Techniques that Boost Accuracy & Productivity —
Data is perhaps the most essential resource your business has. It is at the core of all operational processes and is an indispensable tool for decision-making. At its best, it offers opportunities for competitive analysis and process improvements. At its worst, it dampens productivity and is a costly drain on resources. Today, we are going to identify the top five techniques for ensuring data quality, and what must be done to operate at peak performance.
1. Strategy
Your organization needs a strategy for data governance. A good strategy includes all levels of operations, from IT to CEO, and outlines methods for ensuring security, accuracy, and usability. Implementing a strong strategy keeps big data manageable as it grows, and facilitates high-level tasks such as performance evaluations or c-suite decision making.
2. Training
Proper certifications, such as CDMP or CIMP, are valuable for staff dedicated to data management; however, education and training for all employees that take part in importing or accessing your business data is a good practice. Teach staff what good data looks like, the metrics that most impact their contribution to the data pool, and make certain they have a good grasp of internal procedures and security protocols.
3. Control
The number one cause of bad data is human error. Putting measures in place to control data quality on the front end, as it comes into your organization, is essential. Training employees to quality check upon entry is best — Removing the human factor where possible, is better. Automating data entry can eliminate a lot of errors and offer opportunities for cleaning the data before it hits your stores.
4. Auditing
A key part of maintaining data quality is routine audits. Auditing allows you to catch data that may be missing key metrics, discover errors and possible process flaws, and ensure consistency. Auditing helps to protect the integrity of your business data and presents opportunities to catch and remedy root causes for bad data.
5. Tools
Technology should not be overlooked when considering data management and quality control. There are a variety of solutions on the market that are capable of automating data management tasks and minimizing the need for FTE and the likelihood of human error. A good data normalization tool will have the ability to facilitate basic maintenance such as cleaning, auditing, reconciliation, and conversion. A great tool can do all of that as well as provide high-level security within a user-friendly interface.
Is your organization lacking the right technology to ensure your data quality? Discover RoboDX™. Our powerful ETL tool puts everything you need, to ensure accuracy and boost productivity, inside a user-friendly dashboard. Benefit from top-level security features and compliance-minded programming.
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