<img width="1024" height="707" src="https://cdn.thenewstack.io/media/2023/04/097db110-opentelemetry-traces-screenshot-e1681419266367-1024x707.png" class="webfeedsFeaturedVisual wp-post-image wp-stateless-item" alt="" decoding="async" loading="lazy" style="display: block; margin: auto; margin-bottom: 20px;max-width: 100%;" link_thumbnail="" data-image-size="large" data-stateless-media-bucket="cdn.thenewstack.io" data-stateless-media-name="media/2023/04/097db110-opentelemetry-traces-screenshot-e1681419266367.png" /><p>Grafana is often considered to be the hands-down most popular observability platform for data visualization. OpenTelemetry has emerged as a</p><p>The post <a rel="nofollow" href="https://thenewstack.io/why-grafana-needs-opentelemetry/">Why Grafana Needs OpenTelemetry</a> appeared first on <a rel="nofollow" href="https://thenewstack.io">The New Stack</a>.</p>

Why Grafana Needs OpenTelemetry. Over 2022, the project has continued to gain momentum and is the second-highest velocity open source project in the CNCF.

The New Stack

AI, or artificial intelligence, is technology that attempts to simulate human cognitive function. AI has made its way into the software development space in a number of ways. Visit the AI article list to expand your AI knowledge.Learn MoreObservability is a way for development teams in an organization to view their program state. Failing to provide developers with insight into their tools and processes could lead to unaddressed bugs and even system failures. Read about the latest observability content hereLearn MoreIn the past, the CI/CD pipeline was simply a place to integrate code. Developers would write their code in GitHub, pass it through the pipeline, and then deploy it. The pipeline has become a much more critical piece of the software delivery lifecycle today.Learn MoreModern cloud-native applications, often leverage microservices, containers, APIs, infrastructure-as-code and more to enable speed in app development and deploymentLearn MoreSponsored By:Always On, Always FastDeploy Distributed PostgreSQL in just 90 SecondsLearn MoreAre TuringBots Up for the AI Autonomous Testing Challenge?Join Forrester’s Diego Lo Guidice to learn about the transformative power of AI in the software development lifecycleDec 6th at 10am CST Register HereMobile App Testing involves analyzing mobile apps for functionality, usability, visual appeal, and consistency across multiple mobile devices. It helps ensure an optimal user experience, irrespective of the device used to access the app.Learn MoreToday’s distributed software environments incorporate a variety of APIs with every interface your software touches, from mobile to microservices. Each API has to be continuously tested and verified to ensure your software functions as it should. Parasoft’s API testing platform makes quick, efficient, and intelligent work of such requirements.Learn MoreEnsure your application’s resilience, and make sure your software performs as expected under diverse operating conditions. (sponsored by Parasoft)Learn More DevSecOps is the DevOps community’s approach to bringing security into the development lifecycle. Businesses want to deliver software, but cannot afford to release unreliable or insecure applications— therefore security needs to be baked in much sooner than it has traditionally been.Learn MoreSecuring an application is just as important as building it in the first place. As data becomes more valuable, there are more people who want to steal it and use it for their own personal gain. Making sure applications are indeed secure has always been a challenge, as hackers try to stay one step ahead of defenders.Learn MoreIn 2023, there was an 18% decline in the number of open-source projects that are considered to be “actively maintained.” This is according to Sonatype’s Annual State of the Software Supply Chain Report.Learn MoreDevelopment Managers need a different type of content than developers… They need to know what platforms, tools, trends, and issues they should be thinking about. SD Times delivers those unique topics hereLearn MoreAgile software development has been around since the 1990s, but didn’t get the name until the famous meeting of 17 renowned software development thought leaders at Snowbird, Utah resulted in an Agile Manifesto. The idea behind Agile software development is to reduce time to market by enabling faster iterations of smaller segments of software.Learn MoreValue stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of resources, time and assets. It is the practice that truly brings the business side and IT side together as partners in creating value for the organization.Learn MoreLearn MoreDevOps is a methodology in the software development and IT industry. Used as a set of practices and tools, DevOps integrates and automates the work of software development and IT operations as a means for improving and shortening the systems development life cycle.Learn MoreMicroservices define the latest iteration of a service-oriented architecture, under which applications are not written as a monolith but are built by joining discreet components — bits of functionality, called services — via application programming interfaces (APIs).Learn MoreAI, or artificial intelligence, is technology that attempts to simulate human cognitive function. AI has made its way into the software development space in a number of ways. Visit the AI article list to expand your AI knowledge.Learn MoreValue stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of resources, time and assets. It is the practice that truly brings the business side and IT side together as partners in creating value for the organization.Learn MoreCopado is releasing a new tool designed to make it easier to test Salesforce applications. Copado Explorer integrates into CI/CD pipelines and automates a significant portion of the testing process. As manual testers are executing their normal activities, Copado Explorer works in the background to create test scripts using its generative AI scripting assistant, Test Copilot. These scripts can then be added into regression suites, shortening release cycles and expanding test coverage. It includes traceability features that allow users to understand exactly where an issue is coming from. It tracks defects against normal Salesforce features as well as user stories, and includes support for Jira and Azure DevOps. Testers can also record the test steps they went through and share that with developers, which results in fewer feedback loops being needed. In addition, Copado Explorer includes annotation and markup tools that allow non-technical subject matter experts to weigh in on issues, which provides additional context to the development team who is responsible for fixing them.“Release quality is a leading driver of risk, cost and speed within the software development life cycle,” said Esko Hannula, senior vice president of product management at Copado. “Copado Explorer makes it easy to automate the manual testing process, drastically reducing the time and effort typically required in traditional testing methods and improving both release velocity and quality. This launch not only underscores Copado’s commitment to innovation, but also sets a new benchmark in facilitating streamlined, effective, and collaborative software testing.”                      AI, Copado, Salesforce, testing                                                  Jenna Barron is News Editor of SD Times. She covers Microsoft, data, programming languages, and UI frameworks and libraries. She likes tabletop gaming, climbing, and knitting.                        Ready to subscribe to SD Times? It's just a click away!Subscribe

Copado releases new automated testing solution for Salesforce users - SD Times. Learn MoreValue stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of resources, time and assets.

SD Times

Copado releases new automated testing solution for Salesforce users

AI, or artificial intelligence, is technology that attempts to simulate human cognitive function. AI has made its way into the software development space in a number of ways. Visit the AI article list to expand your AI knowledge.Learn MoreObservability is a way for development teams in an organization to view their program state. Failing to provide developers with insight into their tools and processes could lead to unaddressed bugs and even system failures. Read about the latest observability content hereLearn MoreIn the past, the CI/CD pipeline was simply a place to integrate code. Developers would write their code in GitHub, pass it through the pipeline, and then deploy it. The pipeline has become a much more critical piece of the software delivery lifecycle today.Learn MoreModern cloud-native applications, often leverage microservices, containers, APIs, infrastructure-as-code and more to enable speed in app development and deploymentLearn MoreSponsored By:Always On, Always FastDeploy Distributed PostgreSQL in just 90 SecondsLearn MoreAre TuringBots Up for the AI Autonomous Testing Challenge?Join Forrester’s Diego Lo Guidice to learn about the transformative power of AI in the software development lifecycleDec 6th at 10am CST Register HereMobile App Testing involves analyzing mobile apps for functionality, usability, visual appeal, and consistency across multiple mobile devices. It helps ensure an optimal user experience, irrespective of the device used to access the app.Learn MoreToday’s distributed software environments incorporate a variety of APIs with every interface your software touches, from mobile to microservices. Each API has to be continuously tested and verified to ensure your software functions as it should. Parasoft’s API testing platform makes quick, efficient, and intelligent work of such requirements.Learn MoreEnsure your application’s resilience, and make sure your software performs as expected under diverse operating conditions. (sponsored by Parasoft)Learn More DevSecOps is the DevOps community’s approach to bringing security into the development lifecycle. Businesses want to deliver software, but cannot afford to release unreliable or insecure applications— therefore security needs to be baked in much sooner than it has traditionally been.Learn MoreSecuring an application is just as important as building it in the first place. As data becomes more valuable, there are more people who want to steal it and use it for their own personal gain. Making sure applications are indeed secure has always been a challenge, as hackers try to stay one step ahead of defenders.Learn MoreIn 2023, there was an 18% decline in the number of open-source projects that are considered to be “actively maintained.” This is according to Sonatype’s Annual State of the Software Supply Chain Report.Learn MoreDevelopment Managers need a different type of content than developers… They need to know what platforms, tools, trends, and issues they should be thinking about. SD Times delivers those unique topics hereLearn MoreAgile software development has been around since the 1990s, but didn’t get the name until the famous meeting of 17 renowned software development thought leaders at Snowbird, Utah resulted in an Agile Manifesto. The idea behind Agile software development is to reduce time to market by enabling faster iterations of smaller segments of software.Learn MoreValue stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of resources, time and assets. It is the practice that truly brings the business side and IT side together as partners in creating value for the organization.Learn MoreLearn MoreDevOps is a methodology in the software development and IT industry. Used as a set of practices and tools, DevOps integrates and automates the work of software development and IT operations as a means for improving and shortening the systems development life cycle.Learn MoreMicroservices define the latest iteration of a service-oriented architecture, under which applications are not written as a monolith but are built by joining discreet components — bits of functionality, called services — via application programming interfaces (APIs).Learn MoreAI, or artificial intelligence, is technology that attempts to simulate human cognitive function. AI has made its way into the software development space in a number of ways. Visit the AI article list to expand your AI knowledge.Learn MoreValue stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of resources, time and assets. It is the practice that truly brings the business side and IT side together as partners in creating value for the organization.Learn MoreGenerative AI has taken the enterprise world by storm throughout 2023 – and for good reason. From a digital transformation perspective, it has the potential to amplify efficiency to new heights at a time when it’s needed most.Most enterprise digital transformation efforts today fail. A recent Gartner report found that nearly 70% of CFOs believe digital spending is underperforming against expected outcomes. The root cause is a disconnect between those outcomes and the software teams’ capacity to deliver them. Planview’s independently commissioned Project to Product 2023 State of the Industry Report found that business leaders believe IT teams in charge of digital transformation efforts can deliver 10x more than their actual capacity. Only 8% of IT and software development plans are ultimately executed, while 40% of digital innovation work is wasted.The simple answer for this inefficiency is disconnected and disparate data. Most organizations leverage a plethora of tools to deliver digital transformation at scale. From Jira and GitHub to GitLab and Azure DevOps, these systems all play a critical role across the end-to-end software development lifecycle. But here’s the catch — none of them are integrated or aligned. Value streams with minimal interoperability cause bottlenecks that hinder digital transformation from succeeding. Data integration and alignment is critical.Enterprise digital transformation is nearing a new era of opportunity amid the rise of generative AI. Because generative AI’s large language models (LLMs) are domain agnostic and don’t reside in any single system, it has unrivaled potential to minimize wasted workflows when integrated with Strategic Portfolio Management and Value Stream Management technologies. The global digital transformation market size is expected to exceed $7 trillion by 2032. Even a 5-to-10% reduction in waste is more than enough to move the needle. The time to act is now.Effectively harnessing generative AI’s power to minimize wasted work first requires a common semantic layer of data. Fusing datasets from heterogeneous systems into a normalized data platform creates an essential foundation for optimizing the flow of value. Once that holistic data foundation is in place, organizations can then engineer prompts that train generative AI LLMs to produce impactful prescriptive insights, identify high-risk workflows, and refine resource allocations. This removes a lot of the drudgery from software team planning processes, essentially automating the fundamental aspects of value stream management with AI-powered productivity.Another prescription could be identifying various underlying dependencies between different product or project initiatives that are causing significant delays — leading the organization to add direct resource capacity or rebalance capacity between teams based on what the data is surfacing. This level of acute data-driven decision-making relative to capacity and allocation helps align financial investments to high-priority projects, accelerating time to market for initiatives with the greatest ROI.Organizational alignment is critical to leveraging generative AI for digital transformation success. It’s important to remember that technology is only as powerful as your ability to deploy it. For generative AI to effectively accelerate value, it’s imperative to eliminate the “black box” that exists between business outcomes and software development. An organization’s business and technology functions must be operating in unison. By synchronizing all the tools, processes and metrics associated with software development and delivery, organizations can optimize decision-making across portfolios, value streams and DevOps teams to link digital transformation capital allocation to impactful business outcomes.This is where converging objectives and key results (OKRs) across strategic portfolio management, value stream management, and agile planning are worth their weight in gold. It doesn’t matter how brilliant the generative AI prompts are – they are incapable of achieving desired outcomes and driving digital transformation without a shared overarching mission. Universal alignment connects gaps between the technology and business facets of the organization with real-time visibility that provides more intelligence, predictions, and prescriptions. By integrating these actionable insights from portfolio management, enterprise agile planning, and value stream management into a single source of truth, a system of record, cross-functional teams have a clear roadmap for transforming ideas into outcomes.It’s no secret that the ongoing generative AI hype over the past 10 months has sparked valid concerns about its ability to replace human workers across industries. However, in the context of digital transformation, we shouldn’t think about the future with a narrow “human or machine” mindset. It’s really about humans plus machines. Applying AI-powered technology to augment manual workflows is what will deliver the greatest impact on digital innovation in the years to come.                        AI, data, generative AI, planview, value stream                                                  Razat Gaurav is the CEO of Planview.                        Ready to subscribe to SD Times? It's just a click away!Subscribe

Data integration and alignment will dictate generative AI’s impact on digital transformation - SD Times. Learn MoreValue stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of resources, time and assets.

Data integration and alignment will dictate generative AI’s impact on digital transformation

AI, or artificial intelligence, is technology that attempts to simulate human cognitive function. AI has made its way into the software development space in a number of ways. Visit the AI article list to expand your AI knowledge.Learn MoreObservability is a way for development teams in an organization to view their program state. Failing to provide developers with insight into their tools and processes could lead to unaddressed bugs and even system failures. Read about the latest observability content hereLearn MoreIn the past, the CI/CD pipeline was simply a place to integrate code. Developers would write their code in GitHub, pass it through the pipeline, and then deploy it. The pipeline has become a much more critical piece of the software delivery lifecycle today.Learn MoreModern cloud-native applications, often leverage microservices, containers, APIs, infrastructure-as-code and more to enable speed in app development and deploymentLearn MoreSponsored By:Always On, Always FastDeploy Distributed PostgreSQL in just 90 SecondsLearn MoreAre TuringBots Up for the AI Autonomous Testing Challenge?Join Forrester’s Diego Lo Guidice to learn about the transformative power of AI in the software development lifecycleDec 6th at 10am CST Register HereMobile App Testing involves analyzing mobile apps for functionality, usability, visual appeal, and consistency across multiple mobile devices. It helps ensure an optimal user experience, irrespective of the device used to access the app.Learn MoreToday’s distributed software environments incorporate a variety of APIs with every interface your software touches, from mobile to microservices. Each API has to be continuously tested and verified to ensure your software functions as it should. Parasoft’s API testing platform makes quick, efficient, and intelligent work of such requirements.Learn MoreEnsure your application’s resilience, and make sure your software performs as expected under diverse operating conditions. (sponsored by Parasoft)Learn More DevSecOps is the DevOps community’s approach to bringing security into the development lifecycle. Businesses want to deliver software, but cannot afford to release unreliable or insecure applications— therefore security needs to be baked in much sooner than it has traditionally been.Learn MoreSecuring an application is just as important as building it in the first place. As data becomes more valuable, there are more people who want to steal it and use it for their own personal gain. Making sure applications are indeed secure has always been a challenge, as hackers try to stay one step ahead of defenders.Learn MoreIn 2023, there was an 18% decline in the number of open-source projects that are considered to be “actively maintained.” This is according to Sonatype’s Annual State of the Software Supply Chain Report.Learn MoreDevelopment Managers need a different type of content than developers… They need to know what platforms, tools, trends, and issues they should be thinking about. SD Times delivers those unique topics hereLearn MoreAgile software development has been around since the 1990s, but didn’t get the name until the famous meeting of 17 renowned software development thought leaders at Snowbird, Utah resulted in an Agile Manifesto. The idea behind Agile software development is to reduce time to market by enabling faster iterations of smaller segments of software.Learn MoreValue stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of resources, time and assets. It is the practice that truly brings the business side and IT side together as partners in creating value for the organization.Learn MoreLearn MoreDevOps is a methodology in the software development and IT industry. Used as a set of practices and tools, DevOps integrates and automates the work of software development and IT operations as a means for improving and shortening the systems development life cycle.Learn MoreMicroservices define the latest iteration of a service-oriented architecture, under which applications are not written as a monolith but are built by joining discreet components — bits of functionality, called services — via application programming interfaces (APIs).Learn MoreAI, or artificial intelligence, is technology that attempts to simulate human cognitive function. AI has made its way into the software development space in a number of ways. Visit the AI article list to expand your AI knowledge.Learn MoreValue stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of resources, time and assets. It is the practice that truly brings the business side and IT side together as partners in creating value for the organization.Learn MoreSalesforce has announced a new set of low-code tools to enable its customers to easily and quickly embed AI into their customer relationship management (CRM) applications. The new platform, Einstein 1 Studio, builds on Einstein Copilot, which is the company’s generative AI solution. The Studio includes three main components: Copilot Builder, Prompt Builder, and Model Builder.Copilot Builder allows users to create custom AI actions for different tasks. It provides access to use familiar tools like Apex, Flow, and Mulesoft APIs so that AI actions can be completed within the flow of work. Custom actions can be activated in any Salesforce application or external system.  Prompt Builder can be used to build AI prompts that can be reused across different experiences. For instance, a user could build a button that enables a contact center agent to get an overview of all of a customer’s escalated cases in just one click.Model Builder provides the ability to build or import AI models. Customers can either use its low-code interface to build their own AI models that are trained on their cloud data, or integrate with existing models from Amazon, Anthropic, Azure OpenAI, Cohere, Databricks, Google Cloud, and OpenAI. Einstein 1 Studio also introduces the Einstein Trust Layer, which is a collection of features to enforce security on generative AI, such as data masking and audit trails. “Customers have always loved how easy it is to customize Salesforce. Our new Einstein 1 Studio makes it easy for admins and developers to build and customize Einstein Copilot and embed AI apps in the flow of work within Salesforce, tailored to the specific requirements of their company and industry,” said Clara Shih, CEO of Salesforce AI. “Built on our Einstein 1 Platform and unified by metadata that safely connects and unlocks data from across an enterprise, Einstein 1 Studio’s low-code tools democratize AI app development, unleashing a new wave of innovation that will transform workflows and augment human capabilities across every team and function.”                      AI, low code, Salesforce                                                  Jenna Barron is News Editor of SD Times. She covers Microsoft, data, programming languages, and UI frameworks and libraries. She likes tabletop gaming, climbing, and knitting.                        Ready to subscribe to SD Times? It's just a click away!Subscribe

Salesforce Einstein 1 Studio enables users to integrate AI into application using low-code - SD Times. Learn MoreValue stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of resources, time and assets.

Salesforce Einstein 1 Studio enables users to integrate AI into application using low-code

We’re so glad you’re here. You can expect all the best TNS content to arriveMonday through Friday to keep you on top of the news and at the top of your game.Check your inbox for a confirmation email where you can adjust your preferencesand even join additional groups.Follow TNS on your favorite social media networks.Become a TNS follower on LinkedIn.Check out the latest featured and trending stories while you wait for yourfirst TNS newsletter.Managing data lakehouses isn’t easy, so LinkedIn created and now has released as open source, OpenHouse, a control plane and interface for supervising a wide variety of data lakehouses.It all starts with data lakes. These are cheap, open storage systems for any data type — CSV, JSON, tabular data, text, images, audio, video, JSON, CSV, etc.  A Data Lakehouse, as defined by Databricks, is an architecture that enables efficient and secure Artificial Intelligence (AI) and Business Intelligence (BI) analysis on a data lake’s data. LinkedIn‘s OpenHouse provides an open source control plane to manage tables within open data lakehouse deployments.This control plane is made up of a declarative catalog and a suite of data services. Users can seamlessly define tables, their schemas, and associated metadata declaratively within the catalog. OpenHouse reconciles the observed state of tables with the desired state by orchestrating various data services.LinkedIn built this because there were no other tools available that could address its issues. Its open source data lakehouse deployments are built on the foundations of compute engines such as Apache Spark, Trino, and Apache Flink;  distributed storage; and metadata catalogs/table formats, like Apache Iceberg, Delta, Hudi, Apache Hive Metastore.” That’s a lot of data in a wide variety of formats and architectures.As LinkedIn admitted, “While functional, our current setup for managing tables is fragmented. The individual building blocks of compute engines, distributed storage, and metadata catalogs operate independently as part of an overall data plane.”OpenHouse was the answer. Since its inception last year, OpenHouse has been a cornerstone of LinkedIn’s data infrastructure, managing over 3,500 tables and serving more than 550 daily active users. Its impact has been profound, notably slashing the time-to-market for LinkedIn’s data build tool (dbt) implementation on managed tables by over six months and halving the end-user toil associated with data sharing. Integrating over 1,000 datasets, including those from AI and Large Language Models (LLMs), into OpenHouse.The inspiration behind OpenHouse stemmed from the perennial struggle between control and flexibility in big data management. Traditional cloud data warehouse solutions, while ensuring governance and performance, often lack the scalability and adaptability offered by open source data lakehouse systems. OpenHouse emerges as a solution to this dilemma, providing a managed experience that liberates end-users from the intricacies of infrastructure management while empowering data infrastructure teams with enhanced control and governance capabilities.At the heart of OpenHouse lies its Catalog, a RESTful table service that offers secure and scalable table provisioning alongside declarative metadata management. This is complemented by Data Services, which facilitates seamless table maintenance.OpenHouse’s key features include fundamental catalog operations, retention management, governance through column tagging, and comprehensive observability tools. These features are seamlessly integrated with Apache Spark. This enables standard engine syntax, SQL queries, and the DataFrame API to execute operations efficiently.Moreover, OpenHouse introduces advanced replication capabilities by extending the Apache Gobblin framework, ensuring high availability and consistency across geographies. Its support for Apache Iceberg as a table format further underscores its commitment to compliance and optimal performance through regular maintenance tasks.Recognizing the importance of adaptability, OpenHouse was designed with pluggability in mind, offering interfaces for storage, authentication, authorization, database management, and job submission. This design philosophy ensures that OpenHouse can be customized to fit diverse environments, from cloud infrastructures to specific table formats.As OpenHouse embarks on this new chapter as a BSD 2-Clause license open source project, LinkedIn invites the global community to explore its capabilities, contribute to its development, and provide feedback. The company is particularly focused on understanding how OpenHouse performs in various settings and is committed to addressing technical challenges as it transitions from Apache Hive to OpenHouse.Community created roadmaps, articles, resources and journeys fordevelopers to help you choose your path and grow in your career.

LinkedIn Open Sources OpenHouse Data Lakehouse Control Plane. You can expect all the best TNS content to arrive. Check your inbox for a confirmation email where you can adjust your preferences. This enables standard engine syntax, SQL queries, and the DataFrame API to execute operations efficiently

LinkedIn Open Sources OpenHouse Data Lakehouse Control Plane

Sed ut perspiciatis unde. Data chiefs in the Asia-Pacific region are pursuing the rollout of artificial intelligence in earnest, according to an international survey of 600 global data leaders from Informatica. India is racing ahead in the region, with 75% of those surveyed already having adopted generative AI.However, APAC-based respondents are facing hurdles around the management of data for AI. These include data fragmentation amid an exploding number of data sources, the quality of data available for AI, and embedding data governance that is robust enough for the AI challenge.Richard Scott, senior vice president of Asia-Pacific and Japan at Informatica, said data literacy is important to support organisational data management. Scott recommended getting cloud data architecture right from the beginning and focusing on people, processes and technology.APAC data leaders said the ability to deliver reliable and consistent data fit for generative AI (40%) was the leading data strategy priority for 2024, together with improving data governance and processes (40%). This indicates AI is driving a mutual focus on data management.SEE: The top 10 benefits of improved data quality for your organisation.The intimate connection between AI and data was also reflected in investment intentions. Three in four (78%) APAC data chiefs predicted their data investments would increase in 2024. Not a single respondent did not plan to invest in data management capabilities in some form.A number of data management capabilities are receiving investment in line with data strategy priorities. Data privacy and protection was named number one (45%), reflecting the necessity of keeping data private and secure amid a rise in a fast-changing cybersecurity environment.This was followed by data quality and observability (42%) and data integration and engineering (40%).“We’re seeing a surge in data quality as an area of focus, and data governance,” Scott said. “So AI is really going to drive in a kind of a new wave of cleaning up of data estates.”SEE: How IBM’s Matthew Candy views Australia’s 2024 pursuit of generative AI scale.According to Informatica’s global survey results, which were sourced from data leaders in organisations with greater than US $500 million in revenue, almost all (99%) data leaders had encountered roadblocks on their AI journey, including those in APAC.APAC data leaders expect data fragmentation and complexity to worsen in 2024. Informatica found 56% of data leaders were struggling to balance over 1,000 data sources. In addition, 78% of APAC data leaders expect the number of data sources will increase this calendar year.“Last year alone, Informatica processed about 86 trillion cloud transactions a month, up 60% from a year prior,” Scott explained. “So while organisations are trying to get their data house in order, the data is still exploding; we are seeing this really explosive growth,” he said.Data quality was named the biggest challenge to generative AI by 42% of global respondents. The potential for bias stood out as a particular concern in APAC due to large language models; 53% of Australian respondents said avoiding bias was their biggest concern (Figure A).“In the era of analytics, if you had bad data foundations, you would get to the wrong decision quicker,” Scott said. “In the same way, if you have a bad data management environment, you will get an answer from generative AI, but it may take you in the wrong direction.”Organisational data literacy is holding progress on AI back, according to data leaders surveyed. For example, 98% of global data leaders said they had experienced nontechnical organisational roadblocks to better data management, such as a lack of leadership support.Improving data-driven culture and data literacy was named by 39% of global data leaders as a top priority for 2024. Improving data literacy was the second most important (42%) measure of data strategy effectiveness, beaten only by getting data ready for AI and analytics initiatives.“Our CEO at Informatica talks a lot about the fact that, with businesses outsourcing applications, buildings, and so many other aspects of a business, for many companies their only asset is data. So it has to be a really high priority for the executive team and the board,” Scott said.The number of data management tools is growing. Two thirds (60%) of APAC leaders say they will need five or more data management tools to support priorities and manage data estates — an increase from the number of data chiefs who needed this number of tools in 2023 (55%).Improving governance over data and processes was named by 40% of regional data leaders as a top data strategy priority for 2024. APAC data leaders also placed the highest emphasis (67%) on enabling more data democratisation across their organisation when using generative AI.This is driving vendors to offer governance services and tools. Informatica recently launched an integrated Cloud Data Access Management tool following its acquisition of Privitar, which helps support the compliant management, sharing and use of data in jurisdictions across the globe.SEE: Data governance to be a renewed focus in IT for Australian organisations in 2024.Informatica also offers a self-service data marketplace designed to ‘democratise’ data access. Users can request and access data based on permissions. Data is served up with data quality and relevance ratings and is tracked so data stewards understand how it is being used.Informatica’s Richard Scott advised regional data leaders to get the right cloud architecture in place to support scale and to focus on people and processes as well as technology.Organisations should start by ensuring their cloud architecture is sound, Scott said, as getting this right from the start will support future efforts to scale.“It’s when you are scaling out and you don’t have the right sort of data management architecture that you get into real trouble,” said Scott.Scott added that getting cloud architecture right from the outset is also cheaper.“Companies with multiple cloud contracts pay a lot of money in the ingress and egress cost between clouds,” Scott said. “Not only does the wrong cloud architecture result in an environment that maybe can’t support generative AI but it is also very expensive.”Informatica client NRMA, one of Australia’s oldest member-based organisations, is working successfully with over 3,000 datasets. Organisations that exert the effort to get the architecture right can get on top of data and have a material impact on their data estate, Scott said.The nature of the data challenge means organisations need to look more holistically at people and processes and technology. Scott said for data leaders in organisations that are trying to fix problems as they arise, it can feel like “putting your finger in the dike to stop a flood.”“What will happen is if you just plug each little hole in the dike by getting a new application or writing some code, you’re going to end up with a very fragmented environment, which is going to be very brittle. You need to look at people, process and technology and have a clear understanding of where you’re headed; then you can bring in technology that’s going to integrate incredibly well and give you that ability to transport data across your environment.”Source link  Save my name, email, and website in this browser for the next time I comment.By using this form you agree with the storage and handling of your data. * Δdocument.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() );Tech dedicated news site to equip you with all tech related stuff.I agree that my submitted data is being collected and stored.✉️ Send us an emailTechToday © 2024. All Rights Reserved.TechToday.co is a technology blog and review site specializing in providing in-depth insights into the latest news and trends in the technology sector.TechToday © 2024. All Rights Reserved.Be the first to know the latest updatesI agree that my submitted data is being collected and stored.

Informatica Claims Data Fragmentation Is Standing in the Way of APAC Generative AI. APAC data leaders said the ability to deliver reliable and consistent data fit for generative AI (40%) was the leading data strategy priority for 2024, together with improving data governance and processes (40%).. Be the first to know the latest updatesI agree that my submitted data is being collected and stored.