Organizations are always looking to improve their ability to use data and AI to gain meaningful and actionable insights into their operations, services and customer needs. But unlocking value from data requires multiple analytics workloads, data science tools and machine learning algorithms to run against the same diverse data sets. Organizations still struggle with limited data visibility and insufficient insights, which are often caused by a multitude of reasons such as analytic workloads running independently, data spread across multiple data centers, data governance, etc. In our ongoing benchmark research project, we are researching the ways in which organizations work with big data and the challenges they face.
Enterprise data platforms (EDP) are designed to address these big data challenges, enabling organizations to leverage their data to deliver better service to customers, operate with greater efficiency and strengthen security. EDP are optimized for hybrid and multi-cloud environments, delivering the same data management capabilities across on-premises, private, and public clouds. This in turn means that organizations have the flexibility and choice over where they host their data, and don’t have to be dependent on their cloud provider.
Organizations are using big data to fuel this digital transformation. The process requires the fluidity to manage all types of data sources and move between different analytics, exchanging data and gaining insights as the data is generated. As the new data types emerge and new use cases come to the fore, organizations must rely on a range of analytical capabilities, from data engineering to data warehousing to operational databases and data science, which should be readily available across a comprehensive cloud infrastructure.
Effectively managing and securing big data collection, enrichment, analysis, experimentation and analytics visualization is fundamental to navigating the data storm in this growing data-driven economy. However, simple analytics that improve data visibility are no longer enough to keep up with the competition. Take ecommerce, as an example. organizations need to process and stream real-time data from multiple endpoints, while predicting key outcomes and applying machine learning on that same data to obtain comprehensive insights that deliver value. We assert that by 2022, more than half of all the data processes will use artificial intelligence and machine learning to boost the value that can be derived from the data.
A modern data platform must not only provide a way to manage big data, but also must provide the functionality to analyze that data, run machine learning algorithms for forecasting, and share that data across various departments with minimal friction to make use of that data. Organizations cannot afford to waste precious time and resources tying together various, separate specialized platforms to meet different data processing and analytic requirements. EDP provides a unified point of control to manage IT infrastructure, data and analytic workloads from one centralized platform, regardless of where the data lives.