atscale

A Sweeter Spot for the CDO?

Peter James Thomas chief data officer ,

Home run

I recently commented on an article by Bruno Aziza (@brunoaziza) from AtScale [1]. As mentioned in this earlier piece, Bruno and I have known each other for a while. After I published my article – and noting my interest in all things CDO [2] – he dropped me a line, drawing my attention to a further piece he had penned: CDOs: They Are Not Who You Think They Are. As with most things Bruno writes, I’d suggest it merits taking a look. Here I’m going to pick up on just a few pieces.

First of all, Bruno cites Gartner saying that:

[…] they found that there were about 950 CDOs in the world already.

In one way that’s a big figure, in another, it is a small fraction of the at least medium-sized companies out there. So it seems that penetration of the CDO role still has some way to go.

Bruno goes on to list a few things which he believes a CDO is not (e.g. a compliance officer, a finance expert etc.) and suggests that the CDO role works best when reporting to the CEO [3], noting that:

[…] every CEO that’s not analytically driven will have a hard time gearing its company to success these days.

He closes by presenting the image I reproduce below:

CDO Venn Diagram [borrowed from AtScale]

and adding the explanatory note:

  • The CDO is at the intersection of Innovation, Compliance and Data Expertise. When all he/she just does is compliance, it’s danger. They will find resistance at first and employees will question the value the CDO office adds to the company’s bottom line.

First of all kudos for a correct use of the term Venn Diagram [4]. Second I agree that the role of CDO is one which touches on many different areas. In each of these, while as Bruno says, the CDO may not need to be an expert, a working knowledge would be advantageous [5]. Third I wholeheartedly support the assertion that a CDO who focusses primarily on compliance (important as that may well be) will fail to get traction. It is only by blending compliance work with the leveraging of data for commercial advantage in which organisations will see value in what a CDO does.

Finally, Bruno’s diagram put me in mind of the one I introduced in The Chief Data Officer “Sweet Spot”. In this article, the image I presented touched each of the principal points of a compass (North, South, East and West). My assertion was that the CDO needed to sit at the sweet spot between respectively Data Synthesis / Data Compliance and Business Expertise / Technical Expertise. At the end of this piece, I suggested that in reality the intervening compass points (North West, South East, North East and South West) should also appear, reflecting other spectrums that the CDO needs to straddle. Below I have extended my earlier picture to include these other points and labeled the additional extremities between which I think any successful CDO must sit. Hopefully I have done this in a way that is consistent with Bruno’s Venn diagram.

Expanded CDO Sweet Spot

The North East / South West axis is one I mentioned in passing in my earlier text. While in my experience business is seldom anything but usual, BAU has slipped into the lexicon and it’s pointless to pretend that it hasn’t. Equally Change has come to mean big and long-duration change, rather than the hundreds of small changes that tend to make up BAU. In any case, regardless of the misleading terminology, the CDO must be au fait with both types of activity. The North West / South East axis is new and inspired by Bruno’s diagram. In today’s business climate, I believe that the successful CDO must be both innovative and have an ability to deliver on ideas that he or she generates.

As I have mentioned before, finding someone who sits at the nexus of either Bruno’s diagram or mine is not a trivial exercise. Equally, being a CDO is not a simple job; then very few worthwhile things are easy to achieve in my experience.
 

 
Notes

 
[1]		  
Do any technologies grow up or do they only come of age?
 
[2]		  
A selection of CDO-centric articles, in chronological order:

* At least that’s the term I was using to describe what is now called a Chief Data Officer back in 2009.
 
[3]		  
Theme #1 in 5 Themes from a Chief Data Officer Forum
 
[4]		  
I have got this wrong myself in these very pages, e.g. in A Single Version of the Truth?, in the section titled Ordo ab Chao. I really, really ought to know better!
 
[5]		  
I covered some of what I see as being requirements of the job in Wanted – Chief Data Officer.

 

 

Do any technologies grow up or do they only come of age?

Peter James Thomas big data, cloud computing, data governance ,

The 2016 Big Data Maturity Survey (by AtScale)

I must of course start by offering my apologies to that doyen of data experts, Stephen King, for mangling his words to suit the purposes of this article [1].

The AtScale Big Data Maturity Survey for 2016 came to my attention through a connection (see Disclosure below). The survey covers “responses from more than 2,550 Big Data professionals, across more than 1,400 companies and 77 countries” and builds on their 2015 survey.

I won’t use the word clickbait [2], but most of the time documents like this lead you straight to a form where you can add your contact details to the organisation’s marketing database. Indeed you, somewhat inevitably, have to pay the piper to read the full survey. However AtScale are to be commended for at least presenting some of the high-level findings before asking you for the full entry price.

These headlines appear in an article on their blog. I won’t cut and paste the entire text, but a few points that stood out for me included:

  1. Close to 70% [of respondents] have been using Big Data for more than a year (vs. 59% last year)
     
  2. More than 53% of respondents are using Cloud for their Big Data deployment today and 14% of respondents have all their Big Data in the Cloud
     
  3. Business Intelligence is [the] #1 workload for Big Data with 75% of respondents planning on using BI on Big Data
     
  4. Accessibility, Security and Governance have become the fastest growing areas of concern year-over-year, with Governance growing most at 21%
     
  5. Organizations who have deployed Spark [3] in production are 85% more likely to achieve value

Bullet 3 is perhaps notable as Big Data is often positioned – perhaps erroneously – as supporting analytics as opposed to “traditional BI” [4]. On the contrary, it appears that a lot of people are employing it in very “traditional” ways. On reflection this is hardly surprising as many organisations have as yet failed to get the best out of the last wave of information-related technology [5], let alone the current one.

However, perhaps the two most significant trends are the shift from on-premises Big Data to Cloud Big Data and the increased importance attached to Data Governance. The latter was perhaps more of a neglected area in the earlier and more free-wheeling era of Big Data. The rise in concerns about Big Data Governance is probably the single greatest pointer towards the increasing maturity of the area.

It will be interesting to see what the AtScale survey of 2017 has to say in 12 months.
 

 
Disclosure:

The contact in question is Bruno Aziza (@brunoaziza), AtScale’s Chief Marketing Officer. While I have no other connections with AtScale, Bruno and I did make the following video back in 2011 when both of us were at other companies.

 
Notes

 
[1]		  
Excerpted from The Gunslinger.
 
[2]		  
Oops!
 
[3]		  
Apache Hadoop – which has become almost synonymous with Big Data – has two elements, the Hadoop Distributed File Store (HDFS, the piece which deals with storage) and MapReduce (which does processing of data). Apache Spark was developed to improve upon the speed of the MapReduce approach where the same data is accessed many times, as can happen in some queries and algorithms. This is achieved in part by holding some or all of the data to be accessed in memory. Spark works with HDFS and also other distributed file systems, such as Apache Cassandra.
 
[4]		  
How phrases from the past come around again!
 
[5]		  
Some elements of the technology have changed, but the vast majority of the issues I covered in “Why Business Intelligence projects fail” hold as true today as they did back in 2009 when I wrote this piece.