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How to Overcome Data Integration Obstacles

As the amount of data accessible to major enterprises continues to grow, data integration difficulties have become more complex. Business executives recognize that their data is a source of potential value, but the amount, pace, and diversity of data accessible today are is overwhelming. It is difficult to keep up with the information streaming from mobile devices, IoT & telematics, clickstream analysis, transactional systems such as a mainframe, and a deluge of unstructured data from social media and user-generated web content.

If your firm is determined to obtain better control over its data assets, it will need to overcome the volume, velocity, and variety-related data integration concerns.

Here are the four most important difficulties that any business owner must consider.

Whether it’s for massive platforms or best-of-breed solutions, organizations are always evaluating alternative investment strategies in technology. Many of the on-premises platforms in the past have been surpassed by specialized systems for marketing automation, logistics, inventory planning optimization, and other specialized business activities. The increased usage of web services APIs and cloud computing have facilitated point-to-point interaction.

Simultaneously, the rising number of distinct systems used by many businesses has made it more difficult than ever to rein in this complexity. These systems may be interconnected to a great degree. In some instances, transactional integrity depends on the precise and timely integration of data across these diverse software systems. Inventory planning and transportation logistics, for example, must have clear visibility and read/write access to ERP data to guarantee that customers, suppliers, and internal workers always have access to updated information. 

 In addition, analytics is playing an increasingly vital role. In financial services, successful fraud detection and prevention systems are driven by rapid access to transaction data. When planners have access to this information in real-time, they may be more responsive to both external occurrences (such as weather) and internal changes (such as issues impacting production or sourcing). In other words, it’s not enough to have the appropriate data accessible in the right location; timeliness is also crucial. 

 Solving this data integration difficulty requires an enterprise-grade integration solution that can connect to numerous data sources, such as legacy, cloud, and on-premise software systems. 

 The fact is that the success of your organization’s big data efforts depends on your performance.

Second obstacle: Inconsistent data formats and models

Many of the issues associated with data integration emerge from disparities in data formats and models across systems. Fixed-length data types, COBOL copybooks, hierarchical databases, and other anachronisms make it particularly difficult to transfer data between mainframes and cloud systems. 

 Even when integrating data across many systems, disparities in data models may make integration exceedingly difficult. Typically, ERP is the system of record, but it must exchange data with a CRM that defines clients quite differently and contains lists of leads ranging from established customers to warm prospects to window shoppers who have no desire to buy.

In these numerous systems, master records are often encoded differently, i.e., they have unique IDs that adhere to distinct alphanumeric patterns and must be mapped according to well-defined business constraints. 

Again, a comprehensive enterprise-grade integration solution can meet the difficulties of data mapping, data harmonization, and master data management (MDM). The approach should begin with a thorough data profile exercise, which will serve as the foundation for an efficient integration plan.

Third obstacle: Poor data quality

This same approach to data profiling is a fantastic beginning point for obtaining control over data quality. Typical causes of data quality issues include human mistakes, differences in the way information’s are maintained across multiple systems, and past integration difficulties, among others. Static data also tends to deteriorate with time. This is particularly true of client information, which becomes obsolete when customers change their identities, move, combine, or cease operations (in the case of commercial customers), or die (in the case of individuals). 

A complete strategy to rectify data quality concerns starts with data profiling and involves implementing tools and procedures that allow line-of-business employees to effectively and efficiently own and manage data quality. In addition to establishing these technological capabilities, executives must create initiatives that raise the organization’s understanding of the measurable cost of poor data quality.

Fourth obstacle: Extracting value from the flood of data

Earlier, we mentioned the three V’s of data: volume, velocity, and variety. At Nallas, we like discussing a fourth “V”: value. Whereas the previous three Vs provide difficulty and complexity, value is the source of genuine competitive advantage. According to our study, more than half of businesses, depending on the strategic use of big data, often include location to improve certain business operations. Examples include retail location selection, improved natural disaster response within the insurance business, and enhanced performance management for bank branches.

Strategic use of data often involves a 360-degree perspective of consumers, which promotes more effective marketing campaigns, improved product development, and enhanced customer service. To get strategic value from your data, you must use a data enrichment approach and a location intelligence viewpoint to add context to your current data. 

Conclusion: Getting data integration right

Nallas is a worldwide leader in data integration, data quality, enrichment, and location intelligence; we provide world-class technologies and experience to assist businesses in achieving enterprise data integration perfection.  Contact us and ask about typical solutions for data engineering services in your sector.