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Customer Data Unicorns: Why how we manage their data is the secret to finding, taming and riding them


The process of how we listen, think, talk and do using this data is not possible without the effective management thereof. This skill enables the business to exploit this asset and ride these Majestic Unicorns.



By Rupert Knight, Data Adventurer

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Customer data is borrowed not owned and we rent it from them on a pay as you go basis. It is the most precious business commodity we have, and in this day an age we have access to lots of it. The process of how we listen, think, talk and do using this data is not possible without the effective management thereof. This skill enables the business to exploit this asset and ride these Majestic Unicorns.

My personal data hero Sherlock Holmes said:

“Never theorise before you have data. Invariably you end up twisting facts to suit theories instead of theories to suit facts.

No one wants to slaughter a herd of wild unicorns. We would rather catch, tame and learn to ride them for as long as possible. Indulge me in a dash of myth mixing for a moment and let me say upfront there are no silver bullets. What I do offer are four silver pointed tranquilliser darts and the best practice black belt disciplines required to become a data ninja fit for engaging with customer data unicorns.

I have put together a short video to illustrate how managing customer data should take the spotlight in the process of finding, taming and riding these illusive customer data unicorns, but let me suggest you read this article first before jumping to it as it will provide you with invaluable context.

Before we jump into the non violent hunting techniques and tools we need to deal with two definitions. And why it's important to understand them.

 

Data and Information demystified

 
It's taken me a long time to understand the difference between data and information and why it's probably the most important departure point of this story.

"You cant have customer information without customer data but you can have data without information."

Why is that ? Data is what we get when we interact with a customer. Information is what we have once we have managed that data. There lies the secret. It is in the process of managing data that it is tamed and transforms into a manageable herd of unicorns that will allow us to unlock the unfound potential we keep hearing about.

 

Management defined

 
So let's briefly look at what management means. Defined simply as : the abilities and processes we require when dealing with controlling things or people. Data is that thing we need to develop abilities around in order to control it. For that we need strategies. Doing things without a reason for doing them generally leads to trouble. Or as this guy put it better,

Sun Tzu: Strategy without tactics is the slowest route to victory. Tactics(Actions) without strategy is the noise before defeat.

So its all about data again. No, its about how we need to manage it. This sounds rather boring. Managing data to me conjures up ideas of sending emails to it about what needs to be done, checking it gets to work on time and of course playing policeman. Not the stuff of the promised customer Data unicorns I spoke of earlier.

Here comes the part I think you are looking for.

 

The four checks to ask before engaging with a Data Unicorn.

 
Later on we will discuss the black belts required when wrestling with a unicorn but first before engaging lets ask if we are doing the following.

At this point I have to own up and say I have re written this next section so many times only to delete everything and start again. There is just to much to say and it eventually it ends up paralysing me. So I have a jotted down some quick questions and notes. I will leave the rest up to you.

 

LISTEN THROUGH data

 
Have you decided what data your customer is talking to you through and which methods are the most important so that you can listen and understand them ? In todays digital world we are overloaded with the noise of data which makes it hard to listen.

"Data is the loud noise our customers make, information is the beautiful sound we hear when we start to listen correctly." me

Also are we listening to what your potential customers are doing elsewhere ? For example there is a huge industry built around social listening but its so noisy we cannot hear them. The process of isolating and prioritising what data we listen to is a very important part of data management.

 

THINK WITH data

 
How are we understanding their data ? At the end of the day customers want to feel understood. It is in these departments of advanced understanding(AU) that we can truly start to cut though all the noise and think carefully with data. As Sherlock said earlier thinking without data can lead to disatrous results.

 

TALK BASED ON data

 
When we decide to talk, can we substantiate what we are saying with data ? In order to convince people to buy our product/service we need to talk to them, but when we talk, we need to show them we have listened to them. We are talking to them through everything we do.

 

DO USING data

 
When we launch a new product/service are we using the data that is generated by the previous three points. Can we react to that data that is being fed back to us when we are doing it ? Have we planned how we going react to that data coming back at us ? Do we have established metrics to measure real time success or failure. If its going really well what can we do to ride our unicorn even faster ?

 

The black belts required to become a unicorn riding data ninja

 
Here are the kata's one needs to master the skills required when engaging with customer data. They are well known in the industry and I will leave it up to you to go an Google the hell out of them. For me the revelation was that you don't necessarily have to become a black belt in the first part of the statement eg. "Data Architecture Management" you have to become a black belt in the last part "Management". How do you manage the customer buildings the our Architects have so carefully designed.

The other key takeaway I got from this list taken from DAMA's body of Knowledge was the new addition of "Data Integration and Interoperability" (Data interoperability addresses the ability of systems and services that create, exchange and consume data to have clear, shared expectations for the contents, context and meaning of that data.)

  1. Data Integration & Interoperability –acquisition, extraction, transformation, movement, delivery, replication, federation, virtualization and operational support
  2. Data Architecture management – the overall structure of data and data-related resources as an integral part of the enterprise architecture
  3. Data Governance management – planning, oversight, and control over management of data and the use of data and data-related resources. While we understand that governance covers ‘processes’, not ‘things’, the common term is Data Governance, and so we will use this term.
  4. Data Development management – analysis, design, building, testing, and maintenance
  5. Data Storage & Operations management – structured physical data assets storage deployment and management (was Data Operations in the DAMA-DMBOK 1st edition)
  6. Data Security management – ensuring privacy, confidentiality and appropriate access to PII, PHI and an individual's private data. Ensuring network security as well
  7. Documents & Content management– storing, protecting, indexing, and enabling access to data found in unstructured sources (electronic files and physical records), and making this data available for integration and interoperability with structured (database) data.
  8. Reference & Master Data management – Managing shared data to reduce redundancy and ensure better data quality through standardized definition and use of data values.
  9. Data Warehousing & Business Intelligence management– managing analytical data processing and enabling access to decision support data for reporting and analysis.
  10. Metadata management– collecting, categorizing, maintaining, integrating, controlling, managing, and delivering metadata.
  11. Data Quality management – defining, monitoring, maintaining data integrity, and improving data quality.

 

How it all fits together ?

 
For me to understand things I use analogies and in this case I used the one of customers being guests in our guest house. As with all analogies they are personal and not complete. Each guest house team performs a certain function with the overall objective of getting new customers to come stay at our guest house and then to come back more often. No one function is more important than the other. The point I am trying to make is that managing the data and metadata across those teams leads to the understanding required which makes guests decide to come and stay longer and more regularly. If you watch the video I put together you will hopefully see how this fits together and the fact that keeping customers happy by how we listen to them through the data they generate when they are staying with us and when they are not is critical in getting them to come back over and over again.

Listening with data is a conscious process and we only start hearing the customer noise properly when we decide on what we listen to and how we listen to it. In order to achieve that we need to develop the strategies and skills to do so.

 
Bio: Rupert Knight is a Data Adventurer who engages exclusively with one organization per Industry to assist them in Strategy, Transformation & Tactical execution. Get the right People, create an evolving artificially intelligent Process and then Implement Technologies to compliment. Strictly in this order. “Gaudet Tentamine Virtus” (Virtue rejoices in Trial)

Original. Reposted with permission.

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