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Analytics Talking Points

Q1. What is data analytics?
Data analytics is the practice of taking ever growing amounts of data and using ever faster computing techniques to generate management insights in order to optimise, redefine or fundamentally disrupt business processes and business models. Q2. What can analytics do for businesses?
Analytics can help a business do more with less effort or less money. For instance, reduce the time and effort that financial analysts spend preparing management reports. Or making sure that trucks deliver goods along the most optimal paths. Or collecting invoiced dues from the people that are most likely to pay first (and maybe neglect the hopeless cases). Or deciding which of your 10000 product catalogue to put on the first page of your website, in order to maximise sales. Analytics can help a business become better, or different. For instance, having a way to listen to the opinions of clients in social media and filtering the right message from all the noise, can allow a business to communicate with that one single individual who has a bad perception. Or becoming a business that engages with clients individually, rather than in “segments”, improving your customer retention and satisfaction drastically. Analytics can help invent new business models or disrupt existing ones. For instance, replacing an outdated taxi service that waits until people call by a proactively – predicted – directed fleet of cars that is present in the most likely hot zones at a specific time. Or by putting the knowledge of medical doctors “in a box” and “accessible everywhere”, so that medical colleagues can share medical knowledge and help patients at any time in any location, without needing physical access to the hospital or human expert. Q3. What do analytics projects or engagements look like?
An Analytics project starts with the question: “how can we optimize the value of data for your organisation”. A great way to do that is have “high impact sessions for analytics”, in which we engage the organisation’s top level in agreeing to a common vision and strategy and priorities for achieving value from analytics. Once the priorities are set, the objectives are translated into projects. A project can start by focussing on the immediate outcome, e.g. by creating test models that predict a customer’s behaviour, etc. Or a project can start by focussing on building or strengthening an organisation’s data foundations. Typically, analytics projects start small and aim at proving value fast. Once the value is proven, the same approach can be rolled out to multiple domains or parts of the organisations. Q4. When is the right time for me to deploy an analytics strategy?
Now. There is actually little time to lose. Markets and industries are becoming increasingly dependent on data and insights into data. What we see in the market is that start-ups have a data strategy from the start, because they know that their differentiation and future depends on it. The often use data to disrupt existing companies. More mature companies struggle more with their legacy systems and invest more in cleaning up the foundations. Or often, they have innovation centres that allow for highly complex analytics experiments while at the same time having foundational projects. Q5. How much time and resources are required to use analytics effectively? If analytics is done right and if it is embedded in the day to day processes, it does not take time, it actually creates time. Would you say that using Google to find something on the internet is an investment in analytics-time? Would you say that booking the most suited hotel for your family on Booking.com is a waste of analytics-time? Do you believe that flipping through your financial reports (with today’s data!) on your iPad is analytics-time? Analytics can only be successful if it serves a clear and valuable purpose. When it does, it creates time, not take time.