Data automation empowers firms to achieve more with less

Implementing data automation can help organisations efficiently and accurately process huge volumes of data with minimal resources.
Nathi Dube
By Nathi Dube, Director, PBT Innovation, PBT Group.
Johannesburg, 27 Oct 2022
Nathi Dube
Nathi Dube

As organisations grow and more systems come online to support new business processes, more data is being generated and gradually it starts to become impractical to process the data manually. Considering this, now is the right time to look at data automation.

Data automation is the process of ingesting, transforming and processing data using automation tools without any manual intervention.

Globally, data volumes are growing exponentially and the sheer volume of this data and the varied formats, both structured and unstructured, in which it is stored, require that organisations employ new technologies to efficiently process these constantly growing volumes of data, accurately and efficiently.

To remain competitive in a constantly-changing market environment, organisations must be able to efficiently exploit their data assets. A truly data-driven organisation has a better understanding of what its customers need and can innovate faster to meet these needs.

To remain competitive in a constantly-changing market environment, organisations must be able to efficiently exploit their data assets.

As organisations embark on their digital transformation journeys, data automation plays a crucial role in ensuring a smooth end-to-end digital experience for users. Removing manual or human intervention reduces errors and improves overall system reliability.

However, the underlying infrastructure supporting the data architecture must allow for scalability so that the data automation tools can be configured to cope with growing data volumes.

Develop a strategy

A well-defined data automation strategy must be put in place before implementing data automation. As part of the strategy formulation, it is important to first get buy-in from all departments that will be directly impacted. The strategy should be documented and approved beforehand.

The following steps can be taken to develop a data automation strategy:

Identify problem areas

A good understanding of the enterprise data landscape is required to be able to identify areas that will benefit the most from automation. These problem areas will typically be human resource-intensive processes that require either many hours or many people to complete. Other candidates for data automation are areas that experience frequent failures usually due to human error.

One of the goals of data automation is to automate existing manual processes, thus freeing the workforce to dedicate more time to higher value activities like analysing data and deriving insights from it.

Data classification

Look across the data landscape and categorise the data based on the format in which it is stored; eg, JSON, XML, relational or unstructured. This will help standardise data ingestion templates according to the source format and if an off-the shelf tool is used, it is important that the tool supports various source formats within the organisation.

Define transformation rules

Determine the transformation rules that need to be applied to the data. Depending on the target requirements, these can be as simple as concatenating two columns; eg, first name and last name to make it a full name, or as complex as dynamically identifying and hiding sensitive personal information to adhere to local regulatory requirements.

Select a data automation tool

Now that the requirements have been defined, the organisation is able to select a data automation tool that meets all requirements. The tool must implement ETL functionality and be able to process or update data at regular intervals as per business requirements.

Schedule the process

Once the ETL has been configured and tested, it is now time to schedule the process to automate the process.

What to look for

These are the features to look for when selecting a data automation tool:

Must do ETL: The first thing to look at is that the tool must be able to perform the three common elements of data automation, namely extract, transform and load.

Support multiple sources: The tool must support various data storage systems (both relational and non-relational sources) and data formats.

Compatibility: It must be able to seamlessly integrate with cloud-based technologies, while being able to support legacy platforms.

Data discovery: It must have data discovery capabilities to facilitate data modelling and data mapping.

Programming language support: In addition to SQL, the tool must support various major scripting languages, like Python and R.

Intuitive: To encourage wider adoption, the tool must have a visual interface that is easy to use. Setting up the tool must also not be difficult.

No code functionality: Even though scripting capability should be built into the tool, it should still allow most tasks to be achieved without any coding.

Built-in documentation: Maintaining up to date documentation is often a challenge and documentation tends to be done as a last project activity that is often not given enough time. Having a tool that generates documentation at a click of a button is a huge advantage.

Data automation use cases

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.

Let us explore some of the use cases below:

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, like ensuring 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 the likelihood of errors is reduced. The automation process also ensures data is loaded in a consistent, predictable manner. And where errors are encountered, fixing the ETL and reloading the data should resolve the issues.

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, and as a result, much effort was duplicated as there was no code-re-use as each developer focused on their own work.

Data warehouse automation 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 big teams.

A good data warehouse automation tool should allow you 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.

Implementing data pipelines

A data pipeline is a series of actions that involve ingestion of raw data, usually from disparate sources, and transforming it into the required format and then moving it to the destination for storage and analysis, or as input for the next pipeline.

In data-driven organisations, data pipelines play a critical role to ensure there is consistent, prompt and reliable flow of data to power decision-making.

Data pipelines can be automated to achieve real-time data processing and analysis, which replaces the traditional batch processing that relies on historical data. This real-time analysis allows organisations to make quick decisions which can give them a competitive-edge.

Data automation can help organisations efficiently and accurately process huge volumes of data and they can do this with minimal resources. All organisations have data; however, those that will stand out are the ones that are able to process and analyse their data efficiently and derive insights faster.

The speed at which organisations move from gathering raw data to generating insights will be a key differentiator moving forward.