3 Ways to Avoid Tool Sprawl in Your Data Integration Ecosystem

Last Published: Jan 12, 2024 |
Sudipta Datta
Sudipta Datta

Product Marketing Manager

Mature organizations are bound to face some degree of tool sprawl, thanks to scale, expansion, team turnover, new business needs and a steady supply of shiny new tech. At some point in the growth journey, however, consolidating the tools and systems for data extraction, transformation and loading becomes inevitable. Especially when you need a 360-degree data strategy to support stronger business insights.

This retrospective approach does not need to be the reality if your business is younger and you are still building your modern data stack. Even if your organization is more mature and undergoing a planned system migration, digital transformation or business merger, you can treat these transformational events as an opportunity for a fresh start for your data integration.

Here are three best practices to help you create a streamlined data integration ecosystem, designed to circumvent tool sprawl from the get-go.

1)  Balance Stakeholder Priorities

Investments in your modern data stack should, from the start, consider the needs and priorities of your stakeholders: business users, data professionals and central IT.

IT wants clean, streamlined systems that enable control over data governance and data lineage, support security/compliance and have proven stability at varying scales of data volume, variety and velocity.

Business users want to function with speed and flexibility so they can respond to changing market dynamics on the fly. They also want to manage their own data analytics needs, without relying on an already-stretched IT for small requests or hiring an army of expensive data tech specialists.

To address the needs of these users, you should build a data integration ecosystem that delivers the right balance of centralization and decentralization. At the same time, it should also address your own behind-the-scenes need for speed, performance, efficiency and cost-effectiveness. 

2)  Build for Today, Optimize for Tomorrow 

You likely are experiencing tool sprawl due to the purchase of ad-hoc tools to solve specific, short-term problems. Often, these types of tools don’t integrate well with your existing stack, and in the long term end up as technical debt, stack clutter or, worse, a security risk.

While investing in a new tool, consider if it aligns with your goal of creating an optimal data integration tool stack, and think about how it will impact your data integration ROI.

Consider factors such as:

  • What is the current and future scope and scale of your data integration? Do you need both extract, transform, load (ETL) and extract, load, transform (ELT) workflows for structured, unstructured and semi-structured data formats? What processing power will you need to handle your growing data integration use cases? Will the complexity of your data sources and formats grow significantly?

  • How strategic are data quality and governance for your business, industry and geographies today and in the future? Do you need specific security and compliance features to adhere to these needs?

  • What are the security and regulatory compliance demands in your geographies and verticals? Is the tool proven to meet them?

  • What technical literacy and skills do you have in-house to be able to use the data integration tool effectively? What degree of standardization, automation and self-serve do you need the tool to provide over time?

  • What are the cost and budget constraints for data integration? Consider the upfront investment, ongoing costs and optional top-up costs for added functionality or more complex use cases. Clarify the impact of scaling up data volume, formats or processing speed on the cost structure.

  • What trade-offs are you able to make based on business priorities? For example, are scale and stability more important than speed and performance? Is cost the final criterion? What are the non-negotiables for your organization?

3)  Adopt a Comprehensive Approach to Data Integration 

Despite your preparation, in a dynamic business environment you can never fully anticipate future business needs. As a result, your data integration ecosystem will always be evolving. Here are some examples of reasons why:

  • Unforeseen business requirements and constraints will come up.
  • The market will continue to innovate with new tools and technologies you may want to try.
  • Systematic audits of your data integration ecosystem may reveal new gaps in data ownership, governance and lineage that occur in the normal course of a high-growth business. Observability and person dependence will remain challenging as use cases get more complex.

A fit-and-forget data stack is too rigid to let you scale seamlessly, respond to business needs or experiment with the next new tools in the market. And an unregulated mix of tools for data ingestion, transformation and loading may offer responsiveness in the moment but will be unstable and unsustainable over time.

Even free and low-cost tools will ultimately cost you more when deployed into a rigid data integration ecosystem that is not optimized for performance at scale. Your use cases may be complex, but your data integration platform doesn't have to be.

A proven approach if you’re looking to build a sustainable data integration ecosystem is to invest in a comprehensive platform that can grow and scale with your needs. Such unified, cloud-native data integration platforms provide the perfect balance of flexibility and stability at any scale.

Typical characteristics of a cloud-native platform include:

Universal connectors

A cloud-native platform should be tool and app-agnostic, allowing you to connect and scale effortlessly once you have created the connectors and mappings. You simply need to change are the source and destination. Universal connectors are not impacted by vendor-led changes and enable interoperability between different combinations of on-cloud and on-premises data systems. You are free to toggle between ETL and ELT workflows, including bi-directional data flows and reverse ETL, as needed.

You also won’t need to hesitate to try new efficiency-enhancing tools or experiment with next-new tech innovations for fear of tool sprawl. A universal connector helps connect new tools easily but also helps roll back anything that doesn’t work out, with minimal disruption.

Intelligent automation

Even DIY data integration stacks need an Artificial Intelligence (AI) co-pilot for optimal performance. Leverage pre-built data integration artifacts to handle huge volumes and scale and reuse connectors and pipelines for low-effort execution of repetitive tasks.

Managing sprawl is not always about the number of tools. AI also helps streamline workflows and recommends the right workflows and pipelines based on your usage history to save time, effort and money. It also automatically documents processes and catalogs data so you can access accurate data when you need it.

A comprehensive data integration platform optimizes storage cost/efficiency and helps ensure intelligent distribution of compute power. The AI even recommends the best use cases for no-code or code reuse, low-code and pro-code, so you can deploy your limited resources most efficiently.

Seamless movement between systems

Can free, pay-as-you-go and full-service integration solutions co-exist in an organization? Yes, when you have a unified, comprehensive data integration platform, you can run various versions of the solution for different use cases, without worrying about scale, integration and alignment. Upgrade from a free version to a paid solution at your pace, or experiment with smaller data sets and scale up seamlessly.

Get The Best of Each World

Time and cost are traditionally a major reason to choose short-term or ad-hoc tools, but the unseen challenges of this approach cause a prohibitive impact on costs later. For example, observability itself may need multiple tools, data governance will demand more resources than anticipated or a security incident could occur in the cracks of your patchwork pipelines.

While you may think a unified, agnostic, intelligent, cloud-native data integration platform is too expensive or too long-term a deployment effort for your current scale of operations, with Informatica this is no longer true. 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 to meet you where you are today and scale with you as you grow.

Start pilot projects or experiment with smaller data sets hassle-free, with zero install, set-up or DevOps time, using CDI-Free. Scale seamlessly to the CDI-PayGo option for large-scale data processing, compliance protection and customer support. Upgrade entirely to the full-service solution when your business is ready.

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

First Published: Jan 12, 2024