Addressing the 4 Most Common Data Integration Challenges

As the volume of data available to large organizations continues to increase, data integration challenges are becoming more difficult. Business leaders recognize that their data is a valuable source of information, but the large volume, velocity, and variety of data available today is intimidating. It’s difficult to keep up with the amount of data coming in from mobile devices, IoT & telematics, clickstream analysis, transactional systems like mainframe, and a stream of unstructured data from social media and other user-generated online content.

If your business is serious about mastering its data assets in 2022, you’ll need to handle some of the most common data integration issues, such as volume, velocity, and variety. The top four challenges that every business leader should bear in mind are listed below.

Challenge 1

Whether it’s large platforms or best-of-breed solutions, companies are always seeking new ways to invest in technology. Many on-premises platforms have been replaced by specialty systems for marketing automation, logistics, inventory planning optimization, financial reporting, and other specific business activities. Point-to-point integration has become easier thanks to cloud computing and the increasing adoption of web services APIs.

Simultaneously, the growing number of different systems in use within many organizations has made managing all of that complexity more difficult than ever. These systems can be highly interconnected. In some circumstances, transactional integrity is dependent on precise and fast data integration across multiple software systems. Inventory planning and transportation logistics, for example, require clear visibility to ERP data, as well as read/write capability, to ensure that customers, suppliers, and internal workers always have access to accurate and up-to-date information.

In addition, analytics is becoming increasingly important. In financial services, timely access to transaction data drives effective fraud detection and prevention programs. When supply chain planners have real-time access to information, they can be more responsive to both external events (such as weather) and internal changes (such as issues affecting production or sourcing). To put it another way, it’s not just about having the correct data in the right place; it’s also about timing.

To solve this data integration problem, you’ll need a comprehensive enterprise-grade integration solution that can connect to a variety of data sources, including legacy, cloud, and on-premise software systems.

Challenge 2

Many data integration issues develop as a result of variations in data formats and models across systems. Organizations that use mainframes are well aware of this; fixed-length data types, COBOL copybooks, hierarchical databases, and other anachronisms make data transfer between mainframes and cloud platforms especially challenging.

Even when integrating data from two or more systems, data model mismatches might make integration problematic. ERP is typically the system of record, but it must share data with a CRM system that classifies customers in a variety of ways and includes lists of leads that range from existing customers to warm leads to window-shoppers with no intention of purchasing.

In those many systems, master records are frequently coded differently – that is, they have unique IDs that adhere to distinct alphanumeric patterns and must be mapped according to a set of business rules.

The issues of mapping and harmonizing data, as well as master data management, may all be addressed with a powerful enterprise-grade integration solution (MDM). The process should start with a comprehensive data profiling exercise, which will establish a baseline upon which an effective integration strategy can be built.

Challenge 3

The same data profiling process is a great place to start when it comes to improving data quality. Human error, inconsistencies in the way information is managed in different systems, past integration errors, and other factors all contribute to data quality issues. Static data degrades with time as well. This is especially true of client information, which becomes obsolete as consumers change their identities, move, merge, or close their businesses (in the case of commercial customers), or pass away (in the case of individuals).

A complete strategy to resolve data quality concerns begins with data profiling and continues with the implementation of tools and processes that enable line-of-business personnel to effectively and efficiently own and manage data quality. In addition to deploying such technical skills, executives must also design initiatives that raise awareness of the measurable cost of poor data quality throughout the organization.

Challenge 4

We discussed the three V’s of data earlier: volume, velocity, and variety. The fourth “V” is – value. Value is where actual competitive advantage can be found, where the first three V’s add challenge and complexity. According to research, more than half of businesses rely on the effective use of big data for strategic gains, with location intelligence often being used to improve specific business operations. Examples include retail site selection, better responsiveness to natural disasters in the insurance industry, or improved performance management for bank branches.

A 360° view of customers is a common strategic use of data that supports stronger marketing initiatives, better product development, and higher levels of customer service. A data enrichment approach, as well as a location intelligence perspective to give context to your existing data, should be included in extracting strategic value from your data.

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