Modernising data analytics at Winnipeg Airports Authority

Posted: 23 March 2020 | | No comments yet

Trevor Strome, Director of Digital Airport Solutions at WAA, presents exactly how data analytics can improve an airport’s operational efficiency.

Data analytics at Winnipeg Airports Authority

International Airport Review’s Airport IT & Security conference in 2020 will incorporate a multitude of workshops and presentations – covering a range of topics including artificial intelligence, operational efficiency, digital transformations and data analytics.

As part of the conference, Director of Digital Airport Solutions at Winnipeg Airports Authority (WAA), Trevor Strome, will be hosting a data-focused workshop at Munich Airport’s ISH on 19 October 2020. Below, Strome gives an overview of the analytics system he has developed, and an introduction on how this can be leveraged by airports.

The analytics modernisation journey

Analytics is the system of tools, techniques and people that enables organisations to gain value from their data assets. Analytics achieves this by generating accurate, validated and trustworthy business insight necessary to drive decision making, guide appropriate action and achieve desired quality and performance outcomes. Analytics can help airports achieve understanding and insight of their quality and operational performance by transforming the way information is used and decisions made throughout the organisation.

Winnipeg Airports Authority began modernising its analytics capabilities to become a more data-driven organisation in mid-2018. At the time when the need to adopt a more systematic approach to data analytics became apparent, WAA was in a position typical of many organisations prior to embarking on a data analytics modernisation initiative. The following section highlights, at a high level, the ‘analytics modernisation journey’ undertaken by WAA to adopt a more modern approach to analytics with a focus on the entire data lifecycle rather than simply deploying a software solution.

Step 1: Identifying the need

WAA is committed to leading transportation innovation and growth to deliver on the needs of the community it serves. This includes enhancing the customer experience by better understanding customer needs and assuring value through measurements relevant to them. To deliver on this commitment, WAA recognised that it could be using data in smarter ways to drive decision making. At the time, there was no centralised analytics function within WAA; most analysis was performed via ‘canned’ reports off source systems or data exported to Microsoft Excel spreadsheets.

There existed strong pockets of analytical skill throughout WAA (namely in the finance and business development areas); unfortunately, these highly-skilled people were hindered because they were lacking the most modern analytical tools. Any data that needed to be aggregated or otherwise analysed beyond the provided canned reports was performed largely by spreadsheet, which can be time consuming and inefficient, especially with large volumes of data.

For these reasons, there was an opportunity to use data more effectively for decision making, which is what initiated a transformation in data analytics at WAA.

The analytics system framework described here will be discussed in more depth as part of the analytics workshop Trevor Strome will be providing at the Airport IT & Security 2020 conference.

Step 2: Building the system

Once there was strong support from key stakeholders and WAA executives, the next key step was to begin building our analytics system. A data analytics system framework I developed while working in other industries, optimised for airports, became the roadmap WAA used to ensure that the analytics system we were developing was able to meet the needs of stakeholders. The data analytics system framework looks at analytics as a complete system with inputs, enablers and outputs with the end goal of generating insight that informs decisions, triggers action and achieves desired quality and performance outcomes.

As the analytics stakeholders were conceptualising what our analytics system would look like, the framework guided effort on the three main inputs to an effective analytics system:

  • Dialogue – from the outset, we identified the proper employees from all levels of the organisation and engaged with them in serious discussions to fully understand their information and decision-making requirements.
  • Drivers – knowing the quality and performance drivers crystallises ‘why’ analytics are important to an organisation. Documenting key drivers for the business (such as wanting information to improve on-time performance, provide real-time parking lot status, etc.) helped establish a clear understanding of why we, as an organisation, wanted to invest in analytics.
  • Data – understanding what data sources we had available, documenting what the content of those data sources were, and how we could access those sources, was a crucial step in setting up a catalogue of data that could be useful within our analytics system.

With a clear understanding of why modernising our analytics was important, what our stakeholders’ information requirements were, and what data we had available, the kind of analytics capabilities we needed (via the three main enablers of an analytics system) came into focus:

  • Technology – Understanding our end-user requirements allowed us to make some key technology decisions that formed the ‘backbone’ of our analytics capability. Because most of our source-system databases are already Microsoft SQL-server based, we standardised on that platform to build our enterprise data warehouse. Recognising that we are moving toward a hybrid environment (with both cloud-based and on-premises systems) and with more vendors supporting APIs for their systems, we selected Dell Boomi as our integration Platform-as-a-Service (iPaaS) because of the complex integrations we can develop using a straightforward low-code/no-code development platform. WAA also continues to plan for the use of more cloud-based analytics within the upcoming year.
  • Tools and techniques – The team selected PowerBI as our business intelligence (BI) platform for its powerful yet easy-to-use functions and features. For projects requiring more in-depth types of analysis, the team is investigating the potential role that software tools such as R and Python can play in creating even more informative, robust analytics.
  • Team – In addition to several very analytical subject-matter-experts already within WAA, the need for dedicated business intelligence, data integration and database administration skills became clear. This was not due only to the increase in demand for analytics in the organisation, but also the clear need for core analytics, integration and database resources to augment the skills and knowledge already in house.

As a major deliverable, we are always working with stakeholders to continually refine the analytic outputs of our system. In this early stage, the preliminary focus is on enabling business intelligence functions using descriptive analytics and providing access to data that is:

  • Accurate (consisting of a single source of curated data that everyone trusts)
  • Timely (is up to date and in real time where necessary and/or possible)
  • Readily accessible (allowing anyone to access the data they need with the tools that we provision).

The analytics team is also preparing to address the needs of stakeholders as their skills and requirements become quickly more sophisticated, which means developing solid diagnostic analytics capabilities and moving toward developing predictive and prescriptive analytics capabilities as well.

Step 3 – Delivering value

One of the keys to demonstrating value of the modernised analytics was to achieve some ‘wins’. Fortunately, our efforts to engage with and understand the requirements of our stakeholders early in the process identified project ideas we could undertake that would almost immediately provide value to the organisation.

The team had early success building and deploying dashboards. In fact, the real success came in the ability for our existing analytical SME’s in finance and business development to pick up the tool and start building dashboards and performing data discovery off our data warehouse almost as soon as it was deployed. This is significant because the IT Department doesn’t need to be the rate limiter in deployment of dashboards and BI within an organisation. This allows the IT team to maintain more of a ‘DataOps’ focus, building and securing data pipelines from our source systems, and ensuring that our data holdings are accurate, timely and accessible.

The team was also able to quickly build some integrations to support more streamlined, integrated business processes. One integration, for example, allowed for the removal of extra manual steps involved in creating work-orders for preventive maintenance items relating to airfield inspections. The team is currently working on several other integrations to further optimise and/or automate using the tool.

Finally, once the data team started to dive into our various data sources (such as AODB, baggage handling, parking), there were some significant datasets that were not previously known to be accessible or available, so it was exciting that the data team was able to unlock significant new value through previously undiscovered data.

Trevor will be joined by over 400 attendees at Airport IT & Security 2020, including C-suite airport executives and IT experts. Make sure you join them in Munich by securing a ticket now.

Looking forward

In truth, our journey towards analytics modernisation has only just begun. As WAA continues to be transformational in adopting new technologies and optimising business processes in pursuit of the ultimate passenger experience in our airport, the need for and use of advanced analytics will only grow.

With a strong understanding of our ‘why’, or knowing our purpose, in the organisation, the WAA data analytics team will continue to focus on providing the insight and information required by our stakeholders to perform their jobs better by doing our jobs better. This means not only evaluating the outcomes of data-driven transformations, but to critically evaluate and constantly improve the analytics we provision, and the value we provide, to our stakeholders.

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