Seven Habits of Highly Effective CDOs Customers are King (or Queen)

I have previously highlighted 7 Habits of Highly Effective Chief Data and Analytics Officers (CDO/CDAO).??In this and subsequent newsletters I will elaborate on them, starting with number 1,?Customers are King (or Queen).

A former CEO I worked for, described his business as only having two types of people, those who serve Customers, and those who serve those who server Customers.??Within the world of Data and Analytics, we are uniquely privileged to serve both groups, external and internal customers!

• The good business is one that can use Data to identify the needs of its customers and continually delight them.??The great (‘Intelligent’) business uses Data to predict the future needs of customers and likewise to use that data to inform the development and sales of those products to customers.

Ultimately most revenue and profit generating Use Cases involving Data, Analytics and AI (DAA), come from capabilities that service external customers.??As the leader of the DAA capability you have a unique breath of customers to serve.??The use cases are too many and varied to describe here, suffice to say that every business function is increasingly an actual or potential customer of Data & Analytics.

There are very many DAA Use Cases, but two of the most important, IMHO are Know Your Customer and Decisioning capabilities as follows:

1. Know Your Customer?(KYC) is key for any front office function, whether to sell and market to them, or whether to understand any risks that they may or may not pose to the business e.g. Fraud, Money Laundering. This is the perfect example of Data & Analytics coming together as Ying and Yang.??In the case of KYC DAA delivers capabilities of managing both Performance and Risk, a classic Buy One Get One Free use case, if ever there was one.
2. Decisioning?(Next Best Offer and Action (NBO/NBA)) capabilities that use predictive, prescriptive and adaptive AI and Machine Learning to ensure that the best product or service (which sometimes may be nothing) is offered to the customer, when and where they want it, within the Channels that your business operates (App, Social, Web, Contact Centre and/or Face to Face In Branch or Store). Decisioning is the leading edge of whether you want you business to be People who are AI enabled, or whether you want to have AI drive People.???????????

NB These use cases are typically prevalent in Business to Consumer Use Cases, but I believe are equally important in Business to Business.???For example in a world where Fraud, Money Laundering and criminal activities are increasingly pervasive, KYC is critical for any business that wants to ensure it is dealing with a reliable and honest customer or business trading partner.

Whilst KYC and Decisioning are increasingly well documented with Use Cases, delivering the best solutions usually exhibits the key paradox of Customers are King, which is best described by the famous quote attributed to Henry Ford: ”If I asked my Customers, they would have asked for a Faster Horse!”

• Despite the thoughts of Henry Ford, or even of Steve Jobs “My Customers will know what they want, when I show it to them…”, it is of paramount importance to engage your customers in DAA product and service development.??
• However, the reality is a great DAA leader knows that Data Products and Services, have always had to be developed in an Agile Way.??It is very uncommon for customers to be able to articulate their full requirements at the start of a programme, and in fact even if they provide a set of requirements, they will only be a fraction of what the end solution requires.??At the other end of the spectrum, Customer requirements may well be well beyond the models and algorithms that can be delivered from the data the business has available.

My approach for delivering DAA solutions is a six step version of Agile, which I refer to as ‘Seeing is Believing’.??

1. The initial step is for the Data Leader (and team) to discuss with the business leader (and team) in a workshop mode what the base solution should look like.??The Data Leader team should bring some example models and capabilities to use to help the business paint a broad outline of the potential solution.??This ‘art of the possible’ will help to articulate the vision and begin the process of developing a business and value case!
2. The Data team can then go away with a limited set of data and requirements and developing a visual proof of concept (PoC).??This should be developed in no more than a few days or couple of weeks and, becomes what I describe as the ‘Art of the Probable’.
3. By visually rapidly developing an ‘Art of the Possible’ and ‘Art of the Probable’ it becomes possible to work with the Business, IT and Finance to develop clarity of the overall Plan and Value Case, to the point where any significant level of investment can be approved.
4. Going forward working with the PoC, and showing it to our customers, we can elicit more requirements, they will give their thoughts on the direction of travel of the solution and together we can (hopefully) identify more data that we can use to build out the platform.
5. This will lead to the development of a Minimum Viable Product, something that is no longer throw away, but can be utilised by a small sub-set of customers to use on a regular basis. Subject to agreement from the business stakeholders, this can be used to go forward to production and critically will also be their trigger to focus on delivering the value case from the solution
6. From the MVP we deliver using continuous iteration, often referred to as 'DevOps'; in other words we continue to develop it as well as support it in Production, , based on feedback and value created by and for customers.

The above may be a statement of the obvious to many, but too often I see the Data Science or Analytical Modelling team in a business take away a set of requirements and some time later, present, 'as if by magic' their transformational solution to the customer, who basically has no idea what they have done, and is rapidly disengaged. The Data Science team, likewise, feel light down and demotivated...

• working in the Agile 'Seeing is Believing' model is not always a panacea but has three critical advantages, it engages the business in ownership of a solution, ensures their commitment to value delivery and critically avoids surprises!!

In conclusion, as the leader of the DAA capability you have a unique breath of customers to serve, and in most businesses I would describe the opportunity as a 'target rich environment'. The only downside of a target rich environment, is that it becomes very easy to dilute focus as you try to serve every potential customer of the business. Ultimately, whilst Customer is King, Value is Emperor! As the Data Leader partnering with Finance to ensure that the Use Cases you deliver are the most valuable for the business is often the difference between success and failure!