What is Data Intelligence?
Data intelligence refers to the tools and methods that enterprise-scale organizations use to better understand the information they collect, store, and utilize to improve their products and/or services. Apply AI and machine learning to stored data, and you get data intelligence.
Data intelligence vs. data analytics
Some may use these terms interchangeably, but there is a distinct difference between data intelligence and data analytics. Both terms refer to the collection of data for the purpose of improving business; But data intelligence is specifically the collection of disparate pieces of data and using AI to determine what happened in the past and why, whereas data analytics is the use of that information to create actionable predictions of what may happen in the future.
Origins of data intelligence
Data intelligence first emerged as a means of gathering accurate background content for the purpose of more accurate and granular reporting. But with the sheer volume of data being collected, it became necessary to attach a value rating to the data itself, which led to a forensic approach to qualifying data assets by asking where they came from, when were they collected, and why were they collected in the first place.
Business value and data intelligence
Ten years ago, the strongest businesses were the ones who collected customer data that led to business insights. However, the new definition of business value is changing to data literacy across entire organizations, data governance as a cultural model, and a curated understanding of data lakes for the purpose of working towards the democratized utilization of meta data-driven insights.
How does data intelligence work?
While business intelligence is the process of organizing information and presenting it in a way that is understandable, contextual, and actionable, data intelligence is more concerned with analyzing the data itself. Intelligence experts are concerned with understanding the stored data, uncovering alternate explanations, resolving problems, and identifying trends to improve decision making.
Organizations use artificial intelligence and machine learning tools to analyze huge volumes of data, which would otherwise be time- and cost-prohibitive if done manually. Additionally, AI and machine learning help store and organize the data in such a way that scrubbing—or detailed searching—large data sets is less cumbersome.
Data is collected from a variety of sources, and businesses want to be able to ingest and integrate said data to make educated and effective business decisions. To handle high-data-volume scenarios like this, businesses use a data fabric, which creates a unified layer of accessible data by ingesting content from those disparate sources and then automating the process of organizing it for a broad scope of users. This data fabric works much like a loom, which weaves a variety of different threads together to form a single piece of useable material.
Data intelligence and data fabric
A data fabric offers several key benefits:
- A comprehensive view of all the data, no matter the location or type
- The ability to ingest, orchestrate, and transform data across multiple sources into real-time integrated dashboards, advanced analytics, and actionable insights
- The democratization of data for all users, while maintaining compliance and data protection with enterprise-wide policies to define access control and encryption
A data fabric unifies different data types into a logical data store. It then cleanses the data, providing access to data in data lakes, warehouses, multiple clouds, the edge, and on-premises data centers. Storing data centrally allows global policies for security, high availability, data protection, and multi-tenancy to be created once and then applied across thousands of clusters distributed around the world. This eliminates the need to access and set policies for each of these capabilities across each application and geographic region.
HPE and data intelligence
HPE has been an innovator in data intelligence offerings across the board, from storage to networks to servers to hypervisors, providing the expertise and cost savings organizations need to remain competitive in a data-driven marketplace. We bring decades of thought leadership and deep market understanding into designing our AI/ML-driven intelligent infrastructure and strategies to support clients in their data modernization initiatives.
How do you make the leap forward to update and upgrade your data intelligence? For businesses looking to bridge the gap between digital vision and reality, HPE Pointnext Services is available to accelerate your digital transformation. With strategic help, operational support, and invaluable training, HPE Pointnext is a trusted guide for any organization looking to automate, optimize, and scale their IT and cloud operations to become modern, fast, and agile.
HPE Ezmeral Data Fabric streamlines data management architecture and workflow process. It uses automated policies to manage the entire data lifecycle, including masking raw data and processing, sharing, and storing it without modifying your applications. IT saves time and effort with a single system that seamlessly scales up and down, eliminating the need to predefine storage capacity and manually intervene when those capacities are reached.
For the streamlined and modernized business, the process of managing infrastructure can be a challenge. HPE InfoSight uses the power of cloud-based machine learning to drive global intelligence and insights for infrastructure across servers, storage, and virtualized resources. The platform radically simplifies IT operations by predicting and preventing problems across the infrastructure stack and making decisions that optimize application performance and resource planning.