Last week, I was fortunate to be able to attend the Chief Data Officer Summit, a 2 day (4-5 July) event with lots of short sharing sessions by senior level data professionals from various companies and a couple of panel discussions. The event provided an overall view of the progress companies are making in their data journeys across the various industries, from banking to e-commerce to manufacturing and more. Less on the technicalities this time.
A couple of themes stood out, from the topics chosen and interactions with other industry members:
1. Growing importance of data governance
While much of the focus for most companies embarking on the data revolution has been on collecting more and more data, regulations are slowly but surely catching up. It can be difficult to pinpoint exactly where and how much data from each individual is stored across a company’s data warehouses. How then can ensure that anyone’s Personally Identifiable Information (PII) is safe? No doubt, forward thinking companies would have in place frameworks to ensure regulations are adhered to, but much catching up may still need to be done for many others. New roles, more regulations, more industry standards, and more businesses may sprout from this increasingly vital area. Another related point to this would be, how should companies tackle data governance while still allowing innovation to flow freely?
2. Managing stakeholders is as important as managing data for anyone taking data analytics seriously
Many misconceptions on what data can and cannot, should and should not do still persist in organisations. Industry experts may fear that data will lead to questioning of their authority. Management may think of using data only to justify their opinions. Others see data as a black box or crystal ball, either ending up fearing to apply any of it, or expecting it to be able to churn out miracles. Communication and education is key, not just for those dabbling in data, but for anyone wishing to make a difference to their organisations, as to where data analytics can most efficiently transform/optimise the company, where it can’t, and what is needed.
3. Data science teams should not work as silos
Having data science teams working closely/integrating with commercial/ development teams are seen to be associated with producing better results. This may be related to the previous point. One speaker even made the bold prediction of Data Scientists no longer existing as a function on its own (as is often the case today), but instead integrated into functions across the company in the near future. Another speaker touched on the often observed gap between data scientists, engineers, and the business folks that leads to friction against achieving efficiencies through data analytics. Data science professionals need to not only be proficient in their analysis and models, but also understand the commercial and deployment implications of their proposals. This can only be achieved though working closer with other functions, and again, more communication.
Are these the hot topics in the near future? What do you think? Do these apply to you too? Let me know in the comments below!
