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By Zoe Ward • April 9, 2019

Dirty Data - Is it creeping into your company?

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Dirty Data
The hype surrounding big data is huge and with good reason. When used to augment customer information, big data provides companies with deep customer insights and massive return on investment. But, big data can come with its own set of problems; dirty data!
 
Dirty Data Manifests in Two Different Forms, including inaccurate data and duplicate data. As customer intelligence is crucial to an organisation's communication strategy, sales and marketing colleagues may find they are unable to make informed decisions about their customers, due to poor data health. The impact can be detrimental to a company’s bottom line. According to a report by Sirius Decisions, dirty data is rife, with twenty-five per cent of the average B2B database classed as inaccurate and 60 per cent of businesses reported “unreliable” data health.  

Data Cleanse
Before taking any action, it’s essential to assess the quality of your current data and face just how dirty it actually is! Evaluate all systems that your company rely on for customer details. For each in-house system, determine which data fields are actually necessary. Obtaining too much unnecessary client information can increase the risk of dirty data and block the systems, In most cases, less is more!
Always ensure you include all in-house platforms and customer acquisition tools, such as survey forms, webinar registration forms, and download forms. If customers are asked to offer personal information too early, they’ll be more liable to falsify information in order to withhold details. Additionally, asking for too much information from customers can often contribute to an increase in dirty data!

Standardisation or Scrub Out!
A lack of standardisation creates the perfect environment for dirty data to fester! Standardisation across the company will help to eliminate this. You will need to consider which input fields should be modified, create consistency across the board with numerical data and case sensitivity. Salutations such as Ms. and Mrs. should be normalised according to one standard dictionary.

Evaluate Acquisition Channels
You will need to work out how to safeguard your data from contamination. What data fields can be made mandatory and input values are required for consistency? By putting these barriers in place, you’ll ensure that data is entered consistently and eliminate the risk of dirty data from reoccurring.

Get Clean
Manual data cleansing takes time and is not a good use of a resource. Human error is always a potential risk where data entry is concerned. A seemingly simple spelling error could cloud things and ultimately lead to lost revenue. HLR data cleansing systems are able to sift through masses of data and use algorithms to detect anomalies and identify outliers resulting from human error.

Update your Data in Real-time
Nowadays, customer and prospect information is changing constantly! Addresses, phone numbers and emails can quickly become outdated and often, your company is the last to be told! There are many internal changes we aren’t informed of, with up to 60% of individuals changing their job roleAlarmingly, B2B data is thought to erode at a rate of 70% per year!

Dead End Leads
Dirty data is a silent killer of sales and marketing departments. It ultimately loses a company revenue, kills off marketing efforts, leads to poor customer experience and misinformed decisions. So what can you do about it?

Disinfect Your Data
DMB offer a Home Location Register (HLR) service, enabling you to make regular data health checks on contacts.
Through our API we can deliver the most accurate information, on every mobile phone subscriber, worldwide, by checking if a mobile number is in use, identifying dead or inaccurate mobile numbers and establishing the network of any mobile number. We can save your business time and money and minimise your message cost.

DMB offers a cost-effective HLR Look up service that will take care of your data cleansing needs.  Inaccurate data is money down the drain - you can't afford that!