The Ultimate Guide to Data Integration

The Ultimate Guide to Data Integration_3Today’s businesses compete on how fast and well they can glean meaningful insights from their data sets to generate better products, services, and, ultimately, experiences. It is experiences that customers use to determine whether they will stay loyal to your brand or buy from your competitor.

The faster you can access the insights from your data, the faster you can move into your market.

But how do you glean those insights when you’re dealing with massive volumes of big data, different data sources, different systems, and applications?

Easy, enterprise data integration.

Well, maybe not “easy.” But, don’t worry. That’s why we’ve put together, “The Ultimate Guide to Data Integration.”

But first, what is data integration?

What is Data Integration?

Data integration is the process of bringing together different sources of data to make the data more useful. For example, by integrating data from your CRM, contact center, website, mobile app, marketing, and sales software, you can create a 360 view of your customer data. Having a 360 view is more useful to the business (and ultimately the customer experience) than just knowing separate data points that don’t seem to connect in each of the systems.

So, data integration is about making data more useful and valuable to the organization.

To accomplish this, data needs to provide business intelligence or operational analytics. Both help your business make more strategic decisions.

6 Reasons You Should Care About Your Data Integration

  1. The Ultimate Guide to Data Integration

    Want to make your applications a trustworthy source of truth across the business?

  2. Want to improve analysis, forecasting, and predictive analytics?

  3. Want to be able to build a 360 view of the customer and business?

  4. Want faster innovation and faster time to market?

  5. Want to increased agility and responsiveness?

  6. Want to improved ROI from tech stack investments??

If you answered yes to one (or most) of these, you’re in the right place.

Data integration is foundational to driving deeper collaboration and to automating business processes and workflows that improve efficiency in real-time, decrease human effort, and create the type of enterprise that delivers exceptional experiences. Wherever you are in your digital transformation journey, data integration is the key to moving forward.

It’s Okay to be Struggling with your Data

Nearly every enterprise today has undergone efforts to digitally transform and harness the power of their data. But, it’s not rare to still be struggling.

With massive amounts of big data being generated, the rapid pace of tech innovation, the costs of change, and a plethora of business analytics tools available to choose from — it’s easy to see why so many businesses wrestle with trying to glean the value from their data.

Data that is not integrated remains in silos where it resides. It takes a lot of effort to manually gather data from each system or application, copy the data, reformat it, cleanse it, and then ultimately analyze it. Because it takes so long to do this, the data itself may easily be outdated and rendered useless by the time the analysis is made. Businesses don’t have time to wait anymore.

Data integration is essential; let’s explore it a bit more in-depth.

What are the Different Components of Data Integration?

  1. Data Migration

    What is data migration? Data migration is all about moving data assets from one location, format, or application to another. You’ll hear this term when data is being moved from an on-premise database to a cloud-based one, for example. But overall, data migration involves data storage migration, cloud migration, and application migration. It’s all about moving data, not making it more useful. You may be able to more easily access data that has been migrated, but you won’t glean more insights from it.

  2. Master Data Management

    What is master data management? Master data management focuses on coming to a standard set of policies, definitions, stewardship, and accountability between IT and every part of the business when it comes to the business’s most important shared data assets. These assets are called master data and typically are data on customers, suppliers, sites, etc. Getting everyone on the same page when it comes to using this data is essential to keeping the business functioning as one organization.

  3. Enterprise Application Integration

    What is enterprise application integration (EAI)? Just like the name suggests, EAI is all about creating interoperability among systems and applications. This is where you get Salesforce, Workday, Redshift, ServiceNow, and Oracle to play nice with each other. EAI is critical to creating omnichannel customer experiences, streamlining workflows and processes, and creating seamless experiences for customers and employees.

  4. Data Aggregation

    What is data aggregation? This is the process of gathering and compiling data to be either stored in its raw form or prepared for analytics. There are three common types of data aggregation.

  5. Data Federation

    A data federation creates an integrated view of data by making a virtual database that does not store the data, but has information about where the actual data is. The data federation presents the data as a single viewpoint, but does not house the data itself.

  6. Data Lakes

    A data lake stores massive amounts of raw data that has not been given a purpose or prepared for usage. This data may or may not eventually be used, but it is stored and held for its potential value.

  7. Data Warehousing

    Data warehousing stores structured data from sources that will be used for analytics and business intelligence. Data from multiple sources goes through what is called the ETL (extract, transform, load) process and put into the data warehouse (like AWS Redshift, Microsoft Azure SQL Data Warehouse, Snowflake, SAP Data Warehouse Cloud, or Oracle Database Cloud Service, etc) where it is then used for reporting, business intelligence, and data virtualization.

The Ultimate Guide to Data Integration

How Can AI and ML Help Improve my Integrations?

Artificial intelligence (AI) and machine learning (ML) used in data integration platforms can process data faster and more accurately than humans. Because they work with algorithms that speed up the ability to identify, access, connect, and move data, AI and ML are important components of data integration solutions.

Data integration solutions that incorporate AI and ML are able to more readily find useful data from different sources and drive faster, more accurate analysis and insights. Part of data integration is dealing with sensitive or personally identifiable information, identifying what should be masked or anonymized, and also discerning what is useful and what isn’t. AI and ML are able to do this automatically to help ensure compliance with HIPAA, GDPR, and other regulations.

What are the Different Methods of Integrating Data?

So, how do you do integrate your data? There are several approaches ranging from manual integration to data integration platforms:

  • Do it manually. This is a time consuming and resource-intensive method where integrations are done point-to-point and must be monitored and continually maintained by IT.
  • Use middleware. Middleware data integration serves as a mediator between data that needs to be normalized and the master data pool.
  • Create uniform access. This type of data integration makes a front end that makes it data from multiple sources appear uniform. The data is left in its original source.
  • Use common storage. A copy of data from the original source is stored in the integrated system and used for the common view. Common storage is the underlying principle in data warehousing.
  • Let an integration-platform-as-a-service (iPaaS) do it for you. An iPaaS, like SnapLogic, does the work of data integration for you.

What Should You Look for in a Data Integration Solution?

Choosing among data integration tools is about selecting a method that will make the process smarter, faster, and easier — and work within your budget.

It’s also about harnessing the power of AI and ML to ensure your integration capabilities can keep your business moving with as much speed and agility as possible.

Here are some things to look for in a data integration platform:

  • Is it AI and ML-enabled to bring speed, quality, and accurate predictability to data-driven decision-making?
  • Is it purpose-built for the cloud? No legacy components. Is it self-upgrading, with an elastic execution grid? Can it scale up or out? Manage environments from public to private and on-premise?
  • Does it offer click, not code for easier integration? Drag-and-drop, and snap-and-assemble? Is it robust enough for developers, but easy enough for your business teams to use?
  • Is the pricing transparent?
  • Is it portable?
  • Does it enable integrations beyond data integration to provide an easy, complete way to integrate data and applications?

The SnapLogic integration platform is here to make your data integration process smarter, faster, and easier. With more than 500 preconfigured Snaps, your business teams and developers can integrate data and applications with clicks, not code. Our Iris AI provides proven recommendations and guidance for smarter integration.

 


Tim White

Tim is the Sr. Director of Marketing at SnapLogic.


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