Data Science For Business- Everything You Need to Know

Table of Contents

What is Data Science?

“Data science is a specialized branch of computer science that combines analysis, programming skills, mathematical, and statistical knowledge to extract meaningful insights from data. It empowers data scientists to leverage machine learning algorithms to build artificial intelligence systems that perform tasks ordinarily requiring human intelligence. These systems can analyze large amounts of data to identify trends and patterns or make predictions in large volumes of text, numbers, or images. Analysts and business users can use these insights to bring tangible business value”.

Data Science for business uses a multidisciplinary approach for extracting meaningful insights from the huge and ever-increasing data collected by modern businesses at numerous touchpoints. Let us tell you in detail what is data science used for. Data science is everything from collecting, preparing, and processing data for analysis to performing advanced data analysis and presenting data insights through a story revealing patterns, trends, predictions, etc. Data science in business helps stakeholders draw informed conclusions and make data-driven decisions.

How Data Science is Different from Big Data?

There might not be much difference between data science and big data, but the doubt has instigated many minds outing them in a dilemma.

Big Data: It is a technique used by many organizations to acquire a huge amount of voluminous information, data, or statistics. Besides, you need to create customized tools or build tailored software and create equivalent data storage to compute big data. Big data helps discover patterns and trends in the data to make informed decisions related to human behavior and technology interaction.

Data Science: It is a field, domain, or area that works with a large chunk of data and uses it to build predictive, prescriptive, and descriptive-analytic models. Data science for business is about digging, capturing, analyzing, and utilizing data. At best, you can say that it is an intersection of data and computing.

Comparison Between Big Data and Data Science

• Big data is a process for collecting and maintaining a huge volume of data that is impossible to handle manually or using traditional database programming. Big data is often characterized by the collected data’s volume, variety, and velocity.
• Data Science is a field that represents a scientific activity focused on processing big data. It is a conceptual technique and follows collecting, processing, analyzing, and utilizing data in various business operations.
• Big data introduces us to a proven technique of extracting valuable information from huge data sets and tracking and discovering complex trends. It focuses on making data more viable and usable.
• Data science is a field of study similar to computer science, applied statistics, or applied mathematics. The primary aim of data science is to create data-dominant products for a business.
• To some extent, you can call big data a subset of data science as it involves data mining, which is a primary pipeline in data science. Big data is often used to add value to the business and customer satisfaction.
• We can consider data science as the superset of big data as it involves data scraping, cleaning, visualization, statistics, and similar techniques. The use of data science in business is mainly for scientific purposes.

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How Does Data Science Work?

Data science for business is a multifaceted discipline that requires various skills to effectively shift through raw data and communicate the most vital bits that will help drive innovation and efficiency.

While using data science for business, it works symmetrically – you can call it a data science lifecycle or data science pipeline. The data science lifecycle contains anything between five to fifteen processes that are continuous and often overlap. The common processes that everyone knows and are easy to follow are as follows –

Capture

This stage involves gathering or collecting raw data from myriad sources. Common data collection methods include manual entry, web scraping, and real-time data capturing from systems and devices.

Prepare and Maintain

Data preparation encompasses all activities required to prepare and maintain data, like cleansing, reduplicating, and reformatting the data. Often raw data is fed to a consistent format for analytics or machine learning and deep learning models. Data science uses ETL (extract, transform, load) or other data integration technologies to maintain data in a single store analysis. Data warehousing, data cleansing, data staging, data processing, and data architecture are other activities commonly involved for the same purpose.

Preprocess and Process

Data mining, clustering/classification, data modeling, and data summarization are common activities during this stage. Data scientists look for biases, patterns, ranges, and distributions of values within the data to understand the suitableness of the data to undergo advanced analytics methods like predictive analytics, machine learning, and deep learning algorithms.

Analyze

Exploratory/confirmatory, predictive analysis, regression, text mining, and qualitative analysis are the major analytic methods used in this stage. Machine learning, deep learning algorithms, and customized AI models are common practices in data science for businesses to extract meaningful insights from the data.

Communicate

It represents the final stage in the data science lifecycle where analyzed data or the extracted insights are presented using data visualization tools. Using tables, charts, graphs, diagrams, images, and other visually appealing methods to represent insights gives a clear picture of research findings is common in data science. Visually appealing reports make it easier for decision-makers and map the impact of data science in business.

Top Five Data Science Trends that Influences the Use of Data Science in Business

Enterprises of all sizes and geographies are vowing for business agility and automation. The digital transformation and cloud-based infrastructure create a higher volume of data faster than ever before. Business industries are looking for different ways of driving tangible values from the huge amount of daily generated data. Data science for business has reached a tipping point as many organizations are finding great opportunities in delivering strategic insights and offering tactical advantages to gain real competitive advantage.

In the pre-pandemic world, business data science used to be a limited scope preposition, but now it has become a ubiquitous service for many organizations.

1. Increasing Dependence on Cloud and Hybrid IT Infrastructure

During the pandemic, global organizations experienced difficulty accessing data from on-premise servers. Hence, more and more global organizations relying on the cloud for efficient IT infrastructure is not surprising. Besides, Hybrid IT that encompasses all the tools, products, resources, and services needed for allocating workloads is emerging as a new normal. Supposedly, it will significantly impact the amount of data generated daily, calling for the best data integration, processing, and analytic capabilities.

The requirement of data science for business has increased over the past few years as several data sources have increased, and many global organizations have adopted a best-of-breed strategy. Specially, organizations using mainframe systems find it crucial to adopt a data science approach as they are storing most of the crucial business data in it.

2. The necessity of Real-Time Data Insights

Data silos remain a substantial challenge for many organizations. The delay in delivering real-time insights is a cause of concern for many. Therefore, many organizations are opting for data-driven automation with the help of tailored data science applications for business.

Modern consumers are accustomed to real-time results, and if you need to wait five to ten minutes for data updating, you are missing business opportunities. Data science enables real-time data updating and provides data-driven insights in milliseconds. For example, Fraud detection algorithms must detect and notify of fraudulent transactions when they are happening. A delay of even a second forfeit the chances of stopping that transaction.

More than ever before, businesses have been considering data science as a savior providing immediacy and reliability at scale.

3. Maturity of AI and ML Technology

Until recently, businesses used to look at AI and ML projects as experimental models for uncovering business potential. Now, the scene is different. AI and ML are looked upon as must-have technologies to enhance business intelligence and achieve process automation.

Data science for business uses ML algorithms and specific features such as deep learning, neural networks, natural language processing, etc., to parse data, learn from that data, and apply the learning to make informed decisions. However, change is constant for businesses. Often they add new data sources, deploy new systems, acquire new businesses, and identify or introduce new metrics. Therefore, a growing business will look at data science for business using mature AI and ML models to ensure business success.

4. Requirement of Data Enrichment

Many organizations have started collecting data of all sorts, structured, semi-structured, or unstructured, and storing it efficiently to be put to good use in the future. Many of them have already started their initiative of getting significant business value through the data. Data enrichment has made it possible.

As of now, many organizations are either using external data or getting external data sourced at their data warehouse. When you use data science for business, it combines the company’s internal data with external data or data generated from third-party sources to get much richer and more nuanced data insights.

The impact of data science in business is huge. It plays a pivotal role in data enrichment by combining geospatial or location-based data with organizational data to offer extraordinarily rich context and valuable insights.

5. Data Security Is a Concern

When it comes to data security, the business size is irrelevant. With increasing technology penetration, the challenges posed by cybercriminals and hackers are greater than before. Besides, every business is dependent on technology, making it more complicated to secure large systems and databases. Today, every business collecting, processing, and storing highly-sensitive consumer information is looking for a data science approach to manage cybersecurity.

Data science uses machine learning algorithms and predictive analytics methods to detect, prevent, and reduce cybersecurity threats. Machine learning algorithms are trained to identify and avert such threats using current and historical information. The predictive analytics models identify patterns that help organizations detect intrusions and predict future attacks.

With applied data science in business, organizations get a chance to create protocols for amalgamating different data sets and finding a correlation between the data to uncover patterns and easily detect hackers’ future behavior.

Top Ten Data Science Tools and Technologies in 2022

Data science tools and technologies are emerging every month. However, an important consideration is for which data science technology has the potential to maintain its momentum and which tool will fetch benefits of data science for business.

1. Python

Python is a multi-functional, maximally interpreted, object-oriented, high-level programming language. It comes with pre-built data structures and properties. Combined with dynamic typing and binding, it makes an ideal proposition for developing applications. Besides, Python has a simple syntax and can be used as a scripting language.

2. R Programming

It is open-source Big Data technology and one of the widely used programming languages in data science for business. Its unprecedented use in statistical computing, visualization, and unified development environments such as Eclipse and Visual Studio assistance communication remains unmatched.

3. SAS

It is a closed source proprietary software and data science tool specifically designed for statistical programming. The in-built statistical libraries and tools are helpful in modeling and organizing data.

4. Apache Spark

You can call Spark a powerful analytics engine, one of the most trusted and widely used data science tools. It is offered multiple APIs that can be programmable in Python, Java, and R and leveraged to access data repeatedly for machine learning and SQL storage. It can process real-time data and is best at cluster management systems.

5. BigML

It is one of the widely used data science tools offering a completely intractable cloud-based GUI environment to process ML algorithms. Predictive modeling is the specialty of BigML, and using Rest APIs provides an easy-to-use web interface. Besides, BigML is equipped with various automation models that can be used to automate the tuning of hyperparameter models and automate workflows.

6. MATLAB

It is a closed source software well known for providing a multi-paradigm numerical computing environment used by data scientists to process mathematical information. MATLAB makes it easier to alleviate matrix functions, algorithmic implementation, and statistical data modeling.

7. TensorFlow

It is an open-source and ever-evolving toolkit preferred by data scientists for its unmatched performance and computational abilities. In a short time, TensorFlow has become the much sought-after tool for Machine learning and is used for advanced ML algorithms like deep learning. It can run on CPUs and GPUs and recently became a powerful TPU platform. TensorFlow offers many data science benefits such as speech recognition, image classification, drug discovery, image and language generation, etc.

8. Julia

Julia is an emerging new-age advanced open source programming language that uses a multiple dispatch approach at runtime, boosting execution speed. It is widely used in data science for numerical computing, machine learning, and other data science applications.

Julia is an emerging new-age advanced open-source programming language that uses a multiple dispatch approach at runtime, boosting execution speed. It is widely used in data science for numerical computing, machine learning, and other data science applications. To utilize it for your project, you can hire Julia developers to enhance your development efficiently.

9. Weka

Weka stands for Waikato Environment for Knowledge Analysis and is an open-source GUI machine learning software in JAVA. It offers a wide collection of ML algorithms used for data mining and various ML tools used for machine learning like classification, clustering, regression, visualization, and data preparation.

10. NLTK

The data science language is called Natural Language Toolkit, playing a bigger role in the data science field. It is used to develop statistical models that help machines learn human language.

Top Five Benefits of Using Data Science for Business

It is a universal truth that modern businesses generate an overwhelming amount of data, and recently data science and its use cases in different areas have become relevant to the organizations. It’s high time for organizations to understand the enormous value in data processing and analysis. Organizations are interested in leveraging data science services to extract actionable insights from gigabytes of data.

Let’s see how data science helps business,

Enhance Business Predictability

Predictive analysis is a part of data science that predicts or precisely analyzes the events going to take place in the future. Predictive analytics helps businesses understand products that will generate more demand, predict manufacturing floor issues before they happen, understand flaws in processes, and much more. Better business predictability allows businesses to prepare for the events that will happen.

Achieve Real-time Insights

The majority of businesses are modernizing their data infrastructure to meet the real-time expectations of their customers. Data science for businesses offers cloud data warehouses and data lakes with near-infinite computing power and the capability to analyze the data in real-time. Businesses can use real-time analytics to enhance workflows, boost marketing and sales collaboration, understand customer behavior and finalize close financial procedures.

Gain Customer Insights

Data science in business is widely used to unlock customer insights such as their habits, demographic characteristics, preferences, and aspirations. The data collected from different touchpoints are then combined in a data wrangling process; then, it is aggregated, processed, and analyzed to identify trends and patterns. Knowing your customers helps in meeting their exact expectations.

Enhance Data Security

Do you know how can data science help a business with complete data security? Let us tell you. It seamlessly protects sensitive data with fraud detection, malware identification, cyber-attack prediction, and managing various data compliances. Knowing data security issues beforehand allows businesses to stay prepared with full-proof cyber defense for the future.

Make Strategic Decisions

A data scientist creates models that translate existing data into various plausible actions. A company can learn which decision will bring the best outcome for the business by measuring, recording, and tracking KPIs and other information. Data science for business can transform any data collected into useful information. It allows business owners to make accurate decisions based on logical facts and figures.

Conclusion

Frequently Asked Questions (FAQs)

Why is data science necessary for my business?

Knowledge empowers businesses, and data science is the field that creates this power. We know that we created 2.5 quintillion data bytes daily in 2020. But, do we know how much data out of these was processed? Data science is a field that empowers your business to use scientific methods, processes, algorithms, and systems to extract knowledge from business-generated data.

What is the importance of data science in business?

Data science allows businesses to take an analytical approach based on hard facts, mathematics, and statistics providing sensible solutions to some of the common problems businesses face. Using actionable and informative insights made available by data science, many businesses can make evidence-based decisions, predict trends, understand customers, detect frauds and do much more unimaginable things.

In short, data science gives you the power to become a superhero. Data science to business is like a hammer to Thor, Shield to Captain America, and metallic armor suit to Iron Man.

Should business owners learn data science?

Degrees and skill sets are irrelevant when it comes to business owners. Therefore, learning data science is a matter of choice and not a compulsion. However, having or learning data science skills is a great initiative and helpful to achieving business growth. Although, it depends on business owners whether to learn data science or hire a data scientist. After all, when any business owner hires a data scientist, they will add value and ensure the business gets optimum results.

How do I hire data scientists?

Hiring a data scientist is not an easy job due to the scarcity of data science experts. It takes time and a lot of effort to find the right person to suit your needs. The other option is to approach companies to hire data scientists who already have vetted and ready data scientists ready to work on independent projects. The process is smooth – shortlist the candidate profile, conduct a test, arrange an interview, and onboard the data scientist of your choice.