A machine readable document can be read by computers. Not all digital content is readable. For example, a PDF file is viewable by anyone with Adobe’s reader. However, a computer cannot process the words in the document or understand the meaning of the document.
Also called computer-readable data, such documents must be in a format a machine can read. There’s a time and place for both human-readable and machine-readable data. There are also formats allowing both humans and computers to process the info.
Will Chatbots Put Data Analysts Out of Work?
Reuters reported that ChatGPT set the record as the fastest-growing user base to date. OpenAI launched the software in November 2022. By February 2023, the platform already boasted 100 million active monthly users and now has around 700 million monthly active users.
Artificial intelligence relies on machine readable data to perform. It can analyze numbers faster than any data analyst and return some thoughts about where companies should head next as far as customer satisfaction, improving their block chain and many other elements of running a company.
Still, it’s unlikely data analysts, marketers or anyone else will be out of a job. First, machines can’t read all data, because it might not always be in the correct format. Second, while roles might change a bit, humans will still have to come up with creative solutions that haven’t been tried before.
Currently, machines can only spit out information already in machine readable format. Computers can’t come up with anything new. They aren’t creative like the human brain. While ChatGPT understands and mimics human language, it can only do so with machine readable data.
Learning to work with the tools of tomorrow is a vital skill for all types of employees and entrepreneurs. With the growing popularity of ChatGPT, expect to see more people implement brainstorming, outlining and research models for their brands.
At Growth Hackers, when we analyzed AI’s impact on analytics workflows, we found that while tools like ChatGPT, Claude AI, Gemini, Grok and Mistral AI can process data faster, human analysts still drove most of strategic decision-making and creative problem-solving.
Examples of Machine Readable Data
How can you know if a document is readable by engines such as ChatGPT or database-driven software solutions? The magic lies in the document format. Computers can read:
- CSV
- XLS
- JSON
- SHP
- RDF
- XML
Typically, machines cannot read JPG, although ChatGPT-4 is now reading images. It also has trouble processing PDF, PPT, GIF and most image files.
How can you make sure computers and bots can read your documents or program? An OpenAPI specification, also called a Swagger specification, ensures the language for a machine-readable format is free of errors and highlights any unusual variables.
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How Do I Make Data More Readable? | Top 7 Tips
Ideally, your data should be machine and human readable at the same time. Not considering one aspect is like ignoring the big picture. Computers are fallible. While they can access and process information faster than most humans, if there is a glitch, you want a person to be able to read and understand what you’ve written.
For example, reactive machines tap into autonomous AI and run tasks based on information humans input into the system at some point. With reactive programming, the computer can play a game of chess pretty proficiently, but it can’t predict the moves the other player might make or remember strategy from one game to the next. If someone inputs the wrong rule for chess, the machine will follow the rules laid out instead of realizing something isn’t quite right and adapting.
What are some things programmers and data scientists can do to ensure input is as accurate as possible and documents readable?
1. Watch for Errors
The internet of things (IoT) is growing at such a rapid pace that it’s hard to keep up with just how many devices connect to the cloud. From smartphones to watches to speakers, it seems everything taps into WiFi and is connected to a main hub these days.
The issue with AI is that if you don’t input data correctly, the machine only knows what you told you. Even though the technology is growing smarter, it still has limitations. If a human pronounced your name incorrectly, they might notice the expression on your face, a wince or a shake of the head and ask you for the correct pronunciation.
A computer, such as Siri, will mispronounce your name with gusto and never pause to ask if it has the correct inflection. Your best bet for ensuring data is at least readable and easy for computers to process is to watch for errors and prevent them. You may want to get a second and even third set of eyes on your documents to watch for typos.
2. Create Web APIs
Machines can more easily read data on websites, for example, when you pull away the layers of design, such as the heading or some images. Presenting just the date allows computers to process the information while ignoring anything unnecessary to completing the task or analyzing results.
Be cautious about stripping too much away from your website, though. You could make it difficult to comprehend the exact purpose of the site or make it much less engaging over time. At Growth Hackers, we treat Web APIs like clean pipelines that deliver only the essential ingredients to machines, and for data processing systems, this improves clarity and automation efficiency.
3. Use the Correct Language
ChatGPT uses natural language processing (NLP) to help the computer understand the general purpose of a conversation and respond based on user intent. Think about the words you use in your commands and content.
Get rid of any slang or regional terms that might confuse the machine. Stick to basic language and good sentence structure.
4. Test Typical Uses
What will you most likely use AI processes and machine analytics for? Run some test processes to identify weaknesses in your data structure. You may need to change your inputs a bit to better pull up reports at a later time.
Whatever syntax you use and elements you input, you must remain consistent over time. Look to the future. What elements might you add to your business over time and how does your current data allow for future additions?
One example might be an Excel spreadsheet where you include inventory. At the moment, you have X number of products. However, as your company grows, you might add hundreds more. Does your database allow for expandability without confusing the computer?
Think about how you’ll utilize information for tracking customer buying behavior. What types of details might be most helpful to the marketing department going forward? If you’re unsure what you should include, talk to your data analytics team about making your system evergreen.
5. Convert Unreadable Data
Although most organizations are open to creating machine readable data and utilizing today’s technology for growth, a majority of brands stated much of their data was still in an unreadable format, such as PDF or images.
Make converting old data into machine readable formats a priority in your organization. Hire someone to take old PDFs and convert them into plain text or a readable format and reupload them.
There’s a place for the visually aesthetics of the older information while replacing it with material machines can interact with more easily.
At Growth Hackers, we worked with a manufacturing company sitting on 2 decades of valuable but unreadable PDF data, and after converting it into structured text formats, they significantly improved searchability.
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6. Identify Missing Links
Use AI to identify holes in your database. Are there big chunks of missing information? How can you fill the gap? Ask the chatbot or run a report on what your data lacks. Such reports help identify areas you should pay more attention to when entering new data.
Over time, you’ll add some rules and processes that help you gather the information needed by machines to drive your sales growth. For example, perhaps you haven’t asked customers some key questions that could lead to additional sales or referrals. Adding a couple of points to the sales process might expand your earning potential.
At Growth Hackers, we helped an industrial business move from incomplete, fragmented datasets to an AI-audited data system that identifies missing links, resulting in an enhancement in sales insights and revenue opportunities.
7. Perfect Your Labels
Machine readable data is labeled clearly. If you don’t have categories and labels for all your inputs, computers will face challenges processing the information. The easiest way to make sure everything is labeled correctly is to come up with a list of possible categories and labels that match. Stick to these for anything you enter, so you may want a miscellaneous label for anything that doesn’t quite fit in another category.
Come up with DevOps for communication amongst various teams within your organization to communicate better. If one department inputs data with one format and a different one with another, the machine might grow confused and spit out inconsistent results.
When in doubt, look at previous entries and see if there is one for the same type of item. Human error is usually at fault behind machine processing issues. Do your best to put processes in place to avoid glitches in the system. If everyone knows where to list specific data entry points, you’ll reduce the risk of using the wrong thing.
Easiest Formats of Machine Readable Data for Those with Little to No Experience
Although you could learn JSON or any other language given enough time and resources, Excel is likely the easiest way for most people to pick up the data entry that will be readable to AI. Most high schools offer beginner information on how to use Excel. You can also access details via video streaming sites and free online courses in how to use the Microsoft software.
Once you’ve input information into Excel, you can save it as a CSV file and upload it to your database for processing. Whatever system you use to store and track data, make sure you stick to a single platform so as not to confuse machines. Consistency is key when it comes to machine learning and readability.
Take advantage of the perks of letting a computer process data for you more efficiently than a human ever could. With a little bit of effort and some tweaks, you’ll save time and bring the machine alongside you to perform tasks effortlessly.
Growth Hackers is certainly one of the best data analytics companies helping businesses from all over the world grow. There is no fluff with Growth Hackers. We help entrepreneurs and business owners harness the power of machine readable data, increase their productivity, generate qualified leads, optimize their conversion rate, gather and analyze data analytics, acquire and retain users and increase sales.
We go further than brand awareness and exposure. We make sure that the strategies we implement move the needle so your business grow, strive and succeed. If you too want your business to reach new heights, contact Growth Hackers today so we can discuss about your brand and create a custom growth plan for you. You’re just one click away to skyrocket your business.
