The Importance of Clean Data in the Quest To Deliver “Value”
There’s no denying that data is vital for businesses.
Data helps organizations better understand their customers, track progress against plan, and develop strategies for long-term success. But many organizations don’t realize that the quality of their data is just as important as the quantity.
This is because inaccurate or outdated data can lead to many problems. Failed marketing campaigns, missed sale opportunities, wasted resources, angry customers, and damaged relationships, to name just a few. That is why it’s vital to ensure that your business data stays clean, reliable, and up-to-date.
Now, the goal of any Business Analyst (BA) should always be to deliver “value” to the business units they support. But, as a BA, how often have you worked on a system change request to a business application only to find yourself challenged by the quality of the data that populate (or more like “pollute”) the application you are working with?
We’ve all been there. We’ve seen it happen too many times to count. The data is just “not clean.” Addresses are incorrect, duplicates abound, or product information is inaccurate. The list goes on. The data keeps getting dirtier no matter how much we try to clean it up.
And you, as a BA, must now come up with workarounds and stopgap solutions to make up for the deficiencies in the data quality to deliver the solution your stakeholders seek.
And the impact of all this “dirty data” on businesses can be costly. For example, a recent study found that poor data quality costs U.S. businesses an estimated $3 trillion per year!
But, what makes data quality “good” or “bad”?
There is no one answer — several factors affect data quality like:
Age
Data doesn’t have a long shelf-life. Businesses move, appoint new management, pivot to new industries, and change their staff and operations. This is why regularly reviewing the data you collect on your existing and prospective customer base is essential. This will ensure your data stays reliable and valuable.
Completeness
Incomplete data will stop you from driving helpful insights. For example, an incomplete view of your prospects won’t yield the kind of targeted, granular insights you need to drive effective lead management processes.
Accuracy
Inaccurate data will damage your trust in your systems, processes, and people. Data inaccuracies can result from many factors: from human errors to software glitches. That’s why it’s essential to validate your data against a trusted source of information to ensure that it’s correct.
Consistency
Variations in formatting, spelling, language, and abbreviations can wreak havoc with your data, leading to duplicate records, poor searchability, and other obstacles that reduce the effectiveness of your business application.
Duplication
Imagine thinking you have 80 prospects in one region, only to find out the actual number is half that because of duplicate records. Duplicate data can happen for various reasons, making your business application bulky, inefficient, and inaccurate, leading to wasted time and resources.
Usage & Relevance
High-quality data is data that is relevant to your organization and is frequently used in reports, dashboards, and other applications. But collecting data without a real purpose will just clutter up your business application and potentially introduce confusion into your automation processes.
There’s no denying that clean data is essential for business success.
So how can you, as a BA, ensure that the system changes you implement keeps the data pool clean and delivers the “value” you have worked so hard to achieve?
- Encourage the business to invest in robust data cleansing and governance tools. Many software solutions on the market can help you analyze, cleanse and govern your business data. These tools can help you identify and correct errors, remove duplicates, and keep your data up-to-date.
- Implement data quality control measures as part of system and tool changes. Put processes and controls in place to ensure high-quality data input into your systems when implementing a system or tool change. This may include setting data entry standards, implementing system validation checks, and performing regular audits.
- Educate your business users as part of the change management initiatives. Make sure your business users understand the importance of clean data and how to input it correctly. This may include training on data entry standards, data cleansing tools, and your organization’s data governance policies and procedures.
By taking these steps, you can ensure that the data entering your systems and tools stay clean and of high quality. Furthermore, this will enable the business units you support to make better strategic decisions, improve operational efficiency, and deliver “value” to the company’s customers.
And suppose you’re still debating the importance of keeping your business data clean. In that case, I hope the following stats from RingLead (a company that provides tools and services to manage data quality issues in Customer Relationship Management aka CRM systems) may help put things into perspective:
It costs $1 to stop bad data from entering the CRM.
It costs $10 to fix bad data.
It costs $100 to do nothing about it.
What other tips do you have for keeping data clean? Please share them in the comments below!
