With the IoT, the cloud or social networks and other communication channels, companies today generate massive volumes of data essential to their marketing strategy. Data integration, provided it is of the highest quality, makes it possible to merge and structure this extremely heterogeneous data. This digital solution provides valuable insights and contributes fully to improve decision-making and marketing actions.
Understanding data integration
Data integration is an essential business process, enabling you to centralize, structure and organize data from a variety of sources (applications, sensors, open data, third-party partners, etc.) in order to provide an overview, notably by eliminating redundancies, and to make it more easy to use.
Two approaches are commonly used:
- ETL (extraction, transformation and loading): here, data is extracted from the various original sources, transformed into models usable for analysis and finally loaded into a data warehouse (storage system), either on site or in the cloud.
- ELT (extraction, loading and transformation): a more recent development, this method extracts data and loads it into a data warehouse or data lake before transforming it. It has the particularity of being able to handle unstructured data.
However, for integration to be truly effective, the data quality is key. Incomplete or inaccurate data can impact analysis and therefore decision-making. Poor quality data costs companies an average of 12.9 M$ (around €12 million) per year, according to the 2020 edition of the report. Gartner Magic Quadrant.
Many challenges ahead
Integrating data is a complex challenge, as data is often wide-ranging (format, structure, multiple sources). Organizations must also take care to guarantee their security and confidentiality, in accordance with the General Data Protection Regulation (GDPR), and thus implement robust safety measures. Finally, as data integration solutions continue to evolve, companies are forced to adapt to them (migrating legacy applications to the cloud, integrating APIs to facilitate data sharing, for example).
Efficient data integration is, however, essential in the fields of artificial intelligence, big data and business intelligence. at the heart of modern marketing strategies :
- L'artificial intelligence requires clean (accurate, complete, consistent, relevant and up-to-date), well-structured data to effectively train its machine learning models. These models, which are capable, for example, of analyzing purchasing behavior and customer interactions to predict their future needs, enable the creation of more targeted and effective marketing campaigns.
- Visit big data involves the real-time processing of massive volumes of heterogeneous customer and prospect data, but must guarantee relevant, accurate and reliable data for analysis, enabling us to derive exploitable insights. Efficient data integration, for example, enables pertinent segmentation of the customer base, and thus a better understanding of customer trends and preferences.
- Visit business intelligence relies on data analysis to provide decision-making insights. When integration is of the highest quality, it ensures that the data analyzed is reliable and accurate, enabling informed decisions to be made. This is essential, for example, to enhance the effectiveness of advertising campaigns, understand the customer journey, identify the most profitable market segments, monitor marketing performance and the relevance of sales actions, or improve strategy.
Read also: How do you choose an IT development company?
Best practices for successful data integration
There are a few best practices for successful data integration:
- Define objectives and requirements It's essential to establish the company's operational and strategic objectives upstream, to give a clear direction and guide all the other stages of data integration.
- Fine understanding business needs (formats, volumes, frequency of updates, etc.) to ensure that objectives are aligned with expectations, and to meet them as effectively as possible.
- Choosing the right integration model based on specific business needs, but also on their constraints (business requirements, data volumes, performance constraints, processing times, etc.), and stick to it to ensure the stability, efficiency and performance of the data integration process. Two types of integration can be distinguished: in synchronous integration, the different systems and applications communicate and exchange data in real time, with system A sending the data and waiting for an immediate response from system B before continuing its processing. In asynchronous integration, data is exchanged via queues, so system A doesn't get stuck waiting for a response, but continues processing. The latter generally offers greater responsiveness, flexibility and scalability.
- Document the integration process(policies, standards, etc.) to ensure that the integration project is well-structured and understandable to all, but also to facilitate updates.
- Implement a data lifecycle management strategy (Data Lifecycle Management or DLM), which defines the policies and processes for managing, structuring and organizing data throughout its lifecycle, from creation to disposal (collection, storage, processing, analysis, backup, etc.). An effective data management strategy ensures that data remains useful and contributes fully to its quality.
- Staying flexible because in the field of data integration, integration technologies are constantly evolving, as are data sources and formats. It is therefore necessary to adapt to these frequent and regular evolutions.
- Continuous testing to ensure the quality and reliability of processed data, and its compliance with business requirements. By regularly testing integration processes, it is possible to identify and correct errors, improve performance and guarantee good data quality.
When it comes to data integration, why choose Vivetic?
Meeting the many challenges of successful data integration requires in-depth knowledge, expertise, availability and the right tools.
At Vivetic Group, we put 30 years of expertise and know-how into engineering and development to support your strategy. Our teams offer you :
- A great expertise and data integration: process automation, structuring and integration of different data sources.
- A global approach and strategic.
- A technology watch
- Solutions forcustomized data integration.
- A commitment to quality and satisfaction
Our teams will advise and support you in defining your data integration project.
