The five reasons why data integration is a critical element for data analytics are:

  1. Data integration improves an organization’s data management and is the first step in analytics.
  2. Data integration increases the quality of input data for analytics jobs.
  3. Data integration stores information in a single repository for easy access during analytics.
  4. Data integration saves time and effort that can be devoted to higher-level analytics activities.
  5. Data integration enables reverse ETL for business users to run analytics workloads.

According to research by IDC and Tableau, 83 percent of CEOs say that they want their company to be “more data-driven.” The study finds that data-driven organizations have observed many positive impacts, from faster time to market to more new customers.

Of course, becoming a truly data-driven company is easier said than done—and data analytics is the way to do it. Using the results of data analytics, organizations can understand how to improve their business on several levels:

  • Optimize their business processes
  • Create more persuasive marketing campaigns
  • Retain more employees
  • Offer greater customer personalization

However, even the most powerful data analytics initiatives will fall flat without a way to access and analyze your data easily. That’s where data integration comes in:

  • Efficiently pulling information from various sources
  • Refining it
  • Collecting it so that it’s easier to understand and mine for insights

Is your organization looking to build a data integration and data analytics pipeline? In this article, we’ll discuss data integration and analytics — plus why data integration is a critical component of any data analytics workflow.

Table of Contents

What is Data Integration?

Data integration (sometimes called data ingestion) is the practice of combining multiple data sources and data sets from different locations in a single centralized repository. This is usually accomplished through a structured data integration process, such as ETL (extract, transform, load):

  1. Information is first extracted from a variety of different source systems and software.
  2. The extracted data is then transformed from its source format in order to fit the target schema.
  3. The transformed data is finally loaded into a destination like a data warehouse, providing a unified view of your information for easier access and data analysis.

Related Reading: Data Ingestion vs. ETL: Differences & How to Leverage Both

The contents of the enterprise data consumed during data integration will differ for each organization. For example, Ecommerce businesses are primarily interested in customer data, while other companies mainly work with specific types of data, such as healthcare data or IoT (Internet of Things) data.

These disparate sources may include internal databases and files, as well as external websites and resources. Some of the different systems you might pull from during data integration are:

  • Relational and non-relational (SQL and NoSQL) databases
  • CRM (customer relationship management) software
  • ERP (enterprise resource planning) software
  • SaaS (software as a service) tools for marketing, Ecommerce, etc.

Although data warehousing is the most common destination for a data integration pipeline, it’s by no means the only possibility:

  • Data lakes are a variant of the data warehouse designed to optimize storing unstructured data (e.g., information that does not neatly fit into database tables, such as text, audio, and video).
  • Data lakehouses combine both approaches, building a data warehouse using data lake technology as the foundation.
  • Data marts are mini-data warehouses that hold information for the needs of a specific team or department.

A solid data integration strategy is crucial regardless of your chosen sources and destinations. For example, the transformation stage of ETL can include data cleansing for more accurate data, ensuring that analytics users can enjoy high data quality.

Establishing a formal data integration pipeline is also a good practice for data management in an organization. A strong data integration workflow can improve business processes, cut costs, break down data silos, and strengthen data governance.

Most organizations don’t attempt to manually perform the data integration process, which requires high levels of effort and technical skill. Instead, they rely on a dedicated data integration solution that uses automation to execute a regular schedule of integration jobs.

These solutions come with pre-built connectors and APIs (application programming interfaces) that help users automatically link up to their choice of data sources. For example,’s universal REST API connector can be used by businesses to connect to any new system in their data pipeline that employs a standard REST API.

What is Data Analytics?

Data analytics is the process of working with raw data and converting it to hidden insights so that the members of an organization can make smarter business decisions. Below are the four types of data analytics:

  • Descriptive analytics attempts to describe and summarize events that occurred in the past.
  • Diagnostic analytics aims to identify problems, errors, and anomalies (e.g., for detecting bugs, fraud, or cyberattacks).
  • Predictive analytics uses current and historical data to make more accurate predictions.
  • Prescriptive analytics hunts for actionable advice that organizations can use in their decision-making.

Employees usually carry out the task of data analytics with the title of “data analyst” or “data scientist.” These individuals have many job responsibilities, including meeting with key stakeholders to define analytics goals, identifying relevant data sources, building analytical models, and delivering results through reports and visualizations.

The terms “data analytics” and “business intelligence” are often used in combination or even as synonyms. It’s no doubt true that there is a great deal of overlap between these concepts. However, when it comes to the question of business intelligence vs. data analytics, we typically make several crucial distinctions:

  • Business intelligence focuses on more straightforward “what” questions (e.g., “What were our most popular products last quarter?”). As such, business intelligence often falls under the umbrella of descriptive analytics. Data analytics focuses on more open-ended “why” questions (e.g., “Why were these products so successful?”).
  • Business intelligence often produces more factual and/or quantitative results, such as a number, a percentage, or a defined entity. Data analytics often produces more qualitative or abstract results and higher-level insights.
  • Business intelligence is usually more accessible to non-technical users, thanks to popular tools such as Tableau and Microsoft Power BI. Data analytics tools often require greater technical skill and knowledge of programming languages such as Python and R.

Another closely related concept to data analytics is data visualization: the graphical or visual representation of data through formats such as dashboards, graphs, charts, plots, maps, and diagrams. While it can often be helpful to visualize analytical insights to obtain a deeper understanding, it’s not strictly necessary to do so for better decision-making. When data analytics and visualization overlap, the result is called “visual analytics.”

These days, organizations have access to more data than ever, making data analytics an even greater challenge. The term “big data” refers to information too complex to be analyzed by traditional data analytics tools. This complexity may come in several forms:

  • Volume (enormous amounts of data)
  • Variety (data in diverse formats, including unstructured data)
  • Velocity (data that arrives very quickly, such as real-time and streaming data)

To properly work with big data, data experts have developed the field of big data analytics. This approach breaks down large data sets into more manageable chunks and then processes each chunk in parallel. Hadoop and Apache Spark are just two of the most popular tools for handling big data.

5 Reasons Why Data Integration is Critical for Data Analytics

Data analytics is a crucial practice for organizations of all sizes and industries. According to a report by McKinsey & Company, businesses that heavily use customer analytics are 23 times more likely to excel at acquiring new customers and 19 times more likely to be highly profitable.

To establish an effective data analytics program, however, companies must first integrate all of the information they have at their fingertips. Below, we’ll go over 5 of the biggest reasons data integration is a key component of data analytics.

1. Data integration improves data management

Data management is an organization’s collection of practices to make its data higher quality, more accessible, and more secure to make smarter business decisions. The field of data management covers everything from data ingestion and analytics to access control, compliance, and backups.

Data integration is essential to data management by explicitly defining your data pipeline's sources, transformations, and destination. A well-defined data integration process ensures that everyone in the organization is on the same page regarding how the first step of data analytics—integrating your enterprise data—is carried out.

2. Data integration increases data quality

In the transformation stage of ETL, information is modified as necessary to fit the schema of the target data warehouse. However, this stage can also be used to improve the quality of your data before it enters its final destination.

One of the steps of data transformation is data cleansing: checking for and removing any out-of-date, inaccurate, irrelevant, or duplicate information. Just a few of the possible transformations in data cleansing include:

  • Deleting useless or bad data (e.g., corrupted or blank information)
  • Consolidating duplicate records (e.g., by merging them or deleting one)
  • Validating data (e.g., ensuring that each field contains the proper format, such as a number or string)
  • Standardizing data (e.g., converting all dates to MM/DD/YYYY format)

Data analytics tools that take in poor-quality information are unlikely to produce useful insights—a concept known as GIGO (“garbage in, garbage out”). The higher quality of your input data, the more likely it is that the results of your data analytics processes will also be high-quality and correct.

3. Data integration makes data more accessible

In most data integration processes, the end result is to store the collected data in a centralized repository, such as a data warehouse or data lake, for easier access and analysis. Without the help of data integration, much of this information could be inside data silos: data sets that are only available for the use of a single team or department, even though others in the organization could benefit from using them.

One of the greatest benefits of data integration is that it breaks down these data silos, collecting previously inaccessible information and making it available for the good of the entire business. This ensures that your data analytics processes have as much input data as possible, leading to more accurate insights.

Data integration establishes a “single version of the truth”: a repository that stores the most up-to-date and accurate version of your enterprise data. This “single version of the truth” provides your data analytics tools with high-quality input.

4. Data integration saves time and effort

Once businesses draw from more than a handful of data sources, performing data integration manually is more trouble than it’s worth. Manual data integration processes require a great deal of technical skill and effort to build and can also break if the underlying data schema changes unpredictably.

For these reasons, most companies use powerful automated data integration tools to perform data extraction, transformation, and loading at regular intervals. By doing so, organizations can free up more time for their data analysts, scientists, and engineers to spend on higher-value activities: running analytics workloads and applying the insights gained to improve the business.

5. Data integration enables reverse ETL

Lastly, many data integration tools like include support for reverse ETL: operationalizing your enterprise data by pushing it out of the data warehouse and into third-party applications and platforms (such as a CRM or marketing tool).

Why would you want to perform reverse ETL after spending so much time getting this information into the data warehouse? The advantage of reverse ETL is that it makes data more accessible to non-technical business users who may not know how to run analytics inside the data warehouse. Instead, reverse ETL makes data available inside third-party systems that are more user-friendly, with their own rich analytics capabilities to offer.

See’s reverse ETL capabilities (and other powerful features) for yourself today with a 14-day pilot.

How Can Help with Data Integration

As we’ve discussed above, data integration is essential to data analytics. To uncover hidden trends and cutting-edge insights through analytics, businesses must choose the right data integration tools for the job, such as is a feature-rich and user-friendly ETL platform for real-time data integration. Based in the cloud, has been designed from the ground up for the needs of Ecommerce businesses. Using, Ecommerce retailers can get a 360 customer view of their audience with real-time analytics while keeping this information safe from a devastating data breach.

With a no-code, drag-and-drop visual user interface, makes it easy for technical and business users to start defining and implementing production-ready data pipelines. The platform comes with more than 140 pre-built connectors and integrations for the most popular data sources, enabling many possible data workflows and use cases.

The platform is packed with valuable features and functionality to help get the most out of your company’s data. For example,’s FlyData CDC (change data capture) feature helps users detect only those records and tables that have changed since their last integration job, saving valuable time and effort. also supports reverse ETL for pushing information out of a centralized data warehouse and into third-party applications, making it easier for non-technical users to access and analyze.

Ready to learn how can help improve your data integration and data analytics workflows? Get in touch with our data experts today to discuss your business needs and objectives or start your 14-day pilot of the data integration platform.