Having access to mountains of data is not enough to create effective AI solutions. What matters most is how you collect, process, and ensure the reliability of that data — transforming raw numbers into actionable insights.
Many organizations collect data from sources as unexpected as cellphone signals and kitchen sensors, not just from forms and surveys.
Using Data Successfully
Having plentiful data available isn’t enough to create a successful AI. Presently, an AI algorithm can’t extract information directly from raw data. Most algorithms rely on external collection and manipulation prior to analysis. When an algorithm collects useful information, it may not represent the right information. The following sections help you understand how to collect, manipulate, and automate data collection from an overview perspective.
Considering the Data Sources
The data you use comes from a number of sources. The most common data source is from information entered by humans at some point. Even when a system collects shopping-site data automatically, humans initially enter the information. A human clicks various items, adds them to a shopping cart, specifies characteristics (such as size and quantity), and then checks out. Later, after the sale, the human gives the shopping experience, product, and delivery method a rating and makes comments. In short, every shopping experience becomes a data collection exercise as well.
Many data sources today rely on input gathered from human sources. Humans also provide manual input. You call or go into an office somewhere to make an appointment with a professional. A receptionist then gathers information from you that’s needed for the appointment. This manually collected data eventually ends up in a dataset somewhere for analysis purposes.
Data is also collected from sensors, and these sensors can take almost any form. For example, many organizations base physical data collection, such as the number of people viewing an object in a window, on cellphone detection. Facial recognition software could potentially detect repeat customers.
However, sensors can create datasets from almost anything. The weather service relies on datasets created by sensors that monitor environmental conditions such as rain, temperature, humidity, and cloud cover.
Robotic monitoring systems help correct small flaws in robotic operation by constantly analyzing data collected by monitoring sensors. A sensor, combined with a small AI application, could tell you when your dinner is cooked to perfection tonight. The sensor collects data, but the AI application uses rules to help define when the food is properly cooked.
How might your daily activities be contributing to datasets analyzed by AI systems, often without you realizing it?
Retailers use data from customer shopping behavior, including clicks and purchase history, to recommend products and optimize stock levels, demonstrating how human-generated data powers real-world AI.
Obtaining Reliable Data
The word reliable seems so easy to define, yet so hard to implement. Something is reliable when the results it produces are both expected and consistent. A reliable data source produces mundane data that contains no surprises; no one is shocked in the least by the outcome. Depending on your perspective, it could actually be a good thing that most people aren’t yawning and then falling asleep when reviewing data. The surprises make the data worth analyzing and reviewing. Consequently, data has an aspect of duality. We want reliable, mundane, fully anticipated data that simply confirms what we already know, but the unexpected is what makes collecting the data useful in the first place.
Still, you don’t want data that is so far out of the ordinary that it becomes almost frightening to review. Balance needs to be maintained when obtaining data. The data must fit within certain limits (as described in the “Manicuring the Data” section, later in this chapter). It must also meet the specific criteria of truth value (as described in the “Considering the Five Mistruths in Data” section, later in this chapter). The data must also come at expected intervals, and all the fields of the incoming data record must be complete.
To some extent, data security also affects data reliability. Data consistency comes in several forms. When the data arrives, you can ensure that it falls within expected ranges and appears in a particular form. However, after you store the data, the reliability can decrease unless you ensure that the data remains in the expected form. An entity fiddling with the data affects reliability, making the data suspect and potentially unusable for analysis later. Ensuring data reliability means that after the data arrives, no one tampers with it to make it fit within an expected domain (making it mundane as a result).
Data reliability in AI is often ensured through rigorous validation processes, including checksums, traceability, and audit trails. In mission-critical systems, data integrity is maintained using cryptographic hashes and secure databases, reducing the risk of accidental or intentional tampering. Understanding and implementing these measures is crucial for AI systems where data-driven decisions can have real-world consequences.
Data that consistently produces expected results, fits within known boundaries, and remains unchanged throughout its storage and use.
The origin from which data is collected, such as human input, sensors, or automated systems.
Why is it important not only to collect data, but also to ensure its reliability before using it in AI algorithms?
Practitioners often spend more time cleaning and validating data than building AI models themselves, highlighting the value of trustworthy data.
Making Human Input More Reliable
Humans make mistakes — it’s part of being human. In fact, expecting that humans won’t make mistakes is unreasonable. Yet many application designs assume that humans somehow won’t make mistakes of any sort. The design expects that everyone will simply follow the rules. Unfortunately, the vast majority of users are guaranteed to not even read the rules because most humans are also lazy or too pressed for time when it comes to doing things that don’t really help them directly.
Consider the entry of a state into a form. If you provide just a text field in the application gathering the data, you encounter a wide variety of possible input:
- Some users might input the entire state name, such as Kansas. Of course, some users will make a typo or a capitalization error and come up with Kanzuz, Kansus, or kANSAS. You see the potential for errors.
- People and organizations also have various approaches to performing tasks. Someone in the publishing industry might use the Associated Press (AP) style guide and input Kan. Someone who is older and used to the Government Printing Office (GPO) guidelines might input Kans. instead.
- And some people might go for other available state abbreviations. The U.S. Post Office (USPS) uses KS, but the U.S. Coast Guard uses KA. Meanwhile, the International Standards Organization (ISO) form goes with US-KS.
Mind you, this is just a state entry, which is reasonably straightforward — or so you thought before reading this section. Clearly, because the state won’t change names anytime soon, you could simply provide a drop-down list box on the form for choosing the state in the required format, thereby eliminating differences in abbreviation use, typos, and capitalization errors in one fell swoop.
Drop-down list boxes work well for an amazing array of data inputs, and using them ensures that human input into those fields becomes extremely reliable because the human has no choice but to use one of the default entries. Of course, the human can always choose the incorrect entry, which is where double-checks come into play. Some newer applications compare the zip code to the city and state entries to see whether they match. When they don’t match (sometimes it’s just a matter of capitalization), the user is asked again to provide the correct input. This double-check verges on being annoying, but the user is unlikely to see it often, so it shouldn’t become too annoying.
Even with cross-checks and static entries, humans still have plenty of room for making mistakes. For example, entering numbers can be problematic. When a user needs to enter 2.00, you may see 2, or 2.0, or 2., or any of a variety of other entries. Fortunately, parsing the entry and reformatting it fixes the problem, and you can perform this task automatically, without…
How do interface design choices, like drop-down lists, help reduce human input errors in data collection?
As long as you have a lot of data, AI systems will work well, regardless of its source or quality.
AI systems require not just abundant data, but high-quality and reliable data; unreliable or inconsistent data can undermine results.
Reliable, well-structured data is the foundation of successful AI — quantity alone cannot compensate for poor quality or inconsistency.
Imagine you are designing a form for collecting address information. Identify three potential ways users might enter state information incorrectly, and propose interface solutions to reduce these errors.
- List three possible errors a user might make when entering a state in a text field.
- For each, suggest a specific form design improvement (e.g., drop-down menu, auto-correction).
- Recognize the importance of reliable data sources, including human and sensor-generated data
- Understand that data reliability is not automatic; active validation and design are necessary
“We want reliable, mundane, fully anticipated data that simply confirms what we already know, but the unexpected is what makes collecting the data useful in the first place.”
Think of a time when unreliable or inconsistent data led to a problem — either in technology, work, or daily life. Reflect on what could have been done to improve the data’s reliability, and how that might have changed the outcome.
What is a reliable data source?
Tap to revealA source that consistently produces expected data within defined boundaries and remains unchanged during storage and use.
Give an example of a non-human data source.
Tap to revealSensors, such as those measuring weather conditions or monitoring robotic operations, are non-human data sources.
How do drop-down list boxes improve data reliability?
Tap to revealThey constrain user input to predefined valid options, reducing typos and inconsistent formatting.
Which of the following most accurately describes a key requirement for reliable data in AI systems?
Designing data collection interfaces and validation processes is as important as the AI algorithms themselves for achieving high-quality outcomes.
The Shift
- Data abundance alone is not enough; reliability and quality are essential for effective AI.
- Both human and sensor data sources require careful validation and consistency checks.
- Designing user interfaces with error-reduction in mind dramatically increases data reliability.