Curious. Useful.

flowchart LR
    id1["Ambiguity"]
    id2["Stimulus"]
    id3["Uncertainty"]

    id4["Discovery"]

    id5["Options"]
    id6["Insight"]
    id7["Action"]

    id1 --> id4
    id2 --> id4
    id3 --> id4
    id4 --> id5
    id4 --> id6
    id4 --> id7

    click id1 "#ambiguity" "More on Ambiguity"
    click id2 "#stimulus" "More on Stimulus"
    click id3 "#uncertainty" "More on Uncertainty"
    click id4 "#discovery" "More on Discovery"
    click id5 "#options" "More on Options"
    click id6 "#insight" "More on Insight"
    click id7 "#action" "More on Action"

A time-boxed service delivered by an inter-disciplinary team.

We employ discovery as a discrete effort, using a set of tools and techniques that have proven useful over many years. They include an iterative approach of research, analysis, modeling, design and operations. We find it most effective when applied before an initiative has been fully funded and staffed - between the idea and the delivery. We deploy the discovery process in order to:

• Provide validated information to the client as a strong foundation for decision making

• Investigate and de-risk key areas of the client’s proposition (value, usability, feasibility, business viability).

• Demonstrate the art of the possible and show the most probable outcomes

• Connect the different threads of potential into a coherent whole

We typically navigate through different phases. Modes might represent, for example, “find the edges”, “get the data”, “leverage domain expertise”, “understand the client’s customer”. This exploration of reality continues until we can produce some testable options in the real world. We uncover information quite rapidly at the start and when saturation of themes and numbers sets in, we turn our attention to generating and de-risking options for solving the problem statement.

Below is an artist's impression of how discovery typically flows. Elements are rarely this discrete and are often revisited based on feedback. Tools and methods are heavily context dependent.

timeline
    title Discovery Phases
    section Discover and Define
    1 : state initial problem : identify the stakeholders : map the organisation
    2 : current technical architecture : find the suppliers : products & services : analyse sales data
    3 : P&L and money flow : identify the primary business processes : understand value as perceived by the customer
    4 : domain knowledge review : identify the weak spots : competitor review : ubiquitous language glossary
    section Design and Test
    5 : restate problem : agree primary hypotheses : start prototype : get backing data: define survey
    6 : identify test segments : run survey : build service introduction plan : concept work
    7 : analyse survey results : customer interviews : identify data gaps : generate architecture options
    8 : summarise learnings : form perspective : prioritise actions : transition plan

“Dealing with ambiguity” sometimes appears on job advertisements, but it also blocks many initiatives - especially initiatives that span multiple organisations or domains. Hallmarks include:

• inability to clearly state and agree on “The Problem”, with people talking past each other using different language and terminology

• difficulty in prioritising competing voices and ideas

• incoherence as everyone has a different perspective and emphasis of “the elephant”.

Why do initiatives happen? Many people focus solely on cost-reduction (as a means to improve EBITDA), but in reality a myriad of forces and events can contribute to a tidal surge of desire for action. Common scenarios include:

• a potential merger or acquisition opening up adjacent markets, new geographies and new service possibilities

• going public, or a change of ownership or leadership

• declining existing market size or share, or a loss of profitability

• competitor improvements and increasing customer expectations

• changes to regulation and legislation

• the need to diversify in the face of changing market conditions and dynamics

• the desire to mitigate one or more business risks

• a innovative idea championed by one or more senior leaders

How should a business recognise and handle such stimulus? Can an appropriate stance be taken without prematurely committing to unrealistic outcomes? With Discovery, we aim to get right into the details of these situations and generate feasible options for progression without taking on too much risk and investment up-front.

Uncertainties affect our ability to create the changes we want in the way we want them. Typical examples include:

• not having accurate cost predictions, or knowing requirements for self-fund or break-even

• not knowing whether our data provides sufficient quality to enable the proposition (commonly a problem in “AI” scenarios)

• not knowing how far to go and when to stop (when are we done?)

• not sure what evidence would support go-ahead (“go/”no-go”) or how to define “kill criteria”

• not sure where to start, or how to identify the most valuable thing to start with

Our Discovery aims to provide more than one way to progress towards the objectives.

To this end, we provide multiple (3+) options. Each satisfies different qualities to varying degrees. For example, some may cost less or take less time, but provide less customer or stakeholder value. Others might excel at satisfying only one or two objectives out of say six.

We design complementary rather than mutually exclusive options, depending on the context. Options produced typically consist of one or more of the following:

• service design

• technical prototype

• ML model

• business transition and service introduction plan

• UX prototype

• system architecture

• service and support model

• cost and revenue projections

The key point of option generation is to apply what we have learned and use it to prove that one or more of the objectives can indeed be satisfied.

Aside from all the information gathering, the finding out, and the crafting of options, one of the benefits of Discovery is the opportunity to develop deeper understanding. As outsiders we provide a perspective that often stimulates new ways of looking at existing dynamics and relationships, sometimes simply through the asking of basic questions. The generation of "what if?" questions allows us to question the status quo and look beyond to opportunities for innovation, at new ways of creating value in the world.

These insights provide useful grist for innovation, where we try to escape the product performance silo and expand into other types of innovation (network, structure, process, service etc.). Ultimately if we can better understand how value is generated across the supply chain, and how our different clients perceive that value, we will have an opportunity to do something that will be considered "state-of-the-art".

We often unearth risks and gaps when doing Discovery, which is unsurprising given we normally cross various organisation and system boundaries. As a result - completely aside from the main quest - there are often numerous improvement ideas that fall out. A few examples:

• increasing the repeatability and automation levels on certain business processes

• connecting different data islands together with an agreed data contract (e.g. shared identities, references)

• filling skills gaps and capacity shortages

• migrating all types of customers to a single identity scheme

• optimising analytics routines that are excessively resource-hungry

• surfacing information to operations teams that was previously hidden

About Us

What We Do

Tackle the Four Big Risks in the early stages of digital initiatives, be they product overhauls, next generation, adjacent market or merger & acquisition integrations.

Why

We feel that we possess a superhero skill that can make the difference between ultimate success or failure of an initiative: having an evidence-based, verifiable perspective on an initiative before its approval for budget. We focus on customer value, technology constraints, operational impacts, and white space gaps. We enjoy Discovery as interesting and creative work, providing an avenue for us to bring the knowledge, experience and insights that we have gleaned from many previous digital propositions. When we participate at the riskiest time - the formation of initiatives, we can give them the best chance of success down the road.

How

Intra-disciplinary micro-teams (1-5 people) that work from customer value back to implementation details across technology, data, organisation, service and support using a variety of proven methods and techniques.

Who We Are

Based in the UK, Gus (technology and data) & Odette (research and modelling) have been working in technology across a wide variety of domains for over 25 years. Together with a team of friends and associates, they specialise in Discovery work and tackling interesting problems. Contact us if you'd like to know more.