OpenText launched the latest version of ValueEdge -- an innovative modular, cloud-based DevOps and value stream management (VSM) platform.
DataOps has emerged as an Agile methodology to improve the speed and accuracy of analytics through new data management practices and processes, including code automation. Simply understood, DataOps is data management for the AI era, powering both automation at scale and friction-free collaboration between humans and machines.
In the digital era, organizations commonly serve their frontline workers' needs with hundreds of applications, collectively generating anywhere between thousands and millions of queries every day. The challenge for IT teams is that these applications are not static; they must constantly evolve to meet the organization's ever-changing needs. By introducing and enhancing automations, DataOps can improve application performance, security, and data analytics with only modest human oversight.
Dataops Enables Organizations to Improve Data Quality and Efficiency
Implemented correctly, DataOps is an enterprise-wide Agile approach designed to ensure every person, system, or machine has secure access to the right data, when and where it’s needed. Rather than simply streamlining the flow of ever-increasing quantities of data, DataOps focuses on improving the quality and speed of data analytics from initial data preparation to final reporting.
Additionally, integrating AI and machine learning can improve productivity by reducing development time — intelligently identifying, suggesting, and testing solutions for issues with code. Automations introduced by DataOps can also augment security, using machines to spot and triage vulnerabilities. Given the vast number of cybersecurity threats enterprises now face, turning vulnerability detection and prioritization over to a machine means freeing precious IT team human resources to focus on bigger issues.
Reality Check: Dataops Depends on Validated, Respected Solutions
DataOps innovations can be incredibly valuable, particularly during a talent crunch that has limited organizations' abilities to expand their human IT resources. However, operationalizing AI at a scale sufficient to meet the demands of today’s data-driven enterprises is no easy feat.
In reality, very few organizations are presently capable of widespread deployment of scaled ML and AI solutions in production environments. Deploying DataOps requires an organization to be aligned with the correct change-focused mindset and select a data platform with trustworthy tools — ones that have already been validated as beneficial by other, comparable companies.
A Successful Dataops Strategy Requires Purposeful Organizational Changes
In order to implement successful and sustainable DataOps practices, companies must ensure that the correct processes are in place to drive operationalization of their results, and that their business cultures are receptive to analytical insights. Broadly speaking, if a DataOps strategy aspires to truly realize the next evolution of data management, it will require the following four steps:
1. Embracing change. Effective operationalization begins with the organization evaluating its existing structure and processes, then welcoming rather than impeding change. Deep adoption may require changing the culture of the organization or specific business units to embrace continuous change through constant learning from both stakeholders and customers.
2. Exalting quality. While AI can rapidly produce high-quality results, unexplained or underexplained conclusions can undermine human trust in the technology. Data governance is important and taking a human-guided approach is key. Without the ability to self-police, the data set will be at risk of bias and drift, negatively impacting the organization's desired or intended results.
3. Mandating teamwork. Historically, enterprises allowed individual business units to manage their own data, leading to everything from incompatible data formats to separately stored and managed information. In the modern era, identifying and improving utilization of high-value data depends upon breaking down old data silos — a step that enables IT teams to work on the entire data set, and determine appropriate levels of aggregation and pre-analysis.
4. Adopting new techniques and tools. Identifying fit-for-purpose AI tools and adopting the agile "test and learn" approach, which enables key stakeholders to see the tools' results and provide feedback to continuously improve their performance, will play a key role in driving AI workflows. As suggested above, the organization's culture needs to embrace and internalize this feedback to improve AI results over time.
Introducing DevOps into data-driven organizations means raising the bar for agility — a structural, cultural upgrade that many businesses will realize is long overdue — and making them more competitive. Moreover, pairing DevOps practices with well-governed AI solutions that are capable of scaling to data warehouse environments will position data-driven businesses for success in an increasingly dynamic world.
Oracle announced the availability of Java 20, the latest version of the programming language and development platform.
Rafay Systems introduced Environment Manager, a solution that empowers enterprise platform teams to improve the developer experience by delivering self-service capabilities for provisioning full-stack environments.
To meet the growing demand for Oracle Container Engine for Kubernetes (OKE) with global organizations, Oracle Cloud Infrastructure (OCI) is introducing new capabilities that can boost the reliability and efficiency of large-scale Kubernetes environments while simplifying operations and reducing costs.
Perforce Software joined the Amazon Web Services (AWS) Independent Software Vendor (ISV) Accelerate Program and listed its free Enhanced Studio Pack (ESP) in AWS Marketplace.
Aembit, an identity platform that lets DevOps and Security teams discover, manage, enforce, and audit access between federated workloads, announced its official launch alongside $16.6M in seed financing from cybersecurity specialist investors Ballistic Ventures and Ten Eleven Ventures.
Hyland released Alfresco Content Services 7.0 – a cloud-native content services platform, optimized for content model flexibility and performance at scale.
CAST AI has announced the closing of a $20M investment round.
Check Point® Software Technologies introduced Infinity Global Services, an all-encompassing security solution that will empower organizations of all sizes to fortify their systems, from cloud to network to endpoint.
OpsCruise's Kubernetes and Cloud Service observability platform is certified to run on the Red Hat OpenShift Kubernetes platform.
DataOps.live released an update to the DataOps.live platform, delivering productivity for data teams.
CoreStack and Zensar announced a strategic global partnership. CoreStack will provide its AI-powered NextGen cloud governance and FinOps capabilities, complementing Zensar’s composable cloud operations offering.
Delinea introduced the Delinea Platform, a cloud-native foundation for Delinea's PAM solutions that empowers end-to-end visibility, dynamic privilege controls, and adaptive security.
Sysdig announced a new foundation that will serve as the long-term custodian of the Wireshark open source project.
Talend announced the latest update to Talend Data Fabric, its end-to-end platform for data discovery, transformation, governance, and sharing.