Data inaccuracy is reported to cost businesses an average of $15 million per year, undermine digital initiatives, weaken competitive standing, and seed customer distrust. So how do you make sure your cloud data is accurate so you can rely on it for critical decision making?
The figure of data inaccuracies costing businesses $15 million per year was reported by Gartner in its 2017 Data Quality Market Survey. The author of the report noted that, while poor data quality was hitting some businesses where it hurts, businesses that had taken steps to make sure their cloud data is accurate benefitted from being able to trade data as a valuable asset to generate revenues.
So, what are some businesses doing that other businesses aren’t? Those that have studied data management and analytics will be aware of the six dimensions of data quality – i.e. data that is “fit for its intended uses in operations, decision-making, and planning” – and will know that making sure your cloud data is accurate is only one of six elements that have to be in place before data can be relied upon.
The six dimensions of data quality
The six dimensions of data quality have to be taken in context of business operations. Data generally only has value when it supports a business process or decision, and therefore businesses need to determine what the data is being used for – and the impact non-compliant data will have on the business – so the six dimensions of data quality can be weighted accordingly:
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Completeness – Are all relevant data sets and data items included?
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Consistency – Can the data sets be matched across different data stores?
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Uniqueness – Is there a single view of the data set (i.e. no duplications)?
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Validity – Can relational data be traced and connected to other data?
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Accuracy – Does the data correctly reflect its “real world” value?
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Timeliness – Is the data relevant to the period being analyzed?
Some data management experts argue that 100% data quality in the cloud is unattainable – especially when data has been migrated from an on-premises database in different or incompatible formats. It’s also the case data can degrade over time due to human error, a failure to keep it up-to-date, and software bugs. Keep this in mind when trying to apply the six dimensions of data quality.
The key to data quality is total visibility
With data sets dispersed in many different locations (both on-premises and in the cloud), the key to data quality is total visibility. With total visibility across your IT infrastructure, it’s easier to identify whether five of the six dimensions of data quality are being met (accuracy being the exception); and, with regard to accuracy, there are steps you can take to make sure your cloud data is accurate.
Assuming the data collection process is effective, these steps include implementing access controls, audit trails, and logs to ensure the integrity of data. Naturally these measures require monitoring to check data isn’t being accessed, amended, or deleted without authorization, but there are automation solutions that can do this for you and alert you to events that could result in data being compromised.
Where data lacks completeness, consistency, uniqueness, and validity, automation solutions with cloud management capabilities can collect data sets from different data stores (on premises, single cloud, and multi-cloud) and consolidate data in one place. It can also check for non-compliant data or data that has been excluded from a data set due to having an incompatible format.
Make sure your cloud data is accurate
Although 100% data quality in the cloud may be considered by some to be unattainable, it’s a goal worth aiming at. If data isn’t “fit for its intended uses in operations, decision-making, and planning”, your business will have unnecessary overheads due to manual processing and reduced revenues. You may even be basing business-critical decisions on incomplete or incorrect data.
Conversely, if your cloud data is accurate and the other five dimensions of data quality are met, you’ll be able to create a sustainable business advantage through lower operational overheads, increased customer profitability, and increased revenues. Many businesses fail to tackle the problem of poor data quality because it’s considered too complex, costly, and time-consuming. However, with total visibility of your IT infrastructure and automation, you should be able to make sure your cloud data is accurate and can be relied upon to help your business thrive in the cloud.
