What are the best data analytics tools for your airline?

For an Airline, key data domains include Schedules, PNRs, Tickets, EMDs, Crew, Profiles, Loyalty Transactions, Fuel, Baggage, ancillaries, maintenance and customer service contacts. In airline technology landscape, these are fragmented across multiple inhouse and 3rd party products. What is absolutely essential is to develop a flexible LakeHouse architecture which can ingest data (both structured and unstructured) and enable analytics on it. AWS through S3, Redshift, Aurora, Glue, Athena, Quicksight and Lake Formation offers a powerful stack for developing such a lakehouse.

It is also important to note the time and resources required to preparing and cleaning the data for integration. This isn’t a trivial step to overlook and one that is between 60-70% of the data lifecycle.

Airlines use many third party tools and in-house tools that store and generate data in different formats and make use of different technologies. Data integration from various sources is one of the major challenges in airline industry. Oracle, SQL server, Azure SQL, cosmos dB, Maria dB, sap etc. All contribute to the airline data for different purposes. It's important to understand the data and maintain a common warehouse or data lake to use this effectively for analytic purposes.

Apart from understanding business context, it’s also critical to capture a high level picture of how business teams operate and how they are gonna use data to make decisions. I strongly believe that most problems with data teams are not tech stack problems but people problems.

When choosing a data analytics tool for an airline, it is important to consider some factors; ? The size and complexity of the airline's data. ? The specific needs of the airline. ? The budget of the airline. However, some popular data analytics tools that can be valuable for airlines: ? Tableau. ? Power BI. ? QlikView/Qlik Sense. ? R programming language. ? Python. ? SAS. ? IBM Cognos. ? Google Analytics. ? Hadoop/Spark. ? Machine Learning and AI Tools. ? Database Management Systems.

In my experience, business doesn’t care much about flashy reports or fancy features unless these are exposed to external clients. They want to see data in an easy to understand way to be able to make quick inferences. Choice of tool would largely depend on what business is most comfortable with, budget, tool performance with large datasets, scalability, maintainability, reliability, reusability, quick development cycle helps as well. Sometimes excel is a better option than Powerbi ??

I had worked on a contract at one time when looking into a Power BI dashboard that had a lot of KPI's on the over-all summary tab and it seemed to come up so slow to where the upper management got so frustrated, so they had me to look into it, including looking into the every DAX query to optimize the queries and validate them at the same time! Consequently, after all the optimization and validations to improve the dashboard reports, which did help a lot, but the dashboard was still slow refreshing, I asked to look at the back-end and come to find out they were using Varchar Max on the backend for datatypes of the SQL Server Data Warehouse! I alerted them to change the column datatypes; automated stored procedures. It sped up about 66%!

Any visuals and reports should be easily understandable rather than flashy. They should meet business requirements,quick in performance and maintain data integrity. Choosing a tool purely depends on the organisation. From my experience Tableau,power bi, looker studio they all fit the purpose!

Power BI dashboards are being used in the operational control centre to analyse various ops data and many close to real time reports. Custom software for crew scheduling and rostering. SSRS reports are the other major.

When choosing data analytics tools for airlines, prioritize data analysis and modeling. These tools reveal hidden patterns, trends, and anomalies, enabling hypothesis testing and predictions. Opt for tools supporting various analytics techniques and data flexibility. To excel in data analytics, consider solutions using AI, machine learning, and cloud computing.

I would not buy an feature heavy expensive analytical tool ( Tableau / Powerbi / Alteryx) until there are significant number of advanced analytical use cases. Open source tools (Python/R , Airflow , Looker studio) or cheaper tools may solve most of your problems. Rather I would invest in hiring expert data specialists. With cloud data warehouses like Azure DW / Bigquery you can shift most of complexity to warehouse itself rather than bringing data into analytical tool on top of it.

Azure platform and Aws are being used majorly for data integration, preparation and modelling. Azure SQL and synapse are being used for data modeling and warehousing for the analytical reports. Aws has a major role in the data engineering part of the data science projects. Most recent one is the databricks which is gaining importance as data engineering and data science module for the data science projects because of the Delta Live table concept and easy to get and compare the historical data for machine learning projects.

Data governance policies, such as data access controls, data encryption, and data masking. Additionally, the tools should have robust backup and disaster recovery mechanisms in place to safeguard your data in case of any unforeseen events. One of the best data analytics tools for airlines that excel in data governance and security is SAS Analytics. With SAS Analytics, you can establish granular access controls, allowing you to define who can access, modify, or delete specific data sets. This ensures that only authorized personnel can manipulate sensitive information.

Data governance and security should be implemented as early as possible in the data pipeline. Best if you can leverage access policies and row level security at data warehouse level itself. It becomes difficult when you have self service users connecting directly to core datasets. PowerBI does a pretty good job of implementing data governance policies at analytical tool level. It also has inbuilt usage Analytics that you can enable to understand consumption and manage cost effectively. Most of the time, data quality issues originate much before data reaches analytical layer. Auto quality checks and ML based data pattern analysis would definitely help.

Microsoft purview , IAM ,Data bricks unity catalogue concept etc make the data governance more easy to manage and implement.

This is step is so important and probably overlooked often. Another aspect of data literally is the importance for admin personnel to enter the data into the database in the same way without skipping fields that are used in the analysis. Making sure that everyone enters the parameters in the same way really affects the outcome of the data analytics. Without correct input, the analysis means little to non. Therefore it's really important to spend some time both exploring and training everyone in the organization on how to enter data correctly and the importance of doing so.

It’s absolutely critical to assess data literacy across multiple business teams. Should be the first step in fact. Once you identify gaps or current level of data knowledge you can spread awareness and start filling those gaps. It should also be taken into consideration while choosing the best analytical tool for your organisation. Standard practice I think is to cluster people based on they level of data experience and organise training programmes based on group.

Data scaling is one of the important factor among airlines when it comes to data engineering and data science. Auto scaling capabilities of cloud providers are necessary to handle the load and at the same time manage costs.

Considering the amount of value analytics delivers to the aviation industry and the number of strategies/policies these airlines come up with, driven by the insights drawn from their data, Tableau is generally the best tool for aviation analytics. It offers the flexibility of creating simple and complex custom visuals powered by Machine learning algorithms in one package. Power BI is a reasonable alternative, but it may not provide the same level of sophisticated visuals that are particularly useful in this context.

Evaluate BI market leaders across your business needs and current data environment. Look for the tool that best aligns with most of your data needs. It’s hard to predict analytical needs so looking at other airlines and how they manage analytical solutions would help. If you are looking for stunning visuals, Tableau is the way to go. Powerbi is more of a platform ( power platform) and will help with a wider set of requirements.

To begin any analytical or data collection endeavor, it's crucial to first comprehend the problem at hand or define the key performance indicators (KPIs) we aim to attain. These issues might encompass tasks like optimizing routes, predicting demand, ensuring safety, assessing market competition, gaining customer insights, evaluating environmental impact, and various others. Identifying the problem serves as a foundation for recognizing the data already available and determining potential data requirements. Therefore, the initial step should involve brainstorming and identifying the business problem.

I would say the most important are the airplanes. Those are the things that transport people and goods from place to place. But don't forget about airports. That's where the airplane's sleep at night.

There are many good data analytic tools available to collect, organize, analyze, and visualize airline data. It all depends on your use case. Start with what you want to know, who is your audience, how big is your budget, where the data is and how much data is there. There is a long list of potentially good analytical tools for airline use cases. So, let your use case, audience, budget and what you want/need for an outcome decide which is the best data analytic tools for your airline.

Understand your business audience/ stakeholders, data Analytics team capability, data producers/ engineering teams. Getting a good idea about the people who will be involved in data Analytics should help make a better decision