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Our Latest

12 Stastistics about Data quality that will forever change the way you think about your Data

12 Stastistics about Data quality that will forever change the way you think about your Data

  1. Sales and marketing departments lose approximately 550 hours and as much as $32,000 per sales rep from using bad data (DiscoverOrg)

  2. Every year, 25-30% of data becomes inaccurate leading to less effective sales and marketing campaigns (MarketingSherpa)

  3. Businesses lose as much as 20% of revenue due to poor data quality (Kissmetrics)

  4. On average, it costs about $1 to prevent a duplicate, $10 to correct a duplicate, and $100 to store a duplicate if left untreated (SiriusDecisions)

  5. Employees waste up to 50% of their time dealing with mundane data quality tasks (MITSloan)

  6. 40% of leads contain inaccurate data (Integrate)

  7. 41% of companies cite that inconsistent data across technologies (CRM’s, Marketing Automation System, etc), as their biggest challenge (dun&bradstreet)

  8. Only 16% of companies characterize the data they are using as “very good” (ChiefMarketing)

  9. In the next hour, 59 business addresses will change, 11 companies will change their name, and 41 new businesses will open (plus all the companies that will close) (Reachforce)

  10. 15% of leads contained duplicate records (Integrate)

  11. £183m is the highest fine so far issued by the ICO for breaching the GDPR. (The ICO), July 2019.

  12. Year 1 of the GDPR has seen 200,000 reported data breach cases, with companies fined over €56m. (European Data Protection Board: EDPB)

 

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How did we do? Client Testimonial

" Excellent collaboration as always the resource supplied was ideal for our requirements."

Paul Turner
Test Manager, Irwin Mitchell LLP
 

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What are the key steps to Data Validation?

What are the key steps to Data Validation?

What are the key steps to Data Validation?

First of all - What is Data Validation? 

Data validation provides the mechanism for checking the accuracy quality and authenticity of your data, typically performed prior to moving or migrating data.
Data validation can also be considered as a form of data cleansing, by assuring that your data is complete. Cleansing highlights blank or null values and assures that data unique and contains distinct values that are not duplicated.

Often, data validation is used as a part of processes such as ETL (Extract, Transform, and Load) where you move data from a source database to a target data warehouse so that you can combine and merge it with other data for analysis. Data validation’s key role is to help ensure that when you perform analysis, your results are as accurate as they can be. 

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