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Lack of data integration and its impact on the organization

The subject of data integration is not new: companies can no longer ignore the need to unify and qualify their data! However, many decision-makers are putting off change: integration, which can be defined as the pooling and transformation of data from multiple sources to make it usable, is often perceived as non-essential in the face of more attractive innovative projects. For these organizations, however, it’s high time to finalize this operation – the cornerstone of transformation! The company can only derive value from projects linked to business applications, IoT, hybrid IS or Cloud infrastructures on the basis of sound, consistent and rapidly exploitable data. Non-urbanized IS architectures not only hold back the company’s technological advances, but also put a strain on the information system’s TCO and result in a poor user experience (both internal and potentially customer).

Data integration is indeed a fundamental issue, and one that needs to be considered in its entirety, across all sources and targets: because beyond IS urbanization, it’s all business processes that depend on the quality of data flows, and therefore end-users!

Even today, many Information Systems are still too unstructured, as evidenced by :

  • The coexistence of old and new applications communicating point-to-point: this mode of operation is no longer feasible, given the diversity of information sources used by companies.
  • Numerous, non-standardized data formats: dialogue between heterogeneous applications slows down communication, generates errors and duplication, and makes data enrichment difficult.
  • Little or no traceability of errors: in the absence of real follow-up, blockages are difficult to identify and resolve.

This lack of structure has technical, organizational and strategic consequences: it leads to tedious error processing for each business application, wasting precious time for business teams and the Information Systems Department. Teams are thus mobilized on maintenance and error correction instead of being able to concentrate on innovative projects. What's more, data is collected incompletely, which hampers cross-referencing and decision-making.

It's worth repeating: proper integration of data is the first step to making the most of it! Data is at the heart of both exchanges and business processes, so its quality is a key issue.

Defects in IS integration are one of the causes of data integrity loss. A loss of integrity occurs when the accuracy, completeness or consistency of data is compromised, calling into question its reliability and value. While there are of course other causes, such as system failures, malicious manipulation or data entry errors, poor orchestration of information exchanges within the Information System is one of the main sources! 

Within a network of specialist garden centers, we implemented the Phoenix platform to enable them to adapt to a high pace of parallel projects within their information system:

At a pharmaceutical laboratory, our teams helped the company gain agility in the evolution of its information system bricks:

  • Guarantee critical flows between Sage X3 and SAP, and between SAP and Notilus for HRIS data
  • Ensure standardization and optimization of interface development

A DIY chain uses Blueway to control data input and output with SAP

  • Replace all interfaces to supervise all flows
  • Manage all exchange flows between warehouses, stores and the website
  • Quickly alert players and easily identify anomalies via the supervision tool

Data integration as a foundation for innovation

Indeed, information circulates throughout the company, feeding into its processes and various systems and applications. Improving the quality of data and its transport therefore means increasing the performance of these processes via :

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Time savings: quality control before distribution, automated synchronous or asynchronous transport, elimination of time-consuming coding tasks...

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Error reduction: automated data collection to prevent oversights and errors, automatic updates and modifications, always up-to-date reports...

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Collaboration: improved dialogue between applications (and therefore businesses), data enrichment by all participants, simplified access from remote sites...

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Continuous improvement: identification of data-related problems, adoption of best practices, preparation of data prior to distribution...

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For CIOs, data integration is not an end in itself: its objectives need to be explained internally within the organization, so that the importance of the project is understood.

So, how do we tackle the issue of data integration for good?

Efficient integration means not only collecting and transporting data, but also being able to centralize and enrich it as and when required, with changes being automatically passed on to the entire application ecosystem.

Simple, complementary data integration solutions exist to meet this need:

1st recommendation

Organize!

The ESB (Enterprise Service Bus), with its application bus, organizes data flows and secures them, two essential prerequisites for companies whose Information Systems are evolving.

Rather than fetching data from each source, business applications can retrieve it from the application bus, thanks to a standardized exchange mode. The ESB’s ability to transform data formats enables applications to communicate seamlessly.

2nd recommendation

Centralize your data!

The unique MDM (Master Data Management) data repository centralizes and manages sound reference data, which applications can use. Duplicates and information silos are eliminated. MDM works in conjunction with ESB to circulate controlled data throughout the IS, distributing the same information to all applications.

Coupling these two solutions results in reliable, traceable and, above all, sustainable information exchanges. Implementing ESB and MDM makes inter-application exchanges more fluid and guarantees data integration: why not take the plunge now?

The interoperability between process and data is central to any agile organization
white_paper BPM and ESB

Choose our Data Foundation Module to support your data integration strategy

It’s a fact: data integration is more than just a question of infrastructure. Urbanizing your data flows and your Information System means providing yourself with the tools to accelerate your company’s digital transformation and innovative projects. The benefits of data integration for business challenges no longer need to be demonstrated. The solutions exist and have been mastered: Data Foundation is our module dedicated to the transport, manipulation, control and exposure of data, within an SOA logic.

Démarche ESB pour urbaniser son SI

… and beyond with the Phoenix platform

Data Foundation is the heart of our Phoenix data platform. In addition to data integration, Phoenix enables you to manage all the dimensions of data exchange (data catalog, interoperability, BPM, MDM and API management).

Combine your data integration strategy with the mapping of all your data, the implementation of repositories, the on-boarding of businesses through the digitization of processes, and the exposure of your data to the ecosystem through APIM!

With Phoenix, unleash the potential of your data, your processes and your teams! Focus on the essentials, we’ll take care of the rest.

Data Platform Phoenix : DQM, MDM, BPM, ESB, Data Catalog

Our strengths in the face of data integration challenges

Graphic interface

to create integration scenarios.

Add user interaction screens

to your integration processes.

Pre-configured processes

to speed up your integration projects.

Supervision platform

to oversee execution and planning.

Real-time, synchronous

or asynchronous data exchange.

Wide variety of drag & drop

triggers (file, e-mail, SQL, TCP, SAP, etc.)

Would you like to discuss your data integration challenges?

Our speeches on data integration

Our data integration FAQ

Understand the company's specific needs in terms of analysis, decision-making and global operations to guide the integration strategy.
Identify the company's existing data sources, as well as the target systems where this data will be used, to ensure smooth and efficient integration.
Assess the quality of available data and implement processes and technologies to improve, cleanse and maintain this quality throughout the data lifecycle.
Select the technologies, tools and platforms that best meet the company's integration needs, taking into account factors such as scalability, security and upgradability.