Digital transformation and cloud migration have increased the ease with which computerization and digital solutions can be offered to customers. This digital revolution is also ensuring that departments, workflows, and processes across departments within an enterprise are being upgraded using sophisticated tools and technologies. The backend process, which was being done manually, is now being automated, thus increasing operational efficiency.
The downside of digital transformation is that it is continuously evolving and growing. Departments are going through automation and computerization as individual units and not as a company as a whole. A huge amount of data is being created and processed in individual silos, which in the long run, is inefficient and lacks accuracy for the enterprise as a whole. Moreover, departments are creating data that is being duplicated which, if appropriately planned, could be integrated as a single point of truth, giving the full advantage of integrated data.
Data Integration - A Primer
As digital transformation is beginning to mature, the need to integrate solutions and data is becoming the priority of the hour. Solutions should be sharing data across departments, vendors, and employees.
Data integration is now becoming the cornerstone for any solution in any enterprise. Data integration is the process of getting all data across departments or processes and placing that data into a single source or a repository. Due diligence and intelligent design is required to decide which data needs to be picked up across all systems, which components and formats need to be used, and how it can be integrated at a single source.
The integration of data thus ensures the quality of data, accuracy, completeness, and consistency of structure such that all software solutions can have access to it and operate in a manner as expected — fulfilling the business needs and customer expectations of a company. To that end, the following are the five data integration principles that businesses must focus on:
- Data Standardization
Data across departments or systems needs to be standardized in a way that it's sanitized, without duplication, and consistent for the role the data is supposed to play. Improving data consistency and reducing data redundancy is one of the crucial steps in the process of data standardization. This increases reliability, accuracy, and usability.
A standard data model is created and ensured that it works and can be adapted across all the systems and applications across the enterprise. Definitions and elements are normalized during the process, and data from multiple sources and views is combined for a unified view.
- Data Quality Management
Once the process of data standardization and normalization has been completed with due diligence, the quality of data that is going to be integrated is to be looked into across all the silos and individual systems across the enterprise.
Data cleansing is undertaken to correct errors, inconsistencies, and inaccuracies in the data being integrated. The data is then profiled, where data elements and values are analyzed along with missing values etc. The profiling exercise results in understanding the relationships between data elements and other related components and ensuring data quality issues are being ironed out in a structured manner. This data is also validated to ensure that it meets the enterprise's business rules and workflows before its integration.
- Data Governance
Data integration is a complex process, and data governance ensures that data is used and managed properly during integration. Processes, compliances, and defined standards are applied to govern the complete data integration lifecycle. Roles, standards, protocols, and responsibilities are defined with metrics to ensure quality metrics and security.
Data governance ensures compliance with business goals and stated regulations as well. Since the integration process is complex, data governance reduces errors and mismanagement of data, which could otherwise result in an increase in cost, low productivity, and reduced trust in the integration process itself.
- Data Security
Data security considerations are a high priority during the process of data integration. Tools, techniques, frameworks, and methodologies are adopted and implemented so as to protect data from unauthorized use, data theft, and cyber-attacks.
Defining access control and privileges and ensuring that confidential data is not accessible without due authorization are primary drivers of the data security protocol during the data integration life cycle. Addressing data privacy concerns while keeping in line with various compliances and protocols are default processes followed during data integration. Additional security protocols that include encryption are generally included in data security policies.
Data security not only includes protecting the data from malicious intentions of the user but also includes data backup and recovery protocols to ensure the continuity of operations. Continuous security audits, along with report generation based on various monitoring logs to detect and mitigate data security threats, are also implemented within the data security plan.
- Data Accessibility
Once data has been integrated into a single point of truth, it should be easily accessible to users and applications requiring the same data. Integrated data should be easy to find and retrieve. The latest updated data should be available across the enterprise in real-time.
There should be an easy way for existing and new applications to plug into the integrated data source, and it should be up and running within a reasonable amount of time. Data should be not only accessible but also shareable across systems and applications within the enterprise. Porting integrated data across systems should also be seamless and easy to do across platforms.
Conclusion
In conclusion, data integration is crucial for organizations in today's digital world. Effective data integration requires proper planning, standardization, quality management, governance, security, and accessibility. By paying attention to these five principles, organizations can ensure their data is accurate, consistent, secure, and easily accessible, leading to a successful digital transformation and increased efficiency and productivity.
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