Data Orchestration Basics: How to Organize Data to Gain Real-Time Insights
Data orchestration automates the organization and transformation of siloed data, increasing data accessibility and visibility. Pohan Lin, senior web marketing and localizations manager, Databricks, discusses how businesses can operationalize data to make more accurate, real-time business decisions by providing increased value and actionability.
Data orchestration is the automated process of managing data to reduce silos between data sources and storage locations. Data orchestration platforms take data from multiple sources, clean, organize, and combine the data, and then send it to storage locations for data analysis.
The more data we possess, the more successful our attempts at bringing about positive, large-scale change. Leveraging the analytics gathered from big data enables the optimization of day-to-day operations. It also supports the improved intelligence of different types of neural networks and AI algorithms and drives the success of data-driven business decisions.
The problem? The more data we possess, the more dark data, data silos, and governance issues emerge.
Despite 99% of businesses actively investing in big data strategies, a Newvantage survey reportsthat only 24% have created a data-driven organization. Only 48.5% of companies are driving innovation with data, and 39% are succeeding in managing data as a business asset.
As data volumes and customer demands increase, businesses struggle to keep up. The tendency to deploy data analytics and Bi tools on an ad-hoc, department-level basis plays an integral role in why big data strategies are so prone to overwhelm, especially if deployed under continuous integration (CI) and deployment techniques.
However, for the businesses that successfully create data-driven organizations, 96.4% report that the investment yielded significant results. Real-time insights and improved internal processes are just two benefits of data orchestration deployment.
See More: Cloud Orchestration Vs. Cloud Automation: How Do They Benefit Your Business?
The exact process of data orchestration varies between systems, but the common goal is to aggregate siloed data to improve data accessibility, visibility, and analysis. Therefore, the data orchestration process falls into four key stages.
Before structuring your data, it needs to be collected and organized. Data orchestration tools use SDKs and APIs to collect all the existing data and incoming data from your business’ customer touchpoints andcloud-storageplatforms. This data undergoes a thorough organization process, including labeling and integrity-checking, before it’s ready for the next stage.
To increase accessibility and speed up the data analysis, orchestration tools transform data into one singular format or language. Take dates, for example, which can take multiple different formats. Where one system might format the date as May 24, 2022, another might format it as 05/24/2022 or 05/24/22.
Disparities are arduous to sort manually and can result in errors that compromise data quality. Orchestration tools automate this while streamlining task management, unifying data from different sources into internally compatible formats for quick and accurate data analysis.
Now that your data is internally compatible, it can be stitched together to create a unified view of your customer.
Rather than leave your website, app, and other touchpoint data to exist as separate parts, orchestration tools unify this data to create intricately individual customer personas. You can make better marketing and sales decisions based on observable customer interactions with an overarching view of your customers’ behavior as they journey across touchpoints.
Data unification also applies to other data forms, like images or languages.
