Organizations are becoming more and more data-driven and are looking for ways to accelerate the usage of artificial intelligence and machine learning (AI/ML). Developing and deploying AI/ML models can be complicated in many ways, often involving different tools and services to manage these solutions from end to end. Accessing and preparing data is the most common challenge organizations face in this process, and consequently, AI/ML vendors typically incorporate tools to address this part of the process. But there are many other steps in the process as well, such as coordinating the handoff between data scientists and IT or software engineers for deployment to production. This can potentially slow down the entire data-to-insights process. End-to-end platforms for AI offer the promise of simplifying these processes, allowing teams that work with data to improve organizational results.
Having just completed the 2021 Ventana Research Value Index for Analytics and Data, I want to share some of my observations about how the market has advanced since our assessment two years ago. The analytics software market is quite mature and products from any of the vendors we assess can be used to effectively deliver information to help your organization improve its operations. However, it’s also interesting to see how much the market continues to advance and how much investment vendors continue to make.
Topics: Big Data, embedded analytics, Analytics, Business Collaboration, Business Intelligence, Collaboration, natural language processing, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing
Organizations are increasingly using data as a strategic asset, which makes data services critical. Huge volumes of data need to be stored, managed, discovered and analyzed. Cloud computing and storage approaches provide enterprises with various capabilities to store and process their data in third-party data centers. The advent of data platforms previously discussed here are essential for organizations to effectively manage their data assets.
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.
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 want to build and deploy their own AI/ML models need to be realistic about the capabilities that are available today. As a practical matter, organizations should anticipate that a robust AI/ML deployment in the current environment requires a set of specialized skills and operational processes, including data operations (dataops) and ML operations (MLops). Collaboration across these disciplines and processes is also required.
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.
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Preparation, Information Management, Internet of Things, Data, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning
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 to cloud-based implementations as an alternative. Public cloud is becoming the new center for data as organizations migrate from static on-premises IT architectures to global, dynamic and multi-cloud architectures.
In this analyst perspective, Dave Menninger takes a look at data lakes. He explains the term “data lake,” describes common use cases and shares his views on some of the latest market trends. He explores the relationship between data warehouses and data lakes and share some of Ventana Research’s findings on the subject. He also provides an assessment of the risks organizations face in working with data lakes and offers recommendations for maximizing the potential of data.
Effectively managing data privacy and security is a high-stakes matter. When an organization doesn’t get it right, it often becomes front-page news and occasionally becomes a subject of litigation. Yet organizations face an equally challenging imperative to ensure that business users have easy access to the data they need. Depending on how they are implemented, data governance policies can inhibit access to data, making it harder to find and utilize the data assets of an organization.