Turning data science into a profitable information product

Getting value from skills investment 

 

A key differentiator of businesses in the digital era is their ability to exploit data through the application of advanced analytics and data science.  Organisations that recognise this will typically establish some form of data science capability.  The starting point for this, unsurprisingly, is some form of data science capability and the recruitment of some data science expertise. 

 

One of the issues organisations come across quite quickly, is how to drive value from this investment in expensive skills.  Even if the data scientists manage to navigate the organisation and systems to deliver some insight, they typically lack the skills (not to mention the desire or interest) to convert that initial insight into tangible business benefit. 

 

 

Industrialising the process 

 

The analogy I see here is with the first industrial revolution.  Whilst this revolution did depend on several scientific advances and the application of science to business, the key driver of value was actually the ability to industrialise the processes concerned.  This continues to be a typical problem in manufacturing – the development of an initial model or prototype may demonstrate a great new product, but if it cannot be easily manufactured at a low enough price, then the invention is of no real value. 

 

This is the exact same challenge that businesses face with data science today.  Data scientists are great at coming up with new insights, models or algorithms (‘products’), but they are typically produced in a one-off, hand-cranked, manner.  To drive value from them, you need the skills to industrialise that process.  Indeed, in the ideal world, the data scientist’s capability should be part of an end-to-end, factory style model that continually produces new, innovative products and then industrialises the production and monetisation of them. 

 

Data science typically produces a product that is analogous to a model or prototype in manufacturing. Whilst it has some value, typically, the real value comes with designing an efficient process for repeatedly producing it and delivering it to the ‘consumer’. The consumer may of course be a business process or an application rather than an individual. 

 

 

The challenges 

 

There are several challenges associated with the industrialisation process: 

 

  • Data supply chain – the analytics are only as good as the data they are based on. The industrialising ‘data entrepreneur’ must identify where a reliable source of good quality data can be found 
  • The source data will inevitably have quality and consistency issues which need to be properly understood
  • A process will be required that can deliver the data into the new process in a regular and consistent manner. This presents both technical and organisational challenges
  • Consideration needs to be given to how to consolidate data from multiple sources into a record that is suitable for processing by the analytical model, as produced by the data scientist
  • All the challenges above need to be considered in the development of a coherent architecture and design, that can be repeated on a frequent, possibly real-time, basis. 

 

 

The solutions 

 

The data entrepreneur must employ the skills to produce a smart solution to these challenges, which balances the conflicting pressures of speed of delivery, with the reliability of the solution delivered. 

 

If the entrepreneur is successful in all of this, that only gets them as far as a reliable, repeatable process to produce his or her data product. They then need to ensure that the product gets to market. The vital last step is in ensuring that the newly developed product gets embedded in the organisation in a way that maximises the benefit from it. 

 

Project One has the skills and experience to put in place this type of industrialised service model for advanced analytics, we can help your organisation do the same. Talk to us to find out more. 

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