Embracing AI and Data Science to make better Business decisions

embracing artificial intelligence

For a long time, businesses have understood the importance of data in the daily operation of their organizations. However, it appears that there is more to the use of data than basic analysis and arbitrary predictions.

This means that, with innovations and methods, organizations can now get more from data, and these advanced benefits spell a lot of good for businesses and corporate organizations. In this article, we will discuss the concept of data science, its importance in the business and corporate world, and the numerous possibilities that come with this concept.

What is Data Science?

what is data science - embracing ai and data science

It is important to state by mentioning that data science is not a lone field. It is a concept that combines statistics, data analysis, scientific methods, and artificial intelligence. However, what this conglomerated concept involves is the collection and analysis of data to provide actionable insights. While this sounds mundane, upon serious scrutiny, you will find that data science involves processes such as data preparation, aggregating, manipulation, and advanced data analysis. After pieces of data have gone through these stages, the data scientists then uncover patterns and trends and allow business leaders to draw conclusions and insights.

Once you delve into the data science concept, you will come across some terms more frequently. Some of these terms include artificial intelligence, machine learning, and deep learning. It is thus important to explain these terms and how they are related to data science.

Artificial Intelligence, also known as AI, is the process of having a computer mimic human processes and activities. Data science is a subset of artificial intelligence as it is the preparatory step. To make the computer mimic human processes (artificial intelligence), you need to train the computer with data. The data used for training the computer is prepared and manipulated through data science.

Machine learning, on the other hand, is the process of training the computer with the data prepared. It is a subset of artificial learning. There are different techniques for training the computer and all of these techniques fall under machine learning. The last term, deep learning, is a subset of machine learning. It involves training the computer to solve more complex problems and carry out complex human processes.

While these three terms are used interchangeably in the artificial intelligence world, it is clear to see that they have different meanings and refer to different processes.

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Why is Data Science Important?

The sole reason for the outstanding importance of data science is the age-long secret in the business world; business decisions are made based on data. It becomes even more important when you realize that Southwest Airlines was able to save about $100 million by leveraging on data. By using and analyzing the data at its disposal, the airline was able to utilize its resources judiciously by reducing its planes’ idle time, thus, saving the airline a lot of money.

It is inconceivable to run a successful business without using data, and data science is the leading concept at the forefront of advanced data analysis and manipulation. Below, we examined, in a more detailed manner, why data science is so important in the business and corporate world.

Data Science Creates Better Customer Experience

Data Science Creates Better Customer Experience - embracing ai and data science

With data science, producers of goods and services can serve their customers better. This is because data science allows the business to understand the preferences of its customers through advanced analysis. Also, with the introduction of machine learning, the business through its E-commerce website or sales portal can make recommendations along the lines of the preferences of its customers. This means that the customers get to spend less time getting the products or services they want, and this makes for a very good customer experience.

Utility in Various Business Verticals

Away from customer experience, data science makes it possible for businesses and organization to improve their offerings and operations. There is virtually no industry where data science would not find its use. Be it medicine, transport, or logistics, as long as the business deals with humans and there is data associated with the operation of the business, there will also be a use case for data science in the business. The versatility makes data science a very important concept that organizations must not only understand but strive to utilize, going forward.

Lastly, the scrum master must understand his role and responsibility. Essentially, he must understand that he is responsible for the facilitation of reference points and events, gearing the team on for continuous improvement, and also removing difficulties for team members. The scrum master, while showing commitment to the due completion of the project must also remember to protect the team and its capacity.

How does a company build data maturity?

How does a company build data maturity - embracing ai and data science

As important as data is to business, there are processes to the use and integration of data in the business. This is why a data maturity model is important. It shows the process of integrating data into the business. As a result, there are different data maturity models for different business processes. For instance, there is a data maturity model for data governance and there is another for customer data. To build data maturity, businesses as well as data intelligence service providers go through four stages-

Explorer

At this stage, the business is new to the use of data in business and has no pre-defined strategy for the inclusion of data in its business process. Of course, this doesn’t mean that the business doesn’t use data at all. But these uses are for mundane purposes such as reporting. And even in this use case, the data used are not sourced and are usually generated in-house.

User

In this stage, the business already understands the importance of data in the business process and is now open to using data internally across all operations. The business also combines the use of ad-hoc datasets at this point. These datasets help to amplify the data generated and used internally. Consequently, the business can make insightful business decisions based on these datasets.

Leader

Organizations at this stage are similar to the User. The business use data to run its operations and make business decisions. However, they take it a step further by utilizing data in competitive intelligence. This business relies on data to have a competitive advantage over its competitors. Therefore, it doesn’t only use data generated internally, the business at this stage utilizes third-party data, in addition to the in-house data.

Innovator

For the last three stages, data have been used for observation and analysis. There hasn’t been a creation or development of something new from the data generated and analyzed. This stage seeks to correct that. For organizations at this stage, the goal is not to analyze, but rather create algorithms and prediction models that help other businesses and organizations stay ahead of the competition. At this stage, the use of data is to revolutionize the business world.

What skills are required of a data scientist?

What skills are required of a data scientist - embracing ai

There is two most important set of skills that you need to have as a data scientist. They are the educational skills and technical skills. Starting with the educational skills, you need to have a solid foundation in mathematics and statistics. A lot of the applications and models that you would build will require this knowledge, so it is important to have these two subjects covered.

As with the technical skills, you need to have seven essential skills. They include:-

Python Programming 

Python is the most adaptable programming language in data science today. It also has lots of libraries for data science processes. These libraries can handle everything from data mining to data manipulation, visualization, and many more.

R Programming 

This is a suite of software dedicated to data manipulation, calculation, and graphical display of data. However, this programming language is more adapted in the academic world than python.

Hadoop Platform

This is an open-source collection of software utilities. It allows you to access large datasets and process them around clusters of computers using simple programming models. This platform becomes very useful in situations where the volume of the data exceeds the memory of the system, or when you need to send the data to different multiple servers.

SQL Databases

SQL is a programming language for managing databases. It is used for managing and querying the data held in the relational database management system.

Machine learning and AI

You are not a perfect data scientist until you know your way around algorithms, models, and the likes. You need to be able to analyze large amounts of data through algorithms and data-driven models. Important topics in machine learning that you need to understand include decision trees, supervised and unsupervised learning, and logical regression, amongst many others.

Data Visualization

This refers to the graphical representation of data using visual elements such as maps, charts, infographics, graphs, and many others. It is quite important because regardless of the excellent job you’ve done cleaning and analyzing data if you can represent the data visually, the utility and essence may be lost on many stakeholders.

Business Strategy

At the end of the day, the analyzed and visualized data will be used to make business decisions. Thus, you must understand how the data analyzed affects business.

Conclusion

Data science is one of the fest rising professions in the business and tech world today, and rightfully so, the concept is very important to business and corporate organizations. It deals with the analysis of data, which is the cornerstone of decisions made by businesses.

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