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The 80/20 Rule of an Effective Chief Analytics Officer

Written by Corinium

The 80/20 Rule of an Effective Chief Analytics Officer

Written by Corinium on Jul 6, 2018 11:20:23 AM

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We all know the problem – data is exploding, the number of analysts is decreasing and expectations on big data and analytics are ever increasing. In such a scenario, how can one best manage this situation? To become an effective Chief Analytics Officer, is there a ‘rule’ to follow?

To shed light on these issues, we spoke to Cameron J. Davies, SVP, Corporate Management Sciences at NBC Universal. Cameron is responsible for both the corporate management sciences and NBCU news group insights teams, including the development and execution of advanced analytics, data and research strategies driving NBCU priorities such as leveraging big data, personalisation of content, monetisation strategies, alternative measurements, and customer insights.

How has your role evolved over the past 12-18 months? 

CD: Most of these roles tend to follow similar paths when a company like NBCU is just getting started in this arena.  Consequently, my last 12-18 months have been in those initial stages.  The first 6 months were largely about building relationships, listening and getting integrated into the business.  It really doesn’t matter how experienced you are either at “analytics” or the industry, your first step should always be about listening to the business, understanding their struggles, challenges, and opportunities.  You can pick up some quick wins along the way but it is largely just learning.  Months 6-12 are about establishing a strategic vision for how you want to prioritise and drive value.  We called it setting a “North Star” of where we would like to see the organisation in 5 years.  We know the path with evolve and shift, but it is important to set it and then make decisions incrementally as you evolve with the business.  You also spend a lot of time in months 6-18 just doing the heavy infrastructure lifting of establishing your Data Strategy (finding it, storing it, curating it, then syndicating it), most of the first stage value comes from driving efficiency and effectiveness through these processes.  The next 18-36 months is where the job gets really fun and exciting, because with the foundations in place we can begin to really deliver value added integrated tools and processes that help the company make better decisions more often.

What advice would you give someone wanting to become a Chief Analytics Officer, and what are the core skills one needs to have to thrive in the role?

CD: The role is only 20% data and math and 80% human behaviour and organisational change. There are a lot of very smart technically and mathematically talented people who fail at these roles because they don’t understand that. Data and analytical skill sets are really just the table stakes anymore. The people that will excel at these roles in the future (especially where the real growth will be outside of the digital natives) are going to be those that can influence an organisation. I would strongly advise anyone thinking of attempting one of these roles to spend as much time reading, studying their Organisational Behaviour and Psychology “textbooks” as they do trying to dig through the math of the latest machine learning algorithm.

Data is exploding, the number of analysts is flattening and expectations and demand are growing – how does one best manage in this scenario? Should the focus be on processes or business problems?

CD: Yes, There is no “OR”, that is like asking whether I should focus on breathing or pumping blood. If you stop doing either, you die.  Business processes are by nature evolved to deal with a business problem.  Any advanced analytics “tool” you want to roll out or put in place has to absolutely be designed to enhance a specific set of decisions the business makes on an ongoing bases AND do so in a way that can be acted upon appropriately (i.e., fit with the business processes).  For example, a LOT of vendors want to sell media companies “real-time tools”.  The idea of being able to see who is tuning in or out of my program in “real-time” sounds exciting BUT… by the time I put a procedural drama on the air (fully produced), there are very few decisions I can make “in the moment” that will impact the content or airing of that show.  Consequently, what value does that information generate for me in that moment?

The idea of being able to see who is tuning in or out of my program in “real-time” sounds exciting BUT…what value does that information generate for me in that moment?

What is the biggest challenge you face within your role today and how are you looking to tackle it?

CD: We are no different than 99% of the folks out there trying to do this.  Our #1 challenge is non-existent, incomplete, or bad data and/or the inability to quickly process all of the data we have stored (new tools like Hive and Spark are helping with this but still an issue).  We are tackling it via an integrated Data Strategy that aligns with our overall Advanced Analytics strategy.  It starts with a consistent and persistent Master Data and Metadata strategy and moves through to Curation and Syndication in ways that create “single” consistent sources for other use cases like enhanced automated reporting, forecasting, etc.

What is the biggest challenge faced by the analytics/big data industry currently and in what ways does this affect your business?

CD: Too many vendors and too few qualified candidates, especially highly qualified data architects.  In particular, those people that are able and willing to dig down into the bowels of the data to create useful repositories.  Everyone wants to be the “rock star” who does cool math but that can only happen if all of the hard work of data procurement and curation has been done.  Consequently, people want to throw that work back to a vendor who really doesn’t understand your data or business and/or a traditional BI group within IT, who by habit and nature tend to think in rows, columns and traditional EDW structures.  There are a lot of start-ups out there right now trying to tackle this particular problem but it is sort of like the promise of a magic diet pill.  Maybe it will exist one day and work well without all of the nasty side effects but for now, it just takes hard work and you have to be willing to roll up your sleeves and get it done.  It isn’t sexy and it isn’t fun but the results can be amazing if you have the tenacity and fortitude to stick with it and get it done right.

Where do you see will be the biggest area of investment in analytics within your industry over the next 12 months?

CD: Distributed computing.  Distributed storage made it possible to gather in and keep all of this “data” but in some ways we are still drinking from the proverbial data “ocean” with a very thin straw.  It is getting better and better and I think this is where you will see the biggest gains over the next year or so.

Want to find out more? Join us at Chief Analytics Officer Europe this April to discover the latest trends in business analytics, AI, machine learning and big data.


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