Have you ever imagined the value that exists in a cloud of data? Well, feel free to come aboard with us!
Living in the era of "Big Data" can be overwhelming. We might not think about it, but we continuously produce and release volumes of information about ourselves with each move we made (cameras, sensors, card movements, PC logins, internet navigation).
If the need for storage was previously the main focus, nowadays, it's how we can process complex and large sets of data containing key information about individuals, groups, and periods of time. Extracting meaningful insights is what Data Science stands for. In this blog, we will be covering the foundations of Data Science and how it can help businesses grow and prosper.
What is Data Science?
Data Science is an interdisciplinary area that uses scientific methods, processes, algorithms, systems, and machine learning principles to discover hidden patterns, trends, and correlations from the extracted raw data.
Data Science emerges to provide a holistic business vision by gathering and filtering valuable/actionable insights that help predict customer behavior and identify new revenue opportunities. The result? It will ease the decision-making process and enhance innovation and efficiency.
Data Science can be used for:
- Detection of anomalies (fraud, disease, crime);
- Automation and decision-making (background checks, credit worthiness);
- Classifications (like classifying emails as “important” or “junk”);
- Forecasting (sales, revenue and customer retention);
- Pattern detection (weather patterns, financial market patterns);
- Recognition (facial, object, voice, text, fingerprint);
- Recommendations (products, services, books).
How is Data Science changing the world?
From 1970 to the middle '90s, the existing data was mostly structured and small in size, which could be analyzed using simples BI tools. Today we face a completely different reality, where about 80% of data is unstructured or semi-structured. Check the figure below:
Big Data makes it possible to achieve research results that cover a wide range of issues and tell us much about developments in the world in many different areas. This data is generated from different sources (databases, text files, social media, forms, APIs, browser searches, etc.), and simple BI tools cannot process the large volume and variety of data.
The demand for data science skills has grown significantly over the years. Having advanced analytical tools became essential to process and analyze data. These tools help data scientists find meaningful insights and uncover solutions to business challenges.
What is a Data Scientist?
Data Scientists not only do the exploratory work to discover relevant insights from data. They also use advanced techniques and algorithms to develop hypotheses, make inferences, and identify customer and market trends. More than analyzing what is currently happening, they are responsible for setting best practices regarding data interpretations.
Data Scientists are a new breed of analytical experts who use industry knowledge, contextual understanding, and predictive assumptions to find trends and manage data from many angles, sometimes not even known earlier. This role is an offshoot of many scientific areas such as mathematics, statistics, and computer science, using the latest technologies (like machine learning and artificial intelligence) to find development solutions and growth within organizations.
How can Data Science improve your business?
We have already seen what Data Science is capable of, but how do we make it useful to us? What are its real-world applications?
In fact, Data Science is revolutionizing many industries (see picture below), providing valuable business benefits that can be summoned into three major categories: business efficiency, product creation, and customer experience. The ability to increase efficiency is the highest value opportunity that Data Science brings to an existing business model.
How Data Science can help improving business efficiency:
Data Creation: improving business efficiency requires more than analyzing the existing data. Creating new and useful data through some tests will help to measure what needs to be improved. Finding actionable insights within this data can result in a significant productivity increase.
Real-Time Reporting: it can improve business efficiency immediately by generating and leveraging micro-scale interaction that makes customer interactions more effective. Companies that prioritize better response times will have their sales processes optimized.
Historical Data Interpretation: historical data allows to analyze customers' past behavior and build predictive models to learn how they will likely behave in the future. More than learning from the mistakes on a person-to-person level, it's essential to predicting the occurrence of future events to avoid those problems altogether.
- Data-Driven Culture: Focusing on data and making the best use of it can't just be the data science team's scope. It must be part of leadership's mentality in encouraging data-driven decision-making at every level, with every employee understanding data’s inherent importance in their job role.
The gathering and generation of large volumes of data have long outpaced our ability to process it manually. The automatic processing and analysis of data is a growing industry all on its own. And guess what? The full potential of Big Data technologies is beyond any business’s ability to realize it without Data Science experts to help.
To be ahead of the curve, it's necessary to have a science team focused on creating and analyzing predictive constructs to boost your business efficiency. Being a strong competitor requires implementing all these cutting edge data procedures and taking the best advantage.