Organizations gather information from a varied range of sources. Challenges arise when the required level of data sophistication, quality and transparency is not acquired from newly evolving IoT and big data solutions. Also, the poor state of data integration tools hampers the growth of your company’s data integration initiatives. Data analytics leaders and CIOs are required to implement strategic disciplines at the center of their IT infrastructure practices.

In this blog post, we have outlined some best practices to cope up with the ever-increasing data integration demands.

Data Integration Practices

• In 2015, a group of organizations were surveyed about their data integration initiatives. This survey highlighted some key areas where organizations would focus on. The most sought after areas were deployment and development of data integration tools along with the maintenance of the existing tools. Organizations must adopt the latest evolving data integration trends that combine both traditional functionalities and modern data delivery styles like data virtualization and support for message-oriented integration.
• Data integration and analytics leaders must examine their vendor’s roadmap to check whether their solution can support the required capabilities or act as inhibitors to your data integration plan.
• CIOs must focus on developing a digital business strategy that can boost an enterprise’s methodology of collecting, manipulating and modifying data. This will improve the scalability and flexibility of your data integration initiative.
• Familiar and repeated data integration patterns that primarily revolved around physical data movement no longer fulfil the needs of effective data integration solutions. Data virtualization acts as an efficient alternative that provides agility in data access mechanisms by creating a single and logical view of data that is combined from myriad data silos. These data silos include transactional systems, cloud data stores, RDBMSes, and big data stores. Data virtualization is an important capability that will develop and modify your overall portfolio of data integration as it matures quickly and proves to be beneficial in creating operational and analytical use cases within a company.
• Many times, analytics users spend a lot of their time preparing data for data analysis. Self-service data preparation acts as an iterative and agile process that explores, combines, cleans and transforms raw data. After this, the raw data gets curated into independent datasets for the purpose of data discovery, data science, and BI. CIOs must adopt the emerging self-data preparation workflows and examine use cases by comparing them with data integration approaches used traditionally.
• A good data integration initiative requires business activities to be conducted in real time so that it can encompass increasing demands such as updating data management capabilities. The focus of data analytics leaders must be on incorporating low latency and real-time mechanisms that will suit the varied data consumption models used in an organization. This will help data integration leaders in anticipating modifications necessary for their organization’s data integration design.

Data integration leaders must choose the right set of data integration tools and techniques, and develop a holistic roadmap that will assist them in evolving and advancing their data integration capabilities, thereby improving their architectural integration decision-making process.