It’s a fact: not all data is created equally. Its differences are most evident when you compare the benefits of working with high-quality data to the drawbacks of using low-quality alternatives. For example, top-notch data can help vessels avoid collisions and aircraft circumvent turbulence, while inadequate information can cause shipping delays and flight disruptions. Identifying valuable information from background noise can seem daunting. But have no fear. There are three telltale traits that all high-quality data shares: reliability, timeliness, and usability.
What Where is the point?
The world is one big data problem, said Andrew McAfee, co-director of the MIT Initiative. While this may sound pessimistic, it highlights one of the most significant issues facing organizations that manage data: the express need to distinguish high-quality information from background noise.
From actionable insights to competitive advantages, the value modern businesses can extract from high-quality data is difficult to overstate. So it’s not surprising that data quality is top-of-mind for most companies. Experian said in a white paper that “the top driver for wanting better data is to find new customers.” Other motivations include improving customer experience, business growth, and reducing risks.
Poor-quality information, on the other hand, can lead to mediocre insights, hampered decision-making, and missed opportunities.
Given the importance of identifying high-quality information, data managers have their work cut out for them. After all, collecting data is only the first step. Ensuring it’s inherently valuable and will benefit your business, that’s another story.
This task may seem monumental at first, but high-quality data has three distinct qualities that distinguish it from the mountains of chaff: reliability, timeliness, and usability. Look out for these traits, and you’ll always be sure to strike data gold.
Reliable data is verified [sourced] data
When it comes to data, reliability means information is accurate, complete, and consistent across all platforms and channels.
Take healthcare, for instance. If a patient’s birthday is April 23rd, 1970, in one system, but September 14th, 1973, in another, then the information is unreliable. Which entry is correct? Are both wrong? You simply can’t trust any data that is inconsistent across platforms.
It’s also important to consider your data’s source when gauging its reliability. Is your data source trustworthy? Is it open about its data creation and collection practices? Or is your source gleaning information from a third-party? These are essential questions to ask.
Make no mistake; reliability is crucial. Inconsistent data pulled from unreliable sources can give rise to dubious insights.
If you’re worried about the state of your current information, you can always audit your data. That’s the most effective way to ensure its reliability across the board.
It’s about service/timing
Speed, latency, refresh rate, and bandwidth are all part of timeliness. Ask yourself, how up-to-date is my information? Was it updated within the past hour, day, or week? If your data was updated in the last thirty minutes, you might consider it timely—unless new information already exists that makes it obsolete.
Timeliness is also relative. Shipping data that is timely for a company transporting iron ore might be sluggish for a company handling highly perishable medicine. Consider what timeliness means to you.
To many, customer service plays an unsuspecting but large role in the timeliness conversation. No matter how hi-tech or futuristic a solution might be, a helpful person is what makes a real difference. How quickly does your data source respond to your inquiries? How soon do they notify you after an outage? Data delays that you experience can, in turn, slow down your interactions with your customers.
Like reliability, timeliness is critical because information that isn’t up to date can cost your company time and money, or even damage its reputation.
More than just a ‘use case’
The most timely and reliable information is only valuable if you can put it to use. That’s why usability is the third trait of high-quality data to look out for.
When gauging data’s usability, look into user-experiences. Is the data you’ve sourced compatible with your technical infrastructure? How seamless is the delivery method? Are you able to efficiently access and integrate the data, or are you hindered by data silos?
Data silos are the worst enemy of high-quality data. They barricade useful information in separate units. This makes it difficult for companies to generate actionable insights and, as a result, can inhibit critical decision-making. Data silos and disparate information can even cause your customers to feel like their needs aren’t being met and prompt them to approach your competitors.
The qualities of data quality
The world may be one big data problem, but companies that tackle the challenge by prioritizing high-quality information will discover competitive advantages and valuable opportunities. Thankfully, there is also help along the way.
Spire Global is a market leader in quality data for vessel tracking, aviation activity, and weather forecasting. In under six years, we have built a fully operational nanosatellite constellation in low Earth orbit that collects global data in near real-time, an unrivaled achievement in the industry.
Spire is now working towards nearly doubling the number of satellites in the constellation so that we can offer customers AirSafe API—a global aviation product that combines data from sensors on the ground and in space. In everything we do, from seas to skies, we focus on providing data that’s reliable, timely, and usable.
For more information, visit spire.com.