What to look for in a data automation tool
Nathi Dube, Director, PBT Innovation at PBT Group
In my previous blog, I discussed some of the steps behind implementing a successful data automation strategy. For this piece, the focus shifts to the features to look for when selecting a data automation tool and understanding some of the applicable use cases for data automation.
At its core, a data automation tool must be able to perform ETL (Extract, Transform, and Load). These are the three common elements behind any data automation project. Furthermore, the right tool needs to support different data storage systems, including both relational and non-relational sources, as well as having the ability to read and process data in different formats.
Of course, a good data automation tool must be able to seamlessly integrate with cloud-based technologies while being able to support legacy platforms. In this way, organisations can link their traditional environments with more modern, agile ones. Data discovery is another essential feature which facilitates data modelling and data mapping.
Furthermore, programming language support is critical. In addition to SQL, the tool must support various major scripting languages like Python and R. It must also be user-friendly to encourage wider organisational adoption. The tool must therefore have a visual interface that is intuitive while setting up must also be easy to do for even a non-technical person.
Many of the newer data automation tools provide no code functionality. This means that even though it will have a scripting capability built in, there are tasks that can be achieved by a person without any coding experience.
Another useful feature that the tool must have is the ability to automatically create documentation. Being able to generate documentation at a click of a button is a huge advantage, as this ensures that high quality documentation is available immediately after development and there is no need to spend long hours documenting the solution before it is delivered.
Find the right use case
Organisations may adopt data automation to address specific business challenges that have been identified to improve operational efficiencies or as part of a digital transformation programme. There are various use cases where data automation can add immense value and support.
One such example is cloud migration projects. As organisations start planning their cloud migration journeys, one critical question they need to ask is how they will migrate their data to the cloud. A data automation tool comes in handy in this instance as it will allow the job to be done efficiently with minimal resources and a higher degree of accuracy.
This means that more resources can be dedicated to other areas of the migration effort. For instance, ensuring that migrated workloads function the same as when they were on-premises and focusing on taking advantage of the new flexibility and capabilities that come with the cloud environment.
Data automation eliminates the need for manual intervention ensuring that the likelihood of errors is reduced. The automation process also ensures that data is loaded in a consistent, predictable manner. And where errors are encountered, fixing the ETL and reloading the data should resolve the issues.
Another potential use case is data warehouse automation. Traditionally, data warehouse projects involved a lot of coding and manual processes. This meant that a big team was required to do the work as fast as possible. Developers often worked in silos. As a result, much effort was duplicated as code was not re-used given that each developer focused on their own work.
Data warehouse automation is another use case example and is a game changer in the sense that it allows organisations to achieve more with less. Correctly utilising a data automation tool can empower individuals to take on work that would have previously required large teams.
A good data warehouse automation tool must enable users to extract and load the data, aggregate it, and provide capability to load to multi-dimensional models to visualise and further analyse the data. The tool must provide data lineage tracing to be able to trace each KPI back to source to ensure data integrity.
Data pipelines can also be automated to achieve real-time data processing and analysis. The real-time analysis provided gives organisations the ability to make quicker and more accurate decisions and this can boost their competitive edge.
Every organisation has data that can be leveraged. Making use of data automation, and within the right context, can play a huge role in supporting the business to analyse data more efficiently and accurately to derive insights faster.