Data integration is not an end in itself, of course. However, many decision-makers postpone making any changes. Integration, which can be defined as the pooling and transformation of data from multiple sources with a view to exploiting it more fully, is often perceived as non-essential in comparison to more attractive projects. Yet is really is high time these organisations completed this particular job, which underpins all other transformation. A business can extract value from projects linked to business applications, the IoT or a hybrid IS only when the data they are using is clean, consistent and readily exploitable. Architectures that are not re-engineered not only slow down a company’s technological progress, they also increase the information system’s TCO.
Data integration is well and truly a fundamental issue, one that needs to be considered as a whole, because over and above IS re-engineering, all business processes depend on the quality of data streams.
The lack of data integration and its impact on the organisation
Many modern information systems are still too unstructured, which is reflected in:
- Old and new applications co-habiting and communicating in point-to-point mode, amodus operandi that really is no longer feasible given the diversity of data sources used by businesses.
- Numerous and non-standardised data formats: running dialogue between disparate applications slows down communication generally, generates errors and duplicates, and makes it difficult to enrich the data.
- Weak or non-existent error tracing when in the absence of a real audit trail, mission-critical issues are difficult to identify and resolve.
This lack of structure has technical, organisational and strategic consequences, leading as it does to tedious error handling for each business application, which wastes valuable time for business teams and the IT department. Employees are consequently spending time on maintaining data and correcting errors instead of being able to focus on innovative projects. In addition, the data collected is incomplete, which is detrimental to the cross-referencing of data and to decision making.
It bears repeating: proper data integration is the first step towards proper data exploitation. Data lies at the core of business interchanges and processes. Data quality is therefore of prime importance.
Whether digital transition is well underway or still in its infancy in the business, it is time for decision-makers and IT departments to examine data integration and resolve the matter once and for all.
Settling the data integration question permanently
Efficient integration entails collecting and transporting data, and the ability to centralise it and enrich it when necessary, with changes automatically carried forward into the entire application ecosystem.
Simple solutions that dovetail together to meet this requirement efficiently:
The ESB (or application bus) re-engineers and secures data streams, these being two prerequisites for any business redeveloping its information system. Rather than fetching data from each source, business applications can retrieve it from the application bus using a standardised data interchange method. The ability to transform data formats using the ESB enables continuous dialogue between applications.
The single master data repository is used to centralise and manage cleansed master data that applications will then be able to use. Duplicate data and data silos are eliminated. The MDM module joins forces with the ESB to circulate quality-controlled data around the IS and distribute it to all applications.
By pairing these two solutions together, reliable, traceable, and importantly, longer-lasting data streams are obtained. The implementation of ESB and MDM smoothes data traffic between applications and ensures data integration: why not take that step now?
Data integration to underpin innovation
Data integration is not an end in itself, of course. It is only there to serve your business objectives and accelerate your transformation.
In fact, data traffic circulates around the whole business and provides input to its processes. Improving data quality and how it is transported therefore means improving the performance of those processes by:
- Saving time: ensuring data quality before synchronous or asynchronous automated distribution or transport, eliminating time-consuming coding tasks, etc.
- Reducing errors: automated data collection prevents mistakes and omissions, updates are automatic and changes automatically passed on, reports are always up-to-date, etc.
- Collaboration: better dialogue between applications (and therefore areas of the business), data enriched by all parties involved, simpler access from remote sites, etc.
- Continuous improvement: identification of data-related problems, adoption of best practices, preparation of data before it is distributed, etc.
Data integration is not an objective for any IT department itself, and its purpose needs to be explained inside the organisation to ensure the project’s importance is understood. Standardisation of data flows at all levels first and foremost serves as preparation for the challenges posed by innovation:
Smart objects are new sources of data, and are likely to be required to frequently exchange data with the information system. These new devices give rise to unprecedented questions surrounding efficiency and security. Data must be exchanged with the same quality level that would exist on-site.
The quantity of data to be processed is perpetually growing. Big data means big volumes, a wide variety of data, and a need for it to be processed quickly. Intelligent automation of data flows becomes essential to make the best use of the resulting data lakes.
Migration du SI Legacy
To handle the rapid changes in formats and new applications, existing IS must dialogue quickly and efficiently. Harmonisation of data streams helps improve communication between old and new applications.
A hybrid cloud infrastructure offers greater agility, provided that its critical flows can be identified, and applications easily separated to migrate them.
To respond effectively to the needs of data display and analysis, data must be reliable, high quality and enriched.
Ajout de nouvelles briques applicatives
It is easier to add new business applications into the IS if data streams are standardised.
The fact is that data integration goes beyond the simple question of infrastructure. Re-engineering data streams and the IS equates in reality to acquiring the means to deliver digital transformation and innovative projects to the business more quickly. The benefits of data integration to resolve issues are no longer in dispute. Solutions exist and their workings are understood. Reason enough to move from words to deeds – there is nothing more to be said, and it is time to concentrate on your business’ new strategic projects.
Executive Vice President Product
A technical and functional expert, Edouard has specialized in IS urbanization and data governance for almost 20 years. A man of action, he supports customers in their projects with his teams, and doesn’t hesitate to use their feedback to shape the product roadmap and gain agility.