Why did Facebook spend billions to buy Instagram and WhatsApp, even when these companies weren’t yet profitable. How can a relative new or weak player challenge big market leaders in Nokia and Yahoo, as Apple and Google once did? How did Uber and Airbnb disrupt the taxi and hotel industry? And how has Amazon managed to take over much of the retail world (even before COVID-19) to become the most valuable company in the world?
Data. The world’s most valuable currency.
The power behind Artificial Intelligence (AI) algorithms, giving them the fuel to ‘learn’ and perfect how to recognize beauty from a photograph … and what a person might do, how he or she will behave next?
It’s also the tool that will help you and your business to fight the threat of disruption, and what is really driving how model countries like Germany and South Korea, to act and react as they have, in their COVID-19 crisis management efforts.
Discover:
- Why data has become so valuable? How has it become a currency?
- What data should your business collect and mine to increase value creation and its own valuation?
- How to implement data-driven techniques to optimize your Operating Model?
… including details of WHAT and HOW Amazon, Apple, Facebook and more have used data and applied AI in their Operating Models. - How to ‘get started’ with the 8 Steps to analyze your Strategy Journey Problem and identify the path of next steps in your Solution Roadmap
- How to fast track the journey to a data-driven enterprise?
[NB: This article is a big update to our introduction covering ‘Why Data has become so valuable and become a currency’ published on March 10, 2017 as ‘Data is the new currency – Does your business deal in it?’ by Julie Choo. ]
Why has data become so valuable?
How has it become a currency?
Data is all around us, especially in social media and other apps that we use daily. It is created when we buy, when we use services, when we take photos, and when we share an update.
It has always been valuable. Over many centuries traders, merchants, leaders and rulers have always sought, bought and traded for data to support their enterprises in whatever business they conduct, from running governments to war, to selling their goods and services. What’s changed is how we have been able to store the data and then use the data to devise better intelligence faster, as well as apply that data to different situations or use cases in the real world. The sheer volume that we can handle using the latest data technologies like AI and Blockchain, supported by cloud architecture, has risen to unprecedented levels in the digital economy.
Successful CEOs know that the value their businesses create today is not about what products they make, it’s the data they have and the potential intelligence they can gain in how future customers will think, act and behave on their customer journeys as they consume different goods and services.
Facebook’s acquisition of Instagram was indeed a smart investment from CEO Mark Zuckerberg, a psychologist undergrad from Harvard. Whilst Instagram had only just begun to monetize, with advertising in the app, its $1bn price tag reflected the value of its platform and users. The platform created a place for content producers to post lovely filtered images of their latest meal. This brought on board consumers, who wanted to see these images and interact with the people who have produced them. Instagram’s community and the data it holds about them gave it value, and this value was recognized by Zuckerberg who understood the true value of this behavior data.
And businesses are happy to show us the value of their data. In a tongue in cheek advertising campaign, Spotify took user information from 2016 and made it into a series of ads for the platform. Their value is not just delivering music to music lovers, it is in the data they have about people’s listening habits.
Sangeet Choudary, author of Platform Revolution and Platform Scale, writes about this shift and the new value of data on his website, platformed.info. He writes about this new focus – a shift in business models. The traditional model for businesses was always a ‘pipeline’. You make a product, market it, sell it to make a profit. All the value flows one way down the pipeline. The value of the business lies in the margin of profit made from each transaction.
But more and more businesses are now moving or have already moved to platform models. The platform business model brings together producers and consumers to interact together on a platform – often formed from digital channels. The value created is then two-fold. There are the interactions on the platform – buying and selling in the case of eBay, as well as the data that is being created by all these interactions.
In fact, some businesses even don’t create physical products anymore. The platform businesses are marketplaces which attract users and sellers to interact. All these interactions create data and build more and more value for the business.
This shift is not just for digital businesses, either. Platform models can be applied where companies are selling physical products. Let’s go back to the example of Apple again, and their launch of the iPhone.
The iPhone is a physical product but the business model behind it is a platform. The iPhone and its operating system were not just a pipeline, a product, they were a gateway to a platform. As Choudary and his co-authors explain in the Harvard Business Review:
It imagined them as a way to connect participants in two-sided markets—app developers on one side and app users on the other—generating value for both groups.
This platform model took Apple from no presence in the mobile phone market at launch, to a 92% share in 2015. As the article notes, this sent its competitors, large entrenched businesses, into freefall because they didn’t see the shift to a platform-based business model.
And Apple has built on its platform by connected multiple products including the iPhone, Mac and Macbooks, iPad tablets, iWatch and even its airpods into the Apple Ecosystem, that has become extremely sticky indeed. We have Apple fans sticking to Apple products because of the convenience and user experience provided by this value ecosystem.
Amazon, the worlds most valuable company and worth over $300billion at the end of 2019, uses this Operating Model, has products too that support its value ecosystem. It uses its Amazon Echo Home Speakers, Firesticks and Tablets to act as gateways or nodes in its effort to collect more data on customer interactions, and channels by which to deliver and distribute some of its services. And this data that it collects is used to predict what goods and services customers might need and want in the future, and what Amazon-branded goods and services the company might manufacture and produce to serve that demand.
NB: We cover how the Operating Model of the Amazon Alexa Service actually works alongside the company’s web services capabilities (AWS) and marketplace, in an in-depth case study within THE STRATEGY JOURNEY Book.
Business Models must transition from traditional pipelines into data-driven platforms if they are to survive disruption.
The value of data only grows as AI technologies begin to learn more from data and predict more activities from data too. And today, data even forms the backbone of cryptocurrencies supported by Blockchain technology. We can also trade cryptocurrencies in digital exchange like Coinbase, which also acts as a ‘wallet’ for digital money. Bitcoin, the world’s first cryptocurrency is actually the best performing asset in the past decade with the highest growth in value at 9,000,000% since July 2010.
With the ability to drive growth and crazy valuations, as well as growth in the value of cryptocurrencies, it’s clear that data has become the new currency.
What data should your business collect and mine to increase value creation and its own valuation?
There are actually many forms of data that an enterprise can collect, hold, mine and use intelligently to increase value creation and its own valuation. The 3 most valuable forms of data for a business and their benefits as demonstrated in THE STRATEGY JOURNEY book and method include:
#1. Customer Data
(especially Customer Journey Data)
As we highlighted above through the activities of Amazon, Facebook, Instagram, Airbnb, Google… the value of a business lies in the data it holds around its customers or rather its users and partners, any person or entity that might co-create value with the business since they are all multi-sided platforms.
It’s important to collect data on how different parties including customers, users and other partners, who would use the business as a platform, where they are, who they are or and most importantly in how they think, act and behave as customers, based on the problems they have.
So, there are actually 3 major types of data about customers that a business should be collecting and then learning from:
Customer Demographic Data has the lowest level of specificity to the customer, but it is representative of the greatest volume of customers. Demographic data is quantitative and relates to a customer’s overall circumstances in life (e.g. age, social grouping, financial worth and spending patterns). It allows customers to be segmented into larger groups, where a Business Model can be established. When used alone, this data is most suited to the design of social and public sector or government services, as well as utility-based services where the goal is to provide fair and equal value propositions to large groups of many customers or citizens.
Analysis applied to demographic data can be used to establish a viable Business Model.
Customer Profile Data makes up the customer persona. There is a mix of quantitative and qualitative data that describes the general behaviors of a particular customer segment (e.g. shopping, investment, holiday preferences/ habits). This data can be used to identify how to co-create value propositions with a customer segment, and is most useful for developing marketing and sales strategies to entice customers into a purchase and repeat purchases.
Analysis of profile related data is useful for marketing purposes in order to
target specific value propositions.
Customer Journey Data describes the thoughts, emotions, and actions of the customer as they undertake their journey to achieve their end goal, that is, the customer experience of using different value propositions or services if they exist to solve their big problem. The data is more granular as it is behavioral but also ‘experiential’, that is, thick* in nature and specific to a customer problem.
* Thick data is comprised of qualitative informative materials, tools or techniques that help organizations gather granular, specific knowledge about their target audience – defined by Brandwatch in 2014
As the data delves into the customer problem, it can be analyzed to identify the outcomes and experiences that customers want within their solutions along their self-directed customer journeys that would also meet their expectations in the Service Design process.
The qualitative nature of experiential data is used to define the value generated to the customer.
#2. Transactional Data (of user interactions with the business as a platform)
Transactional Data is the most common form of data that businesses collect, through different payments in the buying and selling process. It’s what businesses need to track in their accounts and report to the tax department, so there should be plenty of this kind of data to re-use intelligently.
But while many companies have this data, they often neglect to collect all the details or attributes surrounding this data. Many have not taken the time and effort to track the sales conversations taken to make the sale, the number of touchpoints the customer has with the business pre-sale in its marketing efforts, across multiple channels. Nor data of interactions during fulfillment and post-delivery. This is Transactional Data that is also Customer Journey Data, with the ability to delve into the customer experience too.
Many companies track complaints in order to capture this data which is extremely valuable.
Jeff Bezos, Amazon founder and CEO is famous for still reading customer complaints and then acting on them. He is said to be customer-obsessed and data-obsessed too, and we know all too way that data collection is part of his and Amazon game plan, to build the most valuable company in the world.
But as illustrated through Amazon, you need to go beyond just complaints , as that is reactive and retrospective. You need preemptive data too that is proactive in predicting customer behaviors based on their problems along their customer journeys where the root of their problems in life lie, and not just their complaints or reactions to how the service or product they have received meets or does not meet their expectations.
Interaction-based Transactional Data is what the banks are missing, as they only have the restrospective Transactional Data, while platforms like Amazon, Facebook, Google, Apple and other networks are busy capturing more and more highly valuable Interactions Data. And in doing so, they can sell ads and generate many different diversified sources of revenue from launching new in-demand products and services, as well as making them and their entire value ecosystem even stickier.
So much more value is being created and can be created from Transactional Data that is Interaction-based – all the thick, detailed data, that AI algorithms can learn from.
#3. Performance Data (including Transformation Data)
Performance Data is only relevant if there is enough of it from having collected, mined, used and analyzed the other two forms of data – our Customer Data and Transactional Data. If you don’t have the other forms of data as input, then your performance is absent of any real value.
When it comes to data science, if there’s no input or rubbish input, then you get no output or rubbish output. AI algorithms need to be feed lots of data in order for them to learn and keep learning from the data before they output various hypothesis or results to help us make decisions. Of course, we can train the AI to make decisions for us, but we have to tell it what decisions to make.
So, when it comes to Performance Data, what you really want to track and learn from is what has changed, or not changed. You want the Transformation Data.
Transformation Data is the data that helps you to make decisions based on what has changed in the performance of a process or interaction with respect to a service or product, so you can take action. This is the data that will help you to overcome and mitigate disruption to your business or Operating Model, to solve problems.
Why Transformation Data makes all the difference in the Coronavirus Pandemic?
If we look at all the data that is being studied around the Conoravirus – COVIT-19 Pandemic, you will see what the data scientists are collecting, tracking and then analyzing, to see that there is a process, starting with Customer Data, followed by Transactional and Interaction Data and then finally, Performance and Transformation Data, both positive and negative.
When poor Customer Data and Transactional Data is collected and then analyzed, you are potentially getting Performance Data that lacks the quality to help you make decisions or to take the right decisions.
Many countries are dealing with their various outbreaks different, based on their own Operating Models and the resources available to them as well as the timing of their specific outbreaks of course. But those countries that have used data and technology to aid them in collected more Customer Data as well as Transactional Data upfront, might appear to also have made better decisions, faster, and are showing that their actions and reactions to their outbreaks are performing better and faster too.
Countries like Germany, South Korea and New Zealand have been seen as model countries because of their early and proactive reactions to the early threats of the pandemic spread, but might this be because they have more and better data. And their data may even have helped them to set up the necessary infrastructures in their Operating Models as countries to deal with such a crisis.
How to implement data-driven techniques to optimize your Operating Model?
We’ve already discussed why data has become even more valuable than ever before and what kinds of data you need to be collecting, mining, analyzing and then using too help you make decisions and take action.
Data and Information are POWERFUL.
When something is so valuable and powerful, it would certainly be a mistake to ignore it, when you are planning your strategy journey for your business or project and in your career too, whatever problems, challenges and disruption threats you might be facing.
So where do you start if you don’t have the resources, including funds or systems by which to collect the data in order to analyze it? Or if you have lots of data, in many old systems with lots of old processes but its all a mess and this mess is giving you poor performance data, not allowing you to make the right or best decisions and to take the right or best actions in your business or project to help you solve your problems?
How can you ‘get structured’ and ‘get smarter’?
THE STRATEGY JOURNEY method provides step-by-step guides using 5 models that mirror the stages of the business lifecycle, on how, where and when you can collect this data, and well as what you do with it.
Every single business or enterprise involved in solving problems, including people, actually navigate through the 5 stages of THE STRATEGY JOURNEY or business lifecycle comprised of:
- Motivation & Leadership
- Business Design
- Value Design
- Business Architecture
- Business Transformation
When you are talking about individuals, then their business is their career and how they make money, and their customers are all the people, businesses and organizations that they interact with.
The method is comprised of a Framework using 5 models that include 25 templated canvases for you to solve your problem with design thinking.
When using each model and canvas, you are applying data-driven techniques to collect and analyze data, including different SMART metrics you need to collect in the data, the co-creation methods you can use to collect the data from customers and users, and how to analyze the data to discover specific intelligence and opportunities for improved Service Design. This is how to increase value in your business through building service stickiness.
The ‘Mission Model’ describes the core purpose of an enterprise providing laser focus on the target mission that it seeks to achieve, while enabling the business to pull followers and people toward its future vision.
The ‘Business Model’ describes what constitutes and drives a business, giving it the means to make profit as well as growing the value of the business itself. It encompasses customers, value propositions, and details of what makes the business grow.
The ‘Value Model’ describes what constitutes value for an enterprise or a customer, encompassing where the value is created, the exchange of value between different stakeholders, and most importantly, how to find new opportunities to create value in the wider global business ecosystem.
The ‘Operating Model’ describes HOW the business runs to support the design, build, testing, and delivery of its value propositions. Comprising processes, data, technology systems, people and governance of the business’s capabilities to operate at a cost to achieve business outcomes.
The ‘Transformation Model’ describes the effort in time, resources, costs and the governance of the roadmap associated with the transformation journey of an enterprise, as it executes changes to its capabilities and improves its business agility, for continued value delivery and growth.
The five models supported by 5 Strategy Journey Canvases enable you to collect data and analyze the data with a specific purpose, during each stage in the strategy journey.
The Strategy Journey method also shows you how to conduct gap analysis using data from each of the models to transform your business and specifically its Operating Model, to help you build the foundations and infrastructure that will set you and your business or enterprise up for success in the future.
How do you ‘get started’?
8 steps to analyze your Strategy Journey and identify the Path of next steps in your Solution Roadmap
Solving complex problems isn’t easy, and you often need to know where to start, so you have to know what is the real problem or root cause of your problems, and as well as what sorts of outcomes you want to achieve too, upfront, before you jump into the problem-solving process.
So which of the canvases or which of the 5 models in THE STRATEGY JOURNEY method do you start with to solve our problem best and fastest too?
When you don’t start right, and jump to conclusions too quickly, then you are potentially creating solutions that don’t solve your problem and wasting your time and effort, those precious resources that will run out. You may be solving the wrong problem too. Either way, you aren’t achieving the outcomes you need and want to be successful. And that is definitely not the best way to ‘get started’.
This is the real cause of failure and why there is so much failure in enterprises big and small with all these statistics.
You are in fact jumping to making decisions and taking actions from Performance Data that is rubbish, or no data at all, and you haven’t collected or analyzed the Customer Data, nor any Transactional and Interactions Data where you can gain insights and intelligence. This is why you fail.
As with all problems, especially big complex ones (relatively speaking of course), it’s best to start small and then work on the right actionable steps by getting quick wins that are also strategically directed, to get you to the big win. You will fail if you go too hard or attack a mountain that you can’t climb and navigate without the right resources or starting from the right path.
This is how you can gain trust with stakeholders, including customers and users too, whom you will need to buy-in to your course of actions and to sponsor and even finance your project to solve the problem.
To ensure you can get started on the right track, you need to assess what is your problem’s root cause, and rationalize what outcomes you want to achieve in your role too within the enterprise or organization that you serve, in order to determine what is the Strategy Journey Roadmap or Path you should take, with the right model and templates canvases to start from.
This involves answering the following 8 questions:
- What is your primary role in the business, organization or enterprise that you serve?
- Do you have ownership or accountability for this business, organization or enterprise?
- What is the size of the business, organization or enterprise that you serve?
- What TRANSFORMATION CHALLENGES are you trying to address, that form your objectives in your current role?
- What is the primary reason for addressing these TOP 3 Transformation Challenges in the business, organization or enterprise that you serve?
- What is your level of experience and your learning aspiration with tackling these TOP Transformation Challenges?
- Which PROBLEM AREAS that impact enterprises in our fast change Digital Economy is your PRIMARY FOCUS?
– Business Model Disruption
– Servicing relevant Customer Journeys
– Sustainable Transformation - How much help do you want or what kind of approach do you prefer to take in what you will learn on your strategy journey to solve your problem?
This process helps you to work out what success means for you by breaking down your problem and hence your strategy journey into the actionable steps or path you should take – to form your Solution Roadmap. And while you are answering these questions, you’re also collecting and analyzing data on the problem you want to solve, and identifying all those actionable quick wins too.
How to fast track the journey to a data-driven enterprise?
THE STRATEGY JOURNEY Framework recommends 4 Priority Capabilities to build Agility and Resilience in a business:
Digital Infrastructure (or Technology Application)
Data Intelligence
Service Design & Innovation
Managing Transformation
In building out these priority capabilities, an enterprise will also fast track the strategy journey to develop and grow as a data-driven enterprise.
By adding and integrating new tools, systems and apps into your enterprise, you can start to collect and accumulate the right data about customers, transactions and interactions. This data can be analyzed to understand performance and develop insights that will inform more intelligent decisions on where to invest the enterprise’s resources. It also enables teams to identify and develop new service innovations that will meet and exceed customer and user expectations to increase the value created, produced and delivered by the business. And finally, as the organization continuously optimizes its processes and improves its performance, it is in fact transforming how it operates, and its business agility.
What’s important during this process to build out these four priority capabilities is to start with the right path, and in doing so, you’ll also begin to capture, collect, accumulate the right data, to empower the enterprise and its future growth through the power of data.