Managing your data in the cloud creates many advantages for organisations, especially as the world continues to shift towards greater levels of connectivity, agile business models and distributed workforces.
Cloud-based solutions have the potential to provide flexibility, accessibility and scale in a cost-effective way. Of course, cloud infrastructure alone does not guarantee the success of a data platform or your data science and AI projects.
It’s not as easy as developing a data lake with a few bolt on applications, and away you go! It’s definitely not that simple for organisations with complex data sources and formats, and evolving use cases — such as in the transport industry.
There’s a reason that global research and advisory firm Gartner believes that up to 85 percent of data lake and AI projects fail. Many face obstacles like poor data quality and a lack of internal skills in managing and exploiting data sets.
That means many data lakes end up as data swamps, and significant amounts of stored data remains under-utilised and can deliver unreliable insights to complex business decisions.
netBI has decades of experience in helping transport agencies and operators establish effective data pipelines in the cloud for trustworthy and targeted analytical insights that streamline reporting, underpin good AI projects, and support improved operational decision making.
You don’t want your project to become one of the statistics. Based on our expertise, here are five factors to consider when choosing or building a modern cloud data platform.
Complicated data ecosystems — where data is being generated by multiple, disparate sensors, software and other sources in varying formats — often have large amounts of data quality issues. Storing it all in a data lake can quickly lead to a data swamp.
Part of building the ideal cloud data platform is working with your different data vendors to address data quality issues at the source, and getting clarity on metadata and context. Additionally, complex data usually needs to be cleansed, combined and categorised — according to your specific organisational needs and business rules — in order to be ready for analysis.
We’ve helped transport organisations fuse data from a wide range of scheduling, ticketing and telematics systems and have encountered every kind of data quality issue including missing or duplicate data and invalid attributes, measurements or reference values. Make sure your platform automates the many aspects of data error-checking and matching to help systematise processes for achieving quality data, which leads to more accurate analysis.
A data lake may seem like a simple and fast way to derive value from your data. However, being able to get started quickly won’t matter if your data and analytics projects fail to deliver results.
Data lakes pool your vast quantities of raw data —whether it’s structured or unstructured — so that it’s available for ad-hoc reporting and data science projects. But for any kind of in-depth analysis and trend-spotting that many business leaders require, you’re going to want to optimise the data processing steps and support fast and efficient reporting.
Again, the level of complexity makes a difference here. If you have to find and blend information from five different systems, with different data standards, in order to extract meaningful answers — a lack of processing of your different data sets is going to limit your capacity and ability to gain insights from your data.
There’s a strong case to be made for taking the time to develop a specialised data warehouse aligned to your advanced reporting use cases — especially since modern platforms like netBI are compatible with data lakes to create lake house models.
Cloud storage has a reputation for being low cost. However, when your storage needs grow (and they will as you store more data sets) and your data platform algorithms fail to scale effectively with the larger data processing requirements, your cloud costs can skyrocket. Costs will increase as you throw more CPUs from your cloud provider at your data crunching requirements. The industry can refer to this as “cloud bill shock”.
It makes sense to store as much real-time and historical data as you can, because it enriches the analytical outputs you’re capable of achieving. However, as the amount of sensors and data continues to grow your data platform will be required to process more sets of data.
When you’re reliant on a data lake structure, the benefits of elastic scale start to become outweighed by the sheer cloud computing resources required to use and analyse your data. Working with raw data chews more resources because each analytical process requires more ‘brute force’.
Make sure your data platform provider considers your future needs and develops very efficient and effective data storage and processing techniques (including the algorithms that are developed) to minimise your cloud storage costs and processing requirements.
The best cloud data platform is one that enables your organisation to benefit from its data in meaningful ways. If you want people making data-informed decisions in their daily workflows, it’s important that your platform democratises data insights across your organisation.
The flexibility of a data lake has obvious appeal for teams looking to meet the needs of internal data analysts, data scientists and data engineers. However, in addition to one-off deployments and experiments that focus on specific use cases, you need to plan for a data platform that drives improvement across all business areas.
Typically, this requires user-friendly data analytics interfaces that can be understood and accessed by users with varying levels of digital literacy. Your data platform development process should involve end users and focus on structures that enable self-service.
For industries with complex data sets, such as the public transit, roads and logistics sectors, it’s important to bring industry domain expertise, as well as technical skill, to the development and maintenance of your platform.
Reliably fusing transport-related data sets requires developers with experience in industry-specific challenges and use cases. Industry experience and in-depth knowledge of data sets will help to mitigate your project risks and platform deployment issues.
Another thing organisations can overlook is that a data platform should never stagnate, and you’ll need access to advanced technical skills to keep your platform performing well.
Your data sources and analytical needs will change over time. That means a continual need to monitor and refine data quality, integrate new systems, adapt to different data formats and standards, and find new ways to fuse and interpret new data to answer emerging business questions. As a result, organisations experience a growing dependency on data and software engineering talent. Given the difficulties many companies face in attracting and retaining top tech talent — it pays to think about this need before you build your data platform. How will you maintain, enhance and extend your platform over time?
One effective option for organisations looking to build the best cloud data platform is to partner with an expert team. netBI designs, builds and deploys cloud-based data warehouse and analytics platforms that can easily dovetail with your chosen data lake solutions.