Organizations have been using data virtualization to collect and integrate data from various sources, and in different formats, to create a single source of truth without redundancy or overlap, thus improving and accelerating decision-making giving them a competitive advantage in the market. Our research shows that data virtualization is popular in the big data world. One-quarter (27%) of participants in our Data Lake Dynamic Insights Research reported they were currently using data virtualization, and another two-quarters (46%) planned to include data virtualization in the future. Even more interesting, those who are using data virtualization reported higher rates of satisfaction (79%) with their data lake than those who are not (36%). Our Analytics and Data Benchmark Research shows more than one-third of organizations (37%) are using data virtualization in that context. Here, too, those using data virtualization reported higher levels of satisfaction (88%) than those that are not (66%).
Despite all the advances organizations have made with respect to analytics, our most recent research shows the majority of the workforce in the majority of organizations are not using analytics and business intelligence (BI). Less than one-quarter (23%) report that one-half or more of their workforce is using analytics and BI. This is a problem. It means organizations are not enabling their workforce to perform at peak efficiency and effectiveness. It means the workforce in many organizations does not have access to the same information by which they are being measured. It means organizations must find other ways to communicate with, and manage, the workforce.
Topics: Sales, embedded analytics, Analytics, Business Intelligence, Data, Sales Performance Management, Digital Technology, Digital Commerce, natural language processing, subscription management, partner management, sales engagement, revenue management, Collaborative & Conversational Computing
Many organizations invest in data governance out of concern over misuse of data or potential data breaches. These are important considerations and valid aspects of data governance programs. However, good data governance also has positive impacts on organizations. For example, I have previously written about the valuable connection between the use of data catalogs and satisfaction with an organization’s data lake. Our most recent Analytics and Data Benchmark Research demonstrates some of the beneficial links between data governance and analytics. In this Perspective, I’ll share some of the correlations identified in our research.
Ventana Research recently announced its 2022 Market Agenda for Analytics, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value from technology investments in order to improve business outcomes.
Organizations today have huge volumes of data across various cloud and on-premises systems which keep growing by the second. To derive value from this data, organizations must query the data regularly and share insights with relevant teams and departments. Automating this process using natural language processing (NLP) and artificial intelligence and machine learning (AI/ML) enables line-of-business personnel to query the data faster, generate reports themselves without depending on IT, and make quick decisions. Some organizations have started using NLP in self-service analytics to quickly identify patterns and simplify data visualization. Our Analytics and Data Benchmark Research finds that about 81% of organizations expect to use natural language search for analytics to make timely and informed decisions.
TIBCO is a large, independent cloud-computing and data analytics software company that offers integration, analytics, business intelligence and events processing software. It enables organizations to analyze streaming data in real time and provides the capability to automate analytics processes. It offers more than 200 connectors, more than 200 enterprise cloud computing and application adapters, and more than 30 non-relational structured query language databases, relational database management systems and data warehouses.
Databricks is a data engineering and analytics cloud platform built on top of Apache Spark that processes and transforms huge volumes of data and offers data exploration capabilities through machine learning models. It can enable data engineers, data scientists, analysts and other workers to process big data and unify analytics through a single interface. The platform supports streaming data, SQL queries, graph processing and machine learning. It also offers a collaborative user interface — workspace — where workers can create data pipelines in multiple languages — including Python, R, Scala, and SQL — and train and prototype machine learning models.
Alteryx is a data analytics software company that offers data preparation and analytics tools to simplify and automate data wrangling, data cleaning and modeling processes, enabling line-of-business personnel to quickly access, manipulate, analyze and output data. The platform features tools to run a variety of analytic functions such as diagnostic, predictive, prescriptive and geospatial analytics in a unified platform, and can connect to various data warehouses, cloud applications, spreadsheets and other sources.
Sisu Data is an analytics platform for structured data that uses machine learning and statistical analysis to automatically monitor changes in data sets and surface explanations. It can prioritize facts based on their impact and provide a detailed, interpretable context to refine and support conclusions. The product features fact boards, annotations and the ability to share facts and analysis across teams. Data teams and analysts start by creating common definitions of key performance indicators, which Sisu then utilizes to automatically test thousands of hypotheses to identify differences between groups.
Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics. It can support AI/ML processes with data preparation, model validation, results visualization and model optimization. Rapidminer Studio is its visual workflow designer for the creation of predictive models. It offers more than 1,500 algorithms and functions in their library, along with templates, for common use cases including customer churn, predictive maintenance and fraud detection. It has a drag and drop visual interface and can connect to databases, enterprise data warehouses, data lakes, cloud storage, business applications and social media. The platform also supports push-down processing for data prep and ETL inside databases to minimize data movement and optimize performance.