How the OQLIS Data Science Automation Tool Changes Data Science Practices
The OQLIS data analysis software is great for businesses and individuals to use to automate the process of analysing big data. The key to our platform’s automation function is to make tasks that are repetitive and labour-intensive much simpler and easier to accomplish for our users. Read more about the OQLIS data science automation tool and its effect on the field of data science.
Over the past few years, data has become a very important factor for success in most businesses. Companies are spending more time collecting and analysing data now more than ever before. Ideally, the person who will be responsible for performing these tasks for an organization would be a data scientist but, with rapid developments in automated tools, machine learning, and artificial intelligence, most people are starting to wonder just how powerful automating many of the data science tasks could be.
The truth behind that matter is that it is still very early to make any predictions on exactly how automation tools can change data science simply because of the fact that technologies like these are still in the primary stages of development and they do have a long way to go.
For most companies, pricing of their products or service, marketing, and the sales performance thereof is so important and vital to the success of the company. Fortunately, with the help of modern technology, automation tools can help data scientists to ease their workload by taking care of the more strenuous tasks during the process of analysing data. It is quite common to find most growing companies make use of the most accessible technologies to help with live processing.
Before we can dive into how automation is changing the world of data science, we first need to understand the concept of data automation.
What is Automation in Data Science?
Data automation refers to the process which deals with attempts at using different tools to make the work of data scientists a bit easier. Attempts at automating data science processes have begun back in 2010. Now ten years later, it is believed that almost forty percent of data science tasks can be automated. Data scientists can use automation tools to perform the entire task or just partial steps during their work.
Data science is typically known to be a very hands-on job. The role of a data scientist is to be able to solve problems within an organization, using data that are collected from different sources. As a data scientist, you would be working very closely with the business to help them reach their goals using the information from patterns identified in data sets.
The most important factor to keep in mind regarding using automation tools in this field is that some aspects of data science will be trickier to automate than others. Tasks such as creating a hypothesis and formulating strategies require the skills of an actual data scientist. This is why there is no chance that automation tools run the risk of eliminating job opportunities for data scientists.
While talking about automation tools for data science, one should also consider the quality of the output. The output in this scenario refers to how valid and meaningful the information is that gets produced using automation. An increase in the number of tasks that get completed with automation does not always mean that you will get quality results.
We at OQLIS know that our users are looking for a simple and effective platform that will assist with the process of analysing data. Our software was created with the intention to help individuals analyse and interpret different patterns identified within the data gathered. Our automation function makes it easier for you to compile in-depth reports and make time for new discoveries using the information obtained from your data sets and the automation tool.
The OQLIS data analytics software was created around three core factors. These are:
- Initiating new product development within a business
- Improving businesses’ sales and marketing
- Assisting customers to achieve great success in their business
Redefining the process of analysing business data is our main priority at OQLIS. Our software, together with the advancements made in modern technology has helped data scientists examine large sets of data in a shorter period of time.
As enticing as it may sound, big data automation can quickly become a very complex task. This is why we encourage individuals and data scientists to use the OQLIS business data analysis software in their business to successfully come up with a solution that would work for their company.
Will Data Science Become Automated?
As it stands now, we are currently still in the very early stages of using automation tools in the world of data science, but one thing we do know for sure is that automation will revolutionize the world of data science. With the help of automation tools, we find that data scientists work at a higher speed to produce an output that gives value to businesses that seek this kind of information.
Most people believe that data science will not become completely automated in the future. This is due to the fact that there are certain steps that require a qualified data scientists’ expert knowledge in order to be done correctly.
Although algorithms and tools are great to use, they lack in having the ability to make decisions on what information is useful, and what can be discarded. This can only be done with human judgment. You cannot teach machines, tools, and algorithms to be creative. After the data scientist gets the results, they needed by using the tool, they then need to work with other people such as the business’s marketing team to come up with the best solutions that will work for that specific company. These types of conversations and meetings are the main source of creative thinking. This is a very valuable skill that no tool in the entire world could replace.
Automation in data science is a great solution for tasks such as creating dashboards and reports. Automation tools help streamline data maintenance tasks and analytical systems.
One of the greatest advantages of using automation tools in data science is that it enables data scientists with the assistance that they need to create new discoveries whilst drastically shortening the time spent conducting these activities. Other great benefits of using automation tools in data science include:
- Automation tools can help businesses save money and time. As we all know in a business, time equals money. By automating tasks that do not require the skills of an educated human being, businesses will be able to spend less time on tasks that take longer due to their difficulty levels.
- Automation also provides data scientists with enough time to focus on identifying new patterns and gaining insight from data.
- The use of automation tools improves productivity for data scientists.
OQLIS Data Science Automation Tool
OQLIS provides its users with an automation module that can be used for data automation. For the convenience of our users, we have integrated an automation function for our users to access on our platform.
In order for our users to successfully create an automation using the OQLIS platform, there are three key factors that are required, these include:
- A key performance indicator
- The automation set up
- A schedule would need to be created by the user when they have tested the key performance indicator using the OQLIS automation setup.
Because of the fact that it is costly to hire a professional data scientist, most companies are starting to look for alternative solutions. This is where the OQLIS automation module comes in handy. It is user-friendly and be used by individuals and professional data scientists respectably.
If you would like to learn more about how to use the OQLIS automation tool, we have provided video tutorials on this topic that can be found on our website for you to use online.
Will Using Automation Tools Change the Field of Data Science?
No. The use of automation tools will not cause any changes to the field of data science. Data scientists cannot be replaced by these tools instead, they are used to promote productivity.
Instead of posing a threat to the field of data science and the future of work opportunities, automation tools will most likely become an extremely helpful assistants to most qualified data scientists, allowing them more free time to run other complex tasks.
The main goal of using data science in a business is to gain useful insights that will help the business improve its productivity and increase profit margins. Automation and machine learning processes help data scientists reach this goal. Automation will not change the field of data science, but instead, it will help individuals to get a better understanding of how to get the most information out of their data and the data science environment while using the best approach for the situation.
No data scientist could ever be replaced with automation tools because automation alone cannot replace the growing need for more professional data scientists. Data scientists are in a constant demand. Their expertise is required by millions of companies across the world, these include government and even security companies. Data scientists are one of the most in-demand jobs for 2021. As the world of business evolves, companies are trying everything that they can do to stay relevant and ahead of the competition. Automation tools empower data scientists to do more and it also has a huge impact on the value of their work.
Everything You Need to Know About Artificial Intelligence in Automation
Artificial intelligence (also referred to as AI) is often confused with automation in data science. It is very important for people to understand the difference between these two concepts before using either one of these in a data science process.
When it comes to differentiating between artificial intelligence and automation, the key to remember is that automation tools require manual configuration and support from humans whereas artificial intelligence deals with how computer systems use large amounts of data to start the process of reasoning and making predictions of what to do next.
Artificial intelligence is known as the ability of a computer program to think as a human would. Most artificial intelligence used today works to provide its users with the next best option. This suggestion is then taken by the data scientists who will then decide if it should be used or not. If the data scientists are not happy with the outcome, he or she would then try to make some adjustments to the option provided by artificial intelligence until they are happy with the results.
In terms of combining artificial intelligence and automation for data science, the results given from this process are known as intelligent process automation (IPA). People who have used an IPA tool before would agree with the fact that these tools are very powerful because it allows the user to have all of the benefits that come with automation while having the ability to also provide valuable insights just like you would get from artificial intelligence. IPA tools are totally different from automation tools because these tools turn information into action.
Some of the advantages you could look forward to when it comes to using artificial intelligence in automation include, but are not limited to:
- It helps to reduce the risk of human error during the data science process. As humans, we are capable of making mistakes in our day-to-day tasks at work. Artificial intelligence in automation will help data scientists to reduce their mistakes when analyzing data.
- Artificial intelligence is available to use at any time of the day, for seven days a week. Unlike humans who require leisure time, artificial intelligence can be used whenever it is needed.
- Artificial intelligence in automation is great to complete those repetitive tasks that are tedious and take up the majority of your time as a data scientist. As a professional data scientist, there are many tasks that you need to get to during your day of work. Using artificial intelligence in automation will help you to automate the boring tasks and get those out of the way. Leaving more time to explore other options in your field of work.
- Artificial intelligence in automation acts as a digital assistant for a data scientist. It helps to inspire data scientists to come up with new and innovative ways to solving complex problems.
Although there are various advantages to using artificial intelligence in automation, we as humans need to keep in mind that we need to ensure that our work does not get overshadowed by these machines, tools, and algorithms, especially since data scientists are the people who put in most of their effort to ensure success at the end of every data analysis process.
How Data Science Jobs Are Becoming Simplified with Automation Tools
One thing that is for sure is that data scientists should not be worried about losing their jobs to automation tools and machines. In order for a data science project to be successful, it will always require the input of a professional data scientist. Even if automation is used to some extent during the project, a data scientist will always be required to manually validate the results.
One of the qualities that makes a data scientist so unique from other professions is the fact that these individuals are required to have a high level of mathematics and computer skills and are also required to have a business mindset. It is very rare to find all of these qualities in one person.
Let’s be honest, hiring a qualified data scientist to work for your business can become a costly affair. Most organizations, especially small to medium-sized businesses cannot afford to pay large amounts of money to hire professional personal but, with the help of data automation tools, companies, regardless of how big or small they are, will be able to enhance their business performance and increase profit margins.
The introduction of automation in data science has proven to be a game-changer in this field of work. Data scientists are now able to complete tasks in a fraction of the time it would take them to perform them manually. Gone are the days when data scientists were required to spend their time performing monotonous tasks that provided little to no value to the end results.
The traditional methods used by data scientists are very complex. Most professional data scientists spend weeks or even months trying to come up with standard practices to gather data, process it, and find solutions that validate the machine learning modules. Using automation tools makes this in-depth profess simpler. All that’s required for the data scientist to do is feed the data into the system to produce results with automation tools.
At OQLIS we like to provide our customers with simple solutions that help extract the complexity in working with data analytics, machine learning, and automation. It is our priority to make sure that our clients can successfully process big data most simply and effectively as possible. Our platform’s automation module was built with the intention to make the process of analyzing big data much simpler and less time-consuming for our clients. When you choose to use the automation function on the OQLIS data analytics software, you will realize that it does not take much skills or knowledge to understand how the system works and the results can be obtained easily in the most convenient manner. Be sure to give the automation function on the OQLIS software a try soon.
Reasons Why Automation Won’t Eliminate Data Science Job Opportunities
Automation tools assist data scientists to make their workload a little easier but, the importance of using the skills of a professional data scientist should not be undermined. People cannot be replaced by machines. Some skills such as listening, analyzing, and interpreting can only be done correctly by the professionals.
Here we discuss some of the top reasons why automation tools will not eliminate data science job opportunities:
- Validating the results obtained from automation
Even when using automated tools to process data, there is still room for common mistakes to occur. A data scientist would be required to successfully analyze and interpret the information obtained by using automated tools.
- All organizations still need professional human judgment
While automation tools can help data scientists to perform many tasks, they do lack in the areas which require a deep understanding of what the data means for an organization.
The importance of human judgment in data science is a very important one that is often overlooked. All businesses need to use a professional data scientist to gather information from data sources, choose the best algorithm to work with, and interpret their findings.
So, if you are interested in obtaining useful information from data sets at a much faster rate than you already are, be sure to use the OQLIS data analysis software automation function today. Our built-in automation module is simple and easy to use. If you feel like you need some assistance on how to correctly use the OQLIS software, be sure to take a look at the video that discusses automation on our website today. Alternately, you are welcome to get in touch with us to request a demo or to schedule a meeting with our team to find out more about the OQLIS data science automation tool. We also provide our customers with the opportunity to sign up for a free trial of our software. All you have to do is simply fill in all of your personal details on the spaces provided on the online allocation form found on our website, before submitting it for approval. Be sure to include a message to us stating your reasons as to why you would like the free trial to be granted to you. The free trial lasts for a period of fourteen consecutive days before expiring. We look forward to hearing from you soon.