Love (for Data) Will Tear Us Apart: Part 2

by Jim Cain

If you missed Part 1 you can read it here.

In Part 1 of this post I talked about the battle around data that is occurring between departments in almost every company – collecting it, accessing it, sharing it, governing it, etc.- in order to use it to transform the business. I explained the problems most organizations are facing, and why I believe 80% of data initiatives fail (that’s a stat from Gartner). If you haven’t read part 1, I recommend you do, as it explains why lack of alignment is a big issue that needs to be resolved in order to solve this problem.

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Let’s think about alignment for a minute and apply the 4 C framework:

If you really think about each of the 4 C’s (Create, Connect, Control, Commercialize), they have a much higher level of appeal to different parts of the organization because of their closeness to departmental core values (aka how bonuses get paid and careers get made). Let’s illustrate this with a visual:

Each one of the C’s has a clear high-level objective with regards to supporting the business.

Create: This type of work ultimately supports the business by ensuring continuity and governance.  Data that is properly exposed, named, and documented protects campaigns from breaking, reports from lying, and key assets (think website) from breaking.  Create projects ensure the trains run on time. and save the business significant money.

Commercialize: On the other end of the spectrum, commercialize projects grow the business by executing campaigns to directly increase revenues.  Data is intelligently used to create new customers and increase the amount they spend. Emphasis is on return on investment (ROI), –  spending the most appropriate amount to make the maximum impact on the bottom line.  Commercialize projects create the trains that people want to ride.

Connect: These initiatives take any available data and bring it together to maximize the network effect of disparate data together. Emphasis is on supporting and accelerating active business initiatives (i.e. reporting, personalization, customer insights) by connecting as many relevant sources of data as possible.  Connect projects get as many ingredients into the kitchen as possible so the chef can make anything she wants.

Control: Control initiatives are wholly focused on making available business data owned.  Unlike a connect project that will use data from any source, a Control project starts with making available data into an asset which is inside the business, and structured for quality, scalability and utility. A great example of utility would be any data science initiative – Data must be controlled before it can be applied to Machine Learning and Artificial Intelligence programs. Control projects are about owning the data supply chain – creating the farms that send the freshest and highest quality ingredients to the kitchen. 

Let’s add the departments we discussed in part 1 (IT, Data, Executives, Business Users/Marketing) to this visual:

The Executive Team is focused on initiatives that grow the business.

The IT organization is focused on initiatives that safeguard the infrastructure of the business.

The marketing organization and other line of business users are focused on doing their jobs more effectively using any available data.

The data teams are focused on initiatives that safeguard the data quality and compliance of the business, while also enabling advanced programs that only owned data can power.


This inherent conflict between the parts of the organization who are tasked with running a solid defence and those who are supposed to execute an aggressive offence is a major part of the reason why so many data-driven initiatives fail.

So far, nothing has been outlined that actually fixes anything, and it might be a little frustrating to read.  Candidly this is frustrating to write. In almost 20 years of being involved in data initiatives, I’ve participated in my share of failures to launch.  Looking at these illustrations and thinking about the ‘ones that got away’, specifically really cool data programs that just never worked, make my hackles go up.

This lack of alignment between offence and defense is why many brands:

  •  Significantly overspend on digital marketing campaigns but can’t identify where the wasted money is.  
  • It’s why many large organizations have bought advanced data products (i.e. Customer Data Platforms) but very few of them are successful with them.  
  • It’s why no one trusts their reports, and why analysts are drowning in information and executives are in an insight drought. 
  • It’s why IT and data teams feel overworked, unsuccessful and scrambling to shore up dikes they know will break.

Whew! That felt good to get that off my chest.  Honestly, there is nothing that makes me angrier than things that don’t work, especially when I know what they are capable of.  That said, in understanding why something doesn’t work, we can finally get started on fixing it.

But for that, you’ll have to wait for my next post. Or better yet, join me for a live webinar I’ll be presenting on this topic on October 6th. Register Here

In the meantime, read the rest of the articles in our 4 C’s series:

The 4 C’s for Executives

Data Democratization and the 4 C’s Framework

Love (for Data) Will Tear Us Apart – PART 1

Jim Cain

Founder and CEO, Napkyn Analytics

See more posts from Jim