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.
Organizations today are working with multiple applications and systems, including enterprise resource planning (ERP), customer relationship management (CRM), supply chain management (SCM) and other systems, where data can easily become fragmented and siloed. And as the organization increases its data sources and adds more systems and custom applications, it becomes challenging to manage the data consistently and keep data definitions up to date. This increases the need to use master data management (MDM) software that can provide a single source of truth to drive accurate analytics and business operations.
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.
Organizations have become more agile and responsive, in part, as a result of being more agile with their information technology. Adopting a DevOps approach to application deployment has allowed organizations to deploy new and revised applications more quickly. DataOps is enabling organizations to be more agile in their data processes. As organizations are embracing artificial intelligence (AI) and machine learning (ML), they are recognizing the need to adopt MLOps. The same desire for agility suggests that organizations need to adopt AnalyticOps.
Talend is a data integration and management software company that offers applications for cloud computing, big data integration, application integration, data quality and master data management. The platform enables personnel to work with relational databases, Apache Hadoop, Spark and NoSQL databases for cloud or on-premises jobs. Talend data integration software offers an open and scalable architecture and can be integrated with multiple data warehouses, systems and applications to provide a unified view of all data. Its code generation architecture uses a visual interface to create Java or SQL code.
How does your organization define and display its metrics? I believe many organizations are not defining and displaying metrics in a way that benefits them most. If an organization goes through the trouble of measuring and reporting on a metric, the analysis ought to include all the information needed to evaluate that metric effectively. A number, by itself, does not provide any indication of whether the result is good or bad. Too often, the reader is expected to understand the difference, but why leave this evaluation to chance? Why not be more explicit about what results are expected?
Our research shows that nearly all financial service organizations (97%) consider it important to accelerate the flow of information and improve responsiveness. Even just a few years ago, capturing and evaluating this information quickly was much more challenging, but with the advent of streaming data technologies that capture and process large volumes of data in real time, financial service organizations can quickly turn events into valuable business outcomes in the form of new products and services or revenue.
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.
Access to external data can provide a competitive advantage. Our research shows that more than three-quarters (77%) of participants consider external data to be an important part of their machine learning (ML) efforts. The most important external data source identified is social media, followed by demographic data from data brokers. Organizations also identified government data, market data, environmental data and location data as important external data sources. External data is not just part of ML analyses though. Our research shows that external data sources are also a routine part of data preparation processes, with 80% of organizations incorporating one or more external data sources. And a similar proportion of participants in our research (84%) include external data in their data lakes.
The technology industry throws around a lot of similar terms with different meanings as well as entirely different terms with similar meanings. In this post, I don’t want to debate the meanings and origins of different terms; rather, I’d like to highlight a technology weapon that you should have in your data management arsenal. We currently refer to this technology as data virtualization. Other similar terms you may have heard include data fabric, data mesh and [data] federation. I’ll briefly discuss these terms and how I see them being used, but ultimately, I’d like to share with you some research that shows why data virtualization can be valuable, regardless of what you call it.