Your Guide to Data Quality Management

Your data defines the critical aspect of your business information. It might be pertaining to your clients, finances, and competition or future goals. Whatever your data represents, the quality of your decisions has a direct correlation with the quality of your data.Your data defines the critical aspect of your business information. It might be pertaining to your clients, finances, and competition or future goals. Whatever your data represents, the quality of your decisions has a direct correlation with the quality of your data.

Therefore, data quality cannot be underestimated. The cost of inaccurate data can be high on organizations. In a recent report published by Data Warehousing Institute, it is estimated that businesses in the U.S.A lose around 600 billion dollars a year due to data quality issues, leading many companies to seek help from offshore data entry services.

Focusing on data quality management will start showing positive results on the decisions you make for your business. Now, the burning question is, where do you begin?

Defining Data Quality

How do you define data quality? There is no one right answer. Simply, quality data could be defined as precise data that facilitate competent decision making. The quality of data can depend on the following criterion:

  • Accuracy
  • Completeness
  • Orderliness
  • Uniqueness
  • Timeliness
  • Consistency

To state it more immaculately, quality data is data that is impervious to inconsistencies, errors, missing data, redundancy, and delayed entry.

Poor data quality constitutes unreliable information, duplicate data, incomplete and ambiguous data, all of which work to collectively bring down the productivity and decision-making process of a business.

Simple Process to Guide You towards Optimal Data Quality

Managing data is probably the most frustratingly complex tasks of a business. However, it also cannot be ignored. To makes things simpler for you, I have conjured up a simple 6 step process that will guide you towards your goal of achieving optimal data quality.

So, without much further ado, let’s get started

1. Define the Process

The first logical step towards managing data quality is clearly defining goals for improvement of data, impacted business processes, and data rules. Clear definitions set a clear path for you to follow. This is necessary, so you don’t move forward with doubts in your mind about what exactly it is that you want to accomplish. Your goal can be any of the following:

  • Make sure all customer records are unique.
  • Make sure all information is accurate.

Have a clearly defined process.

2. Assessment

Once you have clearly defined rules. Assess the set of data against your rules of definition. Assess it against multiple dimensions, including timeliness of data, consistency, accuracy, etc. Depending on the volume and size of data to be processed, you can perform a qualitative and quantitative assessment using profiling tools. This is also the stage where you have a chance to assess existing policies.

3. Analysis

After an assessment, it’s time to analyze the assessment results on multiple fronts. Analyze the problem areas that persist. Analyze the root causes of inferior data quality. Analyze the gap between business goals and available data.
For e.g., If customer information is inaccurate, then analyze the root cause. Similarly, identify what is causing the inconsistency between the financial system and business order entry system.

4. Improvement

Once your problems are clear, device improvement plans to combat them. Developing plans should be easy because of the groundwork done by the process of analysis and assessment. Make sure the plans are comprehensive and comprehend time frames, resources, and costs involved in the process.

5. Implementation

The improve stage should leave with a clear solution to your problem. It’s time to implement it and get the desired result. Try to implement changes that are both technically and operationally comprehensive. Ensure that everyone in your team involved with implementing the change is well trained. The idea is to avoid chaos. So, the ‘change plan should be carefully implemented.

6. Control

Once you have the plan implemented, it’s time to monitor the process. Periodic verification must be conducted to ensure that data is consistent with the goals and rules established. Any deviation from the plan should be caught immediately and rectified. Communicate the data quality metrics to each and every member of your team associated with the management of data. The designed data quality discipline should be followed by due diligence.

The Bottom Line

Data quality management is not a one-time process. As the inflow of data is a continuous thing, the process should be followed throughout the lifetime of a business. Yes, it can be overwhelming and frustrating. But it is essential, and we hope that the above process can get the job done for you.

If you are looking for an alternative to managing data in-house, then I highly recommend you outsource the service to third-party firms that engage in data entry services. In fact, it might be a wiser decision to make, considering the cost-cutting benefits of outsourcing. When going for an offshore firm, research its reputation and good standing in the industry.

Go through their reviews and ask friends and colleagues for references. We hope the above process will successfully help you with data quality management.

News Reporter
This article has been shared by Jessica Ervin who is a professional writer and blogger.