Integrated data for more efficient business management: when is it possible?

The use of data in business management has well-known and widely recognized benefits. However, companies today face a new challenge: the multiplication of data sources due to an increasingly pervasive digital presence within business processes.

In this context, data integration-that is, the ability to aggregate and harmonize information from disparate systems-becomes crucial to ensure efficiency, both from a strictly operational and decision-making perspective. But what exactly is meant by data integration and in what situations can it be applied? And most importantly, why is it so important?

The advance of artificial intelligence is helping to raise awareness in this area: among the entities that are approaching AI, in fact, the percentage of those aware of the costs associated with poor quality of data rises to 41 percent.

Data integration is undoubtedly an effective strategy to guard against this problem: a proper approach, in fact, requires a series of preliminary steps aimed precisely at eliminating redundancies and dissimilarities, creating a uniform and normalized corpus, typically through a data lake.

Current definition of data integration

Data integration is the process of combining information from different sources to create a unified and consistent view. This approach involves the aggregation, transformation and standardization of data, making it accessible and usable across the enterprise. Integration tools help break down information silos and create a Single Source of Truth, a single, trusted source for the entire organization.

There are several possible techniques for achieving data integration, among which we mention the four most popular:

• ETL (Extract, Transform, Load): a traditional method that involves extracting data from source systems, transforming them, and loading them into a target database or data warehouse;
• ELT (Extract, Load, Transform): a variant of ETL in which data are first loaded into the target system and then transformed as needed;
• Data Virtualization: provides a virtual view of data without physically moving them, so they are accessible and integrated in real time;
• Data Federation: combines data from different sources, presenting them as a single source, often through one or more virtualization layers.

These techniques make it easier to consolidate information and enable advanced analytics and the adoption of technologies such as artificial intelligence and machine learning.

Approaches to data integration

Data integration is feasible in a variety of circumstances, provided there are systems that are compatible or can communicate through standard protocols. The main approaches include:

• Manual integration: useful for small-scale contexts, where only a few data sources need to be combined. This approach scales poorly and is time consuming.
• Middleware integration: uses intermediate software that acts as a bridge between applications and systems, facilitating the exchange of information automatically.
• Data warehouse: concentrates data in one centralized repository, where it can be analyzed and queried.

In today’s reality, especially in manufacturing, it is possible to intervene in systems to make data usable-a good data integration platform usually has all the necessary tools. This will make it possible to converge data into an advanced analytics platform.

From there, with the support of experts and consultants such as those made available to its clients by Regesta Lab, business data can be enhanced according to the specific needs of each reality and sector.

Benefits of data integration

The benefits of data integration extend to virtually every area of business management where decisions, including operational decisions, need to be made based on information. The more complete and accurate this information is, the better the end result will be. We summarize here the most interesting and generally applicable benefits:

Data-driven decisions: single source of trust enables informed decisions and reduces the risk of errors due to inconsistent, incomplete or poor quality data.

Operational efficiency: once information silos are eliminated and the problem of duplicate or redundant data is solved, the benefit extends to efficiency and reduced operational time, as well as, of course, reduced resources needed for calculations and processing.

Personalization: also in the field of customer experience or user experience in general, the integration of data from different touchpoints allows for a better understanding of customer or user needs and the delivery of tailored experiences.

Increased regulatory compliance: an integrated system facilitates data management and monitoring in compliance with current regulations.

Support innovation: with access to comprehensive and up-to-date data, companies can implement advanced technologies and develop new business opportunities in a more agile and informed way.

In these cases, data integration, through its ability to break down information silos and democratize data within the enterprise, manifests its full potential and becomes an enabler for companies that want to move toward an increasingly data-driven approach.

Data integration: better information for better decisions

Data integration should be a preliminary and fundamental step for all companies wishing to embrace the data-driven philosophy, but not only that: entities seeking greater operational efficiency can also benefit, especially through the rationalization of resources that this presupposes.

Finally, Data Integration underpins most of the solutions offered by Regesta Lab for business efficiency and cost reduction, from reducing scrap and downtime to analyzing purchasing behavior. Having complete visibility into your data and the opportunity to put it to value means gaining a major competitive advantage.

Want to learn more? Learn from the voice of our experts about Regesta LAB’s success stories and best practices.

Note ¹: Corrierecommunication.co.uk.