Word from the Wise: Quality Control for Data
Firms must ensure better data quality to make wiser business decisions
As the world starts to find its feet surely but steadily while navigating through the COVID-19 crisis, businesses are facing severe issues surrounding management – primarily a lack of strategy and direction. Globally, most firms lacked adequate auxiliary measures to match up to the challenges being thrown by the ongoing pandemic. This has led to heavily deficient communication and decision-making processes, poor data quality and information loss, and questionable resource management in firms across all sectors. As a countermeasure, firms are coming up with innovations using Artificial Intelligence, machine learning and data analytics in order to prioritise issues such as maintaining sound business practices and high data quality.
A primary tool in this regard is to optimise firms’ master data management methodologies, which outlines the collection, aggregation, consolidation and distribution of data. Automation and other advanced tools are also central to assuring data quality, especially in bypassing the tedious task of manually culling through data and in its standardisation, thereby making it available for widespread usage. This is especially important in healthcare and bio-engineering sectors – critical in battling and finding solutions to the coronavirus pandemic through development and trials of essential drugs and vaccines. Additionally, can better predict the extent and direction of spread of the virus, in assessing resource and personnel availability and in evaluating the economic impacts of the pandemic.
Research estimates from IBM have shown that almost 90% of the total data possessed by firms are hardly ever used – often present as sensor-generated IoT output or in old hard copies. Although most of this data might be superfluous, digitising it and allowing AI and machine learning to peruse through and eliminate the unnecessary can produce a database invaluable in decision-making processes. During digitisation, unlike-data aggregation often poses to be quite challenging – and this is where intelligent machine learning systems become indispensable. For example, consolidating a weather report from 1987 with a corresponding photograph of the storm from a different input stream is a crucial aspect of smart data aggregation and standardisation.
Power BI, a data analytics software from Microsoft, uses advanced machine learning to spot issues in data quality and enhance its applications. Data profiling, the systematic process of data investigation, is central to its functioning – improving both data processing and accuracy. It also allows for better understanding of the completeness and uniqueness of the dataset, along with the patterns and ranges within it.
Refined data quality leads to the improved understanding, management and monitoring of data and translates directly into greater prudence in business strategy and decision-making.
Quality first, always.
