Coming to terms with data automation
Nathi Dube, Director, PBT Innovation at PBT Group
Data automation is becoming increasingly important as South African companies expand their operations and embrace cloud-driven environments to support new business processes. With data volumes growing exponentially, it is getting more and more difficult to process data manually. This is where data automation comes in. It is the process of using automation tools to ingest, transform, and process data without any manual intervention.
Remaining competitive requires business and technology leaders to leverage their data assets to better understand and deliver on customer needs, innovate faster, and create solutions that are more agile to organisational demands. Data automation can help organisations achieve their digital transformation goals by removing manual interventions which reduces errors and improves overall system reliability.
However, the underlying infrastructure supporting the data architecture must allow for scalability. This will enable the data automation tools to be configured to cope with growing data volumes. As such, local businesses must have a well-defined data automation strategy.
The steps behind an automation strategy
The first step in developing a data automation strategy is to identify problem areas. This requires a good understanding of the enterprise data landscape. With this in place, decision-makers can identify the areas that will benefit the most from automation. Typically, the problem areas will be human resource intensive processes that require either many hours or many people to complete.
The next step is data classification. Companies must categorise the data inside their environment based on the format in which it is stored. For instance, JSON, XML, relational, or unstructured. In doing so, this will help standardise data ingestion templates according to the source format. It must be noted that if an off-the shelf tool is used, that tool must support all the data formats inside the company.
After data classification, businesses must define transformation rules. These rules will determine how the data is transformed before it is loaded into the target system. Depending on the requirements, these rules can be as simple as combining two columns or as complex as hiding personal identifiable information to adhere to POPIA compliance requirements.
Once these requirements have been defined, companies can select a data automation tool that ticks all the boxes. As a minimum, the tool must implement ETL functionality while being capable of processing and updating data at regular intervals.
The final step in developing an automation strategy is to schedule the process. This will enable a company to automate its existing manual processes and free up the workforce to dedicate more time to higher value activities.
Data automation has become mission-critical for companies to efficiently process the growing volumes of data they are generating. By automating data ingestion, transformation, and processing, South African businesses can reduce errors and improve system reliability. Data automation gives them the competitive edge to gain a better understanding of their customers’ needs while reducing the potential for human error when it comes to data collection and analysis.