Analyst Perspectives

Ventana Research recently announced its 2021 research agenda for the Office of Sales, continuing the guidance we’ve offered for nearly two decades to help organizations realize optimal value from applying technology to improve business outcomes. Chief sales and revenue officers are experts in their respective fields but may not have the guidance needed to employ technology effectively. As we look to 2021, we are focusing on the entire selling and buying journey and the applications that...

I’m proud to share Ventana Research’s 2021 market agenda for digital technology. Our focus in this agenda is to deliver expertise to help organizations prioritize technology investments that increase workforce effectiveness and organizational agility, ensuring ongoing operation during any type of disruption.

Ventana Research recently announced its 2021 market agenda in the expertise area of Customer Experience. Most organizations have some degree of focus on managing how they interact with their customers, but it is often a disjointed and constrained process. Developing an effective customer experience has become an investment priority in recent years as organizations increasingly recognize the importance of good experiences to profitability, customer longevity and advocacy on behalf of brands.

Ventana Research recently announced its 2021 market agenda for Analytics, continuing the guidance we’ve offered for nearly two decades to help organizations derive optimal value from technology investments to improve business outcomes.

Ventana Research has announced its market agenda for 2021, continuing the tradition of transparency in our efforts to educate and guide the technology market but also our independence as we do not share our market agenda or analyst perspectives with any external party. Each year, we proudly formulate our market agenda that is not biased by clients or the technology industry, focusing on education rather than the prospect of consulting or software revenue. We review and refine our plan...

The industry is making huge strides with artificial intelligence (AI) and machine learning (ML). There is more data available to analyze. Analytics vendors have made it easier to build and deploy models, and AI/ML is being embedded into many types of applications. Organizations are realizing the value that AI/ML provides and there are now millions of professionals with AI or ML in their title or job description. AI/ML is even being used to make many aspects of itself easier. Organizations that...

Data is becoming more valuable and more important to organizations. At the same time, organizations have become more disciplined about the data on which they rely to ensure it is robust, accurate and governed properly. Without data integrity, organizations cannot trust the information produced by their data processes, and will be discouraged from using that data, resulting in inefficiencies and reduced effectiveness.

Organizations are dealing with exponentially increasing data that ranges broadly from customer-generated information, financial transactions, edge-generated data and even operational IT server logs. A combination of complex data lake and data warehouse capabilities are required to leverage this data. Our research shows that nearly three-quarters of organizations deploy both data lakes and data warehouses but are using a variety of approaches which can be cumbersome. A single platform that can...

Businesses are transforming their organizations, building a data culture and deploying sophisticated analytics more broadly than ever. However, the process of using data and analytics is not always easy. The necessary tools are often separate, but our research shows organizations prefer an integrated environment. In our Data Preparation Benchmark Research, we found that 41% of participants use Analytics and Business Intelligence tools for data preparation.

Traditional on-premises data processing solutions have led to a hugely complex and expensive set of data silos where IT spends more time managing the infrastructure than extracting value from the data. Big data architectures have attempted to solve the problem with large pools of cost-effective storage, but in doing so have often created on-premises management and administration challenges. These challenges of acquiring, installing and maintaining large clusters of computing resources gave rise...