Research

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.

Research Objectives

Based on a survey of 742 senior IT decision makers at midmarket and enterprise organizations, the 2023 Technology Spending Intentions Survey analyzes private and public sector technology spending expectations, the business and technology priorities driving those plans, and year-over-year trends in technology adoption and customer preferences. This year’s study from TechTarget’s Enterprise Strategy Group finds that enterprise technology spending will generally slow due to global macroeconomic conditions and the threat of a worldwide recession, but aggressive investments are expected to continue among more digitally transformed organizations.

For more information on this research, please visit www.esg-global.com/2023-technology-spending.

Research Objectives

Ascertain IT budget outlook for 2023 overall and for specific technologies. Determine key business and technology priorities driving 2023 spending plans. Establish a profile of digitally mature companies in terms of the technologies and processes underlying their IT strategies. Monitor YoY trends with respect to technology adoption and changing customer preferences.

Abstract:

Building on existing trends favoring digital transformation initiatives, the pandemic sparked a further shift toward data-centric business models, with virtually all enterprises now using data to support the business and a remarkable one in five claiming data as their core business. To treat data like the business asset it has become, it must be de-risked, protected, made compliant, and leveraged. This will require IT professionals and vendors alike to develop intelligent data management strategies that not only preserve and protect data but also ready it for use in achieving business growth objectives.

Abstract:

Driven by data's rapid growth, the menace of cyber-threats and the enterprise push for digital transformation, among other factors, intelligent data management is fast becoming a priority for data protection and storage professionals. More than two-thirds rank it among their organizations' top five data protection and management priorities, a dramatic change from two years ago when only a third of organizations considered intelligent data management a top priority. Among digitally mature organizations, adoption is even higher, suggesting that interest in this approach will continue to grow as digital laggards achieve digital transformation maturity. One in five organizations believe intelligent data management will replace their overall data protection strategies. Learn why now is the time to shift to intelligent data management.