Small and Agile or Large and Powerful: Size Matters

Tim Pattenden


Data Science

Recent hype surrounding data science, and in particular artificial intelligence (AI), has led to an increasing number of businesses looking to adopt the technology to improve processes and gain greater cost efficiency. In fact, Accenture Research and Frontier Economics suggests that AI will enable 38 per cent profit gains by 2035.

However, although many businesses would like to make profit gains and derive value from data science, they're facing a new challenge: where does this new capability fit in their company?

How should you implement data science software? 

One option is a central data science team tasked with supporting the whole company. Another is several smaller teams that work with specific business units. Another is to outsource data science. And of course, it is possible to implement some combination of these approaches.

Centralised or smaller models?

A centralised data science team can ensure the benefits of new tools are widely shared, and that work is not being duplicated in different parts of the business. As a focal point for data science, this can help to make the potential applications and benefits visible to all. And, a central team may be large enough to justify investment in training and infrastructure that will make it more productive than smaller teams.

However, a centralised data science team can run into problems. It may not be able to respond quickly to the needs of different business units. It may standardise on technologies which do not fit with the needs of some parts of the business, slowing down solution development and frustrating staff. Centralisation and standardisation have many benefits, but a delay in getting that first application into production is particularly an issue for data science, which for many companies is a new and unproven capability which needs to demonstrate its value.

Smaller teams that work within business units can be more flexible and will have a better understanding of their unit’s requirements, which can help deliver better results.

There is, therefore, a trade-off between creating a centralised data science team which will be large enough to develop significant technical expertise, or smaller more flexible teams within different business units which will better understand the domains in which they work.

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Outsourcing data science 

Outsourcing data science can give companies access to a pool of staff larger than even the biggest internal data science teams, who will therefore have the right specialist skills and abilities for a wide range of tasks. An organisation with such a large pool of data scientists is also well placed to provide the mentoring, training and career development that leads to significant technical expertise.

The wrong outsourcing model can exacerbate the problems of the centralised team, with the outsourced data scientists invisible and inaccessible to staff in the business, but the right outsourcing model can help overcome this and other problems. Embedding at least some of the outsourced staff within the company helps visibility and access, and the scale of a dedicated data science organisation means they are likely to have the varied skills necessary to meet the needs of many different business units.

The ideal situation is to have the data scientists easily accessible to all parts of the business while keeping a firm control on costs. There are many ways to do this, but the best ways require a level of trust, which means that this is a relationship that both sides should be viewing as a long-term investment.

Effective data science requires the right strategy

Companies starting with data science should begin to see useful data come through within weeks and actionable insights should be available within one year, maximum. If they continue to persist with a platform or approach, that is not delivering value, the company will not only lose money but could also see relationships sour and staff members lose faith in data science. This could mean that businesses could be missing out on the transformation capabilities because they hadn’t planned their previous attempts well enough.

Data science began its business life much like a cottage industry, with staff members interested in the field exploring it on a small-scale. If organisations are ever going to get to a point where they can deploy and institutionalise data science to deliver sustainable value, decision makers must think carefully about where data science fits in their company. To find the right data science strategy for your business, contact us here

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