How to Minimize Dirty Data?

If all the data analysts of the world were put together in a room and asked to agree on one thing, one

would find them united in their exasperation and frustration at dirty data.

When a CRM is created, the un-compromisable objective is to earn profit. However, ‘dirty’ or ‘bad’ data

– which includes redundant, null, unreasonable or simply, ineffectual data – often renders the effort

applied into various departments a waste. What makes the situation even sadder is that dirty data is so

common a problem that it can easily qualify to be an epidemic in the big data world. The question, now,

is to how rein in the situation.

Here are some useful pointers.

1. Training: This is unarguably the most effective step to be taken at the outset. To keep your

database free from dirty data, don’t let it enter in the first place. Regular training sessions for

the staff to train them in identifying, populating, and valuing clean data are entirely worth the

time and effort.

2. Smart CRM: Another great primary step is to choose and invest in a CRM that allows syncing

between different sources, duplication search catalogs, rule-defined fields, and so on.

3. Clear channels: One of the major sources of null or unreasonable data is unauthorized sources.

Narrow down the number and kind of sources that should be used, since the dizzying number of

channels often leads to strengthening data ambiguity.

4. Periodical audits: Despite all the checks, there is a good chance that dirty data will creep in a

CRM system sooner or later, since human and technical error is a reality that cannot be wished

away. A periodic audit helps ease bottlenecks and ensure clean and smooth data flow.

And here’s a nugget on how to avoid dirty data from impacting your customer relations.

Coordination: Coordination is crucial between channels and divisions when it comes to communicating

with customers/prospects. Imagine sending an appreciation note to a regular customer who has visited

every exhibition of the company except the one for which the note was sent. Or, imagine a company

sending two promotion mails where the same product is priced differently in each mail. The lack of

effort becomes painfully evident, and hits customer relationship.

Again, coordination will be effective only when the data that it operates upon is clean, cooperative and


Is dirty data your pet peeve too? Rant on in the comments section below.

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