Data Driven Innovation: Focus on Building Analytics Talent

Politician Entitlements Cost $100M in 2014
August 4, 2015
The Business Value Potential in Self Service Data Blending
November 5, 2015

By: Raj Dalal


Over the last few years we have witnessed the hype around 'big data', drummed up by analytics gurus and evangelists along with the news and print media. However, this hype has not been without substance. Moreover, the advent of Internet of Things and the ever growing mobile penetration across the globe are only accentuating the issues and challenges arising as a result of the explosion of data.

The rapidly evolving consumer, operational and competitive landscape around organisations is causing a lot of stress on their ability to stay competitive. There is a growing acceptance of having a data-driven approach to decision making underpinned by 'analytics' especially of the 'big data' kind. But to lay that foundation one has to first go after the low hanging fruit that is the capture, storage and manipulation of big data. The research conducted by BigInsights over the last two years has shown that the technological considerations around the aforementioned processes is of low concern to most organisations[1]. This is mainly due to the fact that there are now more than two dozen data platforms and data management product vendors. Most of the technological challenges have already been overcome. However, what has definitely become more critical now is 'what to do with the analytics'.

Analytics Dividend

A recent study conducted by the MIT Sloan Management Review entitled the Data and Analytics Global Executive Study, brought out some interesting findings. Prominent among them are, (a) technology is no longer a barrier in creating value out of data, and, (b) it's more about people now.

As can be seen from the following graph, the biggest concern amongst the respondents in this study is translating analytics into action.

MIT survey

Source: MIT SMR Survey

The BigInsights Big Data Study 2014-15, also points out the following top three challenges –

  1. Inadequate analytics skills in-house
  2. Deciding what data is relevant
  3. Not understanding the benefits to the business

It is noteworthy that there are some overlapping themes here – mainly, skills gap from the perspective of technology but more so from the business perspective in terms of deriving business benefits from analytics. Being able to identify the relevant data for analytics by asking the right business questions is being repeatedly highlighted as a key challenge in these surveys.

Furthermore, similar to the MIT SMR study, BigInsights also found that certain organisations are more likely to identify being data driven as part of their culture, along with a clear strategy to achieve that. And these organisations are much more confident when it comes to in house analytics skills.

Building the Capability

The MIT SMR study categorises organisations into three buckets based on analytics maturity, namely – analytically challenged, analytics practitioners and analytics innovators. By strategically focusing on people (analytics talent), the analytics innovators are not only able to build their competitive advantage but also able to sustain it, compared to the analytically-challenged organisations.

The analytics innovators have focused on nurturing a data-driven culture in their organisation, and as a result they are able to attract the best analytics talent whilst providing them an incentive to stay by giving them the opportunity to work on interesting projects. This way these organisations are able to capture the analytics talent dividend more effectively.

So where does this leave the 'analytically challenged' organisations in terms of attracting the right analytics talent? How can they build their own analytics capability and find the right talent? Can the traditional data workers and people with analytical skill set within these organisations be forged into the new analytics talent using some form of training – in-house or otherwise?

These are critical challenges before most organisations who want to take advantage of big data and analytics but who are facing an analytics talent deficit. Interestingly, this deficit is not in the 'quant' job profile i.e. 'data scientist' but in the job profile that is about translating analytics into business decisions or specific actions i.e. managers. The MIT SMR study authors point out that the end users of the insights generated by data scientists and data analysts do not have the right skills to consume those insights into actionable decisions.

Given that 74 percent of analytics innovators believe that talent drives success with analytics compared to 17 percent of analytically challenged organisations (MIT SMR study), it is  no surprise that analytics innovators are putting a higher emphasis on on-the-job training, formalised training and hiring university graduates. By putting less emphasis on outsourcing this work to either contractors or other companies they are aiming to strengthen their own capabilities in this area.

Getting It Right

We at BigInsights understand that it is still early days for many organisations in Australia on their journey to be data-driven. We suggest moving beyond focusing on producing analytics and instead to start focusing on infusing the organisation with the right talent – either from within or from outside. To start with, a separation of roles is key - data scientists for producing analytics; analytically minded & technology savvy managers/business talent for translating the analytics into action. Business graduates with a grounding in data science or with a technology & data focused background could very much fill the latter roles. Furthermore, specialist third party providers such as analytics training providers could be tapped to up-skill suitable in-house talent. But to ensure these investments bear fruit in terms of achieving a sustainable data-driven culture, it is imperative to establish a well defined analytics strategy.


About Authors

Raj Dalal is Principal at BigInsights.

  [1] BigInsights 2014/15 Big Data Study    
You're welcome to share this page: