As your business generates large amounts of data from different sources and feeds it into the data lake for future use, one issue you need to address is data consistency. Consistency means that data values in one data set should tally with the values in another dataset at the same point in time. This is a guarantee that a complete write will reflect the same value in the future.
Data consistency is a vital part of your data management best practices. Here are 5 ways to ensure data consistency in your business:
Using locks for data consistency
Locks ensure that your data remains consistent even when many users access it at the same point in time. Enforcing locks is essential because it prevents unauthorized users from changing data at any given time. It also ensures that users don’t access uncommitted data, hence, reducing chances of using incomplete or inaccurate data.
If you are running a startup that does not have sufficient resources to execute data consistency by enforcing locks, you may consider outsourcing data management services to data experts. Professionals can clean your data and also ensure it is consistent and accurate.
Two common processes that your data systems face in the course of their lifecycles are updating and replacement. Unfortunately, these processes may result in gaps where old systems don’t align with new systems, compromising data integrity.
Data consolidation is the perfect solution for this challenge. It allows you to create databases from different data sources, which you compare with internal data to check for any discrepancies. To achieve this feat, select a single platform that can host all the software and applications your business uses.
Since your business receives data from different sources, discrepancies may occur in spelling and formatting, and take a toll on data consistency. You can address this setback by embarking on a data normalization program, which ensures consistency for lead segmentation, and lead scoring, and more. Develop normal forms beginning from the lowest to the highest for cleaning and organizing the data by following a few data normalization basics.
Automate repetitive tasks
Automating data collection does more than just save your company time. It also eliminates many of the errors that can compromise data quality and consistency, especially when handling repetitive tasks. Examples of repetitive tasks include the following:
- Data entry
- Data field and mismatch updates
- User input
You can use UX-based CRM to automate user input and data entry. UX-driven CRM syncs with email and popular apps to import critical data such as email, telephone, company name, and client name, quickly and conveniently. This ensures that your teams receive only accurate information, hence, enhancing data consistency.
Data control and profiling
Some data your company receives comes from sources outside your firm’s control. This includes data from other businesses or third party software. You can never be too sure about the quality of such data, hence, the need for rigorous data quality control. To accomplish this task, you need a good data profiling tool that can examine these aspects of incoming data:
- Data consistency on each record
- Completeness of the data
- Data patterns and format
- Data value distributions and anomalies, if any.
It is also vital to automate this process to ensure the quality of incoming data is checked for consistency and accuracy as soon as it is received. Manage each piece of incoming data with the same standards to ensure uniform data quality.
Is data management in your business a little overwhelming? Helios can take care of all your data management needs so that you are free to attend to other important duties in your company. We are the data experts, and you can trust us to propel your business to the next level through efficient data management. Contact us if you need any help with your data.