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.