What we call analytics is both an major driver for business agility and a powerful value creation level. As a matter of fact they allow to go beyond traditional data mining and reporting, providing a comprehensive view how what’s happening and discovering patterns that help to predict how things will be tomorrow.
Analytics support change
So analytics are a change lever – even a continuous change one – since they provide people with the data they need do understand and manage an unique situation or case, contrary to the systematic application of a standard procedure that may work in 80% of cases but will never provide the best solution for each individual case and will even be counterproductive in the 20% left. Analytics help to import a operating models in real time according to the context, what improves agility.
A perfect example can be found in this post explaining how Qantas Airlines used analytics to improve customer service [fr].
The “Insight and Innovation” teams have developed predictive models to predict the probability a customer chooses Qantas and adapt marketing actions accordingly. “To many businesses confuse analytics and reporting. Understanding what our customers want is not enough. We need to understand what they will want tomorrow. Analytics are predictions, a tool that supports change.
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The information gathered on each traveler are accessible to ground and flight crew through a mobile terminal. “That’s a simple interface that does not show all the available information, only the relevant one”.
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On each flight, crew members have a list of the 5 passengers they should care the most of. They are not necessarily the most profitable ones, other criteria being taken into account. Bottom line : ” employees love this tool because it makes their job easier and give them more opportunities to interact with customers”.
For the opposite example, have a look at Air Canada : no data nor possibility to adapt to the customer.
But thinking that an analytics strategy is only about gathering, processing and presenting data is a big mistake. Still from Qantas :
before you staret you have to make sure your organization is ready for the change caused by data and, most of all, that it desires them. Data will change everything in the organization, not only marketing and analytics.
Change is needed to make the most of analytics
Same story in this MIT post
But in all the classroom discussions that surfaced around the big issues from big data — privacy, security, costs, infrastructure, data volume, data quality, and data governance — the reality that many organizations grapple with is change management: whether or not they can manage the human and process changes necessary to make the most of their analytics initiatives.
In brief, if analytics help to make a desired change possible, they also need some changes – that are not always desired – to happen before. Making subsidiarity easier (solving problems and making decisions as close to the customer as possible), analytics require at least empowered employees, management not hoarding information and the end of information and applications silos. As a matter of fact, considering the two airlines mentioned in this post, the Qantas system in the rigid context of Air Canada would not be very effective.
