In the history of IT there has never been so much data to collect, process, analyse and turn into actionable insights. For CIOs charged with enabling data and analytics strategies it can mean they need to let go of strongly held ideas about data control and embrace a \u2018test and learn\u2019 approach to new projects.\nAt a recent CIO roundtable discussion with Amazon Web Services and Accenture, CIOs shared their experiences of applying new data-gathering models across a range of sectors, and for a variety of reasons.\nAccenture managing director for applied intelligence Fergal Murphy says businesses are applying data and analytic programs throughout the entire organisation, seeking to achieve a number of results.\n\u201cAn effective data and analytics model can result in many things, such as boost revenue, improve post-sales customer care, monitor the sustainability of supply chains and even improve staff wellbeing. In short, it can benefit every part of your organisation,\u201d he says.\nAWS head of data and analytics, Asia Pacific and Japan, Cameron Price concurs with this view, noting that there is an increasing appetite among businesses across Australia to experiment with new ways to collect and analyse data.\n\u201cThere are plenty of new data types and models that organisations are exploring, from full scale automation, through to targeted IoT deployments that achieve specific business-focussed outcomes,\u201d he says.\nCompass Group Australia General Manager Lea Cornelius says that the benefits of using data and analytics to enhance business outcomes for their customers are threefold.\n\u201cIt\u2019s about understanding the evolving demands of our customers so we can be part of their journey to grow and change service requirements, and identifying new opportunities to improve our internal efficiencies and optimise the services for and to our customers. It\u2019s also about providing thought leadership to our customers through the use of data and insights,\u201d she says.\nUsing IoT and analytic programs to drive better outcomes\nDuring the roundtable discussion Pernod Ricard Winemakers IT director Simon Bennett described an Internet of Things (IoT) project designed to assist the winemaker in critical and timely decision-making. This includes from when to pick the grapes, through to the tanks they should go into (there are 3000 tanks at just one winery in the Barossa Valley).\nHe outlined one example where an electrical conductivity sensor was put into a wine press to find out the optimum time to run the press for. It took several iterations before the winemaker was able to use the data in a way that pinpointed the best timing.\n\u201cI think this is an excellent example of \u2018test and learn\u2019. You look at the data, you don\u2019t find any insight, and then you keep different datasets and you make a correlation between different elements that suddenly unlock insights you\u2019d never have seen before,\u201d Bennett says.\n\u201cAn important point around IoT and Big Data usage is that ability to use platforms to spin up in a relatively low-cost environment a proof of concept, or a pilot, that you are then able to test and learn. We\u2019ve probably thrown away 98% of all of the things we looked at but there are nuggets of gold gurgling around at the bottom of the stream. That\u2019s what you\u2019re aiming to get to.\u201d\nDealing with legacy and new data sets\nWhile new data can unlock greater insights, it also has to be managed alongside legacy datasets and methods of ingesting, collecting and analysing data. During the roundtable discussion, many CIOs agreed on the necessity to move at \u201ctwo speeds\u201d \u2013 the slower, more deliberate approach, especially when dealing with legacy constraints, and the faster, more nimble projects involving new datasets.\n\u201cYou have to do both at the same time. It is easier to ingest brand new data, test and learn, play with it and then create new insights and new dashboards and outputs. It\u2019s much easier and quicker to do that then it is take legacy data sets and then ingest that into the new platform, perhaps re-do all of the business logic that you have built up over the last ten years,\u201d Bennett says.\n\u201cYou don\u2019t lose the drive for data integrity and there are some datasets that I always want to be 100% accurate, and I will put time and energy into making sure that\u2019s the case before it deploys. But there are other datasets that you can be much less controlling of, especially new datasets with the likes of IoT data.\u201d\nBennett describes three layers of an effective data strategy - the technical architecture layer, the governance layer and the data usage layer. The role of IT in the data and analytics program is to get the architecture and the governance in place and let the business gather and apply the insights.\nSkills required for today\u2019s \u2018data science\u2019\nANZ associate director for analytics Sally Wang notes that what makes a great \u2018data scientist\u2019 is a hot topic and she identifies three key skills \u2013 analytical, technical and communication.\nAnalytical skills are required for statistical modelling, AI and machine learning, and data visualisation, and these people traditionally have qualifications in maths, statistics or econometrics. \u201cThey are strong on data modelling, programming (R programming, python, SQL) and good at using statistics software such as SAS, Matlab and Octave,\u201d Wang says.\nThe technology skills are usually found in people with a computer science background. They understand data architecture \u2013 how data can be generated, stored, processed and cleaned, and linked together.\n\u201cIn many organisations I think analytics and technology are separated into different areas or teams. But there are advantages for them to be together or having staff with both analytic and technology expertise, especially when we deal with unstructured data (such as emails and photos). It is very important for data consumers (analytical experts) to understand how data are processed,\u201d Wang says.\nFinally, it\u2019s important not to underestimate the value of good communication skills. \u201cIt can be very challenging to transfer data and information into insights, stories and business strategies. A good data scientist with great communication skills is very valuable,\u201d she says.\nThe role of the citizen developerDuring the roundtable discussion, many CIOs expressed the need for ensuring business buy-in, for finding those win-win projects that produce immediate, and enduring, results.\nMurphy pointed out that Accenture often works with organisations to ensure data projects are owned not by only technologists, but by specific business areas. He cited an example working with marketing executives on using data from multiple sources to continually fine-tune campaigns based on customer behaviour data.\n\u201cWorking in close collaboration with one client we developed the next generation of marketing mix modelling, pulling in data that included social media activity as well as competitor analysis. As a result, they were able to reduce time-to-insight by 80%,\u201d Murphy says.\nAt Pernod Ricard Winemakers, Bennett\u2019s team have pushed the data analytics back onto the business, and in an overall company staff count of 1700, around 200 are \u2018citizen developers\u2019. These are people in the business \u2013 such as the winemaker \u2013 who are trained on how to use data dashboards so that they can find the insights themselves.\n\u201cThat is a key to unlocking the value - you put it in the hands of the people that understand the data and business process. You give them the capability to analyse data and to draw insight from it in a very rapid environment. They build a dashboard, they create some insight, some different data points,\u201d he says.\n\u201cIt\u2019s relaxing control over the things that IT is not the best at, such as understanding the data and using the data to draw insights that improve products and decision making. But retaining within IT what IT needs to do, which is the architecture, the governance around it.\u201d\nEvolving data journeysThe roundtable revealed the attendees were at various points on their data transformation journeys, but were keen to ensure momentum is maintained. Some CIOs noted that their initial months-long strategy is turning into a multi-year data and digital program.\nFor others, who were early in their journey, the focus is on prioritising the right data initiatives and moving at speed, through use of cloud capabilities while building out their operating model.\u00a0\nMeanwhile those who are further through their journeys said the focus is on deploying more advanced capabilities at scale to establish ever-increasing business benefits across the value chain.