5 Megatrends Driving the Chief Analytics Officer Role
Written by GoodData Author |
By now, the impact of a Chief Data Officer has been quantified. Forrester estimates that organizations with CDOs are 60% more likely to report higher business agility from Big Data initiatives than those without. However, the Chief Analytics Officer role is still in its nascency, relatively untested next to its older cousin the CDO. Yet, there is significant promise in a role that will wrangle the often-fragmented analytics systems and strategy for organizations. As ComputerWorld puts it, “Although still not as prevalent as two other newish C-suite roles -- the chief digital officer and chief data officer -- the CAO may represent an inflection point in an organization's digital journey, signaling a move from managing data to applying it more strategically across the business.”
And at this “inflection point” for CAOs, there are concrete challenges that will determine the ultimate success of the role. Current data and analytics decision-makers report that 46%--far less than half--of their organizations’ decisions are based on quantitative analysis. This means that most organizations are still using their guts to shape their business, despite concrete proof that data-driven decision-making leads to business success. This baseline problem drives five major trends which will determine how the CAO role grows and changes going into 2016 and beyond.
1. Enterprises are increasingly focused on business agility. Competition is at an all time high in most industries. The rise of SaaS companies puts a significant level of pressure on large enterprises to be more agile and data driven in every aspect of their business. In order to deliver on that expectation, the line of business managers are expected to know, understand, monitor and make decisions based on their analytics every single day.
2. Business user self-service is a key requirement for all companies. In order to empower business managers with data and analytics, CAOs and analytics vendors are challenged to provide intuitive tools that are easy to use, but don’t compromise the enterprise capabilities that their IT teams require. Essentially masking the complexity of the tool to simplify the end use experience.
3. Operationalized best practices will accelerate decision-making. With so many different types of analytics on the market, many enterprise organizations are starting to put a heavier emphasis on in-product best practices and benchmarking capabilities as well. In previous years this has resulted in pre-packaged offerings, but enterprises need to create their own differentiated, data-driven strategy so this need for best practices is manifesting more in starter templates, recommendations and even benchmarks to guide how they craft their own view on the data.
4. Personalized everything. In the digital age we (consumers) have developed extremely high expectations from the tools that we use. Google, Amazon and every other technology giant have trained us to expect the services we use to be tailored to our specific needs. This requirement is ever-more prominent as business managers become the primary users of analytics. They are frustrated not only by usability hurdles, but having to sift through dashboards and reports that are not customized for their departments, products and regions becomes an urgent complaint. The expectation is that their analytics will be served on a silver platter, and available on every device.
5. Security, security, security. Enabling more users inside and outside of an organization to access enterprise analytics puts a magnifying glass on the security of the system from almost every angle: Data privacy, user permissions, access locations, administrative access and rights, operational considerations, you name it. Enterprises are becoming increasingly open to outsourcing their analytics, but not without a serious vetting of their infrastructure and operational security.
Written by GoodData Author |
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