Data entry is a key area in business development.
The golden rule in strategy- design is very simple: Strategy follows numbers, not the other way around.
If data entry is flawed, business will be formulating strategies with compromised information. Despite the increased presence of automated processes, data entry largely remains a human function.
Humans are valuable but are flawed and imperfect assets. As emotional beings, they are prone to errors and distraction. They can get sick and miss work. The solution to accurate and consistent data entry may lie with Machine Learning.
What Is Machine Learning?
Machine Learning (ML) is a process whereby algorithms and related technologies are used to analyze data. Programming language is created to detect trends and changes in patterns on old and current data.
Machine Learning algorithms have been applied with great success to processes of various industries. It has proven to be an effective way of automating data entry.
Data entry is mechanical work that is left in the hands of humans. This leaves the process vulnerable to errors, inaccuracies, and duplication. Big Data is crucial for business success.
It gives businesses empirical bases to track changes in demand, market behavior, and consumption patterns. Mistakes in data entry can lead to flawed strategies which could end up costing businesses a lot of money.
This is why companies are starting to automate the data entry process.
Machine Learning algorithms are more accurate, precise, and dependable compared to manual data entry. Businesses can be more confident in implementing their strategies.
Machine Learning algorithms will continue to drive the new forms of technologies that are created to make life and work easier. A good example is your smartphone.
When you begin typing a word, the smartphone processes several possibilities. The algorithm has learned your texting habits. It knows the most common words you type or search for.
Within a few seconds, your smartphone it will can your database and display words that could fit what you are typing. This is Machine Learning at work!
Compare that to another mobile device where every word has to be constructed in full or in abbreviated versions. Machine Learning has made your activity faster, easier, and more convenient. What’s more is there are no mistakes.
Automated data entry processes using Machine Learning algorithms will reduce the time it takes to complete a project.
Data entry automation is here to stay. If you want to remain competitive with the rest of the players in your industry, you must integrate Machine Learning in your data entry process.
What Are The Applications Of Machine Learning?
Studies have shown that Machine Learning can help resolve efficiency issues with challenging digital processes. Here are a few examples of how companies are using Machine Learning to make processes easier and more efficient.
- Scanning – Machine Learning algorithms are used to search and identify data within an unstructured format.
For example, automated processes can be used to scan and find a specific face or object within an image. Doctors use programs that help find tumors, lesions, and other forms of potential damage in organs.
By scanning for specific data whether in image, text, audio, or video format, Machine Learning saves time and allows humans to perform other tasks.
- Manage Large Volumes of Data – Humans have physical limitations. Machine Learning can go through massive amounts of data without losing a beat.
It can track and retrieve valuable information that may appear buried within an avalanche of data.
These programs are crucial for stock traders who need to find reliable data to support their fundamental and technical forms of analysis.
Sometimes people interchange the function of data mining with the purpose of Machine Learning. To be clear, data mining extracts data that will be analyzed by humans.
Machine Learning analyzes the data that has been extracted or mined. It will find trends, patterns, relationships, among the extracted data that can be used to make processes faster.
How Will Machine Learning Improve Data Entry Processes?
Machine Learning is still in the early stages of its evolution. While it has been applied and implemented with great success in different fields, some industries still rely on the human asset to exclusively run its primary processes.
For example, financial analysis and financial planning remains exclusively the domain of humans. It still uses the same manual processes to analyze data.
It should not take time for software engineers to develop Machine Learning algorithm that will make the process of analyzing data easier and more accurate for those in the finance industry.
As mentioned earlier, some algorithms are already in use by stock traders. The Machine Learning algorithm searches and analyzes data that can be used as basis for both technical and fundamental analysis.
Accountants find relationships and trends among numbers. They will look for ways companies can find the equilibrium point. This is the level where costs can be reduced without compromising revenue generation activities.
Financial planners use data to come up with the best options to fit your lifestyle and income capacity. They find patterns in spending and how your stream on income comes in.
In time, the financial industry, as well as others, will realize the benefits of automating data entry processes. These benefits are the following:
- Find Significant Trends; Changes In Patterns and Behaviors
Every day data extraction programs mine thousands and even millions of usable information.
Properly utilized, these data units can lead to a discovery or a major breakthrough that could prove to be a potential game changer in the industry. In business, time is money.
Opportunities are won and lost by those who can capitalize on these discoveries in the fastest amount of time.
Even with an army of human data entry specialists and analysts on payroll, they will not be able to compete with a Machine Learning algorithm.
These algorithms can run different scenarios with the extracted data.
From there, it will identify trends, changes in patterns and behaviors. It will not matter if the data extracted are structured or unstructured.
The algorithm can be applied to other sources of data whether these are through chat logs, social media, comment sections or emails.
- More Detailed and Structured Forecasts
Data analysts need to flex their creative and analytical muscles to spot relationships between numbers and other types of information.
Some are advised to “think outside the box” when it comes to data analysis. For sure, this process will take a lot of time, trial and error.
Machine Learning will take away the guesswork. It will give you the analysis you need with precision and accuracy despite the large volume of data.
Algorithms can organize, categorize, and identify the variables that can help businesses make the most educated decisions.
A company that wants to manage inventory more efficiently will benefit from a Machine Learning algorithm.
It will track patterns and relationships between inventory levels, purchase orders, consumption, wastage and other variables that will prove valuable when improving inventory management processes.
- Flexible and Highly-Adaptive System
Proponents of Human Behavior will say machines can never duplicate the ability of humans to foresee changes in trends and behavior because you cannot engineer or design empathy.
However, Machine Learning is designed to be flexible in its analytical processes.
As mentioned earlier, it can run various possible scenarios by analyzing relationships with data within a few seconds. It can quickly adapt to changes in human behavior such as consumption or purchasing patterns.
Once implemented to data entry processes, Machine Learning algorithms will make them faster, more efficient, and most of all, cost-effective.
In addition to your smartphone making word suggestions while you are still texting, algorithms have been used to make search activity faster and more convenient.
Search engine giant Google uses predictive algorithms to find URLs that are relevant to your query and does it within a few seconds.
Its video streaming platform, YouTube, which is also a search engine, uses predictive algorithms to recommend content based on your search history. It analyzes the search queries you have done in the past.
Based on your online behavioral patterns, it will look for content that is based on what you had searched for in the past. That is why you often find “recommended channels” on your YouTube home page every time you access their network.
Take note that the predictive algorithms go through massive amounts of data and make its analysis within a few seconds.
No human; even with the best trained and most physically and mentally qualified, can do what a Machine Learning algorithm can on a regular basis.