Warning Signs Your Data Is of Poor Quality and You Need to Update it

There’s one basic rule of any business which says that the quality of input will always determine the quality of output. Data is only as valuable as its accuracy and its quality. A small error, say a miscalculation, can make a huge difference in impacting your decision-making and other decisions of any organization. No wonder data quality issues aren’t effects to encounter under the hairpiece. rather, you need to proactively think and resolve the quality issues for better, more data growth. And of course, indeed the most important data analytics platform can never be a crystal ball.
To resolve the quality issues for better, more data-informed opinions and business growth. So, in this haze-to-nuts companion on data quality issues, we’ll bring to light the top problems you need to be aware of and how to break them with genietalk.ai.

Fact- Finding
Gartner research has found that organizations believe poor data quality to be responsible for an average of $15 million per year in losses while 94% of businesses believe the data they hold is inaccurate.
Forrester found that one-third of business analysts spend more than 40% of their time vetting analytics data before it can be used for decision-making.

Now you must be thinking,  how do you know if there’s a data quality problem within your organization? Here are the signs to look out for…

1. Inaccuracy: All the data exists (the data fields are filled in), however, they may be in the wrong field, misspelled, or inaccurate.

2. Inconsistency: Although data may technically be correct, it is not presented in the same format or value without a dialing code, and names are written as an initial rather than full.

3. Invalidity: Data fields are complete, however, said data cannot be correct in such context with negative displays.

4. Redundancy: Where the same data is entered multiple times but expressed in slightly different ways and formats.

5. Non-standard data: Data is in non-standard formats which cannot be processed by the system (e.g., ‘percentage’ rather than %, hyphen.

6. Incompleteness: Your data Crucial pieces of information are missing with fields left empty and incomplete.

Want to fix your organization’s bad data?

Data inconsistency, inaccuracy, overload, faking, and duplication are some of the leading problems that negatively impact the quality of data reporting. Not to mention, but human error can lead to bigger issues down the line and that can turn out into disaster.
Genietalk.ai have a -in-one solution that solves these issues without requiring work from your end.
GenieVerify is a Virtual Agent trained to make an outbound phone call to verify contacts and data enrichment. Create AI-powered virtual superagents, free up your human agent’s time, and bring more focus on specialized capabilities, and efficient processes. Genieverify is:

- Upto 1000X Faster than Human Agent.
- Constant Performance 24X7X365.
- Zero management.

So what are you waiting for? Gather, organize, and use data seamlessly – Book a demo with our AI experts.

CONCLUSION
Good quality data is a key commodity that isn't only desirable but necessary for managing systems, running a business, avoiding fraud, assessing performance, controlling finances, and delivering services efficiently. Give your business data the attention it needs, so you can profit from better business opinions, better deals vaticinations, and better openings and deals. For expert, targeted help in addressing precisely the data quality issues your organization is facing, speak with our Genietalk.ai expert today for better data quality services.