Most organizations are turning to data science for help in their decision-making processes. This has led to an unprecedented increase in demand for data scientists but also a shortage of professionals, who can meet this demand, including of managers with a sound understanding of the benefits that data can deliver them. This unexpected scenario makes it an excellent time to be a manager. If you’re a manager, who is even minutely interested in data, there is tremendous opportunity for you to learn how to run a business or organization that is driven by data and capitalize on the opportunities that have been unexplored. Here’s what you need to know to get acquainted with the science of data!
What Is Data Science?
Data science is a broad field that draws from multiple disciplines like mathematics, statistics and computer science. Of course, it also requires domain knowledge for it to be effective in a specific context of say pharmaceuticals or finance. In the mind map above, you will find a concise introduction to all the key concepts in the field that have thus far proved to effective for managers.
Core Components
If look closely, the mind map puts what forms the basics of data science – mathematics and statistics and programming languages – at the centre. Since data science involves it data, it is obvious why mathematics and statistics play such a crucial role in the field. Every machine learning algorithm has its basis in mathematics. Therefore, a basic understanding of probability theory, linear algebra and statistics is a necessity if you’re to succeed in your data science endeavours.
While math and statistics help you come up with algorithms that will be effective in dealing with data, the task of implementation remains. For this, you need programming languages. For a manager, it is not so important to know exactly who to build an algorithm or understanding everything about each programming language. But an overview of which programming language is capable of what will help you understand how to use them better for various tasks.
Machine Learning
The right side of the map goes one step further into data science. More specifically, it enters the sphere of machine learning. For managers, it is critical to understand basic machine learning algorithms and their uses. This will give them a sense of which algorithms are suitable for different industries.
Stages in Data Science Process
The left side of the map illustrates the different stages in the data science process. This includes data engineering, data storage, big data, data analytics and data visualization. Together, these various stages help gather data, transform it for analysis, interpret it in meaningful ways and then communicate it through visual representations.
Data Engineering
Data engineering is the process of scraping and ingesting data into the organization’s systems and cleaning it in preparation for analysis. It’s difficult and sort of rare to find data that is ready for use. Generally, data tends to require some form of manipulation and pre-processing to become ready for analysis. Once this is done, data must be stored in a way that it is easily retrievable.
Data Storage
Data storage is an effective way of cataloguing data for easy retrieval. For managers, it is important to learn the use of cloud services, the differences between SQL and NoSQL databases and how to best categorize and store different kinds of data for different tasks.
Big Data
Then we have big data solutions. These solutions are useful in handling large volumes of data. They are instrumental in storing and retrieving data in different computing systems or for streaming data in real-time from various sources to enable dynamic business solutions centred on data.
Data Analytics
Data science is all about garnering insights from large volumes of structured and unstructured data. Everything that we have discussed leads up to this goal. But what achieves this goal is the successful implementation of data analytics. Data analytics is the art of observing and drawing meaningful insights from data. It is arguably the most critical stage in the data science process that helps clinch the business or organizational goals that you set out to achieve.
Data Visualization
Finally, we come to data visualization. Another critical stage for managers. Data visualization helps easily communicate insights from data to various stakeholders. It helps business leaders, investors and even other team members make sense of dense numbers and figures by enabling them to see visually what they mean. This stage of the process is extremely important for the manager because it helps them coordinate efforts towards the business/organizational goals. It helps them reassure their investors and other stake holders of progress. And, perhaps most importantly, it helps them make decisions that are in their best interests.
Data Science for Managers
The goal of this article was to briefly introduce data science to managers. Each of the core components of data science – mathematics, statistics, and programming languages – and the various stages in the data science process are worthy of more scrutiny and study. As is machine learning, which is revolutionizing how organizations and businesses function. The mind map and this article are only good starting places for any manager looking to venture more deeply into this complex field to understand how they can use data science in their unique position.
