With organizations rapidly adopting data-driven systems, procedures, and strategies, and executives attempting to “Moneyball” everything from recruiting to software development, the necessity of data quality has never been greater. However, as data quantities expand, ensuring the quality of the underlying data that drives choices is becoming increasingly difficult Ipass Loans.
Rapidly evolving technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are constantly adding enormous amounts of data to corporate systems.
According to IDC, the quantity of data “produced, recorded, duplicated, and consumed globally” will continue to grow exponentially. According to the market research agency, the quantity of data generated over the next three years will exceed that created over 30 years. The globe will generate more than three times the amount of data in the next five years than in the previous five years.
The Exorbitant Price Of Low-Quality Data
Business executives are becoming more aware of the negative effect of incorrect data on their bottom lines. According to a Gartner poll, “organizations feel that poor data quality results in an average of $15 million in annual losses.” Gartner also discovered that over 60% of respondents were unaware of the cost of incorrect data to their firms because they did not measure it in the first place.
IBM’s 2016 analysis is even more startling. IBM discovered that poor data quality costs the US economy $3.1 trillion annually in lost productivity, system disruptions, and increased maintenance expenses, just a few of the negative consequences of poor data quality.
Similarly, Forrester Research discovered that the persistence of low-quality data across corporate systems saps business executives’ productivity by requiring them to vet data to assure its accuracy constantly. Additionally, Forrester discovered that “less than 0.5 percent of all data is ever evaluated and utilized” and predicts that if the average Fortune 1000 company increased data accessibility by only 10%, it would produce an extranet revenue of more than $65 million.
A word of caution: There are several causes for low data consumption. A significant one is a poor quality. According to a recent survey by research company Vanson Bourne (commissioned by SnapLogic), 91 percent of IT decision-makers say their businesses’ data quality has to be improved, while 77 percent express a lack of confidence in their organization’s business data.
In principle, increasing data accessibility might help Fortune 1000 companies. In actuality, data access alone will not be enough.
Fare errors, algorithmic biases, and erroneous credit scores
In the airline sector, inaccurate data usually results in issues, so travel specialists search booking sites for “error pricing.” The incorrect data that results in mistake fares comes from some sources, creating the illusion of a succession of isolated, one-off occurrences. Human error, currency miscalculations, and technological problems have all resulted in incorrect prices, creating a Catch-22 situation for airlines: They risk losing income if they honor the mistaken fares or generating negative PR if they do not.
Credit scores are a more ubiquitous example of how incorrect data may damage a company. If the data used to calculate credit scores is inaccurate, issues may arise. Today, financial institutions depend on big data technologies to mine data from retail transactions and social media. Most data is obtained from data brokers that operate with minimal control and are not rewarded for maintaining high-quality data. Instead, these brokers work on volume.
Based on inaccurate data, borrowers classified as “risky” may discover that “algorithmic bias” clings to them due to a lack of clarity on how such judgments are made. When incorrect data is magnified inside these systems, individuals may find themselves in a financial death spiral, unable to get credit or other financial services, and not comprehend why. Consequently, persons on the blocklist may be refused mortgages, vehicle loans, and even rental housing due to inaccurate data.
How to Ensure the Quality of Your Data
Three key actions can assist you in ensuring that your firm avoids these poor data issues:
1. Keep an eye out for broader patterns.
When incorrect data causes harm to your business, it is critical not to assume that this is a one-time occurrence. Rather than that, corporate executives should zoom out to examine the broader workplace trends.
Determine if your company has the tools necessary to monitor and assess its data quality. Otherwise, your systems may already be harboring your next poor data issue.
2. Ditch obsolete tools that are incapable of keeping up with contemporary issues.
For adverse data-driven outcomes mentioned above, the afflicted businesses had regular IT and Application Performance Management (APM) systems in place, but the poor data got through. No monitoring tools identified a single instance of infrastructure or application impairment.
Businesses want modern data management systems that enable insight across the entire data lifecycle, from generation to display on end-user devices.
3. Consider your data stack to be mission-critical infrastructure.
As organizations increasingly rely on data, reliable data has become a mission-critical asset. Consider it as such.
This may include implementing current data architectures, such as ELT-based systems, or implementing technologies such as data pipelines, warehouses, and lakes. This includes identifying data management solutions capable of monitoring data across all of your assets and, ideally, using AI or machine learning to identify issues automatically, without human interaction.
While the bad data issue is unlikely to go away anytime soon, you’ll be better equipped to identify and remediate insufficient data before it causes system disruptions, revenue loss, or adverse publicity if your business follows these three steps.
