• Maciek Lasota

10 Reasons Why You Need Reliable Data Quality

Updated: Dec 17, 2020


Good, Bad Or Ugly

In this data-driven age organizations are looking to leverage data to enhance business efficiency and effectiveness. The decision on all levels are made with the use of BI and Advanced Analytics tools, better the data better outputs those tools provide and that leads to the creation of opportunities, generating more revenue and improving operational excellence.

Garbage in Garbage out - if data you want to analyze is not accurate then it`s not useful


Data quality is crucial part of your analytics solutions

Here are 10 reasons why you need to keep your Data Quality in order:

1. Better strategic decisions - the higher the data quality is, the more you can benefit from it. Within data-driven organizations you are more confident of your decisions and risk can be mitigated

2. Better Targeting and Customer management - Makes marketing work so much more efficient. If your customer data is clean your target audience is better suited and sales activities can be planned strategically to fulfill your customer's needs. Finding customers with similar attributes to ones you already have is at the same time much easier

3. Improved CMR - Your marketing content can be now more tailored as you get to know your Customer. You can create accurate recommendations systems and patterns can help you identify your most valuable customers and more eager to leave customers

4. Competitive advantage - data is an asset and should be used as one to derive insights that can lead to an advantage. A more mature company can use it to finding critical opportunities for new products and customers.

5. Compliance - decreasing risks of fines with not fulfilling the regulations. The protection of personal information can be simplified with a mature customer master data capability It is generally the avoidance of cost – For example, maximum fines under GDPR on protecting personal information is 4% of worldwide revenue. Trading with blacklisted customers or blacklisted materials can also be costly

6. Decreasing operational costs - In large organizations with multiple entry points keeping consistency data is key. Consistent data helps keep every function in your company on the same page when it comes to analyzing and meeting the needs of your clients.

7. Eliminate double work - If your entries are not standardized it leads to duplicate and doubling the work and cost of maintaining the same record twice.

8. Eliminate unnecessary actions - In the review of one client’s data, it was estimated that 20% of direct mail advertising was going to the wrong addresses for a cost of $1 per mailing. For a company mailing out millions of pieces of mail per year, that drives a significant amount of waste

9. Productivity - good data quality allows Analysts to be more efficient and not waste so much time on the data cleansing process when creating dashboards. Just ask any analyst what`s the pain of trying to get something out of dirty data

10. Reputation - It can vary from the small, everyday damage that organizations may never be aware of too large public relations disasters. Efforts to improve customer experience may also be undermined by bad data resulting in sending communications containing wrong information to customers. On the larger end, poor data in banking can affect the working of KYC and due diligence it could lead to inadvertent trade with sanctioned or suspected institutions resulting in PR fallout on top of punitive fines.



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