Dr Raoul-Gabriel Urma, CEO, Cambridge Spark

All organisations aspire to modernise and operate more effectively or risk becoming irrelevant in today’s fast-moving competitive environment. As CEO of @CambridgeSpark, the specialist Data Science & #AI capability partner for organisations, I advise our clients and partners on how to achieve business goals by developing their AI strategy and workforce capability. 

Let’s deconstruct the question: “Why develop Data Science & AI strategic capability in your organisation?”

  1. First - What is Data Science and AI? 
  2. Second - What is strategy? 
  3. Third - What do we mean by capability?

Data Science and Artificial Intelligence (AI)

These two buzzwords that often have confusing definitions depending on who you ask. To address this, you can simply think of them in terms of what they can do for you:

  • Data Science: Enables you to produce actionable insights and foresights with data to deliver business value
  • AI: Enables you to reduce the cost of predictions generated from data, repeatedly, and at scale

Let’s now turn our attention to strategy

One way to think about what strategy means to an organisation is to answer the simple question - “How are we going to win?” 

Professor Richard P. Rumelt from UCLA breaks it down into three components in his classic book “Good Strategy / Bad Strategy”:

  1. A diagnosis: What are your organisation’s key strengths and weaknesses that hold you back from reaching your goals?
  2. A guiding policy: What direction will you choose in the context of uncertainty, dilemmas and trade-offs?
  3. A set of coherent actions: What steps and resources will you take to carry out your guiding policy?

So where does Data Science & AI come in then?

Data Science can produce your 'diagnosis' by summarising internal and external data into updated KPIs and recommendations. Imagine if you were not able to do this? Your strategy would be ill-informed. 

AI gives you your 'guiding policy' and 'coherent actions' by quantifying and reducing uncertainty thanks to predictions based on data.

When AI is embedded into your processes and ways of working, it can do so at scale, enabling you to be more agile and adapt more quickly than your competition.

Developing your workforce capabilities

Now that we’ve established how Data Science & AI can fuel an organisation's strategy, how can you go about developing the capability of your workforce?

There’s little point investing in Data Science & AI platforms and projects if the engine of your organisation, your people, are not equipped with the skills to react and respond to maximise the opportunities that Data Science & AI can offer.

To address this issue, I recommend investing in flexible, company-wide Data literacy, Data Science and AI upskilling programmes for your workforce. 

Such programmes will upgrade your organisation’s ability to stay relevant and maximise success: 

  • Data Literacy: Ensure everyone is confident and understands the value of data in the organisation
  • Data Analyst: Develop self-serviced teams that leverage data to answer custom business queries
  • Data Scientist: Develop the capabilities to produce forecasts and recommendations based on data
  • Machine Learning Engineer: Develop the capabilities to embed predictions generated from data into your processes and products
  • AI Leader: Develop confident leaders that can identify business opportunities and understand the risks and realities associated with Data Science & AI

So perhaps my opening question / statement  should have been “How do I develop data science and AI capabilities within my organisation”? 

The answer is very simple - Talk to Cambridge Spark!

Dr Raoul-Gabriel Urma, CEO, Cambridge Spark

Cambridge Spark is the only provider offering a full-stack solution of training programmes for practitioners from Data literacy and Data analytics to Data Science and AI Executive Education. These programmes utilise your apprenticeship levy, presenting a fantastic professional development opportunity at all levels in your organisation.

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