In today’s data-driven culture, few organizations can stand apart from the competition without good data quality to generate good quality insights. Gaining competitive advantage is no longer just about the need for analytics, but about speed to insights: how quickly analytics can be run to address questions across the enterprise. Without these two elements—quality data and quick access to analysis—organizations will lack vital information needed to understand customer behaviors, ensure accuracy in reporting and forecasting, and proactively manage operational and risk concerns.
The highly competitive nature of today’s on-demand economy has raised the bar when it comes to the need for accurate and reliable data, as has the amount of data being collected. But as technology grows more sophisticated, increased automation has reduced the need for human intervention as data is ingested, moved, stored, and reported. As a result, the potential for unusable data to reach the data analysis stage has decreased significantly, yet the need for quality has sky-rocketed. While eliminating data errors marks the pinnacle of success, reaching that point doesn’t happen overnight. To avoid these data errors, organizations are embracing a culture of data quality, along with the enabling technologies to ensure its success – what we’re calling analytics-enabled data quality.
A driving force and major asset for most modern enterprises is their data, and the first step toward maximizing its value is ensuring accuracy. Data errors can be reduced through multiple controls and audit checks, which can verify that transposed numbers, invalid data fields and empty values aren’t corrupting your data. Automating the process allows IT to step in and correct the issues at hand, otherwise a great deal of time and resources are wasted on manual quality checks of the data, which are far less efficient and effective.
Layering analytics into your data quality process can also improve your overall data quality assurance and monitoring. Performing analytics and machine learning alongside traditional controls and quality checks allows for continuous monitoring for improvement, as machine learning employs self-learning and enhances data quality over time. This is a key component of an analytics-enabled data governance program, and can increase data utilization as it builds reliability and trust among business users.
With the increasing number of organizations using analytics, it is becoming harder for some companies to keep an edge. When analytics were first introduced to improve business decisions, they created significant competitive advantage among those who were first to embrace the technology. But the competition caught up and the playing field leveled out as more and more businesses implemented analytics capabilities. Today, reducing the time to generate better decisions using better quality data and speedy analysis will help organizations protect their advantage. Self-service analytics that can be performed directly by business users can reduce the time to insights to within hours, not weeks, with far less burden on technical resources.
Organizations should look for a platform that can apply machine-learning algorithms and analytics for varied data management applications, including data quality and pure analytics. Analytics-enabled data quality should include traditional data validation, but also encompass analytics to buttress data profiling, completeness, consistency, timeliness, reconciliation/balancing, and value conformity. It should also fold in data governance, where machine learning can provide ongoing quality monitoring and improvement. Competitive advantage begins with empowering business users with self-service solutions, allowing them to quickly and easily access and analyze all organizational data assets and convert them into actionable insights. By promising analytics-enabled data quality, that is quality to build user trust and analytics to improve results, businesses can ensure that their data assets are being utilized to drive better decisions, improved customer experience, and positively impact revenue and retention.
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