Until recently, the Chief Data Officer (CDO) was the person who took care of the data of the business. The role evolved to become Chief Data and Analytics Officer (CDAO) and recognized that the exploitation of data using analytics was essential to value creation.
While many authors have published case studies showing value creation using data and analytics, there remains precious little empirical evidence or repeatable frameworks for the leaders of the Data, Analytics, and AI (DAI) function to utilize to enable their businesses to create value sustainably. Thus far, there is no ‘5 Forces’, Value Chain, or 6 Sigma for the world of DAI.
The advent of Generative AI and its ‘non-identical twin’ Reinforcement Learning is leading to fundamental disruptions of entire industries, making the need for frameworks and methods for data, analytics, and AI leadership a critical imperative.
We need to see DAI Leaders as people who help their businesses surf rather than get swamped by this wave. Everybody has access to technology and that continues to develop and raise the bar but it is the skills needed to exploit the technology and ride the wave that make the big differences.
This is often described as a data-driven business, but I have defined it as an Intelligent Business:
An organization that utilizes predictive and adaptive insight to dynamically reconfigure itself in response to the expected needs of its customers. Also, it can simultaneously anticipate and respond to changes and events in the external environment.
Combined with 25 years in the Data (and Analytics and AI) industry, as a ‘Chief’ for multi-nationals in multiple industries and global consulting Leader at Capgemini and KPMG, I am also researching for a doctorate which aims to cross this chasm and deliver repeatable methods for how around how DAI capabilities can deliver sustained and sustainable competitive advantage for a business.
We hear a lot about data strategies, but surely, they are insufficient to help an entire organization transform. Having looked at well-known management theories, my doctoral research has focused on ‘Resource Based Theory’ (RBT), which focuses on the capabilities that businesses and organizations use to create sustained competitive advantage.
The Resource Theory came to prominence in the 21st century and along with another theory ‘Dynamic Capabilities,’ it seems to help explain how organizations can really survive and thrive in the complex and volatile world where CEOs are not only facing the challenges of AI, but also squaring the circle between financial performance and their own and investor needs to deliver ‘sustainable’ (Net Zero) performance.
RBT identifies truly differentiating capabilities if they are Valuable, Rare, Immutable (difficult to copy), and Organizationally embedded (VRIO).
In the case of digital, data, analytics, and AI, we have a fixation on tools and technology! The technology is the table stakes but it is not the source of competitive advantage. If we consider the VRIO of Resource Based Theory. Technology does not pass the VRIO ‘sniff test’.
Technology is increasingly one of the biggest tangible assets on many company balance sheets. It can be uniquely valuable and companies can organize themselves to capture the value (albeit many aren’t successful). However, while technology can be costly to imitate if you are buying commercial off-the-shelf software (CRM, Cloud, ERP, BI, etc.) it is by no means costly to imitate and far from rare!
Organizations thinking of spending money on the latest Data Mesh, Data Platforms, Analytics, and AI platforms need to fundamentally recognize the fact that the tools themselves may be essential, but by no measure are a source of competitive advantage.
Technology continues to be seen as the driving force for truly Digital or Data Driven businesses. It is with the skills you must exploit the technology.
The first step in this journey is that using the VRIO principle – Data and analytics (including AI) are like Yin and Yang. The leaders who bring them together can truly differentiate their businesses from businesses that have either a CDataO and/or CAnalyticO. The differentiated organization is led by a Chief Data and Analytics Officer.
1. Customer is King (or Queen): Data is central to understanding your customers – what motivates them, what their needs are, what they are inclined to buy, and for how much. Great companies combine all the data they must predict the needs of their customers and design products and services that meet those future needs.
They achieve that when their data leaders work in partnership with product, marketing, and customer stakeholders and identify the opportunities where data, predictive, and prescriptive analytical models (machine learning and AI) can be deployed to create transformative products and services for customers.
If you think that a typical Credit Rating agency uses about 100 data attributes to provide a Customer Credit Score, a leading Grocery Retailer has 1,000 data attributes to tune products and promotions for their customers. The likes of Amazon and Google have 25,000 (!!) attributes of data about each of their customers.
How data leaders can collect, manage, refine, and fuel data for marketing, product, and supply chains will be one of the critical, if not the most critical value drivers of your business.
2. Masters external environments: In addition to collecting great customer data, the real advent of ‘big data’ was the need to start to collect and leverage data from outside the organization including video, audio, social media, etc. That data can be used to augment an understanding of customers and in addition, central to understanding our external environment, competitors, and suppliers.
Companies that live in highly competitive markets have already had to understand this. They use data and analytics to understand what competitors and new entrants are doing and aim to be at least one step ahead.
As markets and environments become more and more volatile, all businesses need to be more agile and data is central to sensing, identifying, and responding to potential external challenges as, and when, if not before they happen!
That long history lesson is the precursor to the importance of being a master of external environments. In my language, in driving insights, we have ‘strong’ signals and ‘weak signals’.
Strong Signals come from your structured data warehouse and the sophisticated analytical techniques they can be exploited with, providing large amounts of data to train Machine Learning models which allow them to develop sophisticated correlations and sometimes actual causation that will help you with immediate models (as in ‘Customer is King’ – Churn, Propensity, Decisioning!).
Weak Signals come from the external environment. The most obvious ones of late would be the full implementation of Brexit, the Russia/Ukraine conflict, and the COVID-19 Pandemic. These would, if correctly interpreted, have told us in March 2020 and March 2022 that the highly curated machine learning models would rapidly start to deliver inaccurate results.
3. Building a data-driven business model: One critical aspect people forget is that data is both an input and an output to business processes. Too many companies and their data leaders only focus on the output data and the metrics associated with revenue, profit, free cash flow, etc.
These measures are critical, but they only tell you how you have done – they are ‘backward looking’ by design. The leading businesses look at input metrics as well as output metrics because, after all, data is the fuel of the business and therefore drives the processes that operate the business.
Input metrics could include page views, stock availability, price, discounts, and convenience. To institutionalize this, you need to build an entendable data model for your business. The model should be built by looking at the value drivers of the organization.
This allows the creation of a KPI/metric model that gives an uninterrupted line of sight between your Level 1 Key Performance/Results Indicators (like revenue, EBITDA, and free cash flow) you report to shareholders, and the underlying operational metrics of the business (customer lifetime value, cost of goods sold, employee costs, etc.); as well as those critical sustainability and ESG metrics.
The data model can be built around that core performance model and will be owned by your data leader. The model will be central to your business operating model and facilitate the migration of your process model into the background.
I weep when people say they can’t measure the benefits of data investments. Why, because the discipline of Data and Analytics includes Business Intelligence and Reporting! In other words, the smart data leader is also accountable for providing the key reporting of business performance, so you have the absolute levers at your disposal to measure whether the initiative you’re accountable for has made a difference to the cost, revenue, and/or profitability of the business!
4. Governance and regulation – your friend not your enemy. Data Governance has been seen as a problem and the enemy of successful data Leaders, but in my opinion risk and performance are two sides of the same data. If you have a single data model, built in partnership with the rest of the business you have the nirvana to manage risk, performance, and sustainability.
That’s easier said than done, and just understanding the components of cost, revenue, and profit in a large business is a substantial undertaking. Building a network of data owners and data stewards in the business, who will be your arms and legs (and use the data every day, so it’s in their interest for it to be right) is critical and cost-effective.
Furthermore, central to the privacy laws of most countries is a set of principles that define how personal data are collected, shared, and processed. However, consumers often do not know the benefits and costs of the data that pertain to them.
Data Leaders need to understand those laws and work closely with legal/general counsel (the data protection officer) as well as marketing, sales, and finance – typically the data you use to market and sell to a customer is the same as a regulator wants to know that you sell and market to a customer and whether you have their consent!
5. Migrating to a data-driven (microservices) architecture in the cloud: The Intelligent Business operating model is one where data serves people and masters process and technology. Businesses need to redefine their operating model so they are not limited by the straitjacket of legacy ‘process models,’ so that they can embrace a much more agile approach.
Processes will need to be dynamically reconfigured to meet the rapidly changing needs of customers and reconfigure what the business is delivering as services. Your AI and machine learning algorithms are your source of competitive advantage. Over time, we can then migrate away from the ERP and CRM platforms and downsize to what technologists call 'Micro Services' - at a fraction of the cost.
Microservices are an architectural and organizational approach to software development where software is composed of small independent services that communicate over well-defined APIs. They can be developed by your CTO, a trusted software vendor, and/or provided open source.
We connect the data model to the micro-services with APIs which can be delivered by your CDAO and CTO working together and mastered by your integrated Data Model. As you build more, you can pull more and more logic away from those complex monolithic ERP and CRM platforms. The business becomes more agile and data-driven by design!
6. Servant leadership – data serves people. It is all about people and three key communities that need to be addressed. The first community is your (external) customers (mentioned in principle 1, Customer is King or Queen) – you must be using Data & AI to develop products and services that make a difference to the end customer.
The second community is the people within the organization – the whole concept of the Intelligent Business is to ensure that the data in your business serves the people in your business to help them make better decisions, which improves service to the end customer and allows the organization to deliver better revenues, lower costs, and improved ESG outcomes.
That means upskilling the whole business and developing ‘citizen’ data scientists as well as ownership of data amongst the whole employee population.
The third population is the Data and Analytics team, and the job of the effective data leader is to create, grow, and nurture a great team and provide them with exciting work that delivers transformational products and services for the other two populations. Data rarely delivers value in isolation. When it is combined with other activities, it becomes an enabler and a multiplier of value creation.
7. Data strategy spins the business strategy: The famous quote of Gen Omar Bradley is “Strategy is for amateurs and professionals discuss logistics!” The key logistics for a business strategy (powered by Data) is that data is both an input and an output.
Too many companies only focus on the output data and the metrics associated with revenue, profit, free cash flow, etc. These measures are critical, but they only tell you how you have done – they are ‘backward looking’ by design. The leading businesses (like Amazon), look at Input Metrics as well as Output Metrics, after all, Data is the fuel of the business and therefore drives the processes that operate the business.
How your business differentiates and delivers sustainable competitive performance, data, and analytics capabilities will be valuable, rare, and difficult to Imitate. The organization will be key to delivering that competitive performance as well as measuring and managing it.
Many of you will have heard of the ‘Fly Wheel’ concept from Jim Collins and his famous business book ‘From Good to Great’. Of those, quite a few will know that Jeff Bezos took that concept, and his strategy was to use input and output KPIs and associate data and analytics to spin the ‘flywheel’ at Amazon faster than anyone else! You need a Data Strategy that spins your Business Strategy.
The Intelligent Business and the seven habits of the highly effective data, analytics, and AI leader are my first steps towards creating an empirical framework that organizations and leaders can utilize to deliver sustained and sustainable value from data, analytics, and AI.
About the author:
Eddie Short is Managing Partner at Transformational Insight Limited. He is a world-class digital/data/technology transformation leader and a proven specialist in global growth, expansion, and innovation, with a specific focus on transforming businesses and creating new products and services leveraging data, analytics, artificial intelligence, and digital capabilities.