Data Science vs Business Analytics
With the increase of digitalization in all business sectors, the availability of data and its usage for operations management and strategic decisions has grown significantly in the past decade, a trend that is expected to continue. Along with the spread of data applications, the number of tools, techniques, frameworks, and models has also increased, rendering their understanding more complex and their differentiation difficult.
Data Science and Business Analytics are two terms that have been widely promoted and are sometimes applied as synonyms. However, although both use data to extract insights that support business decisions, the tools and techniques resourced in each case are different. Before launching a project or team in any business, leaders must understand which approach is more relevant for the company or the challenge.
Differences between Data Science and Business Analytics
First, it is essential to understand what differentiates Data Science from Business Analytics. Regarding the width of scope, Data Science is a broad field of study and application that stores and processes data for different purposes. Problems worked on by Data Science are usually complex and require the development of intricate algorithms. On the other hand, Business Analytics concerns using data analytics tools to interpret data that will cater to more informed decision-making. These tools and applications are usually planned in the Business Intelligence Strategy of the business.
As the scope of Data Science is more extensive, so are the handled datasets. Business Analytics usually focuses on specific business areas or challenges, which typically involve smaller datasets, with the information already more standardized, requiring less computing power to deliver the wanted analysis.
One could say that Data Science is the umbrella that encompasses all Data Storing, Processing, Mining, Analyzing, and Visualization techniques, including Business Analytics.
What is Data Science?
Data Science is the field of work where data is collected, stored, analyzed, and presented to identify trends or patterns and predict outcomes. The tools used may include mathematical models, statistical analyses, machine learning, and data visualization, which are applied to complex and large datasets.
This can be applied in any industry, whether it is to predict sales, manage stocks, anticipate extreme scenarios, test the impact of different variables, or many other applications. Apart from business applications, Data Science plays a vital role in research and health.
Skills and tools required for Data Science
As mentioned previously, the scope of Data Science is vast, which implies that the list of tools used to apply the different methodologies involved is also extensive. Tools may be used to store, manipulate, visualize, or share data. To name a few, the following skills and tools are commonly applied when solving complex problems using Data Science:
1. Data manipulation and data analysis tools are used to simplify, organize, and standardize data to make it more user-friendly for processing and visualization. These include Pandas – for data analysis tasks in Python, NumPy – to manage numerical data, and SciPy – for symbolic expressions, among others.
2. Big data tools are used to mine large datasets that are not manageable through traditional means. Data can be structured, semistructured, or unstructured, influencing the chosen tool to be applied. For large datasets, data scientists may use tools such as Hadoop, Apache, HPCC, and others.
3. Programming languages are resourced to solve Data Science problems. The choice of language applied depends on the computing complexity and the size of the dataset. Python, Java, C++, Go, R, PHP, and SQL are some of the languages used.
4. Machine learning techniques allow computers to learn continuously from growing datasets, delivering results without explicitly programming every rule and scenario.
5. Statistical modeling uses finite datasets to build algorithms that identify relationships between variables and make predictions from data.
6. Data visualization tools visually present data so that it can be shared with different knowledge-level stakeholders and still be understood. Some visualization formats could be geographic maps, charts, sparklines, infographics, heat maps, or statistical graphs. Microsoft Power, Tableau, Qlik Sense, Matplotlib, and Seaborn are some of the most used visualization tools.
7. Collaboration and version control tools are needed when several people are involved in a Data Science initiative. These tools facilitate simultaneous programming and help to keep track of different versions and updates. Examples of these tools are Git, Apache Subversion, and Jupyter.
8. Domain-specific knowledge is not to be neglected among the more technical skills required for Data Science. Understanding the environment in which the data is inserted is crucial to extract the correct findings.
Data Science: application-top 5 industries
Data Science is a broad field that can be applied in almost any economic sector. However, its application is already more mature in some industries, such as:
1. Banking & finance industries benefit from Data Science for operations such as credit risk modeling, fraud detection, and financial forecasting.
2. Healthcare industry has been using Data Science since its very beginning to study disease behavior or predict patient outcomes. On a more operational level, it is also used to enhance resource allocation and maximize capacity.
3. Retail organizations use Data Science to predict consumption, optimize pricing strategies, and develop customized marketing campaigns.
4. Manufacturing industries optimize production planning and stock management, predict equipment failures, and prevent product quality issues by using Data Science tools.
5. Technology companies use Data Science mainly to test different scenarios, study user behavior and optimize marketing efforts. Often, this is done by leveraging CRM platforms to improve the customer experience.
What is Business Analytics?
In contrast with Data Science, Business Analytics is a narrower scope within the field, where analytical tools and models are implemented to transform data into business-relevant insights. It uses statistical analysis, data visualization, and predictive modeling to analyze business data, interpret it and present it in a way that is understandable and actionable by leaders.
Skills and tools required for Business Analytics
The tools and skills required for Business Analytics are very similar to the ones used broadly in Data Science, as data also needs to be stored, manipulated, analyzed, and presented.
Both business analytics and data science require skills and tools like data manipulation, machine learning, collaboration, version control tools, data visualization tools, and domain-specific knowledge. However, when the complexity of the problems varies, so does the complexity of the tools:
1. Similarly to Data Science, in Business analytics processes, data manipulation and analysis tools are also used. For more straightforward problems, one could use Excel or SQL, while Python could be used for more complex challenges.
2. Machine learning techniques are also leveraged in business analytics. Commonly used algorithms in business analytics include linear regression, decision trees, and neural networks.
Business Analytics: application-top 5 industries
Like Data Science, Business Analytics has proven to be effective in driving business growth in a variety of industries:
1. Banking & finance industries benefit from Business Analytics for analyzing financial data and identifying trends or patterns that can help define and price products.
2. Healthcare industry uses business analytics mainly to enhance resource allocation, maximize capacity utilization and reduce costs.
3. Retail organizations rely on Business Analytics tools to support stock management, pricing strategies, and marketing efforts.
4. Manufacturing industries optimize production planning, stock management, and supply chain leveling with the support of Business Analytics.
5. Technology companies use Business Analytics on a daily basis to manage product development priorities by analyzing user behavior and testing different product features.
How do Business Analytics and Data Science contribute to business value?
Each organization’s Business Intelligence strategy will determine how data and analytics should be applied in each department to generate the most value for the businesses. From product development to operations, sales, and strategy, the insights that data science and business analytics can bring may be crucial for the business’s success over its competitors. These are some examples of how Data Science can benefit a business:
1. Enhancing Innovation and Research:
Data insights can help to identify new trends in consumer preferences or correlations between variables that may lead to more robust product or service development.
2. Gaining Strategic Competitive Advantage:
Organizations with advanced business analytics skills can identify new business opportunities and revenue streams sooner than their competitors, giving them a head start in product or service innovation.
3. Increasing Productivity and Efficiency:
By continuously modeling actual operational data, organizations can predict failures, identify constraints, and plan operations and stocks more efficiently. It will also be possible to identify operational and cost-saving opportunities more easily since they will be more visible to all stakeholders, which will help make change more agile.
4. Overall Improvement of Decision-making:
Information-based decisions can substantially lead to more predictable outcomes. If teams and leaders can access reliable data and previous scenarios, they can make more conscious decisions. This is equally valid at the strategic, team, or project level.
By gathering insights on consumer patterns and choices, together with seasonal events, Marketing & Sales leaders can create campaigns that meet the needs of their target audience at the right time, thereby boosting sales results. The Sales strategy can also be driven by Business Analytics insights, helping leaders to identify untapped markets or develop more effective sales approaches.
For more mature organizations, setting up a Business Intelligence Strategy that covers all areas of Data Science applications is already part of the annual strategy review. However, the sheer scale of Data Science can be daunting for small companies looking to take their first steps into data analytics. Starting with the most common tools used in Business Analytics applications should be the first step in introducing data and its potential into the organization, as well as preparing the data structure and team skills for a more complex future structure.
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