Here is the technical description used by HR in the recruiting and hiring process:
Chief Data Officer (CDO) is a corporate officer responsible for enterprise wide governance and utilization of information as an asset, via data processing, analysis, data mining, information trading and other means.
I think there is so much more to it, phase one is a holistic relationship between data partnerships, the team that sells the data and the client goals as well as past results to deliver the best audience experience for the people and the client.
Recently for an opportunity I am very excited about I have been analyzing what other titles around, under and partnership of this role are and my findings are new and old based upon experience internally and externally with clients and companies.
Yes there is no doubt the term “big data” is being used and abused from PR, to HR, to Start Ups and investments.
It does remind me of years ago when it was so difficult for a company in need of data analytics for research, marketing and efficiency but was confused as to who really was the true source, whose data was truly valid, updated properly and timely, easily accessible and built into products versus large files and tons of filters.
In all my data discussions (pitches, advice and conversation) I lead with well what data can you omit from your current environment and partnerships versus constantly adding and then what data should be coupled together to make the buying, implementing and usage of that data easier for the end user.
This was very difficult in the days of direct mail and telemarketing data without a true partnership between us and the client so we could take the input data and then the result data and go back to work to create better models (now called audiences), achieve the right reach and market at the right time.
We have evolved from all forms of data traditional to cookies to IP data to social data and now there is so much more to take in and build goals from…reverse the goal into the data!
Being done already…yes parts of it in the programmatic space on messengers, apps, DSPs and social networks but has exciting room for growth and dare I say optimization.
The model I personally feel needs to and will change as the data becomes more actionable, smaller and more result driven based upon actual company results is PRICING.
This is not easy and takes a lot of “proof” to start deploying, but paying more for a smaller audience should be the goal. The right audience based upon demand, past transactions, intent, search, consumption and sharing.
Robert Scoble posted something on Facebook the other day and I am paraphrasing here but it was like look even Mark Zuckerberg has reach issues out of all is fans on Facebook only 1.5 million liked the post announcing that he will be having a child, look at the posts of Jeff Weiner, CEO of LinkedIn and you will see some interesting numbers there as well as to who is interested in his posts and comments about LinkedIn…
Anyway so my point is those 1.5 million people are really paying attention and maybe about 15,000 of them would even like to send Mark and his wife a gift or congratulatory message beyond a like that would be a lot of gifts from people connected to someone just via Facebook and have never met.
This is another window to look into when you look at People they are the data and if you only crunch numbers, files, selects, etc then you will lose touch immediately in your data build process with making sure the People come first.Here is a small part of my data process beginning that starts with the people:
First analyze the people, their desires, goals, their research, timing and the level of market they’re in.
Next we take this data and combine it with the goals of an advertiser. Start the process with a brand, product or service, or even the ROI. Just start with the people!
I hope this helps anyone else that is tasked with multi-compiling and cross compiling data to merge into one file and create audience driven products…it helps me.