7 critical KPI's to maximum migration & transformation of unstructured data

As businesses increasingly move towards digitalization, they generate vast amounts of data. This data is often unstructured, meaning it is not organized in a predefined manner. Unstructured data includes text documents, images, audio, video files, social media posts, emails, and more. This data is essential for businesses to make informed decisions and stay competitive in the market. However, to extract insights and value from unstructured data, it needs to be transformed into structured data.
Migration and transformation of unstructured data can be a challenging and complex process. To ensure the success of this process, businesses need to establish and track key performance indicators (KPIs). These KPIs are metrics that measure the progress and effectiveness of the migration and transformation of unstructured data. In this Blog, we will discuss 7 useful KPIs that businesses should establish and track during the migration and transformation of unstructured data.

1. Data Quality:

   Data quality is a critical KPI that businesses should measure during the migration and transformation of unstructured data. It measures the accuracy, completeness, and consistency of the data being transformed. Poor data quality can result in incorrect insights and decision-making, which can be costly for the business. Therefore, it is essential to establish data quality standards and track them regularly.
   To measure data quality, businesses can use various metrics, such as data completeness, data consistency, and data accuracy. Data completeness measures the percentage of data that is available for analysis, while data consistency measures how consistent the data is across different sources. Data accuracy measures how closely the transformed data matches the original unstructured data.
   For example, a business may want to migrate and transform its customer feedback data into structured data to analyze customer sentiment. To ensure data quality, the business may establish a data completeness standard of 90%, a data consistency standard of 95%, and a data accuracy standard of 98%. By tracking these metrics regularly, the business can ensure that the transformed data meets its quality standards.
   
   

2. Data Volume:

   Data volume is another essential KPI that businesses should track during the migration and transformation of unstructured data. It measures the amount of data being transformed and provides insights into the scalability and performance of the transformation process. This KPI is particularly important for businesses that deal with large amounts of unstructured data.
   To measure data volume, businesses can track the total size of the unstructured data, the amount of data transformed, and the time taken to transform the data. By tracking these metrics, businesses can identify potential bottlenecks and optimize their transformation process.
   For example, a business may want to migrate and transform its video surveillance data to monitor its operations. To ensure data volume, the business may establish a data transformation rate standard of 100 GB per day, with a maximum transformation time of 24 hours. By tracking these metrics regularly, the business can ensure that it can transform its data efficiently and effectively.
   
   

3. Data Security:

   Data security is a critical KPI that businesses should measure during the migration and transformation of unstructured data. It measures the level of protection provided to the data during the transformation process. Unstructured data is often more vulnerable to cyber-attacks, making data security a critical concern.
   To measure data security, businesses can track metrics such as data encryption, data access controls, and data backup processes. Data encryption ensures that the transformed data is protected from unauthorized access, while data access controls limit access to the data to authorized personnel only. Data backup processes ensure that the transformed data is backed up regularly, reducing the risk of data loss.
   For example, a business may want to migrate and transform its financial documents to improve its financial reporting. To ensure data security, the business may establish a data encryption standard of 256-bit encryption, data access controls based on role-based access control, and data backup processes that
   occur on a daily basis. By tracking these metrics, the business can ensure that its transformed data is secure and protected from cyber-attacks.
   
   

4. Data Integration:

   Data integration is a critical KPI that businesses should measure during the migration and transformation of unstructured data. It measures the ability of the transformed data to integrate with other data sources and systems within the organization. This KPI is particularly important for businesses that rely on multiple data sources to make informed decisions.
   To measure data integration, businesses can track metrics such as data compatibility, data consistency, and data mapping. Data compatibility measures the ability of the transformed data to integrate with other data sources, while data consistency measures how consistent the transformed data is with other data sources. Data mapping ensures that the transformed data is mapped correctly to other data sources.
   For example, a business may want to migrate and transform its social media data to gain insights into customer behavior. To ensure data integration, the business may establish a data compatibility standard that ensures the transformed data can integrate with its customer relationship management system. The business may also ensure data consistency by mapping customer feedback data to existing customer data. By tracking these metrics, the business can ensure that its transformed data is integrated seamlessly with other data sources.
   
   

5. Data Governance:

   Data governance is a critical KPI that businesses should measure during the migration and transformation of unstructured data. It measures the policies and procedures that govern the management and use of the transformed data. Effective data governance ensures that the transformed data is managed ethically and transparently, reducing the risk of non-compliance and reputational damage.
   To measure data governance, businesses can track metrics such as data privacy, data ownership, and data retention. Data privacy measures the protection of personal information within the transformed data, while data ownership ensures that the transformed data is owned by the appropriate stakeholders. Data retention measures how long the transformed data should be kept for, and how it should be disposed of.
   For example, a business may want to migrate and transform its employee data to improve its human resources management. To ensure data governance, the business may establish data privacy standards that comply with local data protection laws. The business may also ensure data ownership by assigning ownership of the transformed As businesses increasingly move towards digitalization, they generate vast amounts of data. This data is often unstructured, meaning it is not organized in a predefined manner. Unstructured data includes text documents, images, audio, video files, social media posts, emails, and more. This data is essential for businesses to make informed decisions and stay competitive in the market. However, to extract insights and value from unstructured data, it needs to be transformed into structured data.
   Migration and transformation of unstructured data can be a challenging and complex process. To ensure the success of this process, businesses need to establish and track key performance indicators (KPIs). These KPIs are metrics that measure the progress and effectiveness of the migration and transformation of unstructured data. In this article, we will discuss seven essential KPIs that businesses should establish and track during the migration and transformation of unstructured data.
   
   

6. Data Performance:

   Data performance is another critical KPI that businesses should measure during the migration and transformation of unstructured data. It measures the performance of the transformed data, including its speed, reliability, and availability. This KPI is particularly important for businesses that rely on real-time insights to make informed decisions.
   To measure data performance, businesses can track metrics such as data processing speed, data reliability, and data availability. Data processing speed measures the speed at which the transformed data can be processed and analyzed, while data reliability measures the accuracy and consistency of the transformed data. Data availability measures the availability of the transformed data to authorized personnel.
   For example, a business may want to migrate and transform its machine sensor data to improve its manufacturing processes. To ensure data performance, the business may establish a data processing speed standard of real-time processing, data reliability standards that ensure that the transformed data is accurate and consistent, and data availability standards that ensure that the transformed data is available to authorized personnel at all times. By tracking these metrics, the business can ensure that it can make informed decisions based on real-time insights.
   
   

7. Return on Investment:

   Return on investment (ROI) is a critical KPI that businesses should measure during the migration and transformation of unstructured data. It measures the return on investment of the transformation process, including the financial benefits and cost savings realized by the business. This KPI is particularly important for businesses that need to justify the investment in the transformation process.
   To
   measure ROI, businesses can track metrics such as cost savings, revenue growth, and efficiency gains. Cost savings measure the reduction in costs realized by the business as a result of the transformation process, while revenue growth measures the increase in revenue realized by the business. Efficiency gains measure the improvement in operational efficiency realized by the business.
   For example, a business may want to migrate and transform its customer data to improve its marketing efforts. To measure ROI, the business may track metrics such as the reduction in marketing costs, the increase in revenue from targeted marketing campaigns, and the improvement in customer engagement. By tracking these metrics, the business can ensure that it is realizing a return on its investment in the transformation process.

Importance of these KPIs to Success:

The above-mentioned KPIs are critical to the success of the migration and transformation of unstructured data. By measuring these KPIs, businesses can ensure that the transformation process is on track and that the transformed data is accurate, secure, integrated, governed, performing well, and generating a return on investment. These KPIs are important for the following reasons:

1. Accuracy: Measuring accuracy ensures that the transformed data is free of errors and inconsistencies, providing a reliable basis for decision-making.
2. Security: Measuring security ensures that the transformed data is protected from cyber-attacks and data breaches, reducing the risk of reputational damage and non-compliance.
3. Integration: Measuring integration ensures that the transformed data integrates seamlessly with other data sources, providing a comprehensive view of business operations.
4. Governance: Measuring governance ensures that the transformed data is managed ethically and transparently, reducing the risk of non-compliance and reputational damage.
5. Performance: Measuring performance ensures that the transformed data is available in real-time, providing timely insights for informed decision-making.
6. ROI: Measuring ROI ensures that the transformation process is generating a return on investment, justifying the investment in the process.

Data to Support the Importance of These KPIs:

Several studies have shown the importance of these KPIs to the success of the migration and transformation of unstructured data. For example:

1. Accuracy: A study by IBM found that data accuracy is the most important factor in decision-making, with 84% of executives saying that accuracy is essential to their decision-making process. Inaccurate data can lead to poor decision-making, wasted resources, and reputational damage.
2. Security: A study by Ponemon Institute found that the average cost of a data breach is $3.86 million, highlighting the importance of data security. By measuring security, businesses can reduce the risk of data breaches and associated costs.
3. Integration: A study by SAP found that integrated data can improve decision-making by 27%, highlighting the importance of data integration. By measuring integration, businesses can ensure that the transformed data provides a comprehensive view of business operations.
4. Governance: A study by Gartner found that poor data governance can result in up to 25% of critical data being inaccurate or incomplete, highlighting the importance of data governance. By measuring governance, businesses can reduce the risk of inaccurate or incomplete data.
5. Performance: A study by Microsoft found that real-time data insights can increase operational efficiency by up to 30%, highlighting the importance of data performance. By measuring performance, businesses can ensure that they have timely insights for informed decision-making.
6. ROI: A study by McKinsey & Company found that data-driven companies are 23 times more likely to acquire customers, 6 times more likely to retain customers, and 19 times more likely to be profitable than non-data-driven companies. By measuring ROI, businesses can justify their investment in the transformation process and realize the financial benefits of the process.
   
   

In conclusion, businesses should carefully consider their KPIs when embarking on the migration and transformation of unstructured data, and ensure that they have the right tools, processes, and people in place to track and measure their progress towards their goals. With the right KPIs and a well-executed transformation process, businesses can realize the benefits of accurate, integrated, and governed data, and stay ahead of their competition in the data-driven economy. The Expede Platform provides these requirements out the box and enables unstructured data to be uploaded and Expede AI will undertake everything else.