After the $billions invested year after year in analytics it is an indictment of line-of-business managers and IT that little real business value is extracted from data.
It comes down to a fact that has been known since the dawn of computing- it all depends on the data. It is sad yet true that too many people rush to implement ideas without considering the evidence based analysis that justifies action. Evidence requires accurate, timely and accessible data.
Take sales and marketing extracting value from data to hit the right targets. Think again!
a field which you would expect to be at the leading edge of
Too often the content takes priority over the data. The "list" is often the last item to be considered and there is too little marketing budget left to ensure it is complete, accurate and relevant.
Take the most accessible data- that held internally on current and potential customers. How accurate is it?
From personal experience it is often terribly inaccurate. Salesforce.com, SugarCRM, Netsuite et al are competent sales and marketing applications. They are often augmented with marketing automation tools like Eloqua and Marketo. Too often the underlying data is plain crap.
- Vertical classification plain wrong
- Or limited by SIC generalisation
- Out of data
- Duplicated
- Wrong location
- Mix-up between head-office and local business unit.
- Wrong company name
We have not even considered the exploitation of external data- the so called big data approach. If you cannot get the internal data management right it is foolish to embrace external data sources.
It is not as though there are not enough data enhancement solutions to avoid this. It's just that too few organisations invest the time, money and prioritisation to get it right and keep it right. Unless you do the most effective BI, marketing intelligence and analytics solutions in the world will do no more than give you an illusory impression that you know your markets, customers and business potential.
Once you prepare the 10% to 20% of accessible data to the righty standard you can move on to the 80% to 90% of external data which needs the same rigour and attention to prepare and maintain.
Then, and only then, can you move on to consider marketing automation, machine learning, AI to gain competitive advantage. Otherwise you just compound the issues raised above.
I was driven to write this after reading the article below (follow link) which describes three barriers to exploiting machine learning.
Most companies today own data assets but, as is well reported, few are successful in extracting any real business value. Often the scale of a machine learning project is too daunting for companies still at the start of their data-driven journey. This poor track record of success stems from three primary obstacles: data inaccessibility and security concerns, inability or reticence to test and experiment, and rigid business processes undermining innovation. Only once these three obstacles have been overcome, will a business achieve the success promised by machine learning experts.
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