Data standardization is the most important part of ensuring data quality. If you lack standardization it would result in bad data, which can have several negative effects. Sending bad emails, mailing to bad addresses and losing customers are some of the negative impacts. This blog post would be discussing three important lessons of data standardization.
Three Lessons Of Data Standardization
- Standardized data bring along with it great benefits to an organization in a long run. But, these effects are not visible immediately making it difficult to obtain and justify the investment. Businesses need to identify clearly the opportunities quality data can bring in for you. That can be in terms of generating and improving member value and/or revenue through key applications/products which mainly depend on the navigation of standardized data. Finding such business case early is important to clear the quantifiable benefits which are essential to get buy-ins and support towards data standardization.
- The process of data standardization is iterative; hence one should start it with an end in mind. If you do not have an end goal in mind you would easily get trapped in repeated work and numerous small fixes. To gradually move up the maturity scale, data standardization takes in several steps and this is an open-ended problem. To ensure that your overall program moves toward the desired direction, balance out strategic objectives and operational costs of building tools/pipelines to steward small pieces of data quality control at scale.
3. Data standardization is not bounded to specific technologies. It is important to see if innovations can help improving data standardization practices. Inserting innovations from Machine Learning, Artificial Intelligence, Natural Language Processing, Databases, Information Retrieval, etc. into big-picture data standardization solutions would ensure maximum value accretion and reduce the biases of your comfort zone.
Data standardization is the initial step to ensure that your data can be shared across the business organization. This creates reliable data for use by other applications in the organization. Ideally, data standardization should be done at the time of data entry. If this is not possible, a comprehensive back end process is necessary to eliminate any inconsistencies in the data.
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