Five key practices for a good data detox: how to regain control over enterprise data
With the arrival of September, many companies are taking advantage of this time to take stock and tidy up their data. After months of constant accumulation of information, organizations are faced with the challenge of reviewing, cleansing and optimizing their digital inventory. Much of that data is not being actively used, which creates unnecessary costs, complicates decision making, and raises security and compliance risks.
In fact,according to Secoda, approximately 50% of the data stored by companies is dark data, i.e., information that is collected, stored and maintained, but not actively used, and this can generate an annual cost of up to $26 million in storage alone.
Against this scenario, Knowmad Mood, a multinational technology company that provides digital transformation solutions, and with the support of its Know4Data framework, has identified five key practices that every organization should follow;
Know4Data, has identified five key practices that every organization should adopt if it wants to extract real value from its information assets and prepare for the challenges of artificial intelligence, automation and regulatory compliance:
1. Detect and eliminate stale data. A Veritas study estimates that 33% of enterprise data is redundant, obsolete or trivial (ROT data). This backlog not only creates cost overruns, but also hinders operational efficiency and increases regulatory risk. Applying data retention policies and analyzing usage patterns allows you to identify what information can be archived or securely disposed of.
2. Purge duplicates and improve data quality. Duplicate and inconsistent data directly impacts business efficiency and costs. In fact, an IBM study reveals that poor data quality can cost companies up to $15 million annually. Therefore, implementing standardization processes and using automatic duplicate detection tools, as proposed by knowmad mood's know4Data framework, is key to improving the reliability and value of information.
4. Adopt modern management architectures such as Data Lakes and Data Fabrics. Having modern architectures such as Data Lakes and Data Fabrics is key to managing large volumes of heterogeneous data and leveraging artificial intelligence. According to a Salesforce study, an estimated 73% of enterprise data is not used for analytics, and 95% of companies struggle to manage unstructured data. This underscores the urgent need for Data Lakes and Data Fabrics to unlock value and accelerate predictive modeling.
5. Decentralize data management and promote data governance as a strategic asset. Decentralizing data management, through models such as Data Mesh, empowers business teams, improves data quality and traceability, and accelerates the value generated. According to McKinsey, treating data as products enables new use cases to be deployed up to 90% faster and reduces the total cost of ownership by around 30%.
In the digital era, it is not enough to accumulate data; it is essential to purify and manage them with criteria to extract their true value. Our approach, supported by the know4Data framework, guides companies in a structured “data detox” that reduces costs, mitigates risks and prepares organizations to face the challenges of artificial intelligence, automation and regulatory compliance”, says Alejandro Morales, Data Governance Consultant at Knowmad Mood.
Alejandro Morales, Data Governance Consultant at Knowmad Mood.
Alejandro Morales, Data Governance Consultant at Knowmad Mood.