Campfire Analytics

Last Updated: 2021-07-06

Introduction

Companies everywhere have lots of data — Big Data — but many still struggle with making good use of it. After decades of developments in business analytics and related fields, we are still far from a world where data genuinely drives decisions, products, and processes. The digital transformation of business is well underway, but it's clear that we don't have the full story yet.

Most data-driven business initiatives fail, and often do so without any meaningful return on investment.

Some say that the business analytics solution space has matured, even commoditised. All the solutions follow the same playbook, and work the same way, more or less. Few vendors have differentiating features: yesterday's innovations are table stakes today. The standard business analytics pipeline has been well established.

And yet, businesses large and small struggle with their data. They haven't found their way to the promised land, where everything is digital and data drives the business. Does that mean that the problem is with the users?

I don't think so. There's something fundamentally wrong with the standard business analytics pipeline. Certainly there's plenty of room for entirely new approaches.

The challenge today is too see past the dominant players of the business analytics landscape. For those willing to rethink the entire analytics pipeline, the perceived maturity of the field presents a great disruption opportunity.

We begin with the pitch. I will present the Campfire Analytics venture in three parts: the idea, the market, and the competition. Given the medium, I'm aiming for a slightly longer form exposition than is customary for a live pitch. That said, this whole piece is very much preparation for in-person presentations in the future.

The second section begins with an "office hours" submission, aimed Forward Partners, a London venture capital fund. This is another step towards in-person pitching. I follow-up by developing the Campfire idea further based on some questions that Forward have kindly provided as guidance to aspiring founders.

The third section is my attempt at filling the Lean Canvas for Campfire Analytics. The canvas is a standard business model development tool. I've written more about the canvas and other frameworks as part of Business Model Tools.

The fourth and final section presents additional reflections on the Campfire business idea. Taglines and ad-libs help with concise expression, while Blue Ocean strategizing and the DARPA inspired Heilmeir Catechism help develop the idea into something worthwhile.

Some of the section repeat or rephrase previously discussed material. This is on purpose: developing a business idea is an iterative exercise. The objective here is to look at the idea from multiple points of view.

As I'm still developing the idea, any feedback would be tremendously valuable to me. I encourage you to reach out to me, if you have any thoughts on the idea, the market, the competition, or anything else related. You can reach me by emailing "antti" at this domain.

"If you want to go fast, go alone; if you want to go far, go with others." — Proverb

The Pitch

The Idea

Every company has a story. That overall company story is made up of countless smaller stories, the ones people tell each other — and themselves — every single day. There's a story behind every product and every feature, there's a story behind every project and every meeting. There's a story behind every person you ever meet.

Business analytics is a business function centred around company stories. Business analytics is essentially a means of introspection for an organisation, and as such it plays a vital role in helping decision makers prepare for the future.

The purpose of business analytics is company improvement. In practice this typically means taking all of the business data available to a company, and then using analytical methods to develop business improvement ideas based on this data. These ideas can target the company's products and services, the overall strategy, the market position, internal operations and processes, and many other things.

Business analytics can be the primary job of dedicated business analysts, but at the same time analytics and data already feature in a variety of roles in every business. In some sense everyone is a business analyst these days. After all, every employee is an authority on the work they themselves carry out. Improvement ideas can and should come from everybody. (This is not a new idea.)

Data has transformed every industry, but the process is far from over. Leading businesses are constantly looking for ways in which they can further empower their employees with data. Companies want to see at least some return on their data investments.

The point is that modern business analytics is not an isolated function, it's much more distributed. More and more people are using business data not just to improve businesses, but to make working life better for everybody. Business analytics is a shared activity, one founded on effective communication.

However, there's a huge problem in realising this vision of "data democracy" and analytics everywhere.

The business analytics tools available today fail their users precisely when they need support the most.

Most business analytics initiatives fail, for a variety of reasons, but the main one is simply that nobody cares. The data-driven insights, regardless of how they were generated, generally fail to translate into action within companies. This is known as the last mile problem of business analytics.

Today, most business analytics software works the same way: you start with the data, you go through some data processing motions, and then you end up with a dashboard or a report. Sometimes the result is a queryable interface. In data science efforts the output is often a machine learned model of some kind.

The trouble is that once these data artefacts have been generated, you are on your own. The tools have little to offer when it comes to turning that output into action within the business. You have to cross that last mile all alone.

The reason why the last mile is a challenge — for tools and people alike — is because it's a complete transformation. In the last mile you take what started out as a tech and data project and you translate that into a people project. All business improvements, all change within a company, happens through people. You have to get your data-driven insight from one brain to another, or nothing will happen. "Hey, have a look at this dashboard" is not nearly enough.

The Campfire vision is that there's got to be a better way to do business analytics. This is founded on three insights:

1. Business analytics is about communication, not data, or even data visualisation.

2. Data storytelling, the craft of translating data-driven insights into compelling narratives that drive action, is the best way to solve the last mile problem of business analytics. Analytics results need to be packaged into data stories in order to be communicated effectively.

3. The business analytics pipeline needs to be flipped around. Don't start with the data, start with the last mile. Start with a business question that frames your data story.

All three can be realised or leveraged in a SaaS solution that automatically, on demand combines story templates and user data into compelling data stories. This lets the user and the business focus on not the technology or even the data itself, but the data story and the conversation around it. Better data conversations then lead to alignment and understanding, which is the only way to drive action and change in a business.

The Market

Based on research reports, business analytics is roughly a $25B industry globally, growing at perhaps %10 annually. The categories and terms in this space are notoriously vague, and many of the dominant players are highly diversified software vendors, so measuring the market is quite challenging.

The data story tool I'm presenting here is probably best understood as a solution in the modern business intelligence space. Business intelligence (BI) is a category under the business analytics umbrella. BI tools typically focus on data-driven insights and workflows where a human looks at the data. This in contrast with data science solutions, incl. machine learning and AI, where a machine looks at the data.

Modern business intelligence is a $6B industry globally. Tableau has about 20% of the market and has $1.2B in annual revenue. Evaluating publicly listed BI vendors, such as Domo at 1-5% market share, gives a similar number. Modern BI is growing at up to 20% annually, to some extent at the expense of traditional business intelligence.

The UK represents probably 3-5% of the global business analytics and business intelligence markets. To put a number on it, let's say $1B and $250M, respectively. That is, £700M for business analytics and £175M for BI.

I believe the small business market segment is underserved by existing BI solutions. That could be the entry point. The next step would be higher annual contract value enterprise customers.

I've written more about the business analytics market in Market Sizing Business Intelligence. I also tried to untangle the business intelligence solution category in A Brief History of Business Intelligence.

The Competition

Half of the business analytics market is held by enterprise software giants like Microsoft (with Power BI), SAP, Oracle, and IBM. Cloud vendors, such as Amazon, Google and Alibaba, have their own cloud-native BI solutions. As mentioned above, Tableau, now part of Salesforce, alone holds about 20% of the modern business intelligence market.

Behind the giants, we have a tier of established players. There are many diversified software vendors with business analytics solutions in their bundle. Infor, TIBCO, Informatica, and Zoho are some of the largest. There are also several end-to-end analytics platform players like Cloudera and Databricks. In the security/intelligence sub-market we have Palantir and Verint, and many smaller players. Mathworks and SAS are major tool vendors for pure statistical analysis.

The next tier is made up of what I would describe as "pure" business analytics vendors, where most of the company revenue comes from business analytics. In the +$500M category we have Qlik, Alteryx, and MicroStrategy. Alteryx and MicroStrategy are public companies, Qlik used to be one.

In the under-$500M category we have the challengers, who are perhaps the main innovators in the space at the moment. Domo, Sisense, GoodData, Datapine, Yellowfin, and others are all trying to figure out how to win more large customers and grow. Thoughtspot is notable for pioneering the use of natural language queries in business analytics, with startups like Sisu Data following suit. Behind the challengers, there's of course a long tail of smaller vendors.

The main competitors for Campfire Analytics can probably be found among the BI vendors that take data storytelling seriously. Many vendors have some sort of blueprints or templates to help users build their analytics views, but these are still geared towards dashboarding. Similarly, many vendors have some tooling support for data stories, but the workflow is essentially the same as with dashboards.

Tableau and Yellowfin are great examples of this authoring approach. The Tableau Stories feature is built around versatile dashboard views. Yellowfin Stories & Present has perhaps the market leading data story authoring workflow, with first-class support for presentations as well. In a sense this kind of functionality brings the PowerPoint/Keynote workflow inside the business analytics platform.

There's a small group of vendors trying to put storytelling first, albeit with a focus on the story authoring experience. French Toucan Toco and Nugit from Singapore perhaps lead the pack with fairly complete solutions. Juice Analytics, datastories, and Datatelling follow close by. Livestories is a "civic analytics platform", a data storytelling company focused on the US public sector. June is a YC-backed, template-powered BI startup, focused on Segment data.

Narrative Science, Arria and Yseop approach business intelligence from the direction of natural language generation, NLG. In NLG, the systems generate meaningful textual representations of data sets, highlighting trends and outliers and more. The NLG companies typically package their solutions as modules that can be integrated into business analytics platforms that the user might already be using, such as Tableau.

A growing number of business analytics vendors believe that the age of the dashboard is coming to an end. Narrative Science, for example, is rethinking the whole business analytics presentation layer with their Lexio product.

I've written about the business analytics ecosystem at length in Business Analytics Landscaping.

The main difference between what the market offers and what I'm proposing is in the level of automation. Today, business analytics means tools for authoring and building bespoke dashboards, reports and basic stories around those views. The presentation obsessed authoring workflow is supposed to somehow magically transform into a consumer workflow where "drilling down" on charts adds value in some way. The dashboard is the conversation.

The Campfire approach is to let the computer do the heavy lifting, so humans can focus on the telling of the data story. Less artisan tinkering for bespoke data goods, more smart automation that generates data stories on demand.

Relying on automation might mean less customisation, but perhaps the business analytics wheel doesn't need to be reinvented in every company separately. Flipping the data pipeline around, focusing on the last mile, should yield better results for all users, especially those who do not have the resources to organise a full data analytics team.

What a Great Idea

Forward Partners runs a monthly Office Hours session for pre-seed founders. Forward pitch the event series as a selective forum for startup founders who wish to present and get feedback on their startup ideas.

Forward have lots of great content on their dedicated guide site, The Path Forward. For Office Hours in particular, they have a post on making an idea great and another one on acing the application.

In this section we start with an Office Hours application, and then work our way through the great idea questions. Thinking about these questions should be useful preparation, regardless of whether Forward are interested.

Office Hours

Question	Answer
What is your name?	Antti Halme
Best email?	antti at campfirebi com
Name of company?	Campfire Analytics
Number of founders?	1
Describe in a tweet (280 char)	A smart self-service business intelligence SaaS tool that generates compelling data stories from story templates and business data, enabling better conversations around data-driven insights.
Startup idea in more detail. "Please be concise and focus on what unique insight that you have that other people have missed. Describe your target customers, the problem and your solution."	Business analytics tools available today fail their users precisely when they need support the most. Data-driven insights, regardless of how they were generated, generally fail to translate into action within companies. This is known as the last mile problem of business analytics. Business analytics is really about communication, not data, or even data visualisation. Most business analytics solutions start with the data and the project goes nowhere. Starting with the last mile, starting with a business question, is much better. The question frames the story and the data provides the context. Data storytelling, the craft of translating data-driven insights into compelling narratives that drive action, is the best way to solve the last mile problem. Analytics results need to be packaged into data stories in order to be communicated effectively. Data stories and the conversations around them lead to alignment and understanding, which is the only way to drive action and change in a business. The Campfire data story generator helps small business owners and corporate firebrands who want to make sense of business data and are keen to share insights with others. I've written about this idea at length in a post.
What type of startup?	SaaS/Platform
Which sector are you looking to disrupt?	Business analytics
What stage?	Idea stage
What is your reason for doing this startup? Let us know a little bit more about your background and how you came upon this idea.	I find that learning and growing are great sources of happiness and meaning in life. Learning is inherently rewarding. Growing as a person, growing into new roles, and seeing things grow are all deeply fulfilling. I've worked in the financial sector for many years now building software systems of all kinds. Right now I feel like taking on a new challenge, a new opportunity to grow. I've always had a startup at the back of my mind, and I feel like this could be the time to try building something great. This business idea is a combination of three things. First, I've come to understand what institutional friction means in an organisation. It's the same reason why digital transformation is hard, why data-driven decision-making often fails to deliver. Making use of data in a system made up of people is difficult. Second, following related readings online, I realised that this is a common problem. After more than ten years of Big Data, we haven't figured out anything yet. Data initiatives fail left and right. At the same time the BI market is becoming stale and commoditised. Something is seriously broken in this space. Third, the transformative potential of data storytelling. Through my readings, I learned about the last mile problem and about data storytelling as a way to tackle it. A few players are already working on these things, but I feel like I have a fresh perspective on the problem. There's a great opportunity for an innovative product somewhere in this space.
Public profile?	LinkedIn
How did you first hear about Forward Partners?	LinkedIn, I think; Forward Partner's blog piece "The solo founder hypothesis" was the boost I didn't know I was looking for.
If your application was via a referral, who put you forward?	No referral.
What would you most like to get out of office hours?	Advice.
Which specific aspect(s) would you like to discuss with us?	Go-to-market strategy, customers, SaaS model, general feedback / sanity check on idea
Is your company based in the UK?	Yes.
What is your UK outward postcode?	E14
Would you like to sign up to our e-mail newsletter?	Sure.

Product Idea

Do you have a clearly stated use-case(s) or solution(s) to a problem?

Data stories help individuals and organisations consume and share data-driven insights. Insightful narratives help companies have the right discussions about the state of the business: the past, the present, and the future. Informed discussions lead to informed decisions, which set the business up for success.

Automatically generated data stories speed up the analytics processes and enable more people to have better conversations. A shared story drives alignment, understanding, action, and change within an organisation.

1) Automatically generated data stories for small businesses that don't have data analytics expertise.

If you are a small business, you don't have a data analytics team. Nobody has the time to build and monitor reports and dashboards. However, if you know that something needs to be done about your business, and you believe in data, then perhaps you are open to a tool that can quickly generate some data-driven insights.

If you have a question about your business, Campfire has a story template for you. As a user, all you need to do is to provide your business data, the context of your data story. The template tells you what data it needs — the opposite of a dashboard tool. You start with a question and get an answer in the form of a data story that encapsulates data-driven insights, ready for you to consider, and to publish and share with others.

2a) As an enterprising line worker, there's something you want to change about your product or service, your processes, your work, your team, your organisation, or there's something else that occupies your mind. You can find the data to support your intuition, but you don't know how to present your issue to your boss. Campfire helps you with a data story that you can use to win over your line manager.

2b) As the line manager, something is troubling you about the team's projects, processes, cross-departmental concerns, silos, employees, planning, priorities, etc. You want to make a case for change upwards in the organisation. You want to convince a director or an executive that we should stop doing A and start doing B instead. Maybe you need funding for an experiment, for a new kind of hire, for a new project or a new solution. Maybe you want to make a name for yourself.

Campfire generates data stories for you so that you can win over the colleague and the manager and the director and the executive. All you need is the right question and the data to fill in the template.

Have you backed this up with some preliminary, objective market research?

Not really. I've seen institutional friction first hand. I have also seen how excited people get, when you have a proper data story to share.

Business intelligence is definitely a market. There is plenty of competition in this space, there are even several vendors looking at data storytelling. Campfire seeks to improve on solutions currently in the market through automation and by rethinking the process. The small business segment in particular is underserved by current solutions, and as such presents a particularly great opportunity.

Deep Understanding Of The Market

What market are you operating in, how big is it and how is it segmented?

Campfire is a data storytelling company. Data storytelling can be seen as modern approach to business intelligence, but, to me, data stories also represent a way to engage a broader set of users than is typical for business data tools.

I like the Competition and Markets Authority (CMA) definition of business intelligence. In this taxonomy, business software divides into individual-use software and enterprise application software. The former is tools people use independently of others to get their daily tasks done, think Microsoft's Office suite.

Enterprise software is made up of shared tools that meet business function level needs: there's CRM for Sales, legal software for the legal team, accounting software for accounting, and so on. Business analytics software is one of these categories: tools for business analysis.

Business analytics can be divided in three categories. The first category is data science, which in this framing includes machine learning and AI. In the most reductive sense, data science is about model building, in one form or another. The model is the unit of work that upgrades products, services, and operations.

The second business analytics category is business intelligence (BI), which divides into traditional and modern business intelligence. The essence of BI is the craft of surfacing and communicating data-driven insights.

There's a nice contrast between the two approaches: in data science, the machine looks at the data, while in BI, the human looks at the data. For completeness, the third category is everything else: industry specific analytics tools, etc.

Business analytics as a whole is a $25B market in annual revenue terms. Within that, modern BI is about $6B.

Tableau, acquired by Salesforce in 2019, had something like 20-25% of the market at roughly $1B in revenue. At $200M, Domo has 1-5%. Market analyst reporting varies quite a bit, but business analytics has seen 10%-20% growth in recent years, with modern BI seeing growth closer to 20%.

Most business analytics vendors sell monthly seats, typically billed annually. The product is often exactly the same for every industry, with just minor customisation options available in the form of templates. Most deals are between $10K and $10M. A medium-sized business intelligence vendor has a few thousand organisations as customers.

I'm particularly interested in the opportunities in the small business segment. For example, while most BI vendors have a customer base in the low thousands, leading accounting software vendors have millions of customers.

I have written more about the Campfire target market in A Brief History of Business Intelligence and Market Sizing Business Intelligence.

In those segments what do consumers value most and who is your target customer?

Ideal customer profiles, matching the use cases above.

1) Small business owner. Somebody who runs a bakery or a coffee shop or an indie fashion label, etc. They gather data about their business — accounting, point of sale, social media, other marketing — but it's perhaps not well organised, and they don't really have a process for making use of it to improve their business. In a low margin game, they are happy to spend one hour and £10 per month to save £100. Or even £10 per week.

2) Enterprising line workers and middle managers at medium-size organisations. They want to change something, they are willing find the data for their argument, but they find dashboard tools unwieldy or ineffective as a means of sharing insights and having a data-powered discussion. They want to see data-driven insights used more in their organisation. They want to make a name for themselves by doing a data project of their own initiative.

For the small business owner, they make the purchase, much like with a SaaS accounting+payroll solution. In the larger organisation the users might need to get an approval from somebody, but at a sufficiently affordable rate, buying the tool shouldn't require too much processing. InfoSec and legal might need to be involved to vet the solution for business data.

In all cases, users value convenience and the guided nature of data story generation. Self-service, not needing a data team to generate immediate results, is highly appealing. Low cost, compared to enterprise analytics platforms. A focused workflow without a thousand features to learn. Only data literacy needed, no need for advanced data science ability. Data democratisation through improved data insight accessibility. Improved communication, focus on the last mile, building conversations around data.

Everybody wants to work in a modern company, where decisions and discussions are not arbitrary and subjective, but rather carefully argued for using data. Data-driven insights help people and organisations move boldly forward.

Benign Competitive Environment

Who are your key competitors, what are their products and who are their customers?

There are many types of vendors in the business analytics domain. Half of the business intelligence market is held by enterprise software giants — Microsoft, SAP, Oracle, IBM, and so on. All major cloud vendors have their own solutions as well. Microsoft's Power BI is the leading platform, followed by Tableau. Tableau was the only $1B annual revenue pure BI vendor before being acquired by Salesforce for $15B in 2019.

Behind the giants there's a tier of diversified business software vendors. Players like Infor, TIBCO, Informatica, and Zoho have grown through a series of acquisitions, including pioneering business analytics boutiques. There are also many analytics focused established players like Qlik, Alteryx, and MicroStrategy, and end-to-end platform players like Cloudera and Databricks. This mid-tier has a substantial share of the market as well.

Challengers — Domo, Sisense, GoodData, Datapine, Thoughtspot, Yellowfin — are the ones trying to innovate. There's quite a long tail of smaller players as well. Many vendors have found success specialising in a regional market.

All the giants are public companies, though they typically do not break out business analytics in their reporting. Alteryx, Domo, and MicroStrategy are also public companies.

When it comes to data storytelling, the picture gets much more interesting. Many vendors have some kind of basic templating: if you want to pull accounting data from Xero or metrics from Google Ads, here's a dashboard frame you can use to get started. Dashboard tools help you organise your story — if you know what story you want to tell.

Tableau and Yellowfin are great examples of this authoring approach. The Tableau Stories feature is built around versatile dashboard views. Yellowfin Stories & Present has perhaps the market leading data story authoring workflow, with first-class support for presentations as well. In a sense this kind of functionality brings the PowerPoint/Keynote workflow inside the business analytics platform.

There are a number of smaller data storytelling focused BI vendors. French Toucan Toco and Nugit from Singapore perhaps lead the pack with fairly complete solutions. Juice Analytics, datastories, and Datatelling are not far behind. Livestories is a "civic analytics platform", a data storytelling company focused on the US public sector. June is a template-powered BI startup, focused on Segment data.

Narrative Science, Arria and Yseop approach business intelligence from the direction of natural language generation, NLG. In NLG, the systems generate meaningful textual representations of data sets, highlighting trends and outliers and more. The NLG companies typically package their solutions as modules that can then be integrated into business analytics platforms the user might already be using, such as Tableau.

Toucan Toco has funding from Balderton Capital. Nugit has funding from Sequoia, 500 Startups, and Wavemaker. Yellowfin is proudly self-funded. June is backed by YC. Narrative Science, trading since 2010, is on Series D, with funding from Jump Capital and Sapphire Ventures.

A growing number of business analytics vendors believe that the age of the dashboard is coming to an end. Narrative Science, for example, is rethinking the whole business analytics presentation layer with their Lexio product.

All kinds of organisations, large and small, use business analytics software. Analytics has something to offer for every industry. Many BI vendors have large chunks of their website dedicated to showing how companies from automotive to retail and real estate to manufacturing can make use of business data. Or, to put it another way, what company in 2021 would not be at least a little bit curious about what is possible through data-driven decision-making?

I've written more about the competitive environment in Business Analytics Landscaping.

What is good about your competitors and where/why are they vulnerable?

Broadly speaking, the business analytics market has matured and commoditised. What used to be a competitive advantage is now just table stakes. Every BI platform vendor has a drag&drop UI, dashboarding tools, a chart library, reports and alerts, collaboration functions, embedding options, mobile clients, etc.

If dashboards are the thing that companies truly need, any off-the-shelf BI tool can get the job done.

In recent years there has been little innovation in the analytics space. In some sense adding more features is pointless, because customers are barely making use of even the basic functionality. The are three main trends or innovations that vendors are currently exploring: natural language, augmented analytics, and data storytelling. All three, in their own way, seek to make data tools more human friendly.

Natural language is an interesting modality for a new kind of non-specialist user. With natural language search, through unstructured queries of the form "show me the top 10 customers in Canada last quarter", more people can access data that previously required data engineering or dashboarding ability. Thoughtspot is a leader in NLS, with startups like Sisu Data pushing the concept further. All major platforms have or are developing basic NLS support.

Natural language generation, presenting numeric data as text, forms the other half of this orientation. NLG is challenging to do at scale and in depth: most auto-generated snippets are quite shallow, and the best results require careful domain data engineering. The problem is quite hard: turning arbitrary data into meaningful insight automatically is not an easy task for a computer.

Augmented analytics, leveraging machine learning and AI in the BI platform, often takes the form of nudges. An automated background process can identify significant changes or outliers or other patterns in data, and present and highlight these finding in the context of the data set. These then drive interactions and the business intelligence workflow. Users add meaning to the discoveries in connection with other knowledge they may have.

Data storytelling is about communicating data-driven insights in narrative form in order to drive change and action within an organisation. Many BI vendors are trying to figure out what that means, with most focusing on extending the data analytics pipeline with data story authoring tools. These are great when you know what story you want to tell, but the real difficulty in BI is in generating the insight in the first place!

All story authoring tools start with the data and leave the user to figure out what's the right question to ask and what to highlight, what's important. Few tools really support data storytelling, or focus on the last mile problem.

Most vendors are too married to the dashboard paradigm and the start-with-the-data model that they would struggle to re-orient their platform around a start-with-the-question model. At the same time vendors actively pursue big accounts, and have little to offer to small businesses that might not have dedicated data people or business analysts.

In short, the BI ecosystem is full of elaborate data dashboarding platforms and unnecessary features. The market is commoditised, but users struggle with getting value out of their data tools. The dominant players cannot really throw out their existing platform to explore new ways of getting data-driven insights to people.

Potential For Future Profitability

What will your margins be when you startup and what will affect them in the future?

A modern B2B SaaS company is doing something horribly wrong, if the gross margin is under 70%. The aim should be around 90% — cloud infrastructure is cheap. The major expense is research and development, which for the core product should be heavily front-loaded. Building an app/platform that is easy to use and scales with the user base isn't trivial, but should be doable with what tech is available today.

Some of the early development work should be fairly easy to get done through contractors. Developing connectors for cloud infrastructure needs to happen regardless of what the final shape of the product will be, and it can happen without many of the details being fully fleshed out. Similarly, the app design work around data sources and data handling should be fairly straightforward to define.

The generated data stories need to be delightful. This will require hiring design and data storytelling talent, perhaps short contracts for consulting expertise in target industries. Staff developers will eventually be needed as well.

I envision a no-touch sales process, a self-service SaaS model. Perhaps a very basic sales function combined with customer success. I would focus on marketing and being smart about where the marketing budget is spent. A thousand junk leads a month and a big sales team calling outbound and closing at 0.5% is not what I have in mind.

Early on, the focus is on small businesses and low annualised contract values. A low-ACV B2B play is almost more like B2C. I would not go after big accounts, but rather the people who are currently not using any business intelligence tools. I'm certain that every business that uses accounting software would benefit from data-driven business insights.

A monthly subscription model is the easy option, but I'm also thinking about true cloud-native usage fee models. Some users might go for weekly data stories, others might prefer monthly letters or quarterly reviews. The platform should support all of them. These stories could be priced individually, subject to the features baked in.

I'm confident that I can build the first version of what I have in mind by myself. Whether it proves viable is another matter. Building out the whole vision I cannot do alone.

Once the app is up and running with a strong data story generation workflow and a library of story templates, the business should run fairly lean. From there it's a matter of scaling things up.

How will you acquire customers at the beginning and as your business progresses?

The main thing is to find the product-market fit. The very first customers probably need to be sourced directly, in person. While this doesn't scale, it should be highly valuable to do just for the feedback alone. This assumes that the product is in a state where people can give it a try.

My plan is to talk to small businesses in a given neighbourhood, such as a London district. The idea is to have a discussion about business analytics in general, in order to figure out what kind of a data story might be useful to them. Focusing on businesses in one particular area has several benefits, including logistics, community connections, and potential for locally relevant data and content.

If there is a neighbourhood business association or similar, I could try to get into that to present to a larger audience. If there's isn't one, I could try and get businesses together in a local coffee shop or something. This doesn't scale, but I feel like it would be valuable to me, and it could be easy to turn into something that is valuable to the businesses in question — even if they don't become my customers immediately.

Once I have spoken to enough people and have applied the learnings and feedback into the product, once the product fits the market, I can start scaling things up by focusing on marketing by demand generation. My plan is to follow the Chris Walker playbook and focus on content marketing, brand, and inbound. This should prove cost-effective, with good levers on awareness spending.

Data storytelling is still a fairly new concept. There should be an interested audience waiting to hear more. Certainly the last mile problem of business analytics is real and is painful to businesses. I'm open to exploring a podcast or a video series on data storytelling, and interviews with thought leaders and influential data people, with an eye towards leveraging their networks and platforms for visibility. With a clear message, a novel product in the business analytics space, it shouldn't be too hard to get traction on platforms like LinkedIn.

Long term, there's opportunities to partner up with Big Data players, building data story modules and deeper integrations with other tools and platforms.

Basis For Long Term Competitive Advantage

What are the key points of differentiation between you and your competitors?

Contemporary business intelligence tools all follow the standard business analytics pipeline. From data acquisition to data processing and transformation, from the intermediate data store into models and data integrations in the BI platform. The final step is exposing all of the data in the repository through dashboards, reports, embedding, or a user query interface. Modern BI tools are specialist data wrangling tools masquerading as business insight solutions.

The natural language search workflow makes more sense, but it only works if you kind of know the answer already. And even if you find things more quickly, communicating the insights you pick up is just as difficult as with regular dashboarding. Few BI tools out there today support conversations and effective insight articulation. The industry doesn't support data-driven people alignment, or sharing views of the future. BI tools don't support people.

The BI workflow begins with data and ends with a dashboard or a report, neither of which helps with the real challenge that is taking the data-driven insight and turning that into action and change in the organisation.

The Campfire approach flips the BI pipeline around and puts the focus on the last mile. Don't start with the data, start with a business question or area of interest: start with the last mile. Once you know what conversation you want to have, find the right data story to support you.

The Campfire story collection has the story template for your industry, for your business, for your need. Once you find a promising story, the template tells you what data it needs. You just provide the data, the context of your business narrative, and the story generator does the rest.

If you don't have the data you need right now, you can look around the business for it, or you can try estimating the data from what you do have available. In the very least, you can set up processes to capture this data moving forward — try again in a month's time. By starting to collect the right data, you get value from the platform even before a single story has been generated.

Once you have your freshly baked data story in hand, you can learn from it yourself and you can share it with others to really get people aligned and moving in the right direction. The sharing can be as simple as forwarding the story as is, or you can reframe the insights and tell the story in your own words, perhaps in person.

In short, Campfire gets people going in the right direction by having them start with a business question, not with an overwhelming supermarket of data. Campfire helps people tell a story that others can connect with, taking business analytics beyond throwing charts at people.

If your business succeeds, how will you defend your position in the market?

It is unlikely that major vendors completely change their focus. After 20 years of dashboard workflows, these companies are not going to change their ways overnight. These products, these vendors will be data-first forever.

Campfire will not be a fully featured BI platform. There will always be room for specialist tools that support deep dives and data engineering. The point is that these standard BI tools should be used by a small fraction of the company. These tools shouldn't be shoved down the throats of analysts and other business users.

When surfacing and communicating data-driven insights is the mission, people shouldn't be using heavy duty data wrangling BI platforms — it's the wrong tool for the job.

There will be pressure from first-class data storytelling BI vendors. However, their focus is on the authoring experience, not auto-generation. Solutions such Toucan Toco and Nugit are great for story authoring, when you know what story you want to tell. If you need a data-backed PowerPoint/Keynote, those could work great for you.

But if you are non-specialist user, or you are looking for new data insights, then Campfire is worth a try. Again, it's unlikely that Toucan Toco, say, would throw out their authoring focus in favour of a simpler auto-generated workflow.

Building up the story generating engine is not a trivial thing to do, and building up a library of useful data story templates is quite an undertaking. It's not easy to build great templates. It would be way easier to buy Campfire than to try building out your own competing solution — especially, if it represents only one aspect of BI for you.

I also have some long terms plans for features that could really solidify the position. The first one is opening up the library for third party data story authors: if Campfire builds tooling for story template authoring, with clean interfaces for the data flows, these third party authors could help build out the library for ever more sophisticated business questions. If Campfire took an Apple tax 30% on stories rendered, that should be plenty to incentivise expert authors to do their own marketing around their story templates. There are powerful platform plays available here.

The second long term advantage could be third party data. There are a few companies that already integrate things like public data sets into their platform, so you can build dashboard that combine your data and third party data. But you are still doing 100% of the dashboard and the insight shaping. If the Campfire platform, a modular system, signs up some third party data vendors, the generated enriched stories could generate huge value.

For example, imagine you run a bakery. With Campfire, you can fill in a story template with your data, and get insights on the state of your business. But if we add in third party data, we can enrich the story with additional segments. These could be things like average foot traffic on the street outside your store this time of year, a chart on the cost of sugar and major suppliers in your area, inflation and financial macro data relevant to any business, upcoming events nearby, businesses around you to target in your marketing, baking influencers or trends of note, etc.

Campfire could even be a broker for freelancers looking to contribute insights to go alongside data stories. Not advertising, just valuable stuff per industry or niche. For our bakery example, this could perhaps be a profile on a fellow bakery owner in a different region. Or maybe it could be advertising, if nothing else seems to work. Advertising would render the platform into something quite different to what I have in mind, but it would probably be a great source of revenue.

Finally, there's an interesting influencer play to make. Let's say Jane Public is the top UK authority on running a jewellery business. If Campfire works with Jane to develop data stories that could be of interest to trading jewellers in the UK, these could be branded as "Jane Public's guide to running a jewellery business", and sold as a premium bundle. Or maybe a "Run a restaurant like [TV Chef]" -bundle, etc. Data stories encapsulating business advice, with Campfire as the platform for them.

All of these added features and the network/platform effects around them would be hard to replicate, if Campfire wins any significant traction.

Team And Skills

Are you the right person to lead this business? Why?

I have a vision for Campfire, and an understanding of the larger context. I see why digitalisation is difficult for companies and why Big Data hasn't been able to deliver. Within business intelligence, I see the small problems that are blocking lots of potential. There's a great opportunity in this space, a chance to rethink some of the decades old practices. And best of all, I believe this opportunity should match well with the kind of company that I want to build.

My background is in technology. I have a Master's in computing and I have been a professional software developer for many years now. I have enough experience to know that the tech stuff, no matter how complex, is still the easy part of any project. I have a deep appreciation for systems and processes involving people and communication. I believe in the power and value of data-driven insights.

I have built cloud hosted systems, but I have never built a full SaaS solution from scratch. Still, I'm confident that I can build the system I have in mind. I know what's easy and hard in software. I know what to look for in technical hires. I value great presentation and user experience. I love stories and the craft of their telling, I treasure insights.

Having worked at an asset management company, working with financial data, I have an understanding of data, analytics, and business. I see the power of stories in investing and in everyday communication, and I see what data-driven insights can do for operations and ambitious projects and many other things.

Personally, starting a company is highly appealing to me, because I have found that learning and growing are the things that make me truly happy. And I hear startups provide ample opportunity for both.

What are the initial skills gaps? Where do you feel you need to hire?

At some point I will need cloud infrastructure expertise, to help me build a robust system that can scale. I should be able to get a basic solution going by myself, but building the production platform at pace takes more than one developer. I'm open to a contractor arrangement, though staff developers will eventually be needed.

Hiring somebody to work on marketing and related things full time will be a key early hire. This person would be in charge of market education, demand generation, and the content strategy. This role would entail working across marketing channels on video, images, and copy. Essentially I'm looking for somebody to run the Chris Walker marketing playbook, potentially with a content specialist. This person would also be instrumental on the customer understanding side of things, contributing to messaging and even product positioning.

Connected to marketing, there's plenty of work for an overall lead designer, possibly working together with freelance creatives. This person would be in charge of the website appearance, all marketing visuals, product UX, etc. Ideally this person could contribute in all areas of business where good taste and clear expression is needed. They could also support customer understanding.

Finally, there's a complex role to fill for someone to own the product itself. This includes data storytelling in theory and practice, the output side of the automated data story engine. This person should be able to help us get the right stuff into data stories, cutting out everything superfluous. This person should have expertise in data visualisation, narratives and data-driven communication. This role will be a challenge to fill.

Lean Canvas

The Lean Canvas is a variation of the celebrated Business Model Canvas, one of the leading thinking tools for startup idea crafting.

This section contains (verbose) entries to the nine segments of the Lean Canvas, divided in two groups:

• The Fundamentals: Problem, Customer, Value Proposition, Solution, and Unfair Advantage, and
• The Flows: Channels, Cost Structure, Revenue Streams, and Key Metrics.

The Fundamentals

Problem

Most companies are still not as data-driven as they would like to be.

Software ate the world, but most companies still don't know what it means for them. Every business is still trying to figure out what to do with all the data being generated and stored. The standard business analytics pipeline, pushing data from acquisition through to transformations and into reports and dashboard views, doesn't seem to be working. Companies large and small are struggling to turn their data-driven insights into action and change.

Most data initiatives fail towards the end, where the main task is to share data-driven insights with the rest of the company. The transition from data and technical solutions to people and business processes is a huge challenge for most companies. This is known as the last mile problem of business analytics.

This ineffectiveness in leveraging data blocks enterprising employees from bringing forward their ideas, and it blocks managers when it comes to presenting actionable projects to executives and directors. As a result, the long promised digital transformation of business has never materialised.

Additionally, because of the rigidity of the standard business analytics SaaS model, small businesses may not even have the resources to experiment with business analytics. Companies that could potentially benefit from data-driven insights have to make do without.

Customer

A B2B setup with two main customers profiles:

1. Small business owners who are happy to try out a lightweight business analytics tool, if it doesn't take up too much of their time.

2. Line managers, analytics leads, CTOs, CDOs, and executive/director level data enthusiasts who are keen to try out new approaches, when it comes to finally getting a return on years of data investments.

The principal users vary from business to business, depending on who is interested in data-driven insights:

1. For small businesses, this is probably the senior management directly, or some employee on their mandate. This can be either someone with a business admin background, or an engineer / data specialist.

2. For larger organisations, the user is any person with drive to change or influence some aspect of the the business, be it projects, products, or processes.

Focusing on the underserved small business owners segment is probably a good way to go to get started. Once there is some traction and market fit, it's much easier to then go after larger organisations.

The ideal early adopter is a tech-savvy small business owner who has been collecting business data from a few different sources for a while now, but hasn't really had time to look into what data analysis could do for the business.

Unique Value Proposition

Insightful data stories on demand from story templates and your business data.

Frame your business question, fill in your business context, and Campfire will generate engaging data stories for you. Bespoke stories help you understand what's going on in your business, and allow you to easily share those insights with others.

Solution

With self-service analytics automation, anyone can generate data stories if they have access to the right data. No data science expertise required.

With compelling data stories, managers in the middle can sell their ideas up to executives and directors and down to their team. Managers can make a name for themselves by being the person who has their story straight and can back it all up with numbers.

With easy-to-use templates, inspired employees can discover metrics and stories they can then use in articulating their ideas. Templates guide people to the business questions that matter the most.

Effective, commonplace data storytelling drives digital company culture. Data stories help people solve the last mile problem of business analytics.

Unfair Advantage

Some weak ones:

• Timing: after ten years of Big Data initiatives failing, companies are open to trying out new things — especially if it's lightweight and not too expensive.

• Inertia: Large players are married to the standard business analytics pipeline. Data storytelling is, at best, a sideshow for most vendors. They couldn't orient their companies around stories even if they wanted to.

• Buzz: Data storytelling is a 2020 hype term/concept. Building on a broader data viz trend, people are intrigued by data stories.

• Marketing: Timing & Buzz should help in bootstrapping content marketing plays, potentially leading to "unfair" customer acquisition cost models.

• Template Library: A comprehensive library of analytics templates is not a trivial thing to build, or to replicate.

Some stronger ones:

• Market: Modern BI is growing at 20% CAGR.

• Time to results: Business analytics projects can take months and involve loads of people. I'm aiming for one person, one hour projects, including minor data herding.

• Marketplace: There are currently no business analytics data story marketplaces out there. Figuring that out could be huge.

The Flows

Channels

I'm a strong believer in inbound marketing. Not only should the eventual product demonstrate value, but all the interactions that happen before the purchase as well. Basically the plan is to run the Chris Walker playbook.

There should be plenty of opportunities for creating content on the theme of data stories and their telling, driving alignment and action through data-driven conversations, and so on. Leveraging the novelty of this self-service, on demand automation, there should be plenty of space for a new take on how to make data work for the business.

Making good use of existing networks is one of the keys to success. For example, LinkedIn is full of insightful data storytelling folks, who I'm sure could be persuaded to participate in a podcast episode, for example. I have a feeling many people will be in agreement that something's not quite right with business analytics today.

Following Walker's playbook, paid marketing channels are for awareness marketing, in a highly targeted fashion.

In the very beginning, interactions need to be direct. Not so much from a sales angle, but simply because of the value in having those conversations and getting a better understanding of potential customers. Getting feedback and learning from target customers is the thing. But first there has to be some kind of a product ready to go.

So product first, then a content strategy around data storytelling, with paid and organic visibility on LinkedIn and maybe FB, Twitter, Instagram. Probably better to focus on one or two, rather spread out too thinly. Experimenting is key. Early on, it's all about talking to customers to figure out both the product and the messaging.

Cost Structure

SaaS business. Aiming for 90% gross margin, the cost of delivering the solution should be low. Worth keeping an eye on, of course.

Hosting the SaaS solution should be free or practically free at least for a few months. There should be plenty of free tier cloud infrastructure options to experiment with, and whatever the cost eventually settles down to should not be a problem to bake into the pricing.

Early product discussions with potential customers: 25-50 interviews at 30min each, with another 30min for prep. Let's say 50h total. Developing a super simple MVP with two people on average, in maybe one month. Let's say 2 * 22 days * 8 hours/day = 352h.

Total effort to launch 400 hours. At £50/h, that's £20K to get a minimal thing going in something like six weeks.

Two people can build a data story generator in three months. A team of four can build a polished basic product in six months. People who really know what they are doing might be able to turn those months into weeks, but I don't think that's realistic.

Six months, four people: two developers, a designer, and a data story specialist. £200-250K on salaries annually, fully loaded at 1.33x for £300K p.a. That amounts to £25K per month, or £150K for six months.

Revenue Streams

£25K per month would be covered by a thousand customers paying £25 per month. Alternatively, that could be a weekly story at £6 a piece for the same 1000 customers. Weekly stories and a monthly summary for £25, perhaps.

Scaling things up, one could keep the £25 per month per seat. One month free if you pay for the year in advance.

Many other plays possible as well. I quite like AG Grid's Perpetual Licence model, where the user receives a license to use a version of the product in perpetuity. This is limited to features available up to one year after purchase, at which point the customer may renew or simply continue using the no longer supported legacy version.

This kind of a licence would cover the cost at £500 annually, if we assume, for no particular reason, that on average 60% of customers will renew, with churn replaced.

Gaining 1000 customers in six months would mean starting with one and doubling the tally every two-three weeks. This kind of growth doesn't really happen with B2B. But just as a thought experiment, let's say that all of the above was possible. That would mean that one could build a sustainable business around this idea in one year for £300K.

More realistically, one might be able to show some traction and some market fit for a solution based on this idea in one year for £300K. This might then be enough to close a second funding round to start growing the company.

For growth, I generally believe in product-led approaches, where existing customers are interested in getting more out of the solution, and are willing to pay more for premium features.

Key Metrics

Obviously for every metric, having the absolute and relative values, the overall trend, and rates of change all at hand is the way to go.

Some general metrics:

• Standard: Burn rate, cash runway, revenue, gross margin, profit/loss, customer lifetime value, average revenue per user

• Users: Activations, daily/monthly active users, churn, onboarding stages, feedback (e.g., NPS, CSAT, CES)

• Marketing: Customer acquisition cost per channel; payback period; awareness metrics (views, likes, follows, comments, etc.); content metrics (e.g., scroll depth)

Some metrics that are perhaps more specific to this idea:

• Stories generated per day, per month, total; session length; number of story templates; average interest in story templates; template popularity

Reflections

More perspectives on the Campfire Analytics business idea.

Taglines

• Reveal the story hidden in your data
• Your data, your story
• From insights to action
• We are the stories we tell
• Make them listen — tell your story with data
• Be the hero of your data story
• Every business has a story
• Stories mean business
• Someone needs to hear your data story
• Better conversations with data

Ad-lib Value Proposition

Our data story generator
help(s) small business owners and corporate firebrands
who want to make sense of business data and are keen to share insights with others
by replacing unwieldy dashboards with compelling narratives
and by actually bringing data into conversations ( unlike most business analytics tools today ).

Blue Ocean Strategy

The Blue Ocean Strategy is an approach to business strategy that emphasises the value of creating entirely new market space. Blue oceans have lots of room for growth and opportunity, whereas fierce competition in contested waters quickly turns the seas red.

What has to go up within an industry in terms of product, pricing or service standards?

Business analytics has been the domain of a favoured data priesthood for far too long. Data science and business intelligence are both needlessly esoteric arts, often detached from the decisions and conversations that actually shape business strategy. There's more to leveraging data than just endless dashboard views and carefully crafted bespoke visualisations.

The standard business analytics pipeline, from data to dashboards, does not serve the whole organisation. The business analytics industry has failed to get the right data-driven conversations going in businesses.

The effective use of data in business has to go up: more people using more data to gain and share more data-driven insights. The use of data stories as the unit of communication and insight transmission has to go up.

The level of automation in the data story authoring process has to go up.

The time it takes from posing a question to having a data story in hand to answer it has to go down dramatically.

What can be removed within a company or industry, to reduce costs and to create an entirely new market?

You can remove much of the expensive data science team, if auto-generated data stories meet the data insight needs of the business. The dashboard modality probably will remain in the tools of the data specialist, but ordinary business users will use something more user-friendly.

Pointless data projects and other similar initiatives can be scrapped, if a story generator has a comprehensive library of high quality data story templates for standard business questions.

What aspects of a product or service aren't entirely necessary, but still play a significant role? What can be substituted?

Dashboard crafting is a huge resource sink, but unfortunately the effort put into dashboards does not translate into action within an organisation. For most users, much of this work can be automated and supplanted with auto-generated data stories.

Building a vast data lake for Big Data might not be necessary, if data stories of interest guide what data is captured and obtained. Most organisations don't need data in a thousand dimensions in order to make informed decisions.

What entirely new product or service can be created to form a new market through differentiation from the competition?

Dashboards and story authoring tools are not for everybody. Having a data story generator at you disposal, fully loaded with insightful business question templates, can help more people tell compelling data stories. All you need is basic data literacy and a willingness to share what you have discovered.

Today, if you don't have a data team or a business analyst on staff, you have to make do without business insights. But if there was an easy-to-use data story generator, a machine that tells you what data it needs, everybody could create data stories and share data-driven insights with others. And shared insights drive change.

Heilmeir Catechism

The Heilmeir Catechism encapsulates the ethos of the The Defense Advanced Research Projects Agency, DARPA. The catechism is a series of sharp questions that program managers can use to evaluate project proposals.

What are you trying to do? Articulate your objectives using absolutely no jargon.

Business analytics is a business function focused on the use of data and data-driven insights in improving a business. The data can come from the company's products and services, from operations, from customers and the marketplace, or from other sources. Similarly the insights can be applied to the company's products and services, as well as to the internal operations of the company.

Most businesses struggle with making effective use of data-driven insights. If they produce any to begin with, that is. There are numerous reasons for why business analytics initiatives fail, but many of them can be traced to the discontinuity between where the data processing pipeline ends and where the rest of the company operates. This gap is known as the last mile problem of business analytics.

Data storytelling, the craft of translating data-driven insights into compelling narratives, is an attempt to bridge the business analytics gap. Data stories emphasise the human dimension of data insights. Effective communication drives action and change in an enterprise through clarity, alignment, and direction. When a group of people have a shared story, they can better understand one another and can work together towards a common goal.

Campfire is a business analytics tool, a software system, that automates the process of authoring data stories. This data story engine takes a rich data story template, tailored to answer a specific business question, and combines it with the user's data that provides a specific context. The data stories are publication-ready and easy to consume and share with others. This tool makes it possible for everybody to generate and share data-driven insights.

How is it done today, and what are the limits of current practice?

Business analytics today means a linear process that begins with data acquisition and elaborate data transformations, and ends with dashboards. Discovering and communicating data-driven insights is left entirely to the data analyst, who has to be quite the character. The analyst has to not only manage the data pipeline, the data engineering and the related infrastructure, but has to also do the analysis and then communicate the results effectively. And, of course, they have to have a deep understanding of business to make the right connections.

It's challenging to find even teams of people capable of doing this work, let alone individuals.

Even if the standard business analytics process is executed perfectly, the company still may not see any benefits, because the insights end up hidden away in reports and dashboards that nobody has time to dive into. Going from tech and data to people and processes is extremely challenging, and the business analytics tools we have today do not support that transition at all.

Most companies have no solutions whatsoever for the last mile problem of business analytics. As a result, most data-driven initiatives fail. Small businesses don't even have the resources to try, so they do without business analytics altogether.

What is new in your approach and why do you think it will be successful?

There's three fresh things about the Campfire approach. First, simply framing business analytics as an exercise in communication is a disruptive proposition. If business analytics is seen to be about people and conversations, instead of tech and data, many solutions immediately appear crude and unsuitable for the task.

The second one is to focus on the last mile — to start with end, if you will. Data storytelling, translating data-driven insights into compelling narratives, is the best way to cross the gap, to move from shallow data presentation to influencing people and processes. Some business analytics vendors have basic support for data storytelling, but few have it as a first class feature.

The third one is the emphasis on automation. Generating effective data stories is not easy. The process requires a variety of technical and creative skills as well as business understanding and effective communication. Carefully thought out rich templates and strong automation can make this whole process much easier.

The claim is that most businesses and most users do not require infinitely versatile authoring tools. They are looking for results. Business analytics solutions should focus on effective communication, not the creative exercise of authoring documents and data views.

Combining business-first data stories and the scale and speed of automated story generation should result in something genuinely new. The proposed approach presents an opportunity to re-imagine what business analytics even means and who the user is. Automation opens up business analytics to businesses that otherwise would have to do without.

There are further possibilities in extending this approach through third party data enrichment, branded story bundles, and a template marketplace, where data story authors could sell their best story templates.

Who cares? If you are successful, what difference will it make?

For the last 10 years, Big Data analytics has failed businesses left and right. The promised land where data-driven decision-making is the norm has not happened yet. Industries are still struggling with digitalisation, data science initiatives haven't made products and services that much smarter, and the public sector is as inefficient as ever. There is so much waste in all areas of business. Companies are not seeing returns on their investment in data, and are falling back to their old ways.

If a wave of automation and purposeful business-first practice sweeped the business analytics field, there's no limit to how much value and opportunities could be created. Imagine if every company could have informed, data-driven conversations and could decide on the future based on the best available information. What would that do to the health of businesses everywhere, to job satisfaction — to the global economy?

If data stories became the standard way in which companies have internal conversations, what would the effect be on the society outside the office? Shared stories build understanding and empathy.

If computers helped us be more human by doing most of the heavy lifting for us, perhaps we could learn to make better sense of all the stories around us, all of the time.

What are the risks?

Probably the main risk is any SaaS operation is security, especially when it comes to customer data. In addition to the guarantees given to cloud hosting providers, SaaS vendors typically complete their own audits and assessments to meet customers' compliance requirements.

There are European, American, and international certifications that companies can apply for, especially if they plan on holding customer data. Bodies that coordinate these include CSA, AICPA for financial reporting, and ISO/IEC, etc.

Every SaaS vendor has to meet the requirements of relevant legislation, such as the European GDPR, Californian CCPA, and the US federal HIPAA regulation for medical data.

SaaS vendors typically provide a cloud deployment option, as well as an on-premise installation option for those who wish to keep a closer eye on application data flows. The standard approach to facilitating both at the same time is to provide the software solution as a machine image that can be deployed to a separate, client-specific cloud instance or on a virtual server in a local network.

In addition to normal business risks, such as financing and legal disputes, SaaS businesses carry additional risk through service level agreements. So called intellectual property disputes can also pose a threat to normal business operations.

How long will it take? How much will it cost?

Two people can build a data story generator in three months. A team of four can build a polished basic product in six months. People who really know what they are doing might be able to turn those months into weeks, but I don't think that's realistic.

Six months, four people: two developers, a designer, and a data story specialist. £200-250K on salaries annually, fully loaded at 1.33x for £300K p.a. That amounts to £25K per month, or £150K for six months.

£25K per month would be covered by a thousand customers paying £25 per month. Gaining 1000 customers in six months would mean starting with one and doubling the tally every two-three weeks. This kind of growth doesn't really happen with B2B. But just as a thought experiment, let's say that all of the above was possible. That would mean that one could build a sustainable business around this idea in one year for £300K.

More realistically, one might be able to show some traction and some market fit for a solution based on this idea in one year for £300K. This might then be enough to close a second funding round to start growing the company.

See the canvas for more details.

What are the mid-term and final “exams” to check for success?

One customer, 10 customers, 100 customers — at 1000 it's working. A thousand times that would be a success.

Accounting software vendor Xero has 2 million subscribers after fifteen years of trading.