Business leaders across multiple industries realize the importance of big data. But to get results, you need to improve the quality of this data and find ways to improve it. If your data isn’t accurate, it could lead to costly mistakes when making business decisions. Since low-quality data costs businesses $15 million per year, which means you can’t make expensive mistakes.
What is data quality? It’s data that’s clean and aligned with your organization, and it shouldn’t be confused with data governance. In the meantime, here are five things you should know about the data management process.
Define Thresholds & Rules
If you want 100% perfect data, you should realize that it will be more challenging than it looks. Achieving 100% is a costly and timely process. Most organizations determine which data issues are critical and focus on which data quality attributes require 100% perfect data. If you need different levels of quality for different levels of data, you need to set different thresholds for each one.
It would be best if you determined how that data meets the thresholds. This is a step where setting data rules is essential, and data management rules are another name for enforcing those thresholds discussed. Once you set data management rules, you should determine which data management attributes will apply to each.
Assess the Quality of Data
Now it’s time to look at your data and see if it meets the thresholds you set. This involves profiling your data or getting statistics about it. For example, you have eight individual records for your first rule. All of your records should meet this rule, which means your data has to be 100% accurate.
Data accuracy consists of three fundamental rules:
The customer’s full name should include a space in between
The customer’s name must have letters, no numbers
Only the first letters of the first name, middle name, and surname must be capitalized
Resolve Data Management Issues
This is the stage when you determine the root cause of each issue. You’ll identify specific problems for that data by implementing clear standards for those data entries, including data-related key performance indicators that can be used in your CRM system. This involves cleaning and standardizing this data. But to avoid any potential issues, you should set a validation rule that meets the format and the threshold.
Monitor & Control Data
This is not a one-time event, and it’s an ongoing process. You should review your data policies and procedures with a continuing attempt to improve. This is important since your organization is constantly changing. There may be a time when your company may decide to improve your customer data by integrating and investing in an external data set that includes demographic data. That means you’ll have to develop a new set of data rules, including an external data set for the data you haven’t monitored yet.
Ensure Data Governance With the Board
A data governance board can help protect your business from costly decisions. This panel should include a combination of business leaders, executives, and IT users. Their role is to set policies and standards that will become the core of your data governance efforts.
Your data governance board should also meet regularly to monitor the data goals and DBQ initiatives. This is where you’ll invest in an objective measurement scale that can be used to improve your data, and it’s an important tool for measuring your data.
Big data is an essential part of running your business in the digital age. It offers competitors and customer insights that can’t be discovered with other resources or tools. Big data allows you to make important business decisions in real-time. Because of that reason alone, it’s often associated with risks that have to be monitored regularly.
To work on your data quality management, you should keep each aspect in mind. The metrics can help you choose the right tools, determine data quality, and describe data rules and thresholds. While this is a challenge, it’s imperative to have this done on an ongoing basis. Low-quality data can negatively impact your data analytics efforts.
