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Introduction: The Soaring Need for Data Integration

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Hey there! With data volumes exploding and companies adopting more applications, I know you‘re struggling with disconnected systems and data silos. It‘s increasingly difficult to get that coveted "single source of truth" needed to compete. But have no fear – data integration is here!

As your friendly neighborhood data geek, I‘m thrilled to walk you through different integration approaches and tools that can help conquer chaos. With the right solution in place, you‘ll be on your way to consolidated data, improved analytics and better decisions. Just imagine – no more spreadsheets patched together or data mysteries to unravel!

Let me start by quantifying the data dilemma. IDC predicts worldwide data will balloon to 175 zettabytes by 2025. For perspective, that‘s 10x growth in just 5 years! Meanwhile, companies use over 100 SaaS apps on average according to Blissfully. Throw in some on-prem databases, legacy systems and new IoT devices for good measure. No wonder data is so dispersed!

With all these disconnected systems, it‘s no wonder 87% of organizations cite data fragmentation as a top challenge according to a Denodo survey. The costs and risks are real. Inaccurate reporting, customer experience issues and redundant tools are just the start. Without integration, data‘s potential remains untapped.

The good news is data integration innovation is booming too. Let‘s explore popular techniques and tools to connect the dots!

There are a few core ways to integrate data, each with their own strengths:

ETL Still Rules for Analytics – Extracting data from systems, transforming and loading into a warehouse remains the workhorse for analytics and reporting. It may not be real-time, but it gets the job done! Informatica and Talend offer leading ETL tools.

Data Virtualization Gets You Closer to Real-Time – This creates a virtual data layer to access and combine information from diverse sources on the fly. No data replication needed! Denodo and IBM‘s solutions shine here.

iPaaS: Integrate Faster in the Cloud – These cloud platforms with prebuilt connectors and transformation tools simplify and speed up linking cloud apps and data. MuleSoft leads the pack.

APIs & Microservices Enable Modular Integration – Exposing data as APIs allows flexible but targeted integration. Microservices break work into modular pieces that integrate through APIs.

Data Replication Sync‘s Things Up – These lighter, nimble tools copy and sync data in real-time across endpoints. Helpful for data migration scenarios. Striim and Attunity solutions stand out.

Of course, many organizations combine these approaches. You might use ETL for your core data warehouse, data virtualization to augment it with real-time data, and iPaaS to sync SaaS applications. The right mix depends on your environment and use cases.

Now let‘s dive into leading commercial data integration tools and cloud platforms:

Informatica – The Data Integration Gold Standard

As the long-time category leader, Informatica offers it all – vast connectivity, first-class transformations, robust management tools and both on-prem and cloud deployment options. It shines for analytics use cases but can handle just about anything you throw at it. Over 5,000 enterprises rely on Informatica including American Express, Pepsi and United Healthcare.

Oracle Data Integrator – A Robust Option for Oracle Shops

ODI is purpose-built to handle complex integration scenarios for Oracle databases and applications. Part of Oracle‘s larger data management stack, it offers tight integration and optimizations for these environments. The downside is it doesn‘t support other data platforms quite as well.

Talend – Powerful and Enterprise-Ready

With strengths in big data integration and native cloud support, Talend offers unified data health capabilities alongside integration. Its Stitch product provides self-serve data pipeline building targeting less technical users. Talend boasts over 4,500 global customers.

Boomi – Cloud Integration Made Simple

Dell‘s Boomi leads the integration PaaS category with pre-built connectors to popular apps, slick visual workflow design and scalable cloud infrastructure. The company claims over 15,000 customers using Boomi to address integration, workflow automation and API management.

Microsoft SSIS – Accessible Data Integration for Microsoft Environments

While not as robust as Informatica and Oracle DI, SSIS provides capable ETL within the SQL Server platform. Because it‘s bundled with SQL Server licenses, it‘s a no-brainer for Microsoft shops to leverage it for their basic integration needs. Larger enterprises often opt for heavier-duty platforms however.

MuleSoft – Agile API-Led Integration

MuleSoft pioneered API-led integration, exposing data via APIs and integrating through API calls vs. bulk data movement. This proves very flexible and agile. Their Anypoint platform handles full lifecycle API management along with integration workflows. Recently acquired by Salesforce.

With all the options out there, selecting the right data integration solution for your needs can feel overwhelming! Here are some of the top factors to consider:

Available Skills & Experience – If your team already knows a tool or has integration experience, build on that. Don‘t force an entirely new platform on them without proper training and support.

Total Cost of Ownership – Look beyond license costs at expenses for development, maintenance, infrastructure, training etc. Cloud options can lower costs.

Security & Governance – Ensure the platform has security capabilities and access controls you require, especially for sensitive data like financials or healthcare.

Scalability & Performance – Pick a solution able to handle your integration volumes now and projected growth. Know peak load limits before you hit them.

Cloud Roadmap – Factor where you are heading on cloud migration and SaaS adoption. Seek platform support for hybrid on-prem and cloud integration.

Ease of Use – If targeting less technical business users, examine visual workflow designers, pre-built templates and self-service access.

Change Management – Don‘t underestimate people challenges. Plan for new roles, training and processes to smooth adoption. Manage integration like code.

Avoid thinking you necessarily need the shiniest new tools either. Start-ups can have great technology but lack enterprise capabilities. Stick with established vendors you can count on for mission critical integrations.

Tools get you started, but you need the right people, processes and governance in place too:

Build a Solid Team – Data integration requires a mix of technical ETL developers, data modelers, quality assurance, data governance leads, architects and business analysts. Getting the right team mix is so crucial.

Test, Test, Test! – Regression test integrations to catch breaking changes from source system updates. Test with realistic data volumes and types expected in production. Fix bugs before go-live.

Start with Business Use Cases – Let business needs, not technology, drive the integration approach. The use case should guide the data scope, SLAs and platforms.

Share and Reuse – Promote sharing of data maps, schemas, models etc. across projects for consistency. Maximize reuse vs. reinventing the wheel.

Monitor Health and Usage – Watch for integration errors and lags indicating issues. Track usage to right-size capacity and leverage tool features.

Document Everything – Metadata describing data flows, business meaning and quality measurements provides a knowledge foundation for the team.

Make Integration a 1st Class Discipline – Give data integration governance, mindshare and investment equal to other IT disciplines. Great integration is the lifeblood of data value.

Even with stalwart platforms like Informatica, the data integration market continues evolving at a rapid clip:

  • Self-Service and Automation – Empowering more business users through automated recommendations and AI-assisted workflow building

  • Smart Data Fabric – Solutions that incorporate knowledge of relationships and usage patterns to provide dynamic, intelligent integration capabilities

  • Embedded Integration – Incorporating data virtualization and other integration technologies directly within applications to erase silos

  • Hybrid Deployment – Support for multi-cloud, on-premises and edge integration scenarios under one platform

  • Everything Real-Time – Minimizing latency by enabling real-time integration, streaming, and data access

I think you‘ll see convergence of automation, analytics and business applications with data integration woven seamlessly into the fabric. The days of moving bulk extracts in the dark of night are ending!

I hope mapping out the integration landscape was helpful. My key advice is to start where you have the biggest pain points. Identify one or two high-value use cases – maybe operational reporting for sales or centralized customer analytics. Prove value and then expand.

With modern data integration powering your business insights, you‘re sure to outpace the competition. So don‘t settle for ad hoc reports and gut feel decisions any longer. Let me know if you need any help taming your data wilderness. I‘m always happy to chat data integration!

AlexisKestler

Written by Alexis Kestler

A female web designer and programmer - Now is a 36-year IT professional with over 15 years of experience living in NorCal. I enjoy keeping my feet wet in the world of technology through reading, working, and researching topics that pique my interest.