Today’s business leaders have greater access to meaningful data than ever before. When correctly stored, analyzed, and interpreted, this data fuels important business decision-making. Businesses are motivated to keep data private, accurate, and powerful. How do they achieve that goal? By recruiting business analysts or data scientists.
Defining Business Analytics vs. Data Science
The fields of business analytics and data science have key distinctions, and each field uses essential tools. Further, business analysts and data scientists play significant roles in developing data-driven business strategies. The roles offer value in different ways.
Business Analyst Role
Business analysts work with large datasets, extracting information to support decision-makers. This work helps executives understand past business performance and current market position with an eye to making predictions about the future.
Business analysts must carefully evaluate data to draw business-relevant conclusions. They also need to present their findings clearly and persuasively to business leaders. This often involves data visualization (presenting information via graphs, charts, or other visual means).
Data Scientist Role
Data scientists work at the front end of data analysis; typically, their job is to build algorithms or other mathematical structures that can aid in data collection. This highly technical role is essential for providing companies with the means to gather and store information, which analysts can later examine to form meaningful business insights.
Essential for data analysts are foundational skills in mathematics and machine learning.
Role of IT
Information technology (IT) connects data science with business analytics. For example, the IT team:
- Works alongside data scientists to create databases where information can be stored
- Ensures business analysts have easy access to databases and can extrapolate the information they need
- Provides security measures to protect sensitive data and ward off cyberattacks
- Promotes data collection standards that protect the privacy of individual users
Data scientists and business analysts rely on IT professionals to help them solve crucial data challenges. For example, IT creates data privacy and security parameters. IT also maintains software and cloud computing applications that ensure accurate data collection.
Business Analytics vs. Data Science: Additional Resources
Find additional insight into the fields of business analytics and data science in the following resources:
- CIO, “What Is a Business Analyst? A Key Role for Business-IT Efficiency.” Learn more about the job description and core responsibilities associated with the business analyst role.
- CIO, “What Is a Data Scientist? A Key Data Analytics Role and a Lucrative Career.” Gain further understanding of the data scientist role and its value to a business.
- Entrepreneur, “5 Ways Big Data Can Help Your Business Succeed.” Discover practical implications of big data use.
Business Analytics Tools at a Glance
Several tools are available to help business analysts analyze and interpret data and provide useful visualizations to key decision-makers.
A core responsibility of the business analyst is taking raw data and turning it into an analysis that businesspeople understand. Tableau helps with this; it’s primarily a data visualization tool that converts raw or unstructured data into an easily understandable visual format. These formats include worksheets and dashboards from which executives and other business leaders can easily spot statistical trends.
To learn more about Tableau, consider these resources:
- Tableau. Learn more about the product and its various features.
- Tableau, “7 Tips and Tricks from the Dashboard Experts.” Get insider tips on using Tableau effectively.
Using both machine learning and artificial intelligence (AI), Oracle BI is a software suite that performs data analytics throughout an organization. Specifically, Oracle BI lets data analysts build role-specific data collection tools, so each individual and department can consolidate and visualize data. In other words, the marketing team can develop a database, while the finance team can devise their own spreadsheets. The data analyst can access, compare, and analyze data from throughout the organization.
For more information about Oracle BI, take a look at these resources:
- Oracle Business Intelligence. Take a closer look at the product, its specs, and its list of features.
- Rackspace, “Tips for Using Oracle Business Intelligence Enterprise Edition.” Learn how to use this software to its full potential.
Sisense is a “big data” processing program that allows businesses to input large or unruly sets of data, then generate relevant trends and statistics from that data. (Big data simply refers to datasets that are too large or too complicated for an individual human to effectively process.) Sisense offers a number of options for visualization and reporting, so relevant findings can be easily shared throughout an organization.
To further explore Sisense, use these resources:
- Sisense. Take a look at the Sisense software suite and consider its unique features.
- Sisense, “Tips and Tricks for Sisense Masters.” View video tutorials and learn advanced features.
Wrike is primarily a project management tool that allows team members to share files, collaborate on projects, and keep track of due dates. However, the program also has an “Analyze” function, which allows team members to create visualizations for each project and assess their progress.
For more about Wrike, consider:
- Wrike. Get more information about the full Wrike software suite.
- Wrike, “12 Ways to Use Wrike You Never Considered.” Explore some unique ways to leverage Wrike.
Like Wrike, Trello is primarily a project management platform. It, too, offers analytic options, allowing users to input datasets and then create and share charts, reports, dashboards, and other visualizations. Trello allows team members to collaborate using foundational, easily digestible data points.
Learn more about Trello at these links:
- Trello. Find out more about Trello’s broad spectrum of collaborative features.
- PC Magazine, “10 Trello Tips Guaranteed to Make You More Productive.” Learn more about how Trello can increase your efficiency and productivity.
More About Business Analytics Tools
To learn more about recommended business analytics tools, consider the following resources:
- TechRepublic, “How to Choose the Right Data Analytics Tools: 5 Steps.” Get advice on selecting the right data analytics tools for your business or team.
- Guru99, “24 Best Business Intelligence (BI) Tools List in 2020.” Get recommendations for additional business analytics tools.
A Look at Data Science Tools
In addition to business analytics tools, businesses benefit from data science tools. Data scientists use such programs to gather data and turn it into usable information.
Apache Hadoop is a framework that allows data scientists to efficiently store and process big datasets. Rather than using a single computer to store and analyze data, data scientists can “cluster” multiple computers with Apache Hadoop, resulting in their ability to quickly process enormous datasets.
To learn more, consider these resources:
- Apache Hadoop. Explore what this software solution can offer.
- IBM, “10 Expert Tips to Boost Agility with Hadoop as a Service.” Learn more about using Apache Hadoop and Spark.
Apache Spark shares many similarities with Hadoop, but it offers a key difference. While Hadoop processes huge data batches with relative speed, Spark processes data in real-time. In other words, Spark is the preferred option for data scientists who need to interact with the data as it’s processing.
For more, check out these links:
- Apache Spark. Find out more about what Apache Spark is capable of.
- Towards Data Science, “Apache Spark Optimization Toolkits.” Discover some resources for using Apache Spark more effectively.
Microsoft Excel is a familiar spreadsheet program that also works well for data science and business analytics. Excel allows data scientists not only to compile large volumes of information but also to run formulas that automatically synthesize data or extrapolate key conclusions. Another benefit of Excel is that, because it is a commonplace program, it allows data scientists to present information in a way that will be understandable to non-data scientists.
Learn more about Microsoft Excel:
- Microsoft Excel. See the full list of software specs and features.
- Excel with Business, “15 Excel Data Analysis Functions You Need to Know.” Explore the ways in which Excel can help with business analysis.
Many data scientists rely on the Python coding language to efficiently interpret data. Matplotlib goes hand in hand with Python, allowing the data scientist to take Python data and convert it into animations and other visualizations.
Find out more:
- Matplotlib. Take a look at Matplotlib’s features and options.
- Real Python, “Python Plotting With Matplotlib (Guide).” Get a full tutorial on using Matplotlib for Python plotting.
Machine learning is an important concept in data science. BigML is a good example. This program allows data scientists to import large datasets. BigML then uses machine learning capabilities to examine that data and offer a set of statistical conclusions.
For more about BigML, consider these resources:
- BigML. Take a closer look at BigML’s features and tools.
- BigML, “Tutorials.” Find out more about how to use BigML effectively.
More About Data Science Tools
For additional insight into data science tools, consider these resources.
- Towards Data Science, “Best Data Science Tools for Data Scientists.” Consult this list for additional data science processing recommendations.
- GeekFlare, “18 Essential Software Every Data Scientist Should Know About.” Check out this article for additional software tips.
Business Analytics and Data Science Languages
When considering business analytics vs. data science, be aware of the importance of programming languages. Through familiarity with these complex languages, business analysts and data scientists can build data-driven strategies tailored to business needs. Here’s an outline of four of the most common languages used in these fields.
Python is a favorite language among data scientists, as it provides a library of existing codes and formulas that can efficiently manage large sets of data. Compared to other coding languages, Python is considered fairly easy to learn and straightforward to use, making it an accessible option for novices in the field.
To learn more about Python, consider: Python for Beginners. This tutorial will walk you through the process of downloading Python and getting started.
While Python is a generalized data science language, R is specifically geared toward statistical analysis. In other words, it’s a language built by statisticians, and it encapsulates their particular discipline. Many data scientists learn both Python and R, believing they work best when used in tandem.
For more insight into R, consider: Free Code Camp, “R Programming Tutorial.” View an in-depth video tutorial about learning and using R.
SQL, or Structured Query Language, is used to access, manipulate, and revise large datasets. Specifically, SQL helps data scientists communicate with relational databases (which store different data points that are all related to one another), allowing for the effective use of these large, interconnected datasets.
Learn more: Programming with Mosh, “McSQL Course for Beginners.” View a full YouTube course about SQL programming.
SAS (Statistical Analysis System) is a command-driven software platform, useful in both data analysis and data visualization. SAS is sometimes compared with R and is generally considered to be more user-friendly, as it can be learned without any preexisting coding or programming experience.
Find out more about SAS: SAS Crunch, “How to Learn SAS Fast.” Check out this full primer on using SAS.
More Information About Coding Languages
To learn more about these languages and their import for data science, take a look at some of these resources:
- Guru99, “R vs. Python: What’s the Difference?” Learn more about the distinctions between R and Python.
- Towards Data Science, “SQL for Data Science.” Find out more about the use of SQL in the field of data science.
Business Analytics vs. Data Science: Harnessing the Power of Big Data
Both business analytics and data science allow large enterprises to use their data effectively and make well-informed decisions about their business strategy. While these disciplines are not identical, both of them provide meaningful pathways for people interested in data, statistics, and business leadership.
Business News Daily, “How Businesses Are Collecting Data (and What They’re Doing with It)”
Business News Daily, “9 Big Data Solutions for Small Businesses”
Business News Daily, “10 Best Resources for Learning How to Code”
Dataquest, Data Science Resources
DreamHost, “The 67 Best Online Resources to Learn How to Code (Updated 2020)”
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Statista, Big Data — Statistics & Facts
Tech Republic, “Big Data: 3 Biggest Challenges for Businesses”
Tech Republic, “Python Programming Language: Best Resources for Developers and Managers”