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Data Integration: best practices to improve customer experience

2019-04-03T11:32:02+01:002019-04-03|Best Practices|

Increasing the volume and diversity of data is both a blessing and a challenge for businesses. It integrates and exchanges within your Information System (IS) from various sources, applications and devices. But the exploitation of information begins with their integration. This step is essential. The goal? Switch from information overload to “useful” information management.


According to the IDC, data stored in business applications is expected to grow by 50 percent a year and reach 40 zettabytes next year. Companies collect, store and analyze more and more data in order to better understand their clients expectations and market trends.

The way businesses interact with their customers has become an essential part of any digital transformation project. But a company’s IT environment consists of dozens, if not hundreds, of computers, internal or external storage space (cloud). As new systems (through acquisitions or mergers) become part of an organization’s scope, the situation becomes more and more complex to manage.




The information system relies on silos, more or less well identified, of disparate  data. A Ventana Research study on customer analytics found that 40% of respondents worked with 14 types of data on at least six different systems in order to gain insight…

data integration moskitosTherefore, integration consists of ensuring homogenization in order to make the information usable and accessible by all concerned collaborators. Without this step, it is not possible to perform pertinent reporting and analysis.

Effectively managing data exchanges between systems and trades becomes a priority. Integration tools are therefore essential because they allow companies to consolidate all their data in one place (or on a few specific sites). Businesses need their data hosted in these disparate systems to be combined in order to form an overview. They can then use this integrated data to fuel their business intelligence.


What are the main benefits of data integration:   

  • Data synchronization between applications;
  • An overview of the customers;
  • Data availability for managers and trades;
  • Analysis, forecasting and decision-making based on comprehensive and quality data.


But be careful not to only focus on the benefits of integration. Rushing head first into such a project is very risky. Data integration is a difficult task. As a result, companies invest considerable sums in such projects. According to one study, about 25% of IT budgets are dedicated to data integration every year. And what about the return on investment? McKinsey estimates that most of these projects offer a value 56% lower than what was expected …

The main challenges are to streamline the data integration process to reduce costs, optimize the use of invested resources and generate a positive ROI.

These objectives can only be achieved if one begins to understand the different methods of data integration. There are many different approaches: the use of extraction, processing and loading softwares (ETL -Extract Transform Load), Managing File Transfers (MFT- Managed File Transfer), Enterprise Service Bus (ESB) or integration platform.


Common mistakes and good practices


By opting for a data integration solution, businesses tend to overlook important factors which result in common mistakes:


  • Not prioritizing the businesses needs


The bottom line: many solution providers offer seemingly similar data integration services. But they can use different approaches, even minor ones.

Best practice: The needs of the business must first be identified. Then, a relevant approach must be determined in order to meet those needs. It should not be the other way around. It is important to ensure that the approach used by the solution provider meets the businesses operational objectives.


  • Being short-sighted


The bottom line: while opting for a data integration solution, businesses tend to consider short-term benefits and aim for a quick return on investment. The feasibility and relevance of the selected long-term solution is then neglected.

Best practice: Data requirements evolve at a rapid pace. The solution provider must be able to adapt to these changes. As for the data integration software, it must be flexible enough to easily integrate these changes.


  • Not thinking about the users


The bottom line: Many business users find data integration technologies too complex to use. In many cases (but it is not specific to this type of software …), these softwares were not designed for use by “non IT professionals”.

Best practice: it is the users that get the most value from the data. Therefore, opt for an intuitive tool with a user-friendly interface that can easily be used by people with little or no programming skills.


  • Not retaining the best platform


The bottom line: performing hand-coded API integrations or insisting on personalized integrations is likely to burden the IT budget.

Best practice: It is important to ensure that your integration platform connects to your current applications (be it Salesforce, NetSuite, SAP, Oracle, etc.) as well as those you are planning to use.


To summarize, the integration of data is of paramount importance to becoming more competitive. But still too many businesses are satisfied with massive and anarchic collection of data. This practice is detrimental to a strategic, structured and relevant approach. The performance and quality of data processing is the cornerstone of the majority of projects run by organizations. Optimized management of information capital is crucial within a strong competitive environment.


2019-04-03T11:32:02+01:002019-04-03|Best Practices|
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