We call this Digital Era because people are digitally more active than ever before. As a result, data is growing in an exponential way. According to a report, IDC predicted, the data will grow by almost six folds by 2025 compared to 2018. Thus, it becomes difficult to make complex data human-readable to draw a meaning conclusion. This is where the role of Data-Scientists comes into the picture.
However, the demand for the Data Scientists is outstripping supply. To compensate this, many organizations are trying to improve the skills of their employees through various ways to data science to their workforce.
But, what other things should the organizations do to compensate for this? Here are the five ways to make the most use of Data Science.
Quality of Data is always important than Quantity
If the quality of the input is of low quality, even the best predictive model could show the worst results, which are not reliable. So first, we need to understand, the quality of data is more important than gathering more data. To evaluate the data whether it is accurate, complete and consistent, we need to know from where and how it came together.
Learn Skills for Tomorrow
Data literacy is one of those skills, which is necessary for almost everyone in the digital age. The employees of an organization should be prepared for tomorrow by improving their skills of understanding, analyzing, and most importantly questioning data.
Some employees may fear for change of a cultural shift but they need to be educated about the importance of learning data literacy. The importance of including Artificial Intelligence (AI) in the workplace to support their role has to be made clear. Once implemented and we start seeing changes, organizations need to take feedback and be ready for the open challenges.
Learn and Upgrade By Doing
Although organizations train their employees to hone skills, they need to participate in other ways of learning as well such as blogs, webinars, video playlists, etc. These fill them with confidence in the new technology.
The technology should be learned to use as a part of their work culture but should not feel like an extra burden by the employees. The aim should not be to turn a company’s marketing team or accountants into Data Scientists but enhance their skill set with the technology in their particular role.
With the knowledge of Data Science or Artificial Intelligence, an employee can analyze and interpret enterprise data avoiding any ambiguity. Natural language processing helps users in finding answers through a search-engine like an experience.
The new technology might be of use in little things but very useful things that reduce the work. It could be a small machine-generated alert, but once the user gets the correct forecast and benefits from the insights, his confidence in the technology grows and curiosity to learn increases.
Start Small Go Big
Small things matter a lot. Even a small feature in AI can give greater insight. A small and innovative project in AI could turn into success and can then scale it up across the entire enterprise. For this to happen, the right employees in the team are much necessary. A person with the right mindset to identify problems and test hypothesis are better than with a good understanding of the algorithm.
A curious, self-motivated who like to try and fail frequently can do the work better in data science technology. Such employees would decipher the mystery of data science and help enhance the projects in the organization.
Go With the Right Approach
Data Science techniques are complex and most time-consuming. Therefore, buying pre-trained AI models that suit your needs and could automate complex processes could be a better idea. Choosing the right solution will help users about the data science process and the factors considered during the model creation. It creates transparency about the reliability and accuracy to the users and helps them in understanding the steps in the process that fail and how to rectify them. In such a process of corrections, users will adopt the best practices for the implementation.
Even though Data Science is based on logical thinking and mathematics, there is never a single right answer, since there are many approaches to a single problem or a question. The key is to find a solution that fits everyone by conducting various experiments and improving continuously. The entire process is to create trust between machine learning models and users by delivering valuable information to the business.