Data Intelligence
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 that has been refined through data intelligence processes, and you get meaningful, data-driven insights.
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. Data intelligence is specifically the aggregation of large datasets, processing the data to make it AI-ready, and using AI and machine learning to analyze and interpret the data to generate actionable insights that inform business strategies and decision-making. Data analytics is the analysis of data based on statistical and machine learning techniques to understand past events and reveal patterns, trends, and insights, enabling the prediction of future events.
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.
The storage and organization of data streamline and improve the processes associated with managing and searching large datasets. Organizations can then 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 enhance processes related to organizing and retrieving data by enabling smarter indexing, categorization, and search optimization.
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 usable material.
Data intelligence and data fabric
A data fabric offers several key benefits:
- A transparent and secure access of all the data, no matter the location or type.
- The ability to ingest, orchestrate, and transform data across multiple sources and multiple formats 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.
Data intelligence and metadata enrichment
Metadata enrichment enhances data intelligence by transforming raw data into a structured, contextualized, and actionable resource. Through the addition of descriptive, relational, and contextual metadata, organizations can improve searchability, discoverability, and interoperability of their data assets. This improves data quality, analytics accuracy, decision-making, and predictions. Metadata enrichment also ensures appropriate data is accessible and secure, which enhances automation, governance, and compliance efforts.
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 Services is available to accelerate your digital transformation. With strategic help, operational support, and invaluable training, HPE Services provides expertise and guidance for any organization looking to automate, optimize, and scale their IT and cloud operations to become modern, fast, and agile.
HPE Data Fabric streamlines data management architecture and workflow processes. It helps users access, manage, organize, and govern enterprise data across a variety of formats in a single, consistent, easy-to-use, edge-to-cloud data plane optimized for AI and analytics workloads. This centralized approach enables AI practitioners to effortlessly access, manage, and secure a diverse range of data assets—files, objects, tables, and streaming data—all in their original formats. By federating data into a unified platform, HPE Data Fabric Software offers a compelling value proposition by streamlining data management processes, optimizing resource utilization, and improving data governance.
HPE Alletra Storage MP X10000 is a high-performance, massively scalable object storage solution that is purpose-built for modern workloads including AI and active data lakes. It accelerates data-intensive workloads with enterprise performance at scale using a disaggregated, all-flash architecture. The X10000 also features built-in, inline data intelligence services to easily and efficiently enrich file metadata to improve speed, accuracy, and outcomes for AI and analytics projects. Furthermore, the GreenLake cloud-based operational experience simplifies and speeds object storage management, agility, and time to value.
For the streamlined and modernized business, the process of managing infrastructure can be a challenge. HPE Data Services Cloud Console-based AIOps 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.