Data Integration Blind Spots to Avoid Before It’s Too Late

How do your departmental users access data organization-wide? They are likely deploying multiple data tools to meet their needs at their own pace, budgets and skill levels. But how does this impact your data management?

At the business end, this approach keeps the wheels of your data initiatives turning day-to-day. But much of it is possible behind the scenes thanks to data engineers like you, who keep data flowing through a patchwork of pipelines to connect disparate systems.

While this data flow may be necessary, the sheer pace at which data integration must occur means you are usually deep in the weeds, firefighting day-to-day challenges like broken pipelines and poor data quality.

This makes it increasingly difficult to create a long-term, cohesive data integration strategy that can scale without losing stability or speed. Over time, hidden foundational problems build up that put data ops efficiency and compliance at risk and can potentially derail the business itself.

So how can you build and run efficient and compliant data ops at scale and speed, while still ensuring a secure data ops foundation that’s built for the long term? Learn more about three hidden challenges and how to overcome them below.

Imagine a library that had an inflow of millions of new books each day. Manually organizing them by category or subject would be almost impossible, let alone storing and retrieving them.

Data engineers already face this very real challenge, as 55% of companies report having more than 1,000 data sources in their organization.¹ Stemming the inflow of data is not viable, as you can’t risk not collecting the data you need to do business. But just collecting data brings no advantage by itself. It’s what you do with your data that makes all the difference.

The challenge is that most organizations start collecting data from diverse systems before they put a framework in place to log and organize incoming data in a way that can be made sense of at scale.

Without the proper data discovery framework, your data becomes a static — and messy — record of what has happened rather than a clean, connected, dynamic repository that holds valuable insights about what’s possible. 

Companies that collect data without a data discovery framework realize too late that they don’t know what data they have, where it is stored, how to access it and connect it quickly, or what sensitive data may be vulnerable to unauthorized access.

A robust data discovery framework helps determine what data:

• requires protection.
• should be minimized to reduce redundant, outdated, trivial (ROT) and avoid costs and risks.
• is ideally suited for migration to cloud applications.
• takes precedence for the business.

Automating processes such as data discovery, metadata inventory and creating an intelligent data catalog with an AI-powered tool brings speed and accuracy at scale. This enables users to spend less time searching for the data they need.

With data discovery in order, the next priority is to implement a long-term strategy based on process rather than people-dependent workflows.

Unfortunately, with the pace and scale of data ops at the function level, tool sprawl is all too common,making it easier to unwittingly circumvent rules and guidelines. Despite the risks, it is hard to control, especially at an intra-departmental level, where centralized policies are hard to implement.

In that case, data engineers may choose to:

• hand code a patchwork of ad-hoc pipelines
• make subjective decisions for day-to-day fixes
• deploy various new tools to solve immediate problems
• experiment without proper alignment, standardization or documentation

When that happens, the entire operation can quickly become dependent on a particular person, skill or vendor. Then, if people leave, teams change, vendors shut down or services sunset, a lack of standard processes and documentation could mean losing tribal knowledge and visibility into possible risks in undocumented workflows. This is a short-term mindset — one that makes a trade-off between speed and discipline.

A data governance framework creates a single set of rules and processes to collect, store and use data. It ensures the same policies and definitions apply to your data organization-wide. Good data governance keeps data workflows secure and helps maintain regulatory compliance at scale.

While a centralized policy may seem restrictive, a robust data governance framework leads to data democratization and allows you to introduce self-service tools across departments. These tools can empower non-technical users who want to find and access the data they need for data analytics while freeing up your time to focus on more strategic work.

With a standard process in place, data portability becomes easier, making it less challenging to scale up, try out or transfer to new technologies without breaking pipelines and disrupting ETL workflows.

As mentioned above, you can reduce person dependency on your IT team by automatically documenting workflows. To reduce skill dependency, you can automate decisions about best-fit workflows and code reuse based on usage patterns and user roles. Data governance also helps control costs by auto-suggesting when to devote resources to pro-code and when low, no-code or reused existing code would better serve business priorities.

AI-powered data governance tools automate documentation and standardize best practices that can be reused for similar use cases across the organization. This removes person dependency and the need to trade off discipline for speed. 

Your business may be dynamic, but your data integration system needs to remain stable and secure. At the same time, it should be flexible enough to adapt innovative new tools that improve efficiency, speed or productivity.

Consider these scenarios:

• A new chief technology officer (CTO) says his previous organization managed similar task loads with lower costs and suggests moving some extract, transform, load (ETL) processes to extract, load, transform (ELT) to drive efficiency.
• Due to a merger or acquisition, multiple new pipelines must be integrated into your existing pipelines, even as data volume/variety spikes and processing power has to scale up.
• A digital transformation initiative requires major changes, such as migrating to the cloud; a shift from Microsoft Azure to Amazon Web Services (AWS) or instances to serverless; a change in processing engine from Hadoop to Spark; or a move from centralized to decentralized data architecture.
• Your organization wants to explore and experiment with new technologies, such as data fabric and data mesh, to stay ahead of the industry curve.

Avoiding change to minimize risk is one way, but it could cost more in the long run, as inefficiencies creep in, and speed, processing power, security or scale of data integration ops are compromised.

At the same time, new tools must be deployed with a clear understanding of the upstream and downstream impact on data management. This includes whether current data integration tools can support and ‘talk to’ the new tools and whether the underlying infrastructure can handle the changes, including expansion and scale. 

The inability to quickly integrate new tools that make data management faster, cheaper, smoother or more accessible is not viable in the long term. To easily adopt new technologies and tools as needed, your data integration platform should be capable of evaluating, aligning and rolling back new tools, if need be, at scale, without impacting data integration operations. This is crucial because data integration solutions are neither fit-and-forget nor one-size-fits-all.

Look for a versatile platform that aligns with the next-new tech and frameworks. And it should be intelligent enough to optimize standardization and low-code or no-code. It should also minimize disruptions. These factors together will help ensure the longevity of your data integration ecosystem and give you the flexibility to try next-new technologies without posing risks to business-as-usual data ops.

The path to friction-free data management starts with an AI-powered platform that can grow with your needs. The ultimate goal is to have stable, secure, cost-effective data ops without compromising speed and flexibility.

As part of the Informatica Intelligent Data Management Cloud (IDMC), Informatica Cloud Data Integration-Free (CDI-Free) service give you the perfect path to standardize core workflows, automate repetitive tasks, pro-code where needed and respond to market dynamics, at scale.

Start pilot projects or experiment with smaller data sets hassle-free, with zero install, setup or DevOps time using CDI-Free.

Get all the benefits of working with a build-as-you-go tool environment without impacting security, stability or scale. With Informatica, your business users can access insights in hours, not days or weeks. And IT leaders can relax, knowing that you’re not compromising data security and compliance.

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

¹Informatica, CDO Insights 2023: How to Empower Data-Led Business Resiliency