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Using Workflow Orchestration to Boost Innovation
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Hi! Brett here and I’m an IT professional that has verified the success companies have gained through workflow orchestration. Innovation is the name of the game for all companies right now and with unified analytics, you can easily increase the agility and productivity of your business.

As organizations undergo rapid change to unlock the hidden value of their business data, it becomes a challenge to keep up with this massive influx of data. Business data enables organizations to attain actionable insights that lead to innovation and help them gain a competitive edge in their market.

As data volumes explode though, how can you be sure you’re utilizing all your collection points to go beyond traditional business applications? Here are 4 options for using workflow orchestration.

Implementing a Unified Analytics Platform

The best way to boost innovation and ensure productivity in your organization is by implementing a unified analytics platform. A unified analytics platform brings together data ingestion and exploration, a training and machine learning model, and production deployment to a unified workflow experience. Unified analytics platforms unify separate pipelines created by engineers to help employees gain significant value of their shared experiences.

Data Ingestion and Exploration

When building any kind of data production pipeline, the first step is to ingest data. Since data comes from a variety of collection points and is often times unstructured, a unified analytics platform helps data engineers ingest and transform this data into accessible information at scale. Once data has been ingested and made accessible, data analysts can then clean up this data to run necessary quarries against it. Since data is so essential to an organization’s success, a unified analytics platform is a necessary tool to have.

Training Model

With a unified analytics platform, your data engineers, analysts, and scientists will have easy access to algorithms for classification, regression, clustering and collaborative filtering of data. Without this, these important data employees will not be able to collaborate and make accurate predictions for the business.

Production Deployment

Since unified analytics platforms allow data engineers to evaluate data further, they’re able to understand the goals of predictive models to process data quickly. With a unified platform, data engineers can easily deploy trained models into production with the use of the MLlib library or even Databricks’ dbml-local library. So, whether they need a low-latency model or not, a unified analytics platform allows them to perform any function they need to.

Now that you know why orchestrating workflows in a unified fashion is so essential to the success of your organization, you’re ready to implement a unified analytics platform to help boost innovation in your business

If you want to learn more about using workflow orchestration to boost innovation, click the link below for more information.