This is one of a series of posts looking at the four key dimensions of data and information that must be addressed in a data strategy – reach, richness, agility and assurance.
In my previous post, I looked at Reach, which is about the range of data sources and destinations. Richness of data addresses the complexity of data – in particular the detailed interconnections that can be determined or inferred across data from different sources.
For example, if a supermarket is tracking your movements around the store, it doesn’t only know that you bought lemons and fish and gin, it knows whether you picked up the lemons from the basket next to the fish counter, or from the display of cocktail ingredients. And can therefore guess how you are planning to use the lemons, leading to various forms of personalized insight and engagement.
Richness often means finer-grained data collection, possibly continuous streaming. It also means being able to synchronize data from different sources, possibly in real-time. For example, being able to correlate visits to your website with the screening of TV advertisements, which not only gives you insight and feedback on the effectiveness of your marketing, but also allows you to guess which TV programmes this customer is watching.
Artificial intelligence and machine learning algorithms should help you manage this complexity, picking weak signals from a noisy data environment, as well as extracting meaningful data from unstructured content. From quantity to quality.
In the past, when data storage and processing was more expensive than today, it was a common practice to remove much of the data richness when passing data from the operational systems (which might contain detailed transactions from the past 24 hours) to the analytic systems (which might only contain aggregated information over a much longer period). Not long ago, I talked to a retail organization where only the basket and inventory totals reached the data warehouse. (Hopefully they’ve now fixed this.) So some organizations are still faced with the challenge of reinstating and preserving detailed operational data, and making it available for analysis and decision support.
Richness also means providing more subtle intelligence, instead of expecting simple answers or trying to apply one-size-fits all insight. So instead of a binary yes/no answer to an important business question, we might get a sense of confidence or uncertainty, and an ability to take provisional action while actively seeking confirming or disconfirming data. (If you can take corrective action quickly, then the overall risk should be reduced.)
Next post: Agility