Data Management

Data Workflows

Confident businessman with tablet, managing data workflows in a modern office space.

Data Workflows use data as input, which is processed to produce output data. A data-driven workflow can use data values to control its logic flow and eventual outcome. A popular use of data workflows is to prepare data sets for analysis.

Why Are Data Workflows Important?

Businesses have become increasingly digitalized, making operational data readily available for downstream decision support. Automating data workflows allows data to be prepared for analysis with human intervention. Workflow logic can be used to create business rules-based data processing, automating manual processes to increase business efficiency.

Increasingly, jobs have become defined by a function’s role in a business process. Software such as Slack has enabled widespread business workflows. Similarly, data integration software has enabled a holistic approach to automating extract transform and load (ETL) processes, data pipelines and data preparation functions.

Automation can streamline business processes to build awareness of problems and opportunities in near-real-time.

Data Workflow Classes

Data workflows can be classified into the following three types.

Sequential Data Workflow

A sequential data flow is formed from a single series of steps, with data from one step feeding into the next.

State Machine

In a state machine, the initial state is labelled, and a process is performed that results in a change of state that is also labelled appropriately. For example, an initial state might be array-data. The process might be sum-data. The output would be labelled data-sum.

Rules Driven

A rules-driven workflow can be used to categorize data. For example, a given data value range could be categorized as low, moderate or high based on the applied rule.

Parallel Data Workflows

Single-threaded operations can be accelerated by breaking them into smaller pieces and using a multi-processor server configuration to run each thread in parallel. This is particularly useful with data volumes. Threads can be parallelized across an SMP server or servers in a clustered server.

Data Workflow Uses

There are many reasons for a business to make use of data workflows. Including the following examples:

  • Gathering market feedback on sales and marketing campaigns to double down on successful tactics.
  • Analyzing sales to see what tactics or promotions work best by region or buyer persona.
  • Market basket analysis at retail outlets to get stock replenishment recommendations.
  • Building industry benchmarks of customer successes to be used to convince prospects to follow the same path.
  • Use data workflows to pass high-quality training data to machine learning models for better predictions.
  • Gather and refine service desk data for improved problem management and feedback to engineering for future product enhancements.

Data Workflow Example

A data pipeline workflow will likely include many processing steps outlined below to convert a raw data source into an analytics-ready one.

Data Ingestion

A data-centric workflow needs a source data set to process. This data source can come from external sources such as social media feeds or internal systems like ERP, CRM, or web logfiles. In an insurance company, these could be policy details from regional offices that must be extracted from a database, making it the first processing step.

Masking Data

Before data is passed up the workflow, it can be anonymized or masked to protect privacy.


To keep the workflow efficient, it can be filtered to remove any data not required for analytics. This reduces upstream storage space, processing resources, and network transfer times.

Data Merges

Workflow rules-based logic can be used to merge multiple data sources intelligently.

Data Transformation

Data fields can be rounded, and data formats can be made uniform in the data pipeline to facilitate analysis.

Data Loading

The final step of a Data Workflow is often concerned with a data load into a data warehouse.

The Benefits of Data Workflows

Below are some of the benefits of data workflows:

  • Using automated data workflows makes operational readily available to support decision-making based on fresh insights.
  • Manual data management script development is avoided by reusing pre-built data processing functions, freeing up valuable developer time.
  • Data workflow processes built using a vended data integration technology are more reliable and less error-prone than manual or in-house developed processes.
  • Data governance as policies can be enforced as part of a data workflow.
  • Automated data workflows improve overall data quality by cleaning data as it progresses through the pipeline.
  • A business that makes data available for analysis by default makes more confident decisions because they are fact-based.

The Actian Data Platform and Data Workflows

The Actian Data Platform provides a unified location to build and maintain all analytics projects. DataConnect, the built-in data integration technology, can automate data workflows and lower operational costs by centrally scheduling and managing data workflows. Any data processing failures are logged, and exceptions are raised to ensure decisions can depend on high-quality data.

The Vector analytic database used by the Actian Data Platform provides high-speed analytics without the tuning required by traditional data warehouses thanks to its use of parallel query technology and columnar data storage.

You can try the Actian Data Platform using a 30-day trial here.