What Is Data Integration? How to Succeed Your Data Integration Strategy?
To understand data integration, imagine these two scenarios:
Scenario 1: The marketing department in a retail company is compiling its monthly reports. To do so, the team requires data from the company’s emailing system, CRM, Google Ads, Facebook Ads, and website. However, since data has been gotten from various sources, it often has different formats, and consolidating it is a headache.
Scenario 2: In retail, a company with multiple outlets needs to manage inventory, sales, and labor hours across its network. All this data needs to be brought together in one place to get a 360-degree view of the business to greatly enhance future planning and profits.
The solution? Data integration.
Without an advanced data integration system, most business operations will be chaotic, which will negatively impact revenue and overall business efficiency.
Let’s look at how to incorporate a successful data integration strategy into your business.
What Is Data Integration?
Data integration is the process of combining data from different sources to a centralized destination known as a data warehouse to help obtain a unified view of the business.
Its ultimate goal is to allow data consolidation which makes a company data ready. A solid data integration strategy ensures that data platforms and tools for data analysis and creating reports can make better data-driven decisions.
In today’s competitive business world, it’s critical to derive value from data. Making sense of it through an advanced integration system is also essential.
How to Succeed Your Data Integration Strategy
Now, let’s look at tips on how to build the right data integration strategy for your business.
Choose a Data Integration Technique
Data integration is a process that can be achieved through different techniques and it is important to know which technique will offer the most value to your business and take the least amount of time to process. Highlighted techniques are:
As a data integration technique, ETL (Extract, Transform, and Load), involves extracting data from the sources, transforming it along the way, and finally loading it into a specified data warehouse.
ETL is best used for synchronizing several data use environments and migrating data from legacy systems.
Use case: Companies that are merging may have multiple consumers, supplies, and partners in common. This data can be stored in separate data repositories and formatted differently. ETL works to transform the data into a unified format before loading it into the data warehouse.
Another common data technique is ELT (Extract, Load, and Transform). Here, data is extracted from various sources and stored in a database. Transformation of the data is done at a later time when needed for particular uses.
ELT is best used in high-volume data sets or real-time data use environments.
Use case: Organizations with large volumes of data. Let’s take an example of meteorological systems like weather services. They collect, collate and use large amounts of data regularly. The extract, load, and transform process allow for quicker transfers of source data.
Change Data Capture
In this technique, data changes in databases are identified in real-time and the changes are applied to a data warehouse or other repositories. It simply means capturing data any time there is a change.
Here, data in one database is replicated in other data warehouses. This is useful for backup purposes and to keep the information synchronized to operational uses.
In data virtualization, data from different systems are virtually combined to create a unified view rather than loading data into a new repository. It allows an application to retrieve and manipulate data without requiring technical details about the data, such as how the data is formatted or where it is physically located.
Streaming Data Integration
This is a real-time data integration technique that implements a data pipeline to ingest, filter, transform, enrich and then store the data in a target database or analytics system to be analyzed later.
Data Governance Strategy
It refers to a collection of roles, standards, processes, policies, and metrics that ensure the effective and efficient use of information in enabling a business to achieve its goals.
Data governance is essential when dealing with large volumes of data. It ensures the security and quality of data being used in your company.
It is a key factor to be considered in data integration for the following reasons:
- Better data quality as a good data governance strategy helps define standards to maintain the integrity of data.
- Data visualization as the complexities that come with bulky data is simplified, making it easier to manage, govern and garner insights.
- Better access to data as proper governance leads to an improved organization that can give your employees easy and reliable access to data.
How to Choose the Right Data Integration Tool
Based on your need, you can use different criteria when it comes to choosing the right data integration tool for your business. Some of the factors you ought to consider include:
- Data sources supported – Each tool specifies which data sources it supports. Be careful to ensure all the sources you require are available for extracting your data.
- Scalability – Choose a tool that allows you the capacity to grow your data warehouse big enough to accommodate your needs at any time.
- Performance – Speed and reliability are essential for day-to-day operations.
ClicData is a fantastic data integration tool due to the functionalities listed below that allow it to create strong and efficient data pipelines.
The functionalities are:
- A lot of connectors – ClicData allows you to conveniently bring all your database and files into one place. With over 250 connectors, ClicData has connectors for many business functions including business applications, social media, cloud and on-premise databases, spreadsheets and flat files, web services, and APIs.
- Integrated Data Warehouse – ClicData allows you to quickly and easily create an integrated and smart data warehouse. Such a warehouse helps bring together relationships between different data sets and different systems to explore, analyze and report on all of your data.
- Integrated ETL – ClicData is keen on helping analysts spend less time massaging data and quickly make sense of everything. The integrated ETL is easy-to-use, intuitive, fast, and sophisticated for a smooth user experience.
Benefits for Companies / Businesses
Improve Efficiency and Save Time
A good data integration strategy helps save time on data management. Manually combining files takes time and is prone to numerous errors.
Data integration simplifies data preparation and data analysis allowing you to focus more on future decisions and business growth.
Having a data integration strategy ensures fast connections from one data store to the other. Additionally, there’s also improved ROI and efficiency due to greater productivity because of time saved.
Improve Collaboration and Avoid Data Silos
All the departments in a company need to work together. Whether it’s Sales and marketing, or marketing and IT, no department should be isolated. In big companies, collaboration can even be difficult inside a department.
But collaboration should go beyond excel file exchange. When you automate this part and make it easier for everyone to have access to data, the real collaboration will start.
Data silos are bad for business since they don’t only slow down your business systems but also waste your storage space. Data silos can be avoided by having an efficient data integration system, which ensures a seamless flow of data.
Better Data Quality
Data integration is all about automation to eliminate human intervention and thus the error. By having an air-tight data integration strategy, the chances of interference of data reduce significantly since every activity leaves a digital footprint.
When managers know they can trust data, they also gain time because they don’t have to run it through multiple checks. Ultimately, this improves productivity which translates to business growth and increased efficiency.
Quality data also ensures improved customer experiences since decisions are made with accuracy and relevancy, which in turn ensures brand loyalty and improved revenue.
Ability to Manage Big Data
By definition, big data cannot be processed manually, which calls for a good and efficient data strategy. Considering the volume, velocity, and veracity of data, especially big data, data integration ensures improved retrieval and reliability.
Depending on the company’s governance structures, handling big data may call for the recruitment of data scientists and engineers to help make sense of such data. With a dependable data integration provider, big data will be handled efficiently, consciously, and transparently.
Valuable and Reliable Data
Improving collaboration, better efficiency, and data quality leads to reliable data and valuable insight. Since information is passed and processed through data pipelines, its quality improves greatly which ultimately helps build trust in the data.
Ready to integrate your data?
Every business has the desire to make its daily operations fast and seamless without compromising on efficiency, which is probably why you’re reading this. With ClicData you are guaranteed easy integration, access to historical data, data consistency, and faster access to your data.
Transform your business using smart and innovative data solutions from ClicData.
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