Appointing A Chief Data Officer To Increase The Economic Value Of Businesses

“There is a new position, the Chief Data Officer. It’s a good idea, but there has been poor execution. What has been happening is taking a CIO and giving them a new title of CDO. However, it should be the Chief Data Monetisation Officer. The job is to determine how to monetise the data you have available. This should be an economics person rather than IT person.” – Jacob Morgan: Principal of Chess Media Group.

Businesses making it a top priority to bring in a Chief Data Officer are doing so as a means to ensure the quality, governance and performance of their Big Data projects are at their best. The threat of losing opportunities from disruptive innovation and the fear of being unable to manage the exponential growth of data has been a key reason for the large increase in hiring for this role.

“For some organisations today, data has become such an explosive part of business that they have created a Chief Data Officer (CDO) position to reside next to the Chief Information Officer and the Chief Technology Officer. This evolution clearly acknowledges that data in the business setting is separate from the systems running it. Beyond that, it recognises that data has a value that, if fully exploited, can help drive profitable business.” – Wired.

With business acumen, ability to lead change and suitable IT awareness as initial qualifiers, there are many other factors that an executive leader should take into consideration before taking the leap and appointing a Chief Data Officer. Here are a few;


#1 – Establishing A Clear Outline Of Roles & Responsibilities

First and foremost, in order ensure the new executive you’re bringing on board is set up on a path to success, it’s important to present the leadership team with a clear definition of roles & responsibilites, and a solid understanding of what the organisation is hoping to achieve. This will help the CDO create a roadmap that is aligned with the organisation’s goals and highlights potential obstacles that need to be addressed, as well as minimise border skirmishes with CIO and CTO peers. The CDO must be suitably empowered and supported to equip them to succeed. The role is unlikely to deliver the required value to the business without authority and support, especially given the CDO’s remit can include challenging existing practices and contributing to digital transformation within the organisation.

“A successful roadmap should divide the implementation into logical phases in order to reduce implementation risk. Phases should be around three months in duration. Taking on all the metrics and goals at the same time or in large chunks is very risky primarily because business users lose interest if they are not engaged on an ongoing basis. Prioritise your roadmap phases in order of importance to your business so that you reap the most benefits from your analytics early in your roadmap and provide justification for additional phases. Strong early success provides the critical mass and positive impression about analytics which leads to stronger business adoption.” – StatSlice.


#2 – Building The Right Team

“As well as a financial cost, there’s obviously also a cost in human resources and time. If you have data scientists bumbling their way through hundreds of projects with no clear aim, or decoding terabytes of data you have no clear, immediate use for, they’re likely to be unavailable, or distracted, when something of real value comes along. Having the right people with the right skills in the right place is essential.” – Talend.

Part of the responsibility of a Chief Data Officer is to hire the right team and effectively navigate the success of Big Data projects. In order to put together an A-level team, there needs to be a clear set of qualities, characteristics and expectations of prior experience that the CDO must look out for in the hiring process. A considered approach to recruitment and selection, recognising the change process the business must navigate, will help to select the stand-out candidates that are most suitable for the role.

One example is hiring a Data Scientist. Some of the most important traits include statistical thinking, good communication skills, creativity, curiosity, and of course, the right technical skills.

“A great data scientist has a hacker’s spirit. Technical flexibility is as important as experience, because in this field the gold standards change with an alarming rate. Data scientists work together, love open source, and share our knowledge and experience to make sure that we can move at the speed of demand. If your data scientist is a quick study, you’ve made a sound investment beyond the current trend cycle.” – Datascope Analytics.


#3 – Strategic Allocation Of Budget & Resources

“Analytics – the ability to find meaningful patterns in data – can help manage costs, lead to efficiency and better decisions, increase services and make better use of capital.” – Carlos Londono: Global Supply Chain VP at Owens Illinois Inc.

A CDO is responsible for the cost, schedule, delegation of tasks, coaching and technical performance of a Big Data project. In order to be able to implement change, invest in the right technology and systems for processing data, oversee and guide the team and achieve a profitable outcome, effective project management techniques must be adopted to keep track of whether objectives and KPIs are being met.

Among these is also the responsibility to determine which which project management method is most suitable for the project, a popular choice among many organisations being the Agile method.

“By delivering the work in small increments of working – even production ready – software, those assumptions are all validated early on. All code, design, architecture and requirements are validated every time a new increment is delivered, even the plan is validated as teams get real and accurate data around the progress of the project. But the early validation is not the only benefit that Agile brings, it also allows projects to learn from the feedback, take in new or changing requirements and quickly change direction when necessary, without changing the process at all.” – Gino Marckx: Founder & Business Improvement Consultant at Xodiac Inc.



For more resources, please see below:

The Rise Of The Chief Data Officer

Six Qualities Of A Great Data Scientist

Developing A Business Analytics Roadmap

Where Is Technology Taking The Economy?

Staffing Strategies For The Chief Data Officer

12 Qualities Your Next Chief Data Officer Should Have

Why Businesses That Use “Big Data” Make More Money

Making Data Analytics Work For You – Instead Of The Other Way Around

How To Turn Any Big Data Project Into A Success (And Key Pitfalls To Avoid)


To discuss this and other topics, please contact the team at Contexti – + 61 28294 2161 |

6 Missing Drivers of Failed Big Data Projects in Australia

Given Contexti’s 50+ Big Data Solutions engagements and having provided Big Data Training to over 1,000 professional from over 200 enterprises across Australia, we’ve seen, heard and been involved with enough projects to draw some insights on what drives big data project success and failure.

At the same time, talking with our international business partners and counterparts, we find our observations, while made in Australia, are not unique to Australian enterprises and businesses across the globe will benefit by understanding the importance of the following drivers.

To help illustrate each driver below, we’ve provided an insight of how Contexti client Seven West Media positively addressed each driver, resulting in the delivery of one of the most successful Big Data Analytics projects in Australia.

#1 – Commercial Strategy

What’s the purpose of your big data project? Are you responding to business disruption? Are you leading with innovation? Which revenue lines have been impacted and which revenue lines do you intend to impact? Having a clear commercial strategy with the right narrative will help to align team members, limit initial scope and set the bar for getting a return on investment. No clear commercial strategy, a poorly defined strategy or the common ‘proof of concept’ approach of ‘let’s throw all our data in a data lake and see what we find’ will deliver a tyre kicking exercise, resulting in wasted time, money and missed opportunities.

Contexti Client Example: Seven West Media recognised ‘television was no longer just in the living room’ and they were now in the business of ‘quality content on any device, anywhere, any time’. This headline narrative guided the commercial strategy for Seven West Media’s big data project which was to find innovative and effective ways to engage with audiences on any platform. Engaged audiences directly correlate to increased revenue.

#2 – Defined Actions

Aligned to your commercial strategy, you need clarity on how you will take action with your new-found insights. Worse than not having the capability to find new insights from your data is when you have the insights but you’re unable to take actions on those insights. This require to step away from the data and think about systems, people, processes, customers and partners that will be impacted when you take action with your new insights.

Contexti Client Example: Seven West Media during the 2016 Rio Olympic Games leveraged their big data platform to find new audience insights and to personalise the viewer experience. Leveraging the insights they executed on a marketing program that delivered 2.7 million emails across 108 targeted campaigns. By defining and acting on their insights they achieved an ROI of a 29% lift in the average minutes streamed by users who were part of this targeted program.

#3 – Executive Engagement

All projects need executive sponsorship and support to get basic budget sign off, however some projects significantly benefit by having executives more actively engaged with the project. Big data projects fall into this category. With the right engagement, executives will provide headline messaging across the organisation of why this project exists, what are the benefits and importantly why departmental colleagues should pro-actively enable and support this project. There will come a time when requests will be made to departmental colleagues to make data available, make people available and make their expertise available and without the right mandate supported by an engaged executive your project is on-hold in the best case or dead in the extreme case.

Contexti Client Example: Seven West Media’s big data project was sponsored by its Chief Commercial Officer who was aware, active, vocal and visible across the project. With an engaged executive sponsor, the Director of Data and Business Intelligence who was driving the project had mandate to align internal departments and external services providers, ultimately delivering a successful project.

#4 – Team Composition

When considering the team composition for big data projects, many will be able to quickly identify the Data Scientist role, but will struggle to name other roles. Many project failures have been a result of unallocated or misallocated resources. A Data Scientist is a Data Scientist, they are not Platform Architects, Platform Engineers, Platform Administrators, Data Architects, Data Engineers, Commercial Strategist, Change Managers, Trainers or industry subject matter experts… to name just a few of the roles that need to be filed in a successful project. If you start with getting clarity on your commercial strategy (Driver #1) and work through how you will action your insights (Driver #2) then that should be a good start to thinking through your team composition. Of course depending on the size of your project and complexity of your mandate and budget you will take a different approach to composition of your team. Some roles may be internally filled, some will be full time roles, others will be part time roles, you might supplement your core team with contractors or you might outsource some functions to third party service providers. Key point here is don’t just hire a Data Scientist then wonder why the project failed.

Contexti Client Example: Seven West Media’s big data project team was comprised of many internally filled roles (Director of Data and BI, Data Architect, Data Scientist, Data Analyst), they also also engaged with other internal departments (e.g. marketing who executed on the targeted email campaigns) as well as third party providers such as Contexti who took care of the design, build and management of their big data platform (meaning Platform Architecture, Platform Engineering, Platform Administration, Data Engineering and Support was outsourced).

#5 – Technology Choice

Your big data solution needs to be powered by an appropriate technology that supports your commercial objectives and use cases (Driver #1). Further, the technology needs to be supported by a appropriately skilled, qualified and enabled resources (Driver #2). Too often we’ve seen technology choices being made in the absence of commercial objectives, use cases and clarity of how the technology will be supported in a production environment. What’s worse is we’ve also seen technology choices and decisions being made because of internal politics, pre-existing vendor relationships and by people who are not close enough to the issues of the project. This not only sets up the project for failure from a technology perspective, it also causes the project team to lose faith in the leadership and decision making process.

Contexti Client Example: Seven West Media had clarity on their technology choices. Leveraging the project leadership team’s prior experience and with clarity of their commercial objectives they identified there were two key challenges that they would focus on. Firstly, the variety and complexity of the data sources drove their decision towards selecting a mature, secure and scalable enterprise-grade data management platform at the core of their data solution (they selected Cloudera). Secondly, given the highly variable nature of the events they were covering (such as the Australian Open, Wimbledon, Rio Olympic Games etc) with variable amounts of traffic and data, they needed the ability to scale up and down in a flexible manner, driving their decision for a cloud-based solution (they selected Amazon Web Services). Further looking at the team composition, their skill-sets and familiarity with existing tools, the overall solution also integrated with Teradata on AWS, Tableau for visualisation and R for advanced analytics.

#6 – Agile Approach

With the first five drivers in place (Commercial Strategy, Defined Actions, Executive Engagement, Team Composition and Technology Choice) you want to execute effectively and demonstrate quick wins. Adopting an agile approach will enable to you test and learn, share the lessons-learned and move to your next iteration to meet your commercial objectives. Without these first five drivers in place, it’s difficult to be agile and we’ve seen many organisations fail as they have talked themselves into building what they perceived to be ‘the biggest, most flexible, most secure big data platform that could solve any big data use case’ but in reality solved none. These non-agile projects lost momentum and ultimately failed having spent so much time waiting for hardware to arrive, waiting for integrators to spend months building and securing their big data platforms, waiting for their internal departments to make data available, waiting for the newly hired data scientists to start and the list goes on. Without all six drivers in place all involved with the project had no sense of urgency, purpose or accountability.

Contexti Client Example: Seven West Media adopted an agile approach from day one. Within six weeks of Contexti being engaged (to design, build and manage a big data solution) an initial pilot program was run to support the coverage of the 2016 Australian Open Tennis event. Following that the solution was expanded to support the 2016 Wimbledon Tennis event, then more work was done to scale up the solution for Seven West Media’s coverage of the 2016 Rio Olympic Games in Australia.


By: Sidney Minassian – Founder & CEO, Contexti – Big Data Analytics


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