Research

Abstract:

You can't improve what you can't measure. This makes analytics a vital part of digital experience initiatives, which require continuous improvement of many experience elements—which might include product choices and recommendations, website layouts, marketing copy, and pricing—to establish and maintain successful return on technology investments. Enterprise Strategy Group research found that organizations deploy analytics in conjunction with digital experience to measure progress toward numerous external customer-facing and internal metrics. Companies planning new digital experience projects should bake analytics technologies into their plans to increase their odds of success.

Abstract:

Headless content management and headless commerce strategies can enable enterprises to keep up with the evolution of devices, apps, and channels their customers use, but traditional content management systems make the headless approach difficult or unattainable. Enterprise Strategy Group research revealed that while many organizations still rely on traditional content management systems, others have made the leap to headless and hybrid CMSs, with more adoptions in the works.

Research Objectives

• Determine the amount and value of data for a typical organization, and how this impacts data management activities like availability, usability, and security.
• Connect the dots between the important elements of data governance like classification, placement, and compliance as ecosystems evolve and become more distributed.
• Help overwhelmed IT organizations find the right combination of process and technology to solve their unique data governance challenges.
• Identify data governance process and technology gaps that need to be addressed in vendor solutions.

Research Objectives

To gain insight into the market dynamics for integrated technology solutions from strategic partners, Enterprise Strategy Group surveyed 352 IT professionals in North America (US and Canada) involved in the purchases of integrated solutions at their organizations.

Abstract:

The complexity of gathering, maintaining, and interpreting huge volumes of data continues to plague organizations. It’s challenging to clean, integrate, and maintain data with goal of gaining rapid insight to help the business. But it’s not slowing down organizations in their prioritization of data initiatives. They recognize the value and game-changing potential of harnessing the power of data. It starts by properly defining objectives and desired outcomes and ends with data driving decision making and action to fuel innovation.

Abstract:

While organizations can have altruistic reasons for embracing and implementing environmental, social, and governance (ESG) initiatives, there are also more self-serving drivers. Indeed, not only is improved brand development the most commonly cited ESG objective, nearly half of early adopters identify it as a benefit they have already realized as a result of implementing these initiatives.

Abstract:

The principles of environmental, social, and governance (ESG) initiatives increasingly matter as a technology brand attribute and are subsequently new and important evaluation factors for IT buyers. It follows then that the vast majority of organizations will pay a price premium for products and/or services from vendors that demonstrate a firm commitment to ESG, a trend that is even more pronounced among younger organizations.

Abstract:

The demand for rapid actionable analytical insights is forcing organizations to prioritize agility, transparency, and speed across their data ecosystems. These enterprises are investing in data-driven initiatives that maximize operational efficiency, improve collaboration, and accelerate time to value. They're turning to DataOps.

Abstract:

Gone are the days when the data engineer was the key persona driving DataOps. IT operations, line-of-business leaders, developers, and end-users can all claim roles in the direction of their company’s DataOps initiatives. While this cross-functional team approach to DataOps is viewed as a positive trend, the ongoing technical skills gap in key DataOps positions continues to plague most organizations, overburden stakeholders, and compromise the value of DataOps initiatives.

Abstract:

Data quality issues, distributed data, tool proliferation, overburdened and underskilled teams, rising costs, and increased risk all contribute to the complexities of today’s data ecosystem that hinder the democratization of data and analytics. As a result, organizations are looking for ways to empower data teams to reliably deliver data and analytics to all consumers. DataOps processes liberate data silos and democratize workloads throughout the data lifecycle, yielding significant improvements in data quality, cross-functional collaboration, and analytics outcomes.