Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise tech. In this feature, StreamSets‘ Girish Pancha offers commentary on how centers of excellence drive digital transformation.

More than a decade after the phrase “digital transformation” appeared in our business vocabulary, pulling off a successful project in that area hasn’t gotten any easier. Around 70 percent of digital transformation programs still fail outright, and nearly 87 percent don’t meet their original expectations. Those are unacceptable numbers, particularly in light of almost 60 percent of executives citing digital transformation as their most critical business growth driver. 

Part of the trouble arises from companies approaching digital transformation as a time-bound project rather than an ongoing initiative. If long-term change is going to be possible, it’s going to take a dedicated team of specialists within the organization. A digital transformation center of excellence (CoE) is the only way to bring all the critical business and technical capabilities into an ongoing effort. 

Gathering people with the necessary knowledge and experience and granting them authority over standardization, operating procedures, and budget allocation s is only the beginning. Without a well-thought-out strategy rooted in managing data, the team will face an uphill battle. Here are a few guidelines that CoEs must adhere to for effective digital transformation.

Centers of Excellence Drive the Best Digital Transformation

Ensure Data Integration and Compatibility 

Data is at the very core of digital transformation. Without data, enterprises cannot make smart real-time decisions, stay competitive, or accelerate innovation. It’s critical, then, that information assets must move seamlessly and at speed throughout an organization. 

CoEs should focus on connecting data, applications, people, and processes to create a unified, scalable integration platform. This platform should facilitate seamless communication between on-premises and cloud solutions, ensuring no data source is lost. In a world where data drives decision-making, maintaining the integrity and accessibility of data is non-negotiable. 

Avoid Data Friction 

Data engineers must take many steps to connect, transform, and process data to build the pipelines that feed digital transformation projects with the right insights. But when data is siloed in multiple systems with inconsistent formats (called data friction), creating bespoke data pipelines at scale is a huge challenge. In one report, almost two-thirds of respondents (65 percent) said data complexity and friction can have a crippling impact on digital transformation. 

Compounding the issue here are often disconnects between the line of business teams and engineering teams on what new solutions can actually deliver, which can have a crippling impact on digital transformation. CoEs must address data friction head-on by harmonizing data formats and streamlining data pipelines to ensure the right insights are readily available. 

Embrace the New Governance 

Data governance is essential for all businesses, especially for enterprise companies managing massive petabytes of data. Standards and policies are necessary to guide how data is gathered, stored, processed, accessed, and disposed of. Identifying and enabling data domain owners is also the best way to ensure data is accurate, consistent, and secure. 

But today’s companies need a modern approach to governance, not the traditional, top-down manner that hampers innovation. Instead, CoEs should promote modern governance practices that allow organizations to maintain control while keeping pace with the speed of business transformation. 

APIs: Making the Connection 

 Digital transformation efforts occur amid a complex mix of private cloud, public cloud, and on-prem hosting. This chaos of connectivity can be tamed through connections made possible by APIs, integration, and microservices. 

Companies can unlock innovations and modernize without building from scratch by enabling systems applications and partners to be relentlessly and seamlessly connected. This brings digital transformation within reach, as existing resources open a pathway for new digital products. 

Conclusion: A Holistic Approach with CoEs 

From one angle, digital transformation efforts can look like impossibly complex puzzles—a web of technology, data, people, apps, processes, clouds, and connected things. That’s why a CoE guided by a data-backed strategy has the best chance of achieving its goals. Digital transformation isn’t about pipelines and projects and rules; it’s about all those things together. A siloed approach won’t get the same result as a dedicated team with resources, budget, and focus. 

As organizations continue to embrace digital transformation as a fundamental strategy, the guidance and leadership of CoEs will be pivotal in realizing the full potential of this transformative journey. 

The post How Centers of Excellence Drive the Best Digital Transformation Results appeared first on Best Data Integration Vendors, News & Reviews for Big Data, Applications, ETL and Hadoop.

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise tech. In this feature, Progress‘s Philip Miller offers commentary on embracing data agility and why it is a vital strategy for business survival.

IT professionals and industry experts are aware of the continuous, exponential growth in the volume and variety of data each year. The ability to efficiently capture, analyze, and leverage this data for informed decision-making is an essential element of business survival. To remain competitive, organizational leaders should prioritize the establishment of a culture that promotes ongoing learning, embraces valuable insights, and seamlessly integrates cutting-edge technologies into their technology infrastructures.

Like any business integration, the adoption of new data formats and the interpretation of the new insights they provide may present challenges. Organizations must guarantee a secure, scalable, and, most importantly, adaptable data platform capable of accommodating various data sources and tools. This platform should be designed to tackle business challenges effectively, provide essential insights, and facilitate innovation and growth.

Embracing Data Agility

What is the Need for Agile Data in Businesses?

Data agility is assessed based on an organization’s ability to achieve its objectives rapidly and flexibly, regardless of whether it operates within a hybrid or multi-cloud infrastructure. A comprehensive grasp of the metadata associated with these data formats empowers the organization’s data analysts to extract and manipulate the complete data set.

To attain genuine data agility, an organization must have a data platform that offers the needed flexibility and scalability.

The conventional approach to data management usually revolves around using various database and data management tools, knowledge models, and rule engines. This fragmented approach tends to be rigid and lacks flexibility, often necessitating extensive modifications to applications and systems when accommodating new requirements. In the swiftly evolving business world today, companies require the freedom to harness new tools and capitalize on emerging opportunities readily and efficiently. Waiting weeks or months to implement and capitalize on the business advantage of a new tool frequently results in missed opportunities and loss of competitive edge.

This underscores the critical role a data platform plays in the survival and success of a business. Companies require a unified and comprehensive technological solution that seamlessly integrates with the entire organization.

The optimal data platform should possess the ability to ingest various data types, whether they are structured, semi-structured or unstructured. An agile data platform for businesses should be adaptable to diverse data models, ranging from traditional SQL to RDF, Geospatial, and Temporal data, all while enhancing data with metadata and applying ontologies and taxonomies. It involves infusing human intelligence on a machine scale. Ultimately, the platform should deliver customized data downstream to meet the specific needs of the business while ensuring top-notch security and governance at the enterprise level.

Embracing a data platform approach may require companies to explore beyond conventional technologies and architectures. However, this pivotal shift guarantees their resilience and competitive advantage in an ever-evolving business landscape.

What Advantages Do Agile Data Platforms Offer?

Implementing an agile data platform diminishes the reliance on IT teams, granting data analytics teams greater autonomy in their roles and fostering alignment between an organization’s IT initiatives and its business objectives. It empowers IT professionals to work within a more adaptable environment to enhance their visibility into infrastructure settings. Data analysts can then more closely monitor data metrics, delivering stakeholders a deeper insight into internal processes.

Once an agile data platform is in place, it can significantly reduce time-to-market for specific projects. This is especially beneficial in organizations with data silos, which can impede team productivity. Agile data platforms streamline access to shared data for both IT and analyst teams.

Agile data platform offers substantial cost savings, as their implementation doesn’t necessitate a complete infrastructure overhaul or additional investments in data tools.

The Rewards to Data-Agile Businesses

Several global organizations have harnessed the power of agile data platforms to revolutionize their business operations with significant impact. In one instance, a prominent aerospace company adopted an agile data platform to transition to a model-based systems engineering (MBSE) approach. This transformation facilitated increased efficiency within their IT teams, process streamlining, seamless integration of disparate data sources, and enhanced data analysis capabilities.

In another scenario, a non-profit life insurance company migrated from legacy mainframes to a modern, agile data infrastructure. This transition aimed to make data from various systems more accessible and user-friendly. By combining current and historical policy data with unstructured data from across the organization, the company empowered its users to effortlessly retrieve comprehensive member policy information for servicing, reporting, and compliance purposes.

The Path to Business Success: Embracing Data Agility

Upon embracing an agile data platform, businesses embark on a transformative journey toward a future characterized by growth, innovation, and ongoing positive outcomes. An indispensable element of this journey is a data platform capable of accommodating diverse data forms, supporting various data models, and enhancing data with metadata. Such a platform plays an important role in driving valuable insights, addressing business challenges, and nurturing a culture of innovation. In the digital age, businesses must acknowledge that data is the cornerstone of success. In an increasingly competitive global landscape, the ability to harness the full potential of data will determine an organization’s resilience and relevance.

As organizations venture into new markets and technologies, an agile data platform assumes a critical role as the enabler of their ambitions. A unified, integrated data platform liberates businesses from the constraints of fragmented solutions, enabling them to swiftly respond to market dynamics, meet consumer demands, and capitalize on emerging opportunities. By adopting a data platform approach, businesses unlock the true potential of their data, fuelling their growth and charting a course toward a prosperous and sustainable future. This journey necessitates a shift in mindset, but the rewards are substantial—an enhanced ability to translate data into actionable insights, create value for customers, and forge a resilient path to success in a world where change and the management of vast data volumes are the only constants.

The post Embracing Data Agility: A Vital Strategy for Business Survival appeared first on Best Data Integration Vendors, News & Reviews for Big Data, Applications, ETL and Hadoop.

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise tech. In this feature, Nstream‘s Aditya Chidurala offers commentary on full-stack streaming for data applications.

Organizations must make efficient, timely decisions to gain a competitive edge in today’s fast-moving business landscape. In recent years, streaming data has provided enterprises with continuous data that can be leveraged in collaboration with stored data. However, the typical data analysis approach requires multiple data systems, which adds to costs and latency. This limits organizations’ ability to capture the true, real-time value of data and make immediate automated decisions.

This is where streaming data applications built on full-stack application development platforms provide more value through the data pipeline. As data is generated, streaming data applications gather, model, assess, and provide insights about data in true real-time. This is significantly different from systems that store and process data at a later time – even if just for a few minutes. When it comes to informed, automated, real-time decision-making and resulting competitive edge, customer satisfaction revolves around the speed with which data is received, processed, and analyzed and insights are gleaned. 

Traditional data processing takes data from many sources (on-premises, at the edge, or in the cloud) and digests and stores the data in applications prior to its incorporation. The storage stage, often called “data at rest,” renders the data static in a database or a drive. While acceptable for many processes, when real-time decision-making requires immediate access to information, data at rest hampers its effectiveness.  

Full-Stack Streaming Data

Key Use Cases for Streaming Data Applications 

Nearly all industries can benefit from real-time streaming data insights – from retailers tracking inventory to transportation companies reducing fuel costs to financial institutions providing personalized recommendations. Fraud detection is another critical and growing use case that is well-suited for real-time data analysis. It requires the fastest insights, the ability to prioritize them, and the generation of rapid responses.

Batch-processed data is too slow, as the information about fraud detected can be outdated, and in some cases, it can take hours to identify and respond. Streaming data applications are faster and more effective at reducing losses and preventing future financial strains.  

Other common use cases include real-time customer 360, inventory management and tracking, and anomaly detection.  

• Real-time customer 360 gives companies an accurate, real-time picture of real-world customer experiences. These insights help to create personalized offers and recommendations. 
• Inventory management empowers companies with information needed to streamline the supply chain process for resource optimization.  
• Fast anomaly detection flags issues such as equipment malfunction or shutdown, transaction fraud, and damage to external events, so automated mitigation tools can react before irreparable damage occurs.  

Quickly Constructing Streaming Data Applications 

Developers with the ability to create streaming data applications in mere minutes instead of months can produce dramatic operational cost savings and quickly deploy technology across a variety of use cases that measurably impact a company’s bottom line. New, open-source, full-stack streaming data application development platforms allow developers to isolate events at a real-world object level and continuously perform stream-to-stream joins at scale.

Integrating platforms with well-known streaming data technologies, such as Apache Kafka, Apache Pulsar, and AWS Kinesis, helps enterprises obtain real-time business insight that can help automate and create better-informed decisions at network-level latency.  

Companies can reduce the months it takes to design, create, and test traditional architectures. This is true even with multiple, complex open-source data systems, UI frameworks, and application servers. There’s no need for stream processing, UI frameworks, or additional servers to build streaming data applications. 

Producing highly efficient stream data application outcomes at unprecedented scale is doable with today’s modern platforms. With a 10x faster time to value and more than 70% lower Total Cost of Ownership (TCO), building and deploying streaming data applications rapidly is beneficial to today’s enterprise organizations. 

The lower TCO compared to traditional architecture means organizations can reduce the complexity in the building and maintaining of streaming data applications. The reasons why? Reduced infrastructure costs and stream-to-stream joins that provide: 

• At-scale performance. 
• Better optimization of human resources from fewer engineering hours. 
• The elimination of hiring SMEs or paying multiple software vendors. 

Organizations can reduce infrastructure bills by up to 80% when scaling architecture on fewer nodes, servers, and data systems. Isolating streams and events at the real-world object level (assets, customers, IoT devices, etc.) versus executing an additional query every time new information joins a real-world object is now possible with these solutions. Now, organizations can keep data in motion at network-level latency throughout the application stack. This helps to free up engineering hours as just one integrated system needs to be maintained with 5x fewer connections.

Time is no longer wasted designing, building, testing, and maintaining complex, open-source data systems. Engineers can now spend precious hours on projects that benefit the company’s bottom line. Organizations with full-stack streaming data applications can also hire one software vendor rather than hiring multiple vendors or SMEs for each open-source data system. Together, these factors drive considerable cost reductions. 

What Makes it All Work 

There are three core technologies that make streaming data application solutions possible and make it easy to respond to real-world events in true real-time.  

1. Streaming APIs allow organizations to observe real-time outputs of business logic and stream incremental updates to API clients without polling for changes.  

1. Stateful services ensure streaming data applications possess the contextual information and data needed to take action when a new message arrives.  

1. Real-time UIs ensure users have an unprecedented 24/7 live view of their operations as broad or granular as needed.  

Streaming data applications built on a full-stack streaming data application development platform empower companies operating in an extremely fast-moving business environment to build applications that help them make decisions quickly with a complete view of their business landscape, both physical and digital, all while reducing costs and latency. Now, real-time insights can help organizations streamline critical business decision-making without waiting for data storage and processing.   

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Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise tech. In this feature, Rocket Software‘s Phil Buckellew offers commentary on how open-source software puta a modern spin on the mainframe.

The mainframe has long been a bedrock of security and reliability for businesses, with a decades-long track record of success to back it up. While the mainframe remains critical to countless business operations, the growing wave of modernization efforts has IT professionals looking outward for opportunities to innovate and bridge the gap between modern applications and longstanding mainframe infrastructure. And as mainframe developers continue to innovate, open-source software (OSS) has emerged to help close that gap.

While OSS is not always commonplace in some mainframe infrastructure, the technology brings a lot to the table when it comes to improving operations. OSS opens up opportunities to accelerate application development while also reducing costs along the way. It is also becoming increasingly common among early-stage professionals who are just starting in their careers, but doing so with open-source. While the mainframe has a long history, it’s not always the most well-known technology for new IT professionals.

As digital transformation projects become more common, tools like OSS will be critical to support the needs of developers. Here are a few ways OSS is uniquely suited for keeping the mainframe and modern applications on the same page.   

Open-Source Software Puts a Modern Spin on the Mainframe

Open-Source Software is a Must for the Mainframe 

Leveraging OSS is becoming increasingly commonplace among businesses, as it means developers can avoid needing to start from square one when building applications, saving resources and reducing costs along the way. As those applications mature and progress through development, OSS enables more individuals to contribute and make changes that account for the needs of systems throughout an organization, including the mainframe.  

Innovation is the name of the game for a majority of businesses, but it’s only possible if the mainframe and the latest applications are able to work together effectively. Mainframe application development is increasingly becoming an important part of the DevOps pipeline for businesses. With more organizations rethinking their infrastructure approach and opting for cloud or hybrid cloud environments, mainframe application developers need the right tools to map out processes effectively. OSS, by its very nature, brings greater visibility into the development process.  

As modernization efforts accelerate, OSS can help strengthen the DevOps processes that developers rely on. The software makes collaboration among developers easier and streamlines the process for delivery of applications on the mainframe. In all, the result is applications going to market much faster than would otherwise be possible. 

In addition to helping build applications, OSS is often used to create modern agile DevOps pipelines. As organizations move from a waterfall approach for their application development, to a more modern agile approach, it is often necessary to create connections to other tools and processes. By enabling the use of OSS on the mainframe itself, many organizations have found that they can include the mainframe in their agile processes, rather than have it be a standalone silo.  This typically speeds up changes so that updates can be pushed more quickly as well as creating more standardization across an IT organization.  

Supporting Young IT Developer Talent 

The benefits of OSS software extend beyond purely technological aspects. When young, early-stage professionals enter the job market, the tools they learn on and are most familiar with often fall into the realm of OSS. Implementing OSS into mainframe application development gives these young professionals and recent graduates the ability to enter mainframe development with a level of familiarity that helps them to hit the ground running.

Considering the mainframe can often come with a reputation of being less innovative, adding OSS tools also helps to combat that image and bring new talent into the mix—an important consideration as the mainframe industry continues to experience a skills shortage. With modern capabilities and tools, organizations can better tap into the talent pool needed to ensure the mainframe keeps up with the pace of modernization.  

Closing the Divide With Open-Source 

The mainframe is responsible for some of the most critical business operations. It’s well-established and reliable. But at a time when innovation and modernization are the priority, that reputation isn’t enough to help move an organization forward. As modern applications grow more complex, the mainframe needs to be able to keep up. It’s a challenge that OSS can provide the answer to. With OSS, organizations can streamline the application development process, reduce costs, and keep a steady pipeline of talent flowing into the business to ensure the mainframe works seamlessly with even the most transformative projects.

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Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise tech. In this feature, Safe Software‘s Dale Lutz offers commentary on how AI will bring light to dark data.

In today’s digital age, we’re amassing data at an unprecedented rate. Remarkably, 90 percent of all the world’s data has been generated in just the past two years. This surge in data acquisition has redefined exponential growth for both geospatial professionals and businesses. Every second, quintillions (equivalent to a billion billions) of data points are being generated and captured by countless people and machines.

Dark Data

Data that organizations are collecting but not using is called ‘dark data’ – and it constitutes a staggering 90 percent of the data enterprises gather. This might include data gathered from routine operations such as infrastructure monitoring, asset management surveys, and environmental assessments. Yet, this dark data often goes unnoticed. Many experts liken it to the submerged portion of an iceberg, with organizations typically only engaging with and drawing insights from the tip that’s visible above the water.

Given the sheer volume of data, it’s becoming overwhelming for organizations to manage – let alone find meaningful insights from it. That could all change with the integration of generative AI. 

AI Will Bring Light to Dark Data

Turning on the Lights

With the introduction of ChatGPT earlier this year, organizations are moving quickly to integrate new technologies into their software. With AI and a modern data integration approach, companies hold the potential to find powerful insights within their dark data icebergs. 

It’s estimated that around 75 percent of AI use cases are spread across four key areas: customer service, marketing and sales, software engineering and research and development. We’ve only just begun to see the benefits of AI in data integration and the economy as a whole. McKinsey’s latest research suggests that it will add more than 2.6 trillion dollars in productivity to the economy, having the greatest impact on software development. AI offers an opportunity for SaaS companies to think about the impact of the data they are collecting to better serve their users. 

At Safe Software, we’ve started integrating AI into a wide range of activities, starting with our localization work. We are also encouraging our engineers to experiment with AI tools like ChatGPT. In the future, we could see AI filtering and aggregating huge volumes of data to provide value through more actionable and analyzable datasets, particularly the mountain of usage statistics we’ve gathered over the past decade, which overwhelmingly has been our largest “dark data” repository.

The aspect that excites me most about dark data and AI is the potential to find patterns in datasets that would typically go ignored. For enterprises, this could include identifying outliers that foretell important risks or opportunities, assessing equipment failure potential, targeting potential customers and markets, and preparing training data for machine learning and artificial intelligence use. In our own usage statistics, this could highlight combinations of operations that we might want to optimize, which are too difficult to otherwise discern.

Modern integration approaches can further extend the utility of otherwise dark data by joining it to other datasets, with a result that the whole is far more valuable than the sum of the parts.

The Future of Data 

It’s an exciting time in the data economy as new technologies like AI could put a spotlight on previously unobservable dark data. With Generative AI, SaaS companies are positioned to break down those quintillions of data points to draw better conclusions than previously possible. 

Ultimately, dark data has the potential to dramatically change the way we look at information, and shine a light on the underside of the data iceberg, resulting in better products and outcomes for all involved.

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Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise technology. In this feature, Alluxio‘s Senior VP of Customer Success Omid Razavi offers commentary on the data-driven heartbeat of artificial intelligence.

Artificial Intelligence (AI) has consistently been in the limelight as the precursor of the next technological era. Its limitless applications, ranging from simple chatbots to intricate neural networks capable of deep learning, promise a future where machines understand and replicate complex human processes. Yet, at the heart of this technological marvel is something foundational yet often overlooked: data.

Understanding the AI and Data Symbiosis

AI’s relationship with data is symbiotic. As the complexities of AI systems increase, their thirst for data becomes insatiable. Data serves a dual purpose:

• AI as Data Consumer: AI systems, especially machine learning models, are trained using vast datasets. The quality and diversity of this data directly influence AI’s accuracy and reliability.
• AI as Data Producer: As AI systems process information, they also produce data – outputs, logs, analytics, and more. This data can provide insights into system efficiency, user behavior, and potential areas of improvement.

Data & AI

The Growing Pains of Data in the Age of AI

The transformative potential of AI has led to its widespread adoption across various sectors, from healthcare to finance, retail to research. This ubiquity comes with an exponential increase in data generation and consumption. Several challenges arise:

• Latency Issues: In an interconnected digital ecosystem, data is stored in multiple hubs, often geographically dispersed. Any delay in data access can have cascading effects, especially in time-sensitive AI applications.
• Cross-Cloud Conundrums: With multi-cloud strategies becoming the norm, managing and moving data seamlessly across diverse platforms is paramount. Different cloud providers’ varying standards, protocols, and structures can complicate matters.
• Economic Implications: Every byte transferred, especially in inter-regional or cross-cloud scenarios, carries a cost. As data volumes swell, so do the associated costs, making efficient data orchestration vital.

Redefining Data Accessibility in the Digital Age

Amidst the intricate data landscape, modern businesses require solutions that not only store data but also optimize its accessibility:

• Data Proximity and Computation: Modern data solutions recognize the need to minimize the distance between data storage and processing hubs. These tools drastically reduce access times by ensuring that necessary files and datasets are near computational resources. This is particularly transformative for applications like real-time analytics, where latency can hinder performance.
• Seamless Operations Across Borders: Globalization has rendered geographical borders almost obsolete for modern businesses. As such, data solutions must reflect this, allowing for unified, seamless operations irrespective of where the data or computation resides.
• Intelligent Data Movement: More than simply moving data from one point to another is needed in today’s dynamic environment. Data orchestration tools are now equipped with algorithms that can recognize access patterns, predict future needs, and move data proactively, ensuring optimal performance.

Vetting through Industry Adoption: When globally recognized giants in the tech industry adopt specific data orchestration systems, it’s an implicit stamp of approval. Their vast operations, stringent requirements, and focus on reliability make them the ideal litmus test for these solutions.

Harmonizing Data Orchestration with AI

Data orchestration isn’t just a support function; it’s a strategic tool that can significantly influence AI outcomes:

• Accelerated Model Evolution: Access to data influences the speed at which machine learning models can be trained, tested, and iterated upon. Efficient data orchestration can expedite this, leading to faster innovation cycles.
• Boosting Real-time Decision-making: In sectors like finance or healthcare, decisions need to be made in fractions of a second based on real-time data. Enhanced data access can be a game-changer in these scenarios, offering competitive advantages.
• Resource Optimization and Savings: Efficient data management minimizes redundancies, reduces transfer costs, and optimizes storage solutions. These efficiencies can translate into substantial cost savings.

The Road Ahead

While AI continues its journey from science fiction to everyday reality, its foundational support systems, especially data management, gain paramount importance. The sophistication of algorithms or the prowess of machine learning models doesn’t solely determine the success of AI applications. Instead, it’s a synergy where efficient data orchestration plays a pivotal role.

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Solutions Review’s listing of the best enterprise information integration tools and software is an annual sneak peek of the top tools included in our Buyer’s Guide for Data Integration Tools and companion Vendor Comparison Map. Information was gathered via online materials and reports, conversations with vendor representatives, and examinations of product demonstrations and free trials.

The editors at Solutions Review have developed this resource to assist buyers in search of the best enterprise information integration tools to fit the needs of their organization. Choosing the right vendor and solution can be a complicated process — one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we’ve profiled the best enterprise information integration tools providers all in one place. We’ve also included platform and product line names and introductory software tutorials straight from the source so you can see each solution in action.

Note: The best enterprise information integration tools are listed in alphabetical order.

The Best Enterprise Information Integration Tools

Adeptia

Platform: Adeptia Connect

Description: Adeptia offers enterprise data integration tools that can be used by non-technical business users. Adeptia Connect features a simple user interface to manage all external connections and data interfaces. It also includes self-service partner onboarding and a no-code approach that lets users and partners view, setup and manage data connections. The platform touts a suite of pre-built connections and Cloud Services Integration, as well as B2B standards and protocol support.

Boomi

Platform: Boomi AtomSphere

Description: Boomi is a leading provider in the connectivity and automation space. Boomi’s flagship product, AtomShere, supports integration processes between cloud platforms, software-as-a-service applications, and on-prem systems. AtomSphere uses a visual interface to configure application integrations. The solution’s runtime tool, Boomi Atom, allows integrations to be deployed wherever they are needed. The AtomSphere platform is available in several editions, based on use case and functionality.

Celigo

Platform: Integrator.io

Description: Celigo offers an Integration Platform as a Service product called Integrator.io. The solution enables organizations to connect applications, synchronize data, and automate processes. Celigo features an integration wizard that includes an API assistant, visual field mapping interface, and drop-down menus. The tool also offers reusable pre-configured integration templates available on the integrator.io marketplace, allowing users to create their own library of reusable, standalone flows.

Cleo

Platform: Cleo Integration Cloud

Description: The Cleo Integration Cloud allows organizations to connect to enterprise and SaaS applications with a variety of connectors and APIs. The tool automatically accepts, transforms, orchestrates, connects and integrates all B2B data types from any source and to any target, and can be deployed via several different methods. Cleo Integration Cloud can also be embedded for SaaS or Information Services organizations and can be utilized as a managed service to offload complex integrations to the vendor’s experts.

Cyclr

Platform: Cyclr

Description: Cyclr is a UK-based provider of embedded Integration Platform as a Service (iPaaS) solutions. The vendor offers a white-labeled, low-code approach to offering in-app integrations for end-users. Cyclr touts a global user base and helps its customers enhance their native connectivity suites while simplifying the creation and deployment method. Flexible deployment options mean that Cyclr is built for companies of all sizes who are looking to provide added automation capabilities to their customers.

Denodo

Platform: Denodo Platform

Description: Denodo is a major player in the data management software market. The award-winning Denodo Platform offers a robust capabilities package for data integration, data management, and data delivery using a logical approach to enable self-service business intelligence, data science, hybrid/multi-cloud integration, and enterprise data services. Denodo touts customers across large enterprises and mid-market companies in over 30 industries. A pioneering company in the data virtualization space, Denodo was founded in Palo Alto, California, in 1999.

Equalum

Platform: Equalum

Description: Equalum offers an enterprise-class data ingestion platform for collecting, transforming, manipulating, and synchronizing data. The product effectively combines batch and streaming pipelines with modern data transformation and manipulation. Equalum touts an intuitive, user-friendly interface that enables users to build and deploy data pipelines via a no-coding approach. The solution also features a drag-and-drop UI that lets different user personas configure, maintain, and derive insights from the Equalum platform.

Fivetran

Platform: Fivetran

Description: Fivetran is an automated data integration platform that delivers ready-to-use connectors, transformations and analytics templates that adapt as schemas and APIs change. The product can sync data from cloud applications, databases, and event logs. Integrations are built for analysts who need data centralized but don’t want to spend time maintaining their own pipelines or ETL systems. Fivetran is easy to deploy, scalable, and offers some of the best security features of any provider in the space.

Hitachi Vantara

Platform: Pentaho Platform

Related products: Lumada Data Services

Description: Hitachi Vantara’s Pentaho platform for data integration and analytics offers traditional capabilities and big data connectivity. The solution supports the latest Hadoop distributions from Cloudera, Hortonworks, MapR, and Amazon Web Services. However, one of the tool’s shortcomings is that its big data focus takes attention away from other use cases. Pentaho can be deployed on-prem, in the cloud, or via a hybrid model. The tool’s most recent update to version 8 features Spark and Kafka stream processing improvements and security add-ons.

HVR

Platform: HVR

Description: HVR is a high-volume real-time data replication solution that solves a variety of data integration use cases, including cloud, data lake, database and file replication, and database migration. The product allows organizations to move data bi-directionally between on-prem solutions and the cloud. Real-time data movement enables the ability to continuously analyze changes in data generated by transactional systems, machines, sensors, mobile devices, and more.

IBM

Platform: IBM InfoSphere Information Server

Related products: IBM InfoSphere Classic Federation Server, IBM InfoSphere Data Replication, IBM InfoSphere DataStage, IBM App Connect, IBM Streams, IBM Data Refinery, IBM BigIntegrate, IBM Cloud Integration

Description: IBM offers several distinct data integration tools in both on-prem and cloud deployments, and for virtually every enterprise use case. Its on-prem data integration suite features tools for traditional (replication and batch processing) and modern integration synchronization and data virtualization) requirements. IBM also offers a variety of prebuilt functions and connectors. The mega-vendor’s cloud integration product is widely considered one of the best in the marketplace, and additional functionality is coming in the months ahead.

Informatica

Platform: Informatica Intelligent Data Platform

Related products: Informatica PowerCenter, Informatica PowerExchange, Informatica Data Replication, Informatica B2B Data Transformation, Informatica B2B Data Exchange, Informatica Big Data Integration Hub, Informatica Data Services, Informatica Big Data Management, Informatica Big Data Integration Hub, Informatica Big Data Streaming, Informatica Enterprise Data Catalog, Informatica Enterprise Data Preparation, Informatica Edge Data Streaming, Informatica Intelligent Cloud Services

Description: Informatica’s data integration tools portfolio includes both on-prem and cloud deployments for a number of enterprise use cases. The vendor combines advanced hybrid integration and governance functionality with self-service business access for various analytic functions. Augmented integration is possible via Informatica’s CLAIRE Engine, a metadata-driven AI engine that applies machine learning. Informatica touts strong interoperability between its growing list of data management software products.

Jitterbit

Platform: Jitterbit Harmony

Description: Jitterbit offers cloud data integration and API transformation capabilities. The company’s main product, Jitterbit Harmony, allows organizations to design, deploy, and manage the entire integration lifecycle. The platform features a graphical interface for guided drag-and-drop configuration, integration via pre-built templates, and the ability to infuse applications with artificial intelligence. Users can run the tool in cloud, hybrid, or on-prem environments, and feed consolidated data to real-time analytics.

Keboola

Platform: Keboola

Description: Keboola is a cloud-based data integration platform that connects data sources to analytics platforms. It supports the entire data workflow process, from the point of data extraction, preparation, cleansing, warehousing, and all the way to its integration, enrichment, and loading. Keboola offers more than 200 integrations and features an environment that allows users to build their own data applications or integrations using GitHub and Docker. The product can also automate low-value activities while accounting for audit trail, version control, and access management.

Matillion

Platform: Matillion ETL

Related products: Matillion Data Loader

Description: Matillion offers a cloud-native data integration and transformation platform that is optimized for modern data teams. It also features built on native integrations to popular cloud data platforms like Snowflake, Delta Lake on Databricks, Amazon Redshift, Google BigQuery, and Microsoft Azure Synapse. Matillion uses an extract-load-transform approach that handles the extract and load in one move, straight to an organization’s target data platform, then using the power of a cloud data platform’s processes to perform transformations once loaded.

Microsoft

Platform: SQL Server Integration Services (SSIS)

Related products: Azure Data Factory cloud integration service

Description: Microsoft offers its data integration functionality on-prem and in the cloud (via Integration Platform as a Service). The company’s traditional integration tool, SQL Server Integration Services (SSIS), is included inside the SQL Server DBMS platform. Microsoft also touts two cloud SaaS products: Azure Logic Apps and Microsoft Flow. Flow is ad hoc integrator-centric and included in the overarching Azure Logic Apps solution.

MuleSoft

Platform: Anypoint Platform

Description: MuleSoft offers a B2B application delivery network that connects data, applications, and devices with APIs. The vendor enables organizations to improve their applications through integration while also providing API connectivity to a wide variety of on-prem and cloud-based applications and systems. MuleSoft provides both traditional and Integration Platform as a Service products and touts a growing capabilities portfolio.

Oracle

Platform: Oracle Data Integration Cloud Service

Related products: Oracle GoldenGate, Oracle Data Integrator, Oracle Big Data SQL, Oracle Service Bus, Oracle Integration Cloud Service (iPaaS)

Description: Oracle offers a full spectrum of data integration tools for traditional use cases as well as modern ones, in both on-prem and cloud deployments. The company’s product portfolio features technologies and services that allow organizations to full lifecycle data movement and enrichment. Oracle data integration provides pervasive and continuous access to data across heterogeneous systems via bulk data movement, transformation, bidirectional replication, metadata management, data services, and data quality for customer and product domains.

Precisely

Platform: Precisely Data Integrity Suite, Precisely Connect

Related products: Precisely Data Integrity Suite Data Integration Service, Precisely Ironstream

Description: The Data Integration service of the Precisely Data Integrity Suite is one of 7 SaaS services. It is complemented by Precisely Connect, an on-prem data integration solution that supports a broad range of source and target systems. Both solutions leverage Precisely’s expertise in mainframe and IBM i systems to integrate complex data formats into modern cloud platforms like Snowflake and Databricks. Precisely Ironstream also integrates mainframe and IBM i machine and log data into IT platforms like Splunk and ServiceNow for IT operations management, analytics, and security.

Qlik

Platform: Qlik Replicate

Related products: Qlik Compose, Qlik Catalog, Qlik Blendr.io

Description: Qlik offers a range of integration capabilities that span four product lines. The flagship product is Qlik Replicate, a tool that replicates, synchronizes, distributes, consolidates, and ingests data across major databases, data warehouses, and Hadoop. The portfolio is buoyed by Qlik Compose for data lake and data warehouse automation and Qlik Catalog for enterprise self-service cataloging. Qlik also offers Integration Platform as a Service functionality through its Blendr.io product, which touts API connectivity, no-code integration and application automation.

SAP

Platform: SAP Data Services

Related products: SAP Replication Server, SAP Landscape Transformation Replication Server, SAP Data Hub, SAP HANA, SAP Cloud Integration Platform Suite, SAP Cloud Platform

Description: SAP provides on-prem and cloud integration functionality through two main channels. Traditional capabilities are offered through SAP Data Services, a data management platform that provides capabilities for data integration, quality, and cleansing. Integration Platform as a Service features are available through the SAP Cloud Platform. SAP’s Cloud Platform integrates processes and data between cloud apps, 3^rd party applications, and on-prem solutions.

SAS

Platform: SAS Data Management

Related products: SAS Data Integration Studio, SAS Federation Server, SAS/ACCESS, SAS Data Loader for Hadoop, SAS Data Preparation, SAS Event Stream Processing

Description: SAS is the largest independent vendor in the data integration tools market. The provider offers its core capabilities via SAS Data Management, where data integration and quality tools are interwoven. It includes flexible query language support, metadata integration, push-down database processing, and various optimization and performance capabilities. The company’s data virtualization tool, Federation Server, enables advanced data masking and encryption that allows users to determine who’s authorized to view data.

SnapLogic

Platform: Intelligent Integration Platform

Description: SnapLogic’s Intelligent Integration Platform integrates across applications, databases, data warehouses, big data streams, and IoT deployments. It allows both IT and business users to create data pipelines that can be deployed on-prem or in the cloud. It features an HTML5 visual designer and a proprietary AI algorithm called Iris that learns common integration patterns and drives self-service by recommending flows. Complete support for complex transformations, conditional operations, triggers, parameterization, aggregation, and reuse maximizes the tool’s flexibility.

Striim

Platform: Striim Platform

Related products: Striim for Azure, Striim for Amazon Web Services, Striim for Google Cloud Platform, Striim for Snowflake

Description: Striim offers a real-time data integration solution that enables continuous query processing and streaming analytics. Striim integrates data from a wide variety of sources, including transaction/change data, events, log files, application and IoT sensor, and real-time correlation across multiple streams. The platform features pre-built data pipelines, out-of-the-box wizards for configuration and coding, and a drag-and-drop dashboard builder.

Talend

Platform: Talend Open Studio

Related products: Talend Data Fabric, Talend Data Management Platform, Talend Big Data Platform, Talend Data Services Platform, Talend Integration Cloud, Talend Stitch Data Loader

Description: Talend offers an expansive portfolio of data integration and data management tools. The company’s flagship tool, Open Studio for Data Integration, is available via a free open-source license. Talend Integration Cloud is offered in three separate editions (SaaS, hybrid, elastic), and provides broad connectivity, built-in data quality, and native code generation to support big data technologies. Big data components and connectors include Hadoop, NoSQL, MapReduce, Spark, machine leaning and IoT.

TIBCO

Platform: TIBCO Cloud Integration

Related products: TIBCO Data Virtualization, TIBCO EBX, TIBCO StreamBase, TIBCO Messaging, TIBCO Spotfire

Description: TIBCO’s flagship Integration Platform as a Service product, TIBCO Cloud Integration, requires no code. It also allows users to create, model, and deploy APIs in a completely guided process. TIBCO acquired Scribe Software in June 2018 and has rolled its capabilities into TIBCO Cloud Integration as a complimentary package. TIBCO offers a fully integrated data platform that can handle a variety of data integration use cases. The company’s acquisition of Cisco’s data virtualization technologies rounds out its product portfolio even further.

Tray.io

Platform: Tray Automation Platform

Related products: Tray Embedded

Description: Tray.io offers an API integration platform that lets users configure complex workflows, integrate applications, and add customized logic. The product features a clicks-or-code configuration for hastened setup and a quick ramp-up experience for users as well. Tray also touts a universal connector for any RESTful API, full API access via custom fields, a growing list of pre-built connectors, and connector versioning to prevent lapses if an API ever changes. Tray.io is available in a number of editions based on functionality.

Trifacta

Platform: Trifacta

Description: Trifacta offers an open and interactive cloud platform for data engineers and analysts. Its Data Engineering Cloud solution enables users to collaboratively profile, prepare, and pipeline data for analytics and machine learning. Trifacta touts multi-cloud support, flexible execution (you can choose between ETL, ELT, or an optimal combination of the two based on performance and cost), and universal connectivity for ingesting data from enterprise sources.

The post The 28 Best Enterprise Information Integration Tools for 2023 appeared first on Best Data Integration Vendors, News & Reviews for Big Data, Applications, ETL and Hadoop.

Solutions Review On-Location: A live event blog of all the expert video interviews from Safe Software’s The Peak of Data Integration 2023.

FME helps organizations connect data and applications across systems, streamline operations with automated workflows, and deliver speed to insights. Safe Software serves customers in diverse industries. The event, which takes place from September 5-7 in Bonn, Germany, will offer learning and networking opportunities and hands-on training with FME.

Safe Software is a major player in enterprise spatial data integration and helps global organizations maximize the value of their data. The company offers FME, an enterprise integration platform. FME helps organizations connect data and applications across systems, streamline operations with automated workflows, and deliver speed to insights. Safe Software serves customers in diverse industries, including Government, Utilities, Energy, AEC, Telecom, and Transportation.

Solutions Review is holding live interviews throughout the show! Our editors will update this post as new videos go live on our YouTube channel.

The Peak of Data Integration 2023: Live Coverage

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The post On-Location at Safe Software’s The Peak of Data Integration 2023: Live Coverage appeared first on Best Data Integration Vendors, News & Reviews for Big Data, Applications, ETL and Hadoop.

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise business software. In this feature, CData Software Chief Product Officer Manish Patel offers a commentary on how to avoid your next data unification project turning into a disaster recovery data breach.

Businesses are dealing with more data from more sources, formats, and structures than ever before. As a result, data unification strategies are now critical to long-term business success: connecting diverse data sources, enabling democratized access to key information, and powering data-driven decision-making throughout the entire enterprise.

But despite its necessity, data unification comes with inherent security concerns.

Without robust data privacy and security practices in place, even the most advanced data unification strategies fall short — just consider that organizations providing full, unmanaged access to their enterprise data are four times more likely to experience a data breach compared to companies that have implemented necessary access controls.

If organizations aren’t careful, they’ll find themselves knee deep in stressful (and costly) data breaches and compliance violations rather than gaining actionable insights, newfound value and functionality from their data operations.

Fortunately, data unification and security don’t need to be at odds. In fact, these two endeavors complement one another when organizations establish a smart strategy and best practices for data management that will support their entire enterprise data operations.

Data Unification & Data Breaches

Why Common Data Methods Fall Short on Security

Until recently, many organizations relied on manual processes to move data between systems and platforms — a cumbersome, time-consuming, and error-prone process.

But manual efforts couldn’t (and still can’t) handle the scale and complexity of exponentially expanding data volumes. In response, many organizations turned to ETL (Extract, Transform, Load) pipelines, which automatically facilitate data replication from the source. Then, ETL pipelines transform this data into a consistent format and load information into a target system or data warehouse.

Although ETL pipelines are a step in the right direction, organizations gathering data from multiple sources still face significant challenges in terms of maintaining that data’s integrity, compliance, and security measures.

Why? By enabling the replication, transfer, and storage of large datasets across multiple locations, ETL pipelines increase the risk of unauthorized access and data leakage, meaning data can be unintentionally exposed, transferred to unintended destinations, or accessed by unauthorized entities. In addition, organizations may hold on to data longer than necessary or leave information behind in unused or insecure locations. These types of errors and inconsistencies at any point in the data unification process impact accuracy and reliability.

Security concerns of this nature are especially problematic in highly regulated industries. Financial services organizations, for example, face strict compliance and privacy regulations that require tight control over access to data. Companies that use ETL pipelines to transfer data to warehouses face significant hurdles when it comes to the cost and complexity of replicating required security measures over time.

Unfortunately, these problems are commonplace for all types of companies. In fact, less than four in 10 companies today have high levels of confidence in their ability to secure data in the cloud. It does no good to unify your data at the expense of security.

Three Steps to Integrate Security into Your Data Unification Plan

Real-time data connectivity platforms offer another approach to connect, integrate and unify data — empowering teams across the organization to seamlessly connect to data sources, obtain the most up-to-date information and insights, and optimize decision-making. But for these efforts to work, they must be paired with robust security measures.

By incorporating the following principles — accountability, access, and awareness — you can strengthen your data security and support an effective data unification strategy.

Accountability

Accountability plays a critical role in monitoring and maintaining your data security. By implementing accountability measures — including routine security audits, testing, and real-time monitoring — your organization can track who accesses what data, where it’s accessed from, and how it’s used.

These proactive measures enable you to assess security practices and effectiveness, identify vulnerabilities, and make improvements along the way. Likewise, accountability efforts provide a higher level of standardization and greater visibility into data privacy and security measures. These insights reinforce consistent adherence to security protocols and facilitate centralized data governance — and offer a window to intervene when change is required.

Accountability practices also ensure your organization remains compliant with evolving security rules and regulations, such as GDPR or industry-specific requirements like HIPAA. As a result, you avoid data breaches and security incidents, as well as penalties and fines for compliance violations.

Awareness

Human error is to blame for more than 80 percent of data breaches. That’s why ongoing security training and awareness are essential steps to ensure your employees stay well-informed about changing data privacy and security best practices, keeping your organization agile and highly responsive to changes in the security landscape.

Regular security training programs transform your workforce from a security liability to an asset, empowering employees to make informed decisions, adhere

to security protocols, and identify and prevent threats before they materialize. By raising awareness about data security, you reduce the risk of accidental data breaches and promote a culture of security consciousness.

Access

Real-time access is crucial to ensuring the security of your data unification processes. By providing immediate access to data directly from the source without delays, real-time data platforms eliminate the need to replicate and physically move your datasets. This enhances operational efficiency and reduces the risk of data becoming outdated or compromised during the replication process.

Real-time access also enables you to monitor data movement and access controls at a moment’s notice, facilitating numerous security best practices including:

• Robust authentication and authorization mechanisms like role-based permissions and other access controls enforce data security and prevent unauthorized access across your enterprise.
• Encryption, data anonymization, and secure file transfer methods protect your data during transit — and safeguard it from interception or tampering.
• Audit trails track and protect sensitive information, while real-time monitoring and alerts for anomalies enable prompt detection and response to potential security incidents.

As enterprise data continues to expand, it’s no surprise 68 percent of business leaders feel their cybersecurity risks are increasing. But you don’t need to fall into that category. By adopting modern data connectivity platforms and prioritizing strong data governance and security practices integrated throughout your data unification strategy, your organization is empowered to realize the full potential of your data — and, most importantly, to unify and protect enterprise data without sacrificing one for the other.

The post Don’t Let Your Data Unification Turn into a Data Breach appeared first on Best Data Integration Vendors, News & Reviews for Big Data, Applications, ETL and Hadoop.

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise business software. In this feature, Ascend.io‘s Jon Osborn offers a brief on the top data mesh challenges and how to solve them.

If you work with data, you’ll have come across the term data mesh by now. This decentralized but interconnected approach to structuring data has become increasingly popular since the term was coined by Zhamak Dehghani 4 years ago.

However, while data meshes have significant advantages for scaling up your data operations, the approach comes with its fair share of challenges: Michele Goetz, VP and Principal Analyst at Forrester, calls it the “data mesh blind side.”

In this article, we’ll break down the main challenges of moving to a data mesh architecture and briefly explore how to tackle them.

Data Mesh Challenges

Data Mesh: 101 

First, a quick primer on the data mesh concept. Data mesh is a way of thinking about and organizing your data to move data ownership and accountability closer to the end users.

The core tenet of the data mesh is to distribute responsibility and governance of your data across different business “domains”. This is the opposite of having a single, monolithic data architecture managed by a centralized data team.

These domains are loosely coupled teams that own and manage data and pipelines in their business unit. For example, human resources data might be owned and managed by the human resources domain on a particular data platform, while the sales data domain is managed by another team on a different platform.

The term “mesh” comes from the fact that all these data domains remain interconnected despite potentially running on different technology stacks. There’s no return to the old days of siloed data warehouses. Rather, the goal is to move ownership and responsibility of the data closer to the subject matter experts who understand it, while maintaining a unified data catalog of data products that can be referenced by any other builders in the organization.

The end result? A more scalable, secure, and speedy distributed architecture that helps you build and maintain interdependent datasets at scale.

The 3 Biggest Data Mesh Challenges 

There’s no such thing as frictionless business transformation (no matter what the vendors tell you!). Data meshes are no exception. If you’re used to operating with a centralized data platform, there will be some challenges when moving to a data mesh.

Here are a few of the most common issues with data meshes:

Challenge#1: Securing Stakeholder Buy-In

Or, to put it more bluntly—office politics. As with any major change to the way you do business, it’s only going to work if everyone buys-in. Here are a few of the hurdles you’ll need to jump over:

1. Moving to a data mesh structure will require distributing ownership of the data to the business domains—and that means giving more work to line of business workers that may not want it!
2. You may also find you get pushback from your central data team, especially if they feel they are losing their ability to govern and secure the data. Michael Ryan, Managing Principal Consultant and Head of Architecture at telecommunications software provider AmDocs, warns that if your data science team “don’t feel valuable, or if they feel their jobs are threatened by a data mesh, they will act against it – even though the [centralized and mesh] architectures are complimentary.”
3. You need to decide which domain owns what data. That isn’t always obvious—competing business priorities can make it difficult to know what all the different domains should be and (even more sensitive) who should manage them.
4. Data mesh structures rely on a higher level of self-service by data users. Not everyone will be a fan of the learning curve involved—especially if your self-service layer is hard for non-technical people to use. This is particularly likely to create issues if the “non-technical people” hold senior roles, and feel like they’re being asked to work harder to find the information they need.

The Solution—A Carrot (Not Stick) Approach

Matteo Vasirani, a Senior Manager of Data Science at developer platform Github, suggests that the key for securing buy-in from the various stakeholders is to “show them the carrot”—the positive outcomes that will result from moving to a mesh framework.

Matteo found that incentivizing data users with “what’s in it for them” is more effective and realistic than simply attempting to mandate the move from the top (the “stick”).

Some of the key benefits to moving to a data mesh architecture that are likely to appeal to end users and business leaders alike:

• Fewer bottlenecks while users wait for an overworked central data team to produce customized reports—instead, users can help themselves to the information via an approachable, self-service “mesh experience layer”
• More reliability and trust in the data because the organization can trust the experts to watch over data products in their own domains
• Additional rigor in thinking of datasets as ‘products’ and assigning a product manager to define and govern them, resulting in better alignment with business needs
• Faster results from less complex data models, as the data is no longer being modeled to provide everything for everyone
• Scalable data architecture that is easy to align to your current priorities and adapt as your business evolves
• Data is owned by the people who understand it best—the data producers in business-oriented data domains

To quote Sharad Varshney, CEO of OvalEdge, a data governance provider,

“Data mesh architecture delivers a scalable, affordable solution that enables you to work with more trusted data, more quickly, whilst taking pressure away from your IT and Data Teams. This enables them to focus on more business-critical tasks.”

In other words, a data mesh makes it easier and faster for the end user to get their hands on the data they need to make business decisions—and who wouldn’t want that?

Challenge #2: Establishing Rigorous Quality Control

When you move to a data mesh, you’re assigning responsibility for the quality of the data to the data domain owners, instead of a centralized data team. That means that your data quality is dependent on multiple teams who may not know each other, and who don’t necessarily share priorities or even a common set of terminology.

Without taking this into consideration before implementing a data mesh, you risk running into quality control issues, cautions Vasirani. “A data producer might change something and then you suddenly notice a dashboard is failing downstream, and then you’ll need to reverse engineer the issue.” Essentially, you’re risking scaling up your problems along with your data architecture.

The Solution—Data Contracts 

To avoid degrading the quality of your data when you move to the mesh, you’ll need an execution model that defines what downstream consumers can expect from any data product they consume. The industry has started calling these data contracts, but regardless of the terminology the concept has existed for decades. Just look at the amount of documentation and governance that surrounds an API.

The key to success with this approach is the addition of a product management overlay to each data domain. It is the PM’s responsibility to understand the use case that each shared dataset was created to address, and ensure that future changes do not compromise the guarantees needed to fulfill these use cases. If a new use case is introduced, oftentimes it will require the creation of additional data products instead of modifying an existing one and breaking contracts.

You may also want to consider training to make sure that everyone involved in working with or inputting data understands the consequences of unplanned changes.

Finally, you’ll need to be sure that the domain data owners are incentivized to keep the quality of their data high—both by reminding them of the positive business outcomes, and possibly by assigning data-quality KPIs to data product owners.

Challenge #3: Building a Solid Foundation for Mesh Success 

A data mesh is not a substitute for a central, unified data fabric or cloud data platform. Introducing ownership of the data at the domain level does not mean moving back to completely siloed datasets without understanding the big picture.

Data silo-ing is a real risk when implementing a data mesh architecture, especially if you’re building on home-grown technology that wasn’t built with a mesh in mind. Some have proposed requiring every domain to use a siloed slice of the current monolithic infrastructure. But this can be challenging if many parts of the business are already using their own specialized cloud services. Let’s face it, the multi-cloud world is not going away anytime soon.

The Solution—Build a Unified Data Sharing Layer

To be successful, your data mesh must follow these key principles:

• Discoverable and shareable—data users can easily consume data products from different domains, and combine them with external data
• Addressable—users can access domain data from the same location each time, with changes to the data published as opt-in new versions.
• Useable—domain data must be structured and published in ways that make it easy to digest, use, and access via the self-service tool
• Trustworthy—there’s no point in having access to data if you don’t trust it to be consistent and accurate, so data domains must be responsible for assuring data quality, usability, and providing adequate documentation
• Secure and standardized—data from different domains should be able to be analyzed together, found easily, understood readily, and stored securely

A unified platform for intelligent data pipelines can help create a solid foundation for the transition to a data mesh and make it easier to achieve these key principles.

If you consolidate all of your data pipelines into a single platform, you can:

• Automatically detect and respond to changes in other data products and ensure data accuracy across the entire mesh
• Get a centralized view of pipeline status and data quality across all your data domains
• Provide a common experience for building data products that is the same for any cloud data platform
• Allow expert data workers to move between domains and fix problems anywhere in the mesh
• Share and subscribe to data products everywhere across the mesh regardless of which data platform they originated on
• Rapidly extract and migrate data away from legacy infrastructure as you build out your new mesh

Overcoming Challenges of the Data Mesh Approach

There are many benefits to a data mesh approach, but before you move towards implementation you’ll need a plan in place to address the main challenges:

• Securing true buy-in from your data consumers, data team and senior leadership
• Compensating for differing skill levels and priorities between domains to ensure data quality
• Building a foundation to make it easier to move from legacy systems to a federated but unified data mesh

The post The Top 3 Data Mesh Challenges & How to Solve Them appeared first on Best Data Integration Vendors, News & Reviews for Big Data, Applications, ETL and Hadoop.