It’s January, the sun is shining and I’m in Miami speaking with a number of CAOs, CDOs and analytics experts whilst trying to spot some dolphins off the Florida coast. I was attending the most recent addition to the CDAO portfolio of events for Corinium, CDAO, Winter. After being a part of, and organising over 6 CAO events for Corinium, with over 700+ executives having joined us over the past year, It was great to be involved in a more intimate networking event, specifically for business leaders. CDAO, Winter was an exclusive networking event aimed at sharing knowledge, insight and future plans with a select group of senior executives. There was a wealth of knowledge and experience in the room and I find it remarkable that, no matter how many events I attend, there is still so much more to learn and share.
One major theme for discussion was the most effective business models to help operationalise analytics. Whilst there seems to be no consensus on this just yet, the most common opinion is to employ some form of centralised group. Be that through a federated model or fully centralised. Whilst teams that are embedded within the business functions can be hugely effective, and have developed really great relationships and buy in from their respective businesses, they will not always have the flexibility or remit to look at future thinking projects or enterprise analytics.
Separate to the operating model debate, one thing that was clear was that the CAO or CDO should be the one leading the way when it comes to solving business problems. As data and analytics leaders, you should be the bridge between the business and IT, understanding your business and its needs and being able to translate that to both sides of the table. Whilst also thinking outside the box when it comes to future needs and efficiency gains for the business. You should always be asking: how can you make a difference in people’s daily jobs and improve efficiencies within departments? How can you save money and boost profits? How can you derive actionable insights from your data which can inform real business decisions and drive value for your business? How do you make those insights actionable?
It was said that in an ideal world, the split for your analysts time would be 50% on the current business problems/activities and 50% on future thinking, however it was also stated that this is not always possible with:
“Some of the highest paid analysts spending 70-80% of their time cleaning data”
One anecdotal situation I have heard referenced many times, is that a business leader will realise the value of data, employ a Data Scientist on a high salary to come in and work their magic, and when the Data Scientist arrives they soon realise that the data is so unstructured, messy and unclean that they spend a massive amount of their time trying to clean up the mess, rather than generating insightful knowledge which can help drive business decisions. With such a large amount of time spent on data cleansing and organisation, the business leader that brought them on board finds it hard to justify the investment.. This is obviously an extreme hypothetical example, however with some of the highest paid analysts spending 70-80% of their time cleaning data, this can also negatively impact job satisfaction and longevity of your Data Scientists.
So, what can you do about this problem? CAOs and CDOs were crying out for a tool that could do this for them. Alternatively, do you employ a dedicated team just to clean and organise data? What else could help?
“It’s all about the right data, at the right place, at the right time. It’s not all about Big Data!”
Well, as suggested by one discussion, one way that this could be worked around, is by understanding the importance of the right type of data. It’s not all about Big Data! Small amounts of well collected, rich data can be equally as valuable. With all the hype around Big Data, it is worth remembering that it is useless if it is not the right data for the question you are asking and, as we all know, correlation does not necessarily imply causation. Just because the data tells you that the per capita cheese consumption correlates with the number of people that died by becoming tangled in their bedsheets or the divorce rate in Maine correlates with the per capita consumption of margarine, does not mean that there is a true pattern or that you can derive any meaning from that correlation. You only need to visit the website Spurious Correlations to know that you can find patterns in any data set, if it is large enough. One top tip from the conference was that Its all about focussing on smart, not dumb analytics; ask the right questions of the data you have, be aware of personal biases and always question what the data is telling you.
“Data is nothing without smart analytics.”
“Science is built up of facts [data], as a house is built of stones; but an accumulation of facts [data] is no more a science than a heap of stones is a house.”
Henri Poincare, Science and Hypothesis, 1905
Whilst addressing common current issues, talk also turned to the possibilities of future and emerging technologies such as Artificial Intelligence, Deep Learning and the Internet of Things. Society is becoming increasingly connected, with smart phones, watches, body trackers, headsets, social media, cars and many other devices and platforms. The amount of data being produced is gargantuan and will continue to grow with societal adoption and technological innovations. This growth also presents many new opportunities, not just in our personal lives, but also for business, especially through the use of bots, drones and automated equipment. So, what do we do with all of this data? How do we store it? How do we manage it? Is all of it useful and for how long? How do we garner insights from such a vast amount of data and make them actionable? These are all questions which still need exploring, however Machine Learning, Deep Learning and Artificial Intelligence present many new opportunities when it comes to utilising this data.
Machine Learning algorithms aren’t new, but the ability to automatically apply complex mathematical calculations to Big Data – over and over, faster and faster – and to learn and adapt algorithms and approaches as new data is added, is a recent development. With steady advances in digitization and cheap computing power, this presents many new opportunities for optimising your business processes and providing new insights. However, there remains an ethical question as to how much trust we place in AI and cognitive computing and the importance of monitoring, questioning and reviewing the data and findings. We will also be exploring this issue at the upcoming CAO, Spring event taking place in Scottsdale, Arizona on May 2-4, in our dedicated Machine Learning, Deep Learning and AI Pre-Conference Focus Day. Join us to follow the evolving conversation.
“Make what is advanced today, routine tomorrow”
One of the biggest takeaways I got from the event was that we should be doing everything we can to make what is advanced today, routine tomorrow. As an analytics leader you need to organise, inspire, grow, be the bridge between the business and analytics/IT, be a translator, push boundaries, drive innovation and be future thinking (amongst many other things!)And, with this, something that has become even clearer to me, is the need to learn from one another’s experiences, experiment and build partnerships. At a time when the industry is evolving and CAOs and CDOs are at the forefront of that change, it is so crucial to talk, learn and share to make what is advanced today, routine tomorrow.
Join us at our upcoming CAO events to continue the conversation and help promote growth within the data and analytics space:
By Vicky Matthews:
Vicky Matthews, Content Director Americas, Chief Analytics Officer Forum at Corinium Global Intelligence. Vicky has organised and launched the CAO brand in the US and Europe and continues to develop and evolve the event series across the Americas. Consulting with the industry to discover what keeps them awake at night, find solutions to their challenges and stimulate valuable cross-industry discussions to facilitate growth in the sector. For enquiries email: [email protected]