What is data integration?
Data integration is a generic buzzword often confused with a number of terms including application integration development, ELT/ETL development, batch/streaming data processing, etc. While all of these key data phrases are important in an end-to-end data service, data integration is an overarching term used to describe the capture of data from disparate sources.
More specifically, data integration is the process of combining/consolidating batch or streaming data from various source systems into one data warehouse or sink. The centralized location the data is ingested into is commonly a flexible data warehouse or a server cluster designed to meet the requirements of some service level agreement. Once the data is in the centralized sink, business units can activate against it using a variety of methods.
How does data integration work?
Nowadays, data integration processes often kick off with some serverless cloud orchestration technology or dedicated build server scheduled to make a call to a server cluster. The server cluster subsequently makes an API request to external systems to ingest data into a staging data lake using an asynchronous parallel-processed or synchronous paradigm/software to meet the SLA. Once the data is in the staging area, some sort of acyclic graph is constructed to launch procedures to clean, deduplicate, and unify the data from all the sources and ingest it into one cohesive data warehouse or centralized server. Once in the data warehouse, many views or data marts are updated with different levels of data aggregation designed to be used for analytical purposes or to activate against it.
Why is data integration important and how does it help businesses succeed?
So, why would an organization want to embark on a data ingestion journey? Well, having all disparate data inputted into a centralized location provides a holistic view of many organizations’ customers/clients and processes. Many of these disparate data sources include advertising platforms, CRM systems, clickstream analytics platforms, business operations data warehouses, supply chain management systems, streaming logging applications, etc. And this only scratches the surface. Having a process to capture, transform, and unify all of these data sources into one holistic view makes it easy for the business to analyze all data, uncovering real value, thus improving business functions, revealing business development/sales opportunities, and improving cross-team collaboration.
Data integration helps build confidence
Data integration improves data sharing across business units and also makes it easy for IT departments to create secure processes. IT departments would not have to deal with managing data in a variety of locations but rather approve data pipelines/applications. This also enables a data governance strategy to ensure different groups have the appropriate identity and access management levels, PII information is encrypted or masked and change management is established across the business globally. Large organizations with federated groups could truly see the benefits of a centralized data warehouse/server with unified data.
Data integration saves time and increases efficiency
Mature, generic data integration pipelines can accelerate analytics functions with automated I/O, transformation, and unification. Business units don't have to rely solely on developing custom connectors and ingest data in a variety of formats across disparate systems. Having a defined data integration ingestion process, data lake, and centralized warehouse will speed up discovery, transformation, and analysis. In fact, employees can focus on analysis with this acceleration, ultimately leading to efficiencies and even new discoveries.
Data integration delivers more valuable data
Business units will also be able to verify the need for certain data by evaluating existing data points within the centralized location. A common problem in larger organizations is the duplication of data applications designed to ingest similar data points. This can lead to a lack of efficiency and clarity especially if a business unit is requesting similar data. More importantly, the inverse usually happens. Centralized data access allows for individuals to uncover relevant information and enrich their analytics. Departments can run descriptive and inferential statistical programs to uncover relationships in advertising efforts, supply chain functions, and other operations.
Data integration reduces errors and rework
Having data centralized into one location through an approved, solid data integration process can uncover anomalies and reduce errors. Employees using different techniques to ingest data into a multitude of sources opens up opportunities for error. This can be extremely detrimental to the business as data is a critical component to success.
Data integration techniques
There are several techniques or strategies which can be followed when doing Data Integration. The right one for your organization depends on the amount of data you have, the number of data sources, and your (business and technical) requirements.
- Manual involves a single resource manually collecting, cleansing, and aggregating disparate data into a common area for access. This technique is usually used by small organizations with limited resources.
- Application-Based Integration depends on a software application to integrate (extract, transform, load) the data from individual data sources.
- Middleware Data Integration leverages a middleware application to act as a mediator, containing and executing the integration logic of all the disparate applications from which data is being ingested.
- Uniform Access Integration leaves data in the source systems but makes data available and seemingly unified, via a set of views.
- Common Data Storage centers on creating a new system to store, transform, and independently access/manage the data ingested from the various systems. The most well-known example of this technique is a Data Warehouse (DW).
How to get started with data integration
So, you are probably wondering, "Well how do I get started with data integration and what is the best approach?" The answer here is that the best data integration approach is unique to the individual business. There is no ultimate, universal solution to integrating data. The formula for success depends on the specific business use case. However, there are a number of questions that should be raised in order to be as successful as possible. Below is a quick list of questions in the logical order you should ask:
- Who are the stakeholders and sponsors across each business unit to leverage to understand the gaps and benefits of a data integration strategy?
- Are the business problems or pain points raised by these stakeholders/sponsors solved by a data integration strategy?
- What data integration technologies can be leveraged already existing in the business and what investments need to be made to be successful?
- What are all the data sources across the business and what is the specific SLA for each data source?
- What Identity and Access Management (IAM) and security protocols need to be set up in accordance with the data ingestion processes?
- Is there a primary/foreign key(s) to leverage for the ID resolution and unification process? Or are there gaps?
- Will a data governance strategy and master dictionary be developed in accordance with the size of the data integration process?
- Are there future plans to update legacy data source systems? How will historical data be ingested into new systems?
- Data lake? data warehouse? What new or existing technologies can be leveraged?
- Do you expect streaming data? If so, will message broker software and streaming technology need to be leveraged?
- ELT or ETL? What data I/O and transformation process do you expect to use?
- How will the process be maintained? Will this be a generic holistic process to be used by multiple business groups? Will CI/CD need to be leveraged?
What are the greatest challenges of data integration
Before you get started, it's important to know where you may face challenges.
- Understanding the Intent - It's more than cataloging the systems from which data will be integrated, and the types/kinds of data within those systems, it's also about knowing how the data will ultimately be used. Knowing how the data will be used, by whom, and what they are trying to get out of the data, will help you design the best solution, and ask the right questions to ensure that the data that you will integrate will be adequate to fulfill those requirements, or if there are gaps that should be addressed.
- Data Completeness - You can face everything from too much data e.g. IoT data sources, not enough data e.g. newer systems, missing data e.g. from legacy systems, data that's not available often enough e.g. from an external vendor, etc., to data that's not at the right level (granular).
- Keeping Up - Once things are in production, the heavy work is done, but there's still maintenance that should occur on a regular basis. Maintenance includes addressing changes from the data sources or errors that may arise, as well as keeping up with best practices and new regulations.
Overall, data integration can be a complicated process to architect. Evaluating existing data integration tools or in-house custom development resources can be quite challenging. Does an existing tool have a transparent pricing model, do the capabilities meet the SLA, is it open-source enough, is it compatible with existing resources, etc? There are a lot of questions to ask. Search Discovery can help evaluate your options.
