Are you sitting comfortably? Then chances are your organisation is behind the curve in its adoption of data and predictive analytics. When business is profitable and markets benign, the impetus for change is limited. It’s simple human nature - change equals threat for most people. It’s also the reverse side of the proof points that data and analytics generate higher revenues and profitability. When these are already performing well without undergoing a transformation, it looks like evidence that they are not needed.
Payal Jain tells an insightful story about the problem. As she told it to the DataIQ Summit in May, the CEO of Barclaycard had given her the go-ahead to create a centralised strategic analytics function of which she would be managing director. The caveat was that she needed to persuade country MDs to provide funding out of their budgets.
Full of confidence and the righteous belief in the benefits of analytics, she headed to her first meeting in Germany. The response? “Why do we need that when we are already doing well?” Taking money and headcount out of a successful business simply did not look necessary and left Jain with the uphill task of finding other proofs to persuade MDs to buy in. (As delegates at the event will know, she not only did it, but was able to drive a further 15 per cent revenue hike for those sceptics.)
So, if comfort is the enemy of change, where else can analytics advocates look for arguments that the transformation is necessary? Typically, these can be found in aspects of the business or market which the organisation is simply not focused on. Here are three examples:
1. The rise of a data-driven disrupter
As Uber and AirBnB have proven, you don’t need to invest in physical assets to take a sizeable portion of the taxi or hotels markets. Instead, you create a platform that meets a customer need and find data sources to drive it. (In the case of AirBnB, the starting point was to eat Craig’s List’s lunch by using the publisher’s home rental listings to populate its own platform in the early days.)
Within virtually all major markets, similar propositions are in development and winning substantial private equity backing. It is easy to assume that your organisation has a lock on customer relationships - usually this is little more than habit or a contractual obligation. It is also very dangerous to assume that the customer data you hold is a barrier to entry - external sources, ranging from social to open data, can provide the jumping off point for a new arrival. If you are not predicting shifts in customer behaviour or spotting new patterns in their data, how will you know when your market is approaching a paradigm shift?
2. Revenues fall off a cliff
Tesco thought it had an unshakeable share of the grocery (and increasingly non-food) market, until it stopped listening to its own data. In what could be described as a reverse analytics transformation, it stopped believing what customer data was telling it about smaller basket sizes and more frequent shopping trips - all of which opened the door to looking at discount rivals, since customers were no longer doing a single monthly shop.
Recovering lost share is hard and may never happen. While predictive analytics are no guarantee that revenue loss will not happen, practitioners who are willing to explore the outliers and look for counter-factual indicators can often raise a warning flag. If the business has put its faith in those predictors, it will stand a better chance of avoiding the cliff edge.
3. Regulators change the rules
Most regulatory reviews are well-notified in advance, but that does not mean companies are ready to adjust once the new rules come into force, as resistance to the Cookies Directive showed. Reaction fo the General Data Protective Directive has been better, yet the majority of companies have yet to review their own data collection, management and usage for its impact. It is easy to assume that somebody else will be in the regulator’s sights or that enforcement will not be rigorous, which is a mistake.
Beyond GDPR, the ePrivacy Directive is due to be opened up again, plus legislation governing contracts, financial services and other sectors all serve to change the terms under which data can be used. Few business leaders enjoy listening to their legal and compliance teams - even fewer make strategic decisions based on what they forecast might happen, rather than what needs to be done right now. But allowing data and analytics teams to model scenarios based on potential limits or constraints on data flows can be very compelling.
Business-as-usual which is profitable is a nice position to be in. However, it seldom last for long because of the depth of external forces at work in a globalised, digital economy. The people who can help your company to prepare for a new business model are likely to be the ones knocking on the door right now asking for support for their data and analytics teams.
Related articles: Download presentations for the DataIQ Summit 2016 here.
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