We seek to up-skill employees and generate assets that deliver value throughout your investment period, long after our engagement has ended.
We use the investment thesis or corporate strategy milestones, be they EBITDA, revenue targets or market acquisition, as the guiding principles of any strategy we create. The underlying goal is to maximise valuation at the next capital event.
What we deliver
Skills, understanding and in-house capability to enhance the return on investment.
What we bring
Leadership – drawn from industry experience in management and executive positions, start-ups, SMEs and corporates, scale-up, growth and exit. We’ve also sat on both sides of the table.
Tools, processes and policies – that work for the current and intended state of the portfolio company.
Best practices – within the company’s industry and from leaders in other industries.
Product & service strategy
"A product strategy sets the direction for the business, aligns everyone around common goals and reduces waste.
Without one, you're relying on luck".
Product management, and the strategy which it strives to deliver, is not about the creation of proprietary technology.
In fact it was British Airways who pioneered the use of commercial Agile product management for their aircraft maintenance, long before any Silicon Valley start-up cut their first line of code.
'Product' strategy, which inevitably involves services as well, is the data-driven process of identifying, quantifying and selecting market opportunities to drive long term shareholder value.
The best strategies are developed through a continuous iterating process that gathers data, interprets for intelligence, and refines the strategic direction of travel. Whilst engaging external sources, such as customers and partners, is vital to product market fit, so too is gaining feedback from within: people who actually deliver the service often have the most prescient insights.
Most organisations have a set of distinct products and/or services, so actually need to manage a portfolio of strategies (in order to prioritise capital expenditure and maximise ROI). The portfolio will change over time, reflecting the changes in markets and customers.
The tangible elements of a product strategy include operating business models and sales strategies for each product/ service, development and deployment processes with KPIs, in-market reporting and performance KPIs. Crucially, a strategy also includes clearly articulated market segmentation, customer alignment, user profiles and competitor differentiation - the rallying call to drive consensus, cross-functional communications, and focus of activities across the organisation.
Data science strategy
Without a clearly articulated strategy, the data science team will inefficiently consume capital, lack transparency, and systematically fail to deliver the promised competitive advantage - regardless of the team's skills and capabilities.
Commercial data science is the creation and deployment of proprietary and third-party* AI and ML models to transform data into intelligence, for commercial exploitation.
Typically for decision making, the intelligence can be consumed programmatically within software products or as part of management information systems embedded within operational processes.
* We have conducted due diligence on many, many models and have yet to find commercial AI that is built with purely proprietary code developed in-house.
Core strategy elements
We segment the development of data science strategy into key areas, each of which have interdependencies:
Approach to modelling - policies for selecting algorithms, training up and for determining optimal algorithm fit.
Bias and transparency - how to avoid embedding bias, detect if it unintentionally exists, and report on algorithm decisions to stakeholders.
Model performance & quality - KPIs and how to measure, what makes 'good', how to monitor and how to systematically improve performance.
Regulatory compliance - policies, processes and auditing regimes to ensue regulatory compliance of data in transit, in storage and during processing.
Legal compliance - mechanisms to ensure the legal right to operate algorithms as the company expands into new markets and new geographies.
AI & ML commercial relevance - how to maintain the relevance of algorithms and identify when substitutes offer superior commercial value.
Model deployment and scale-up - approach to acquiring data at scale and training up models within revenue deadlines.
People and skillsets - how the team will grow its skill base and manage key person risk.
Organisations need to create data strategies that match today’s realities – few companies have adjusted their approaches to capturing, sharing and managing corporate data assets. Their behaviour reflects an outdated, underlying belief that data is simply an application and process byproduct.
A data strategy is the rationale and plan for how you acquire data, process it into intelligence, and distribute that intelligence as actionable insights that deliver commercial value.
Having a clearly articulated strategy avoids duplication of data, processing overlaps and replication of work across teams.
Without a strategy it is almost impossible to facilitate communicating, collaborating or sharing data methods and practices across projects, systems and operational processes.
Core strategy elements
The six core elements of a data strategy, which provide the foundations of a company's data management, can be defined as:
Purpose - what you intend to do with the intelligence the data can provide.
Identification - the means to identify and represent the content contained within the data, regardless of its source, structure or how the data is stored or where it resides.
Storage - persisting data in structures and locations that support easy, shared access and processing, and which meet regulatory requirements.
Provision - packaging and partial-processing of data so it can be easily reused and shared, along with guidelines and rules for access.
Processing - how to move, combine and process data to provide a unified, consistent data view and maintain regulatory compliance.
Governance - the information policies and mechanisms for effective data usage and compliance, and how to manage and communicate them.