Almost every organization is data-oriented now, and they understand the applications and benefits of data analytics in the decision-making process. Every year, an increasingly large amount of data gets generated across the globe. In the year 2020, 64.2 Zettabytes of data were generated globally. This large corpus of data necessitates efficient storage mechanisms and logic to keep the data available and error-free.
Dirty data can lead to loss of revenue and wastage of time as it may point stakeholders in the wrong direction. Businesses undergo heavy losses due to poor data quality. IBM sources peg this number at $3.1 trillion annually; and these are just the US numbers. .
Data analytics works by feeding processed data through code logic into a user-friendly interface of an analytics tool. Therefore, if the stored data is not clean and not properly stored in data sources, the chances of delays and errors creeping up on the user interface skyrocket.
Data storage hygiene best practices
Data analytics will rely on data storage hygiene best practices to serve the needs of multiple users quickly, accurately, and in near real-time.
Data audits: The most crucial aspect of data storage hygiene is to make sure that organizations regularly audit the data and identify the issues that may be prevalent in it. There might be multiple issues, and it may not always be feasible to fix them all, but with a data audit process in place, issues can be prioritized.
Such processes lead to many benefits. Primarily, the benefit applies to a scaled-up storage system, where the data is stored across multiple sources. They interact through pipelines and scheduling mechanisms, and there might be expected or unintended data delays across the sources.
With a data auditing process in place, you can not only identify issues with your data but can also identify the issues with the data sources and which sources no longer are relevant to your system. You can manage data cleanliness as your use cases evolve.
Over time, you can use automated data auditing tools which can scan your data storage and list out audit results automatically, so it's easier to maintain.
Removal of redundant data: As more people begin to work on data maintenance and analytics, multiple data tables and data variables get added over time to fit specific data analytics cases. These tables and variables might end up storing the same data points, leading to redundancy and overlaps.
In addition, data sources get plagued by unnecessary, unused data that gets fed into the storage and processing systems. Thus, as part of data storage hygiene, removing redundant data frees up storage space, which can be used for more relevant purposes.
Removal of redundant and unnecessary data also reduces the time lost in processing this data. It also leads to code maintenance so that data overlaps are mitigated too.
Keeping data updated: While there is continual inflow of data, it gets outdated too. This can happen whenever data sources sync or when some user-driven changes render the previously captured data obsolete.
If analysts work with obsolete data it can lead to undesirable outcomes in their analysis, ultimately impacting revenue and leading to rework. Identifying that the data has gone obsolete and keeping automated checks in place to capture such identification can lead to building systems that automatically refresh data and thus keeps the data relevant and ready for use.
Standardization of data entry and maintenance: Data entry is not restricted to a dedicated group of people anymore; it is now done by every user. While code logic needs to be present to ensure the entered data is correctly captured, and fed into the data storage, it is also important to set up a standardized data entry and maintenance process.
This would identify incorrect data at the entry stage, and alert systems would track such data errors and flag them. The data auditing systems could then read these flags and utilize them to clean up the data.
A standardized data entry and maintenance process would help with data storage hygiene from the get-go, easing the pressure from the entire storage hygiene setup. Findings from this process can also be used to educate users responsible for data entry so that user-driven errors can be controlled and fixed.
The diagnosis and fixing of errors in complex data cuts on an analytics tool would take a long time, causing a bottleneck in the data-driven analysis and decision-making process.
Hence, it is important for organizations to focus on data storage hygiene for an interrupted, improved data analytics process.