Data integration is now an integral aspect of business. With the ongoing competition among industry leaders and more startups gaining traction, more and more businesses are acknowledging the value of data integration. And enterprises that have embraced it are reaping its benefits.
It is a no-brainer that the coronavirus pandemic has pushed businesses to explore ways to streamline their processes and boost their employees’ efficiency and productivity. One notable trend is the transition to teleworking policies, which necessitates the use of numerous applications.
Generally, your startup is likely to use about 928 apps, excluding your on-premise data storage systems. This points to the bulk of multi-format data that you must manage and decipher. But with data integration, visualization and decision-making get easier and better.
What is Enterprise Data Integration?
Enterprise data integration entails centralizing several sets of organizational data and information, usually spanning varied formats and sources, into one consolidated interface.
Advantages of Integrating Your Business Data
As a fact, enterprise data is the backbone of contemporary business. Data removes uncertainty, helping your firm thrive in the prevailing hypercompetitive business landscape. By tapping into business intelligence and data analytics, you stand to attain:
- Digital transformation,
- Data-driven marketing,
- Enhanced customer acquisition,
- Increased customer retention, and
- Above-average profitability.
How to Integrate Your Business Data
When seeking to execute your data strategy, you are open to multiple approaches. The best data integration approaches include:
Data consolidation naturally gathers data from multiple secluded systems, combining and unifying it in one repository. This approach aims at reducing the number of storage systems and avail business information promptly.
The ETL process plays a key role in data consolidation. It obtains data from varied source systems, converts it into a logical format, and conveys it to the appropriate destination system — data warehouse or database.
Alternatively, you can use the ELT process, which obtains data from the source system, relays it to the destination system, and then conducts database transformations at query runtime. Rather than the ETL process, the ELT process is recommended when handling large data volumes.
Data warehousing meets the need for responsive access to enterprise data organized in a manner that enhances organizational performance and offers quick, accurate, and relevant data insights.
Another benefit of data warehousing is how it consolidates data via the ETL or ELT approaches and loads it into a data warehouse designed to expedite business intelligence, reporting, and ad-hoc requests. This presents all the integrated data assets together with their accompanying details on an integrated interface, easing pattern identification and decision-making.
Data virtualization, unlike the ELT and ETL processes, develops an abstracted layer to offer a nearly real-time, unified view of organizational data from multiple source systems. This presents data on a unified interface although the data is not stored onsite.
In addition, data virtualization improves business intelligence since virtual structures are easier to access and manage than physical structures like data warehouses. Data virtualization also refines speed-to-market due to speedy solutions.
Data virtualization hastens business processes, promoting overall performance. What is more, it is cost-effective since it eliminates data discrepancies, extra hardware expenses, and additional software licensure, among other burdens.
Data federation is technically a variant of data virtualization; it stores data virtually and creates a common data model for varied data from several sources. This approach blends data, sending it from one access point. However, it enforces stricter data models than data virtualization.
Data federation gives you a consolidated and updated view of critical business data. This helps you understand your customers better without the hectic and fault-prone physical data transfers.
Data federation substantially lessens the overhead linked to incessant data collection, transformation, and transfer. It simplifies business intelligence and other key analytics by leveraging multi-source and multi-format information.
For the record, each of these approaches has its merits and demerits. So, choose an approach that supports your firm’s systems, automation features, and on-premise transformations.
Although data integration is straightforward, you might find it overwhelming to execute. But don’t fret.
At Helios, we care about your startup’s data. And data integration is our forte. Contact us today to help your company adopt a reliable, custom data integration solution.