Research

Abstract:

Data teams and developers continue to serve as the lynchpin to businesses, overcoming shortcomings associated with more rapidly and reliably gaining insight from growing data sets. With improving data analytics for real-time business intelligence (BI) and customer insight consistently ranking as one of the business priorities driving significant technology spending, how are organizations enabling more end-users to actually leverage data? Skills gaps, collaboration, and accessibility have created several barriers for democratizing analytics across organizations, and pressure is being placed on data and software teams to make business intelligence easier to leverage and consume. But with the dynamic nature of the business being what it is today and the constant shifting of priorities, timeliness of delivery and accessibility of simplified analytics are being scrutinized. Embedded analytics is increasingly becoming the answer.

Abstract:

While AI is still considered nascent, the impact it is having on organizations that are embracing it early and often is profound. This serves as a key component to why organizations continue placing bets on AI. Even as skills gaps remain when it comes to incorporating AI into the business, organizations simply cannot afford to wait in adopting the technology as they risk being disrupted by the competition using AI today. With the rise of AI tools that simplify and automate several, if not all aspects of the AI lifecycle, expect adoption of AI to continue exploding for years to come.

Abstract:

Organizations continue to prioritize AI investments with a goal of achieving a more data-centric future. While business objectives point to several areas where AI can help improve businesses both internally and externally, time to value continues to be scrutinized as organizations make massive investments in people, processes, and technology in support of AI initiatives. Opportunities to reduce time to value continue to pave the way for AI technology vendors that can help simplify the adoption and use of AI technology to support a growing number of use cases throughout the business.

Abstract:

Though the cyclical AI lifecycle is riddled with complexity, the last mile of AI is proving to be the greatest challenge for organizations in their quest to leverage AI. Between diverse and distributed application environments, the rate at which growing data sets change and create data drift, and the dynamic needs of the business, several contributing factors lead to organizations suffering from AI deployment challenges. Both new and mature businesses leveraging AI continue to prioritize opportunities to simplify the last mile of AI—deploying AI into production—with a goal of reducing the amount of time it takes to get from trained model to production. This has paved the way for the emergence of technology to better enable businesses to deploy, track, manage, and iterate on a growing number of ML models in production environments.

Abstract:

As organizations strive to utilize more data, data lakes are increasingly becoming an attractive option with limitless potential. Data lakes enable organizations to unite disparate data silos and make data more accessible across the business by serving as a centralized repository or collection of data, regardless of shape, speed, or size. Organizations can then leverage a data lake to feed other data-centric tools or utilize tools that sit on top of a data lake to work with the data in-place, such as query optimization solutions that can minimize data movement while enabling improved processing and analysis. And the economic advantages cannot be understated as organizations increasingly leverage cost-effective cloud storage and minimize operating costs through the consolidation of infrastructures silos.

Abstract:

As organizations look for ways to streamline operations, improve efficiency, and reduce costs, they are increasingly embracing automation technology like robotic process automation (RPA). While some view RPA as an ultimate destination to achieve peak business and process efficiency, those organizations that view themselves as digitally transformed have already embraced it and have their eyes set on the next phase: intelligent automation, where RPA is paired with artificial intelligence (AI) and machine learning (ML) to not only interact with systems but also to predict future insights/outcomes based on trending data.

Abstract:

ESG research shows that using a formal data science team is tied to better business outcomes.

Use this infographic to understand what that means for IT organizations in terms of total data use, the public cloud, serverless analytics, and more.

Abstract:

As organizations look to prioritize data-driven initiatives, the success of those initiatives will be directly tied to people, processes, and technology. While data science may seem aspirational or even foreign to some organizations, ESG research shows direct ties between organizations with a data science team and better use of data, better use of technology, and better business outcomes. For those organizations looking to drive greater business value through the use of data, a formal data science team can help.

Abstract:

With the value of data continuing to increase, organizations are constantly looking for better and faster ways to store, access, and analyze it. While many organizations have existing technologies to help stream, collect, store, and analyze both structured and unstructured data, challenges remain that are preventing wider usage of analytics within these organizations. With a goal of consolidating infrastructure and operational silos to address a constantly growing data footprint, data lakes are increasingly becoming the technology of choice.

Abstract:

Enterprise data warehouses (EDWs) have existed for about 20 years, serving as the foundation of insight-driven organizations, delivering timely analysis and reporting of structured data; handling large analytics workloads; and supporting the high levels of concurrency that organizations demand. But while EDWs have been a familiar presence in many organizations, as companies look to reduce their data center footprints, increase organizational agility, and incorporate as much data as possible into their analytics workflows, the architectural rigidity, complexity, and cost of traditional EDWs have paved the way for modern data warehouses to better respond to the dynamic needs of the business.