Too many organizations start their data projects at the wrong end of the pipeline. Although challenges with data quality, integrity, access, and visibility are all important issues to address, a project should never start with the data. The reality is that all investments in data are meaningless if no business value can be gained. And this requires starting at the other end of the spectrum - evaluating the business problem to identify how data can help drive change within the organization.

Current data architecture is going through a revolution. Enterprises are starting to shift away from the monolithic data lake towards something less centralized: data mesh. Data mesh is a relatively new concept, first coined in 2019, that addresses potential issues with data warehouses and data lakes that can cause businesses to be slow, unresponsive, or even suffer from data silos. Data mesh benefits are able to provide a wealth of advantages to your business.

Enabling data and analytics in the cloud allows you to have infinite scale and unlimited possibilities to gain faster insights and make better decisions with data. The data lakehouse is gaining in popularity because it enables a single platform for all your enterprise data with the flexibility to run any analytic and machine learning (ML) use case. Cloud data lakehouses provide significant scaling, agility, and cost advantages compared to cloud data lakes and cloud data warehouses.

Automated software testing powers faster releases and higher-quality user experiences. As developers make changes to the applications, automation engineers, test leads, and developers work together to resolve issues that occur on some of the automated tests. Test failure analysis activities allow these teams to perform root cause analysis (RCA) for these failures. These activities can take increasing amounts of time as the scale of automated testing grows.

Test automation is becoming a critical part of the development lifecycle across all types of products and industries. As automation exponentially increases test complexity, testing teams need a unified, structured solution for automated test management to ensure the highest quality product. To meet this need, the latest Helix ALM Release 2022.2 delivers enhanced automated test management capabilities.

Manufacturing companies face complex challenges in a competitive landscape. To meet these enormous challenges, manufacturers have long invested in continuous improvement methodologies like lean manufacturing. Also, increasing numbers of manufacturers are adapting and investing in Industry 4.0 technologies to tame the complexity by increasing automation, adopting smart machines, and enhancing smart factories.

There is no question about the usefulness of big data these days. However, if you want the best data, you need it to be as accurate as possible. That means that your data has to be up-to-date, correct, and clean. Using one of these top data cleansing tools can help you be sure of this.

Demand for both entry-level and highly skilled tech talent is at an all-time high, and companies across industries and geographies are struggling to find qualified employees. And, with 1.1 billion jobs liable to be radically transformed by technology in the next decade, a “reskilling revolution” is reaching a critical mass.

The AlloyDB connector from Integrate.io empowers organizations to rapidly consolidate all of their data into AlloyDB—a high-powered, Google Cloud database that is 100% compatible with open-source PostgreSQL. By serving as an easy-to-set-up, high-speed data pipeline to AlloyDB, Integrate.io helps businesses modernize their legacy proprietary databases by migrating them to open-source, PostgreSQL-compatible systems.