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Data’s Most Pressing Challenges and Opportunities: How do you Compare?

With more than 50 data and analytics experts scheduled to speak at CDAO Auckland, 21-23 November 2022, we want to give you a chance to get to know a few of them a bit better. We spoke with the following speakers for a deep dive into what they think are the most pressing challenges and opportunities in data:

  • Mazen Kassis, Head of Data & Analytics, foodstuffs North Island
  • Kevin Sweeney, Principal Advisor, Stats NZ
  • Anwar Mirza, Data Strategy & Governance, FedEx Express EU

We asked them:

  1. How have you seen data and analytics become a supporter of business goals in ways it may not have before? 
  2. How do we foster a data-positive culture and empower teams in the broader organisation to use data? 
  3. What are the biggest challenges that you see in data and analytics and what is your strategy to address them? 
  4. What are you most looking forward to about the event? 

Mazen Kassis, Head of Data & Analytics, foodstuffs North Island

Tell us a bit about yourself and your background. How did you end up in data?   

I’ve had a consciousness about the importance of data for as long as I can remember. To me, gravitating towards fields like computer science and biostatistics at university seemed preordained. Having spent my early career working in the public health domain and observing early on how data, when used properly, can positively and meaningfully impact people’s lives, pursuing a career in the space was a must.    

How have you seen data and analytics become a supporter of business goals in ways it may not have before? 

My experience is that there’s an increasing consciousness amongst non-data stakeholders (i.e., data laypeople) about how data can augment business practices, particular senior stakeholders. It feels like so many organisations are on customer-driven journeys and because of this, they have had to contend with the necessity of data to the success of these sort of transformations. As a result, gone are the days when you had to make a case to convince people about the importance of data. Now, decision makers within organisations have matured to the point where the conversations have become more sophisticated – it’s less about “getting” data and more about how to safely access it from wherever it resides and how to use it get to insights and actions, quicker.  

How do we foster a data-positive culture and empower teams in the broader organisation to use data? 

The simplest (but by no means easy) way to do this, in my opinion, is to enable data-leaning people across the organisations – not just centralised data and analytics teams – to do this work for themselves. What a centralised data and analytics team should be focussed on is helping derive outcomes, not just outputs. That means that efforts should centre on developing guidelines, protocols, procedures, and standards that others can use to guide them on how to best use data to get to the business outcomes they seek, without needing to rely solely on data SMEs to do this work for them.  

What are the biggest challenges that you see in data and analytics and what is your strategy to address them in your organisation? 

As I see it, so much of the success of data and analytics as an enterprise supporting business outcomes lies in our abilities to tell our data stories. While us data geeks get excited when talking about data and analytics infrastructure, cloud-based technologies and how data should flow within our organisations, such technical details aren’t typically what will convince stakeholders to get behind various initiatives that we need their support on. The art of storytelling is one that deserves increasing focus, as it’s not an area that we have traditionally been trained in. Nevertheless, the most consequential decisions often come down to how well conveyed a message has been and how well the story (of course I’m referring to evidence-based stories, not the fairy-tale type) has convinced people that they should follow a path you’ve set. Our approach has been to take every opportunity to share the data story with our stakeholders across the business. The more engagements we have, the more opportunity to refine the message and the more people will hear, understand, and hopefully start speaking a similar language.  

When your stakeholders start telling the story better than you do, you know you’re on a winning path.  

Could you share a bit about your session at CDAO Auckland? What are you most looking forward to about the event? 

I’ll be participating in the Data Governance, Privacy and Ethics panel. Data governance is an important topic at Foodstuffs, and we’re looking to enhance our capabilities and continue maturing in the space. So, I’ll be keen to hear from and talk to subject-matter experts and try to learn from their experiences. 


Kevin Sweeney, Principal Advisor, Stats NZ

How have you seen data and analytics become a supporter of business goals in ways it may not have before? 

I think a lot of the shift to data and analytics in this sense comes from the wider reach of data and the democratisation of data. Data is getting into so many more hands.  And that’s the case for data within business environments as well, not just for that out in the public.
There’s been a big increase in data accessibility, I think sitting behind this. I’m old enough to remember when the data for the business I worked at was reams and reams of dot matrix print-outs. Literally stacks of it, and somebody would have to go through all of that line by line and analyse the results.  That’s just horrible, right?  I mean, who would ever want to do that?
But now data is everywhere. But not just everywhere – it’s also much more accessible and offered in a way that’s much easier for people to interact with. It’s more readily discoverable through devices or through apps so that a much wider audience can more easily engage with it. I think that has helped make a big difference in data, and I’d also say analysis, becoming more intuitive for people.  So you get more people who are keen to step into that space.
There also seems to be a shift, particularly lately, around the idea of fit for purpose data.  You see that expression popping up a lot more often, particularly in regards to data quality. And that speaks to this same idea. If data is fit for people’s purpose, it’s more approachable and more easily engaged. On the other hand, if you try and force a perspective on data, particular regarding quality, that’s less likely to resonate with users.  So their level of engagement, and therefore the potential value realisation from that data, might not be as high. Applying a fit for purpose approach might mean that the data could be less accurate say, than a user might have thought.  The point is that it delivers to their purpose. That supports use, and it’s only through use that data generates measurable value.

I think that it’s becoming more common for more types of users to readily engage with data, meaning more people can contribute positively to business goals. We certainly saw that as an outcome of the COVID pandemic here in New Zealand, when fit for purpose data and a much broader range of data use was paramount to our response.

How do we foster a data-positive culture and empower teams in the broader organisation to use data? 

Data culture seems to be getting a lot of traction lately, which is a good thing. Maybe I’m a bit biased as I have a background in anthropology, where culture is everything. I think a lot of culture as we describe it for data really comes down to human behaviour. If data is presented within your organization as something that is easy, accessible, positive, then it presents a really strong value proposition. And a strong culture can follow on from that.  In that scenario, staff come to see data as not only an important part of their work, but a positive part as well.  It becomes established within the identity of their work roles.
But in terms of establishing culture, this is an area where I think there’s a key role to play for executive leadership especially. This is due to their level of influence, their reach, and the position they’re in to drive things like change across the organisation. Because if you’re getting the message right from the top about how important data is or how useful it is, I think it will resonate with people a lot more.

Also, as part of fostering culture, if you’re giving your staff freedom to access data, to experiment, to innovate with data, being open about it, that can be a powerful driver. There are risks with that approach of course, and if your leadership is inherently risk-averse, that will be a tough sell. But those risks are manageable and I think pale in comparison to the potential benefits.

The right attitude serves to empower staff at the operational level to grow the data culture.  And culture comes from both directions. It’s going to be associated with a position or idea or vision promoted from the top, and that all staff are meant to embrace. But it’s ultimately those staff in that organisation that determine what that culture actually looks like. So it takes both genuine leadership along with empowered staff to establish and maintain a positive data culture. 

When you talk about things like data literacy, are we talking about moving that literacy throughout the organization to the to the non-data teams and does that include the executive team?

Absolutely. I think that gets back to the fit for purpose idea, where that literacy around data is going to mean different things to different people or groups of people in the organisation. As part of our work to develop a holistic data governance approach for instance, we identified three general classifications of staff within an organisation, each with a different perspective on data.
At the top is the executive perspective, which is based on strategic thinking primarily. Below that is the management perspective, with those in that space concerned with translating strategy into delivery, so expressing their thinking through policies and frameworks. And thirdly is the operational perspective, where use and delivery of data value is the focus. All three perspectives are important. And while they are in some ways distinct, they also need to work cohesively, in support of one another.  More than that, they need to be holistic, where their individual value is acknowledged and maintained, in the midst of their functioning in a joined-up manner.
So data literacy is going to mean different things to each of these groups. For instance if you have a data analyst or data scientist working at the operational level, their data literacy is going to be quite technical in terms of methodology, accepted analytical practice, and the like.  Whereas the data literacy of a chief executive is likely to be associated with the ability to understand the business value, or risks, or financial implications of decisions associated with various scenarios of data practice enacted across the organisation.  If either of those types of literacy, which are quite distinct, are deficient, then the organisation is likely to suffer repercussions.  So it’s important to understand and consider developing data literacy in a way that acknowledges that one size doesn’t fit all.

What are the biggest challenges that you’re seeing in terms of data and analytics?

There are quite a few potentially, but I think the one that I’ve seen stick around, is the challenge of effectively communicating the value of data generally, and good data practice specifically.  This is about communicating value in a way that’s meaningful to those who aren’t data practitioners. The main recipients are going to be senior leaders, or decision-makers.  Considering that the awareness of data has increased tremendously for some time now, and is widely seen as the critical asset it is, the persistence of this challenge is puzzling.  But it persists.

Those with the authority to use data strategically or to make decisions with it, and those directly working with data, are still operating to some extent in two different worlds.

To that point, I remember a slide I included in a presentation I did at CDAO NZ a few years ago, which described a conversation between a senior executive and a data analyst around a data request, and the way they talked past each other.  That proved to be an especially popular slide, even getting called out on social media.  It clearly resonated with people in the audience.  And that told me pretty clearly that the communication issues existing between these two perspectives was a common challenge.

It’s really about different languages in the end.  These two perspectives, though both drawing on the same data and both trying to get value out of that data, each operate with their distinct language.  And translating between them, though critical, is often really difficult when it’s attempted.  Or worse, it’s not even recognised as an important thing to do. But it is a challenge that has to be addressed, if data is to deliver to its potential.

In my previous career, when I was the administrator of the national Geospatial Office, I would sometimes be asked to provide a guest lecture for uni students graduating with a geography or geospatial qualification.  The idea was to provide them perspective on the realities of working as a geospatial professional after graduation.  While these students were often focussed on their technical proficiency in GIS software, my first piece of advice to them was to enrol in a business class or two.  This to gain a basic understanding of the language of those under whom they would be working.  The ability to communicate their technical understanding, including the results of analysing geospatial data, to decision-makers would make them an invaluable employee at any organisation. It was the quickest path to standing out, I suggested, in what was becoming a crowded field. I think I’d still offer that advice today.  That communication challenge is still prevalent.

At Stats NZ we’ve established a Data System Leadership branch, to support the Government Chief Data Steward and help us deliver to our role as the lead data agency for government.  A basic goal there is to help other government agencies lift their data capability.  And of course that often involves change from current practice.  No easy feat.  But a particularly effective approach we’ve found is through the use of good storytelling.  It’s effective because storytelling is a form of communication that can speak to different types of people, to those with different data perspectives.

Can you share a little bit about your session for CDAO Auckland, you’re most looking forward to?

I’ve been leading work for about the last year or so to develop a data maturity assessment for New Zealand government agencies, and will be presenting on that topic.  But one aspect of that work I don’t often touch on, but that’s really important for anyone considering a maturity assessment, is the idea of getting the foundations of that sort of assessment right.  Those foundations can either make or break the delivery of the assessment and determine how useful it can be.

I thought I would touch on three of those foundations as especially important.  One is around the idea of maturity itself, what it consists of how it’s used to characterise good data practice.  The second is around the selection of the dimensions of data that are measured by the maturity model.  So this is the heart of the model, the core of what it is you consider important in terms of data practice.  And thirdly is the reporting of the results of the assessment.  There are various ways to deliver those results and understanding the audience and the best way to provide them a view of data maturity is absolutely critical to success.

We’ve done most of the work to develop the data maturity measurement model at this stage and are in the midst of testing right now – in the form of prototyping and piloting with other agencies – so maybe a good point to take stock and review the thinking that’s got us here.

As is the case every year, I’m really looking forward to attending CDAO NZ in 2022.  The conference offers a great opportunity to present and get feedback on my work from a large audience, including many from outside of government, as I also gain a lot of insights about work going on elsewhere in the New Zealand data system.  And of course it’s an opportunity to catch-up with colleagues and enjoy some great networking.


Anwar Mirza, Data Strategy & Governance, FedEx Express EU

Tell us how you got into data:

Many years ago, I started out in Management Accounting. In that role, I gathered and analysed data using tried and tested means (which in those days meant spreadsheets !!). The first hurdle was to convince Management that my data and analysis was trustworthy.

From there, I worked in Internal Audit which specifically for me meant, finding areas of concern to the business, establishing the root cause and helping local management to bring that business area back into an acceptable level of control.

As the Head of the Trade Receivables function in finance, I was primarily a data consumer and relied heavily on data quality and integrity to make decisions.  I co-implemented SAP globally to try and improve our processes, data, reporting and business performance. This led to being responsible for Financial Systems across many financial, commercial and other business disciplines.

These experiences lead me to moving into the ‘World of Data’ and eventually my current role of ‘Data Strategy & Governance’ at FedEx Express EU.

What are the biggest challenges that you see in data and analytics and what is your strategy to address them? 

‘Data and Analytics’ is a strange collective noun used to describe a multitude of things. I usually interpret it as ‘Data Management’ and ‘Data Analytics’. The latter is usually called out separately because that is the largest area of demand and expectation from Leadership for insights, innovation and more.

As far as challenges go, there are many and I will just comment on the first two that come to mind.

Firstly, challenges arise because ‘Data’ is a relatively new discipline and consequently, many of an organisation’s foundational data capabilities are either inadequate, outdated or simply just not there !!

Secondly, a gap in Enterprise Data Literacy causes delayed decision making and huge ‘data debt’ in business operations and this heavily impacts the approval and progress of operational projects.

As far as strategies to address the issues go, it is essential to promote and publish a Data Strategy expressed in simple digestible terms. I am constantly surprised by the number of CDOs that supposedly have a Data Strategy and their own teams have never seen it.

With regards to data governance can you tell us how you’re seeing data and analytics being used to support business goals in ways that you perhaps hadn’t seen in the past.

That’s an easier question to answer 😊 A well-oiled Data Team will provide their leadership with the right models that predict multiple business scenarios as well as suggest how to deal with those in business operations. But the real change to the way this was done in the past is that a good collaboration between business and IT functions yields speed, scale, consistency and acceptance at a phenomenal pace.

How do you have a data-positive culture that empowers teams within the broader organisation to use data?

For me, Data Culture goes way beyond a sense of feeling. Data Culture is fostered by the organisation taking time to consider many different things that all affect the entire Data Lifecycle. Some examples are

  • Processes and decisions which are centralised, decentralised or distributed
  • Activities, projects and initiatives which are business led or IT led 
  • Wanting to or being Data Centric, Data Driven or Data Led
  • Choosing to give internal autonomy or hold control centrally
  • Data structures/layers driven by hybrid organisational hierarchies (geography / product / function)

The choices leadership make in the way a company is run directly drives how data is architected, managed and consumed. Organisations can significantly reduce the cost to supply data, reduce the amount of data debt and reduce the amount of confusion by NOT applying hybrid models of the examples provided.

We’re going to be hearing from you in the 21st of November, around data literacy and what can people expect to hear from your session on the 22nd the keynote in the plenary you’re going to be opening speaker. Can you give people a little bit of an idea of what they can expect?

Well, I’m really looking forward to meeting the community in New Zealand and providing some really practical takeaways for organisations of all types and sizes.

For the Data Literacy session, I’ll be giving insight into what it takes to become a Data Literate organisation from the vision, the challenges, the do’s & don’ts, the ‘how to’ and much more!

For the opening keynote speech, I will be cramming in five key imperatives messages and giving practical tips and tricks for each:

  • Why we must put data in context of business value
  • Make Data Governance the primary value enabler
  • Communicate the Value Roadmap and Data Journey
  • Deploying the correct the Operating Model to accelerate speed, scale and consistency
  • A Data Literacy programme that makes each individual in the organisation, aware of their data responsibilities.

Don’t miss your chance to hear from these speakers plus many more at CDAO Auckland 21-23 November 2022.