Data is the most valuable asset in the digital economy. Be it nurturing existing clients or expanding into new territories, data is the foundation on which new initiatives are built and execution is reviewed against.
High-quality data gathered from genuine sources is the sure-shot arrow in any company’s quiver. The keyword here is ‘high-quality’ which is decisive for mining insights that contribute to the growth and increase the efficiency of doing business.
Now when all new-age technologies, be it Big-Data Analytics, Artificial Intelligence or Machine Learning are gluttons for data, it is challenging to get high-quality data in ginormous size. So even if you are upbeat for going all out disrupting the market, you need data to do that.
In 2017, poor data quality data cost organizations around $15 million. A seemingly harmless data error has the potential to support a flawed decision, and therefore it is crucial to place data quality checks at strategic points in the data life cycle.
Here are a few points at which Data Quality check can save the sweat later.
1. Data Ingestion
As obvious it can be, feeding bad quality-data into your process can cause the whole cascade of bad-data and bad-decision. If the data ingestion begins from manual data entry, it is prone to human errors like missing value, inconsistency, redundancy, ambiguity, etc.
The solutions for maintaining data integrity at the point of data-ingestion are:
Data Entry Automation
There are hundreds of tools available for automating data-entry based on the source and destination of the data. These tools can copy data from one app to another or from an excel file to the web form, and more. With predefined rules and acceptable range, a certain extent of quality can be achieved.
Outsource Business Data Processing
If your organization is facing issues with the data-quality then you can consider outsourcing data entry or processing and analysis or all to the data processing services. It not only saves operational costs but also improves accuracy by making people do whatever they are best at.
Even if you have amassed high-quality data, storing it in a database with bad architecture with flawed retrieval process can render it useless. A stringent check is required to break data-silos so that every process can access an updated version of the required data.
If the organizational structure allows then data-silos can be mapped and integrated into the organization. The more people have access, the more updated database will be. For the rest, which will be the case for most organizations, data-silos need to be checked for quality and mapped for competent data-architecture.
Not a point per-se and an ongoing process instead, Data Maintenance requires thorough checks for data-quality—to weed out irrelevant entries and maintain only validated ones. The risk of ignoring quality at this level is higher as strategic decisions supported by ‘assumed high-quality data’ can have serious implications, such as marketing campaigns fired for non-existent users.
Professional Data Cleansing Services can help maintain an updated database because they have the right set of Data Maintenance tools and human intervention with a neutral perspective.
Apart from these plainly apparent check-points, an organization needs a solid governance framework for the complete data lifecycle. This particularly helps when distortion creeps in a data-model and you know where to trace it back. To prevent recurring data-quality issues, stringent policies and procedures should be implemented to enforce data reliability.