<img height="1" width="1" style="display:none;" alt="" src="https://dc.ads.linkedin.com/collect/?pid=306561&amp;fmt=gif">

CDAO New Zealand

Auckland, 4-5 November, 2020

Interview with CDAO NZ speaker:
Mazen Kassis, Head of Data & Analytics, Foodstuffs North Island

Mazen Kassis square

Q1. Tell us a bit about your background and how you ended up in your current role.

My background is in computer science and statistics. After cutting my teeth in the public health sector as a biostatistician, I applied myself in a variety of industries, including financial services, media and entertainment, before settling in my current role in the FMCG/CPG sector with Foodstuffs North Island.

What attracted me to the role is the seemingly infinite possibilities for data & analytics in general in the industry, but specifically in our co-operative, due to the stature it holds in Kiwi society and the abundance of data it is the custodian of.

Q2. Based on what you’re seeing what do you think the big trust issues are that will create barriers or opportunities?

I see engendering trust as a core competency in my role, both for our customers as well as our employees and suppliers, especially as we are on a journey to be one of the most customer driven retailers in the world. This objective will only be realised if we continue to put customers at the centre of everything that we do, meaning continuing to seek from them what they want and them being willing to participate in the conversation, because they trust that we will use their information appropriately as custodians, collecting it for the sole purpose of providing them with value in return.

Ensuring that appropriate privacy training, awareness, policies and procedures are embedded, such that the whole team participates in good practice when it comes to personal information, as well as solidifying a privacy-by-design framework for our initiatives will be important to success.

Q3. What is the importance of real-time data in your organisation?

The prevailing perception is that the trend amongst our customers and suppliers is that real-time information is becoming increasingly important. There’s obvious value in being able to obtain real-time views of pricing and whether an item is in stock at the local store, for example. The COVID situation has, in my mind, strengthened the case for real-time data. Due to the uncertainty of the times, particularly due to unparalleled customer demand for products (e.g. hand sanitiser, masks, cleaning material), having a real-time view of the world, like how many customers in stores, how many are in line, how many are on the website and what are they ordering, would serve to maximise the ability of organisations to cater to such circumstances.

Q4. How do you move up the value chain to ensure your data and analytics team is achieving its full potential?

In a word, organically. The first step is to ensure the more traditionally capability of understanding and being able to visualise what has happened (i.e. business intelligence and reporting) needs to be running efficiently and effectively. Next, the capability of observing what has happened needs to be augmented by the capability of understanding why it has happened (e.g. why have sales gone down last period, why did loyal customers increase?). Once these steps are at the right levels, the data and analytics team stand a better chance at being able to synthesise the what and the why and use them to estimate what’s likely to occur in future.

Q5. What happens when you cannot use historical data to make decisions?

When things like COVID19, and the related significant shift from historical patterns it brings with it occur, it’s understandable that the utility of more traditional data science techniques is called into question.

In my view, COVID19 has forced a rethink in approaches and frameworks to using data science to solve business problems. In the relatively volatile COVID19 context, the speed with which one is able to obtain answers has been of the utmost importance. This is almost inversely proportional to the comprehensiveness of the required insights. In other words, moving from more traditional and perhaps more comprehensive data science methods to more heuristic, smaller scale but quicker results approaches has been really helpful. As our CEO points out, decisions need to be made with between 40%-70% of the required data/information, refining actions as more of the picture becomes clearer. Having less than than 40% of the data and that’s likely insufficient, but anything more than 70% and you’re likely dedicating too much time/effort to the approach.

Q6. How are you building sustainable data governance?

One of our strategic objectives is to raise the level of data and analytics in the co-op and embed good practice. To summarise, we’ve adopted a 5-stage approach:

  1. Create a data governance vision and mission statement
  2. Defining data governance Scope
  3. Develop and implement an appropriate framework
  4. Develop an associated strategy
  5. Structure of the data governance team accordingly.

Watch on-demand