What is Data Integration? – Steps, Why do Need, and More
Table of Contents
What is Data Integration?
Data integration is generally implemented in a data warehouse using specialized software that houses large data stores of internal and external resources.
Data extract pooled and presented in a unified way. A user’s complete data set can include information extracted and combined from marketing, sales, and operations, connected to form a comprehensive report.
It is the process that allows you to combine heterogeneous data from many different sources in the form and structure of a single application.
It makes it easier for different types of information, such as data matrices, documents, and tables, to be merged by users, organizations, and applications for personal, business process, or role use.
The integration supports large data sets’ analytical processing by aligning, combining, and presenting each collection of information from organizational departments. And remote and external data sources to meet the integrator’s objectives.
A data integration project generally involves the following steps :
Access to data from sources and locations, whether it is local, in the cloud, or a combination of both.
Data integration so that records from one data source map records to another.
For example, even if a data set uses “first name, last name” and another “name, ape,” the integrated set will ensure that in both cases, the data goes to the correct place.
It is a type of data preparation essential for analytics and other applications to use the data successfully.
Delivery of integrated data to the business right when the company needs it, whether in batch, near real-time, or real-time.
Why do Need Data Integration in Company?
The business world is increasingly focusing on the consumer. Focusing on customer service and listening to you for feedback was vital in the past.
Still, today’s businesses need to reach more in-depth insights into what customers want, collecting data ranging from patterns with products to publications on social media.
A Price Waterhouse Coopers study found that despite this new focus on customer experiences, 24% of CEOs think they don’t have enough information about what customers want. Almost two-thirds of respondents said that understanding what users value is among their top concerns.
Using data to have more precise customer demands is vital, and doing it profitably is essential. However, many companies lack the tools to do so.
An Experian study found that only 30% of organizations feel-good data integration in marketing departments.
Reasons a Company Should Focus on Data Integration
These are the five reasons why a company should focus on data integration:
1. Reduces the Burden on Business Analysts
Business Intelligence professionals face an overwhelming workload trying to filter the vast amounts of data that enters the business on a day-to-day basis.
Eliminating data silos allows users to access different sets of information based on their specific needs.
Giving teams direct access to relevant information leaves analysts with one less topic to worry about, allowing them to focus on more complex data sets that create business value.
2. Eliminate Double Work
On many occasions, some organizations analyze clients to find out their demands and soon after discover that a similar project completes a few months ago in another department.
With data integration, you can avoid that redundancy and not just in terms of large projects.
The most common problems companies face having to do with records of customer data in multiple places, documentation of processes in different systems, etc.
3. Maximize the Value of your Data
An analysis that can be effective for 20 employees will be considerably less valuable if only five of them receive the report.
Unifying data across channels enables organizations to leverage different data types in conjunction with others to maximize their potential and ensure that user groups have the visibility they need.
This transparency can be extended to both internal and external stakeholders, fostering collaboration within the company.
4. Improve Decision Making
Giving users access to critical data embedded in the applications and services they use allows them to make better decisions when interacting with customers and partners.
Integrating data into relevant systems makes information actionable in daily operations, providing users with the insights they need to work as intelligently as possible.
5. Various Types of Data
While leveraging various data types is related to maximizing data value, it is crucial to recognize that different kinds of information create unique challenges.
It information from spreadsheets, highly structured databases, social media reports, diagrams, technical documents.
And many other sources must come together to obtain complete information on operations, especially when emerging technologies such as the internet.
Of things bring even more data to business ecosystems. This diversity can quickly become overwhelming and leave data abandoned if users do not have access to it to intuitively tap into information across their departments.
Advantages of Cloud Data Integration
As cloud technology matures, the ability to transfer complex data and processes to cloud environments improves. It allows companies to integrate applications for immediate efficiencies while enjoying better information management.
An appropriate data architecture that can support on-premises, cloud, and hybrid solutions. It will help control costs, whether it be licensing, storage, data integration, or bandwidth.
Additionally, a business-aligned cloud architecture increases scalability and reusability. In turn, it makes workers’ tasks easier and improves their ability to meet changing business needs.
Working with a technology partner who helps understand the complexities of dividing application functionality across multiple solutions, both in the cloud and on-premises, and the implications of data integration and data management perspective.
It helps companies efficiently take advantage of cutting-edge technologies, benefiting from the leading solutions.
Cloud data integration offers the following advantages over older methods:
All users can access personal data in real-time from any device with an Internet connection.
It is possible to integrate personal data such as calendars and contact lists offered by different applications.
The same login information can use for all personal applications.
The system efficiently passes control messages between applications.
Silos of data avoid formation integrity is maintained, and data conflicts that arise from redundancy eliminated.
Cloud data integration offers scalability to enable future expansion in terms of the number of users and applications.
In recent years, cloud data integration has gained favor among organizations and government agencies that implement SaaS (software as a service), a software distribution model in which applications host by a service provider and are made available to users over the Internet.
The Fundamentals of Data Integration
Managers who put in charge of a data integration project are often unsure of where to start or what to do. Therefore, several fundamentals will mark the starting point to know how to approach this process:
1. The Metadata is Everything
The source of the data will guide how the integration of the data should begin.
The company must understand the information stored in the source and destination systems and find a single, reliable and authentic source.
2. Understand the Flow of Information
Once the source identifies, it is necessary to determine how the data will flow from one system to another. While most data integration flows are simple replication.
It is also possible to change the structure and content of the information that flows from one system to another.
The target infrastructure receives native data.
3. Unite Security and Data Governance
These are two elements that often do not complement each other well in data integration environments.
This problem becomes more critical as we move to the cloud, as data is physically out of control. Integrators must encrypt the data, and once encrypted. The information will be more secure.
In the context of data integration, Data Governance involves the use of active policies around the use of data, flows, transformations, etc.
It allows us to prevent someone from changing a flow or varying the structure of a target system and breaking the integration solution.
Whether it is the first time an organization is integrating data or already a consolidated system, the fundamentals of integration remain the key to its successful development.
Best Practices and Mistakes to Avoid in Data Integration
Data integration is among the top three strategic technologies companies employ. But the first concern of organizations is that they are not getting the most for their investment in data integration technology.
Maybe it’s time to look at both the basic best practices that have been around for years and other new best practices. It may companies do not know about and review the errors to avoid related to data integration. Let’s look at three good ways and 3 data integration mistakes:
Practices-1: Understanding the Data is Key
Those who want to integrate data without defining it at the metadata level are bound to make huge mistakes.
And these failures may not be easily undone, such as the absence of the critical information set needed to support predictive analytics. Or other operations that require access to historical data.
The problem with this good practice is that defining the data is mostly unknown in data integration.
We currently have tools and technology. It helps us determine the data found in our source and destination systems and manage ongoing metadata.
As things change, we can automatically redefine as well as make changes to our data integration technology.
Practices-2: Security Can’t be an Afterthought
The security of data integration must be systematic. It does not include substance whether you plan to encrypt both stored data and real-time data.
The security approach, models, and technology must determine before implementing the data integration solution.
Today, the excellent newscast is that there are options that were not available a few years ago, such as Identification and Access Management (IAM), for example.
While this may not be appropriate for all domains of data integration problems.
There are many cases where identities can be a perfect job for the types of security services needed to support data integration.
Practices-3: Gather Skills Before Building
What are the most difficult skills to find?: The data integration specialists. The competition for the right talent in this area is fierce.
Some of the best technicians in this discipline changed their professional careers to focus on newer topics, such as cloud computing.
Companies should begin searching for suitable talent before beginning their journey to a well-integrated company.
Those who try to find these professionals at the last minute will find that this approach does not work.
Mistake-1: Not Understanding the Types of Data to be Integrated
Although we have commented on it in the first best practice, we indicate it again as an error due to its importance.
It seems obvious, but most of the most significant data integration mistakes can trace back to failures around understanding existing data on the source and destination systems. There could be data stored in data objects, relational databases, and even proprietary data stores.
The data must be defined in terms of physical storage and structure or lack of system, if applicable.
From there, you need to determine which approach is best for data integration, including live data translation and transformation.
And whether the framework needs to be applied before the data integration engine uses the data.
Mistake 2: Not Considering Performance
Another widespread failure is to assume that data integration technology has no latency. That is never the case.
If you eat a large amount of data from many source systems, processes will determine your data integration solution’s performance.
If the processing is intensive or complex from input to output, things will be slow. There is little processing. Then things will speed up.
The only way to deal with performance is to understand the data integration technology and the use cases we plan to integrate.
Not understanding those parts means that the version will be hard to predict.
And you could fail just because the fix is too slow during production. It is a problem to solve when it occurs.
Mistake 3: Forgetting about Management
In the same way that we essential to understand the data, we also need to ensure that we control it.
And know-how the data changes over time and restrict who can change and access the data through policies.
Security must be consistent in data integration environments. We also have to address compliance issues and laws that determine how information should manage efficiently.