In-house vs Outsourced – Building an analytics function that hits the ground running

In-house vs Outsourced – Building an analytics function that hits the ground running

We live in a data-driven economy and failure to build a data analytics competence of some kind leaves you at a competitive disadvantage. And we know that businesses need to become much smarter about how they use data to retain or attract customers.

One of the first choices you face is how to build out your analytics function – do you want to build a team in-house, or partner with external experts, or even choose a hybrid model? This decision will have wide-ranging consequences for your ability to exploit your data in the future.

DIY data analytics

In theory, building your data analytics capability in-house has one major advantage – you can begin analysing your data almost immediately. Obviously, you will still need to deploy predictive analytics tools, but you can save time that would be otherwise spent identifying potential partners and agreeing service contracts.

But this course of action assumes you already have data science and analytics skills in-house. If not, you will need to hire suitably-skilled staff. And that’s where you start to run into delays and risks.

Paradoxically, you need data science experience in order to hire your first data scientist - otherwise you cannot properly evaluate their technical skills. It is also incredibly important to realise that you cannot simply bolt data science onto existing operations - you must change your culture to be able to act on the insights being generated by your data science team. Too often, businesses make this mistake and never realise the full potential of their investment.

Expensive skills shortages

Data science skills are in very short supply helping to drive salaries up. According to Payscale.com, the average annual salary for a senior data scientist is currently €70,318 – and rising. And you’ll need a broad range of skills that are rare to find in one person – according to research from McKinsey, “Best-practice companies rarely cherry-pick one or two specialist profiles to address isolated challenges. Instead, they build departments at scale from the start.”

Although you will realise significant benefits, building an in-house team to turn your company’s data into money will involve a substantial initial outlay.

Instead of making new hires, you could retrain existing staff. But this will greatly increase the time to get your advanced analytics program up and running – and longer still until you see returns on your data analytics investments.

Why you should consider outsourcing

Keeping analytics in-house creates a huge burden on time and resources – at least during the initial stages of building your data analytics capability. Over time they will deliver value, but many CFOs will baulk at the time it takes to generate a return on investment.

Partnering with an external provider offers a much quicker return on investment because the entire process is shortened. And because your partner already has a suite of pre-configured analytics tools, they can begin unlocking value from your data almost immediately.

Outsourcing can be a transitional process too. One way to get the best of both worlds is to outsource all of your predictive analytics functions initially while you build an in-house data science team. As those capabilities come on stream, you can then start bringing functions back in-house.

Using third party consultancy also helps you avoid the staffing issues inherent in trying to maintain operations in-house. Your business doesn’t have to attract suitably skilled data scientists, or deal with rapidly increasing salary demands.

Outsourcing can be implemented in different ways too. Hybrid outsourcing allows you to split responsibilities with your analytics partner for instance. Under this model you retain responsibility for some elements - for example, the underlying database infrastructure, while the outsourcer provides others, such as the hard-to-come by modelling and analytics functions. The hybrid model is fully flexible because no two scenarios or deployments are ever the same.

This allows you to maximise the use of your own staff resources and minimise outsourcing costs without limiting your analytics projects and obtain the skills you really need for data-driven operations.

Speed is everything

When it comes to improving the customer experience, speed is incredibly important. Giving people what they want, when they want it is a key aspect of all customer retention strategies.

As you roll out your data analytics program, speed needs to be a factor at every point – including before you even begin analysing data. The faster you can get your predictive analytics capability in place and generating insights, the quicker you can begin to realise a return on your investment. McKinsey even put a figure on operating profit improvements, suggesting “first movers” account for around 25% of the gain Why? Because they have more time to integrate analytics with workflows than their competitors.

In reality, if your business has never used predictive analytics tools before, choosing to implement data-driven strategies in-house could be a mistake. Any initial cost saving will be quickly cancelled out by the extended time it takes to begin generating actionable insights. Far better to outsource the work to the experts initially, and have your outsource partner train and gradually hand over hand over responsibility for analytics as it comes up to speed.

For more help and advice on finding the optimum mix between in-sourcing and outsourcing for your data analytics team, please get in touch.