Structured data is the life blood for the decision process in the enterprise, and it's essential to building models that help identify root causes, predict outcomes, and prescribe actions.
To do all that, structured data must be accurate, trusted, protected, and accessible to users in the enterprise. Data management is also the fastest growing category of infrastructure spending in 2022, according to a recent report from IDC.
unstructured data, which is usually in forms like text, images, video, sound, and voice, was incredibly difficult to work with only a few years ago.
However, with the advancements in artificial intelligence and machine learning (AI/ML), it can now be turned into a structured form that provides insights, which help people and applications to act on the information that was hidden in the unstructured data.
“Data is managed differently at different parts of its lifecycle, and can have varying value as well,” explains Bret Greenstein, partner, data and analytics at PwC. “Some data is very time sensitive -- milliseconds when validating a transaction, to days for supply and demand forecasting, to months for pricing -- depending on the industry and use case.”
Additionally, as data moves through an enterprise, it often is transformed as it goes, so its meaning and impact can change.
“It is important to understand the value of data at every step to understand if it can be discarded, used for historical reference, or aggregated with other data to generate a new insight,” Greenstein says.
He explains that the current best practices for structured Data management include leveraging cloud native data warehouses in order to integrate data from multiple sources across the enterprise (ERP systems, HR systems and CRM systems) into an enterprise data model.
Such a model includes tables and views designed to make it easier for consumers of the data to build insights, reports, dashboards, and models.
“Each major cloud provider has their own data warehouse technologies, and several companies have cloud data warehouses that run on any cloud as well,” he notes. “You will often hear data lakes mentioned as well.”
Greenstein says those are good places for companies to manage their unstructured data along with their structured data, to make it easier to work with advanced analytics and AI/ML.
Ed Macosky, chief innovation officer at platform-as-a-service provider Boomi, says before managing structured data, leaders first need to devise a comprehensive and nuanced data strategy. “In a time where organizations are dealing with massive amounts of data, it is imperative for leaders to understand that not all data is equal,” he says. “Not all data requires data management or governance.”
For example, personally identifiable information (PII) is one type of data that must be secured, managed, and governed to corporate standards. However, data that is not PII or critical to the business or driving decisions can live in its natural state for analytical needs.
