Data quality is often a blocker when it comes to implement Predictive Analytics. But it shouldn’t be.

Here are 7 reasons why data quality isn’t a show stopper:

  1. Your sales data is probably in good condition as you use this to bill your customers. This, in turn, is good for us as your sales data is our main source of learning.
  2. Your customer master data may be poor, but don’t worry; we cleanse and enrich your master data matching on: company name, address, URL and email address, so we can help correct these.
  3. You may have duplicate records and multiple accounts set up within one customer – so we roll these up into one parent customer record.
  4. Your weak data is often your worst customers. Typically, your more valuable customers will be better maintained. As we focus on value, poor data quality accounts are likely to not be as relevant to us.
  5. We use your data to cluster accounts. If your data is poor we get a large number that do not ‘cluster’ and form a ‘None’ cluster – you can use this to highlight where gaps are in your data.
  6. We use probabilistic models, so our data does not need to be 100% accurate, unlike financial reporting. Therefore we do not need to wait for such accurate data to be able to add significant value back to the business.
  7. If your data is genuinely rubbish, our model accuracy will be too low and not reliable enough for us to use. We will quickly determine this.

If you want to get started with Predictive, but still aren’t convinced, look at our Predictive Opportunity Assessment. We can tell you quickly if your data is good enough to get started.

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