Getting started with analytics


In today’s digital era, business managers and organisations must be aware of a multitude of different aspects of their business and the environment around them. Many of those aspects – internal organisational capability, competition strategy, customer demands and sentiment, and rapidly evolving markets – can be captured with data.

Whilst organisations capture data, to really take advantage of all the benefits that data could bring, requires some form of analytics. Often the organisation knows it has valuable data but doesn’t know how to use it to add value.

Over the course of my career, I have set up and grown analytics and data science capability for several organisations.

So, what do we mean by analytics? There is spectrum of analytics that starts with relatively simple metrics, and travels through to artificial intelligence (AI).

A left to right arrow showing simple arithmetic on the left, statistics in the middle and artificial intelligence on the right when looking at increasing complexity of analytics projects

No matter what the size of the organisation, from small and medium sized enterprises to large multi-national organisations, analytics and data science can play a role in improving financial performance and customer success.

As organisations grow, they typically hire additional people to help with sales, marketing, operational tasks, and IT. Those differing teams are typically (and quite rightly) focussed on ‘business as usual’ infrastructure (websites, order taking, payment systems) rather than focussing on analytics enablement.

Which leads us to the question: How do you start analysing all that data to add value?

Ask the right questions

There are lots of questions that need to be considered when starting out on the analytics journey and building an analytics or data science capability. They can be grouped into 5 broad topics:

  1. Need
    Are you a start-up, scale-up or mature organisation? What is it that you want to achieve with analytics?
  2. Show me the data!
    Based on the need identified, do you have data to support that need? Where is it stored? How much history do you have? What is the quality like?
  3. Technology
    How should the data be accessed? How will it be stored it as it grows over time? What analytics tools will be invested in?
  4. Skills
    What analytical skills currently exist in the organisation? How will skills be acquired? Will you know the right skills when you see them?
  5. Business case (ROI)
    What potential value could analytics provide your organisation, e.g. increase in revenue, reduction in customer churn, process optimisation and/or cost reduction.

Most of the literature and information available online suggests that a long-term strategy and vision is the place to start. This is typically followed with recommendations for investment in technology platforms and hiring data science staff.

But by the time all that effort has been put in, and staff with technical skills have been hired, it can result in a financial commitment of AUD $500k or more. Depending on the quality of your hires, return on investment may not meet expectations, or cover the outlay.

Rather than ‘jumping in the deep end,’ with a significant up-front investment, an alternative is to ‘dip your toe in the water’.

This is where a ‘quick-win’ project can prove the value of analytics to your organisation and set you on the right path for future analytics initiatives. Some of that up-front thinking is still required, but by partnering with an analytics organisation, the benefits can be brought forward while the investment is slowly scaled up.

Getting started with that first project is the topic of my next blog.