How Many Tools Do You Really Need? Streamlining Your Data Integration Ecosystem

While data is more abundantly available than ever before, it has no intrinsic value by itself. The real competitive advantage comes from insights generated from clean, connected and complete data. That explains the rapidly rising importance of data integration to modern businesses.

But with more data flowing from more sources, your data integration ecosystem — the network of tools that connect data to make it valuable and actionable — becomes increasingly dense, complex and (often) inefficient.

To design the most effective data integration strategy, you need to start by streamlining and right sizing your tool ecosystem. In a constantly changing business landscape, however, this can be a challenge. Keep reading to learn an alternate way to address it.

Growing your business may add new tools — often at the functional level without alignment with central IT — to accelerate go-to-market (GTM) efforts, run experiments or execute functional-level analytics faster.

Functional leaders at mature enterprises often want to move faster than legacy systems allow, necessitating workarounds to connect structured, unstructured and semi-structured data spread across older on-premises and new cloud systems.

These scenarios are common and even understandable in a highly competitive environment. They address immediate problems and needs, so they often work well for small businesses. But at scale, ad-hoc or siloed deployment of data integration tools will significantly impact the effectiveness of your data outcomes.

The challenges with tool sprawl become apparent and even magnified when, at some point in the growth journey, you need to implement a 360-degree data strategy for comprehensive business insights.

Such insights are possible when data flows are streamlined and integrated across functions and geographies. While multiple cloud and on-premises systems do collect, store and transfer data, the integration story is quite different.

A patchwork of ad-hoc free, paid, open-source and proprietary data integration tools results in:

1. Deeper data silos and fragmentation: This leads to a vicious cycle of more stand-alone tools and ad-hoc pipelines. Multiple vendors create data integration bottlenecks without a single source of accountability.
2. Broken data pipelines: This occurs when different tools stop supporting each other, partnerships end, software/app upgrades occur, new tools don’t sync with system-specific pre-built connectors or hand-coded pipelines can’t scale. It's beome more frequent for instances of stand-alone data integration tools to withdraw features and support, putting organizations’ data strategy at risk. Emerging data integration companies often overcommit and then fall short on execution and providing ongoing support for the needs of their scaling customers.
3. Duplication: This happens when multiple sources collect and process data, or when functions replicate workflows that could be otherwise consolidated. This leads to inefficiency and confusion, and ultimately data lineage and governance issues.
4. Threats to data privacy and security: It’s hard to keep track of your data as it’s being ingested, transformed and loaded across tools.  If your ecosystem is fragmented, it compromises visibility, observability, governance and compliance. Despite centralized access control systems set up on a data integration platform, when users can deploy piecemeal tools, they can unknowingly bypass the system.
5. Lower data integration ROI: Your data engineers shouldn’t spend their time on integration clean-up, firefighting and admin tasks over strategic work. Tool switching also negatively impacts resource efficiency, onboarding and the learning curve.

These factors together mean your business users cannot access comprehensive, accurate and timely insights or leverage data as a competitive asset.

While your business aims to grow and scale, it also poses a quandary. Your IT department wants streamlined, controlled data integration workflows for better governance and security. But your other departments — like marketing, sales, finance, etc. — want fast and flexible data ops, free of IT dependence or systemic bottlenecks. As a data engineer, you need to balance centralization and decentralization while building an optimal data integration infrastructure in a hyper-dynamic environment.

If you have multiple tools already running recurring and ad-hoc extract, transform, load (ETL), extract, load, transform (ELT) or reverse-ETL tasks, a situational audit will reveal:

• High-priority business use cases and outcomes for data integration
• Current data sources, formats and tools across the business
• Current scope and scale of data integration workflows — ETL, ELT, reverse ETL— in the context of connectivity, latency, processing power, access and storage
• Data quality, governance, security and compliance requirements
• Where data integration and analytics bottlenecks are recurring
• Gaps in budgets, skills and support available and needed

Your challenge is to control sprawl by standardizing, combining and consolidating tools to create efficiency.

To consolidate, evaluate each of your tools based on:

• How well it meets departmental needs
• In how many active use cases it can be leveraged
• How well the tool can address cost concerns
• How familiar the current user base is with the tool
• How easy it is to adapt and use (and the level of community or customer support available)
• Its implications on cost and speed efficiencies
• The number of unique capabilities it has versus other tools in the stack
• How fast it can implement new technologies

You can gradually pare down to an ideal stack by removing sub-optimal or duplicate tools and keeping tools that best suit the needs, data realities, budgets and regulatory context of your organization.

As you evaluate tools, it helps to remember that:

• Your modern data stack will keep changing in response to your business environment. A fit-and-forget approach to data integration would be futile, as you will need to constantly deduplicate and consolidate your stack.
• Data integration cannot be either-or. You will always need an optimal mix of AI-assisted designing and custom coding capabilities to fulfill your various use cases.
  
• Costs and time will remain at a premium when it comes to data integration.
  

Another approach you can take to minimize ongoing tool sprawl while keeping things democratic is establishing policies, procedures and a common data architecture to help govern the selection, implementation and maintenance of new tools. If data engineers throughout the organization follow the policy, it should help avoid duplication and maximize the efficiency of new tools.

But while this approach will give your departmental users more flexibility, adherence may be difficult. The policies may be interpreted differently at a departmental level depending on their context and urgency. Common data integration frameworks across multiple low-code, open-source and pre-built connectors can slow down departmental users who are seeking speed and may end up seeming counter-intuitive.

While data sprawl may be a reality, luckily tool sprawl doesn’t have to be. Consolidating tools periodically is necessary but can take up a vast amount of administrative time in a constantly changing environment.

If your goal is to reduce complexity without impacting efficiency, effectiveness, governance, stability or cost, then consider a unified, cloud and app-agnostic solution that will help you balance stability, flexibility and cost.

An agnostic, AI-powered data solution offers a compelling value proposition:

1. Universal connector: An application and cloud-agnostic data integration solution seamlessly links various combinations of on-cloud and on-premises data sources and targets with thousands of universal and reusable pre-built connectors that recognize metadata. This makes it easier to run complex integrations while protecting pipelines from software or app upgrades.
2. End-to-end workflows: Your varying functions will need different solutions at each stage of growth. A single cloud-native solution that can address data ingestion, cleansing, transformation and loading efficiently at scale will help you support diverse business use cases.
   
3. Cost and time effectiveness: Cost and time/speed are common reasons for data engineers to resort to DIY, hand-code or ad-hoc data integration tools. But this creates a pipeline and code soup that impacts governance and 360-data outcomes.  A unified solution that offers concrete time and cost savings would include:
   • An intelligent optimization engine and automated cost and compute power adjustments to lower the TCO
   • Reusable low- and no-code pipelines to bring significant time savings to design, development and integration tasks while avoiding the technical debt of hand-coding
   • Wizard-driven interfaces with point-and-click connectors, eliminating the need for custom code and freeing up your time
4. AI-powered: Considering the ever-widening scope and dynamics of modern data management, manually overseeing the data integration strategy and tools across the organization is almost impossible. Centralization may have negative connotations, but empowering your different departments with a single, comprehensive, intelligent tool offers the perfect balance of stability and flexibility. AI acts as the perfect co-pilot by recommending the next best action to optimize compute power, storage, choice of code or costs for the given context.
5. Stability and flexibility: Your business users need simple and fast access to comprehensive data and insights. A no-code, no-setup, agnostic data integration tool sets up a robust and secure foundation for a range of data integration tasks while reducing dependence on your central IT team.

Ultimately, a unified, agnostic, AI-powered data integration solution lets you turn your attention to more strategic tasks such as leveraging data as an asset for business users and monitoring data integration performance in terms of data availability, accuracy, access and timeliness.

Informatica Cloud Data Integration-Free (CDI-Free) and PayGo (CDI-PayGo) services offer an ecosystem of free and pay-as-you-go data integration solutions. CDI-Free allows you to start pilot projects or experiment with smaller data sets hassle-free, with zero install, setup or DevOps time. It also gives you the ability to scale seamlessly to the CDI-PayGo option for large-scale data processing, compliance protection and customer support.

CDI-Free allows you to start pilot projects or experiment with smaller data sets hassle-free, with zero install, setup or DevOps time. It also gives you the ability to scale seamlessly to the CDI-PayGo option for large-scale data processing, compliance protection and customer support.

With Informatica, your business users can access insights in hours, not days or weeks. And IT leaders can relax, knowing that data security and compliance will not be compromised. Connect and upgrade across solutions seamlessly, at your pace.

To learn more and get started with CDI-Free today, visit www.informatica.com/free-paygo.