Key takeaways

  • Data science has been identified as a crucial skill shaping Australia’s transition to a knowledge economy.
  • Rock star data analysts champion the data cause to management, form cohesive teams and rally around standardised data mining processes.
  • A well-run data analytics initiative can unlock a ‘win-win’ scenario for customers, managers, and rock star data analysts alike.

The term ‘rock star’ is being redefined in the business world. Where it once meant a famous musician or celebrity, it’s increasingly being applied to standout performers in any mindset or profession, including innovators, investors, entrepreneurs and data analysts.

As a data analyst myself, this isn’t something I made up. Last year, Prime Minister Malcolm Turnbull named a hundred so-called Australian rock stars as part of the National Innovation and Science Agenda¹. Dubbed the Knowledge Nation 100, this group is working to transition the national economy away from digging up assets under the earth, moving it towards harnessing the ingenuity of the people walking above it.

Of these 100 individuals, approximately 20% work closely with data, highlighting the role data analytics is expected to play in the building of Australia’s new knowledge economy.

But what defines a rock star data scientist? And how can other organisations use analytics to create new value for their operations?

Rock star rule #1:
Walk the data analytics talk

It may sound like a self-fulfilling prophecy, but the first rule of being a rock star data scientist is to act like a rock star data scientist. The gig should be approached with confidence and ambition, in full awareness of the business value and commercialisation opportunities that data brings.

This sense of swagger is important because organisations are often guilty of under-utilising findings from analytics projects. Senior leaders either fail to recognise the value that data can deliver, or they’re reluctant to initiate any business changes identified as necessary by the findings.

This status quo is shifting as the story of data analytics is continually told on the world stage. Several megatrends define this narrative, including the lowering cost of connected devices, an increasing awareness of data’s value, and a continued evolution of the term ‘big data’ – a phrase I both love and loathe.

These megatrends can be distilled into what’s commonly known as the Three Vs of Big Data:

  • Volume: If we were to quantify all of the recorded data created by human society from the very beginning to about 2003 – every cave painting, every play ever acted in, every broadcast and newspaper – we would come to about five exabytes (or one billion terabytes). We are now making that much data every two days. In four years’ time, we’ll be making that much every hour.
  • Variety: Traditionally, the word ‘data’ referred to information that was relatively structured – tabulated numbers or problems that could be easily organised. Today, much of the data is comparatively unstructured, comprising reams of freeform text, audio, or video, all of which require additional processing and analysis.
  • Velocity: With data now created and captured in real time, the opportunity arises to use analytics to speed up important decision-making processes. As the overall speed of data increases, the window of opportunity to leverage analytics effectively begins to shrink. Time really is of the essence.

Rock star data scientists are aware of these megatrends and strive to accelerate data’s role in transforming organisations. They champion data’s cause to managers, advocating the potential for data to be a goldmine of insights and returns.

Rock star rule #2:
Form a band, learn to play in time and in tune

The second rule of being a rock star data scientist is to form a band; a group of like-minded individuals with the desire to succeed in the fast-evolving data analytics landscape. Bringing different capabilities to the group, each band member wields a different analytics technology or skill as an instrument, coming together to play in tune.

A wide range of new these technology-instruments are available to the world of data analytics, many of which didn’t exist in the recent past. Examples include programming languages, modelling tools, and storage or retrieval mechanisms.

The speed in which these tools are emerging means the band shouldn’t exclusively focus around particular technologies or techniques. Instead, they should focus around process and business value. This can be achieved by institutionalising a standardised data mining process, adopting a community language and establishing a shared knowledge repository. Taking these crucial steps, the band will be able to keep abreast of technology changes, adopting new innovations as they emerge.

Several standardised data mining processes are available. At PwC, we use our own DnA2.0 process when conducting analytics sprints with clients, a methodology based on the Cross Industry Standard Process for Data Mining (CRISP-DM). Developed 14 years ago, CRISP-DM is the most widely used process by data miners globally.

Project
rockstar

The rock star analogy is how PwC approaches data analytics. We’re not trying to provide a standard set of analytics tools or to teach identical statistical packages to all clients. Rather, our goal is to unify the approach to data analytics, centring it around a technology agnostic standardised process and helping to make data analytics a core part of Australia’s knowledge economy.

One of the beautiful things about data is that it’s a non-perishable asset. Using analytics, the same data source can keep generating new value and ROI for an organisation. And because it’s being created from an inexhaustible digital resource, this value is a net positive – consistently created where there wasn’t any before.

As a result, analytics can make mythical win-win scenarios a reality. Customers can enjoy better services and experiences, business leaders receive exceeded returns on investment, and the rock star data scientists enjoy getting to work on new data problems with increasingly high-powered tools, singing from the same hymn sheet of a standardised process.

 

Contributor

Matt Kuperholz

Matt Kuperholz is a partner and chief data scientist at PwC Australia.

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