The modern organization leverages the benefits of Data over physical assets when determining their future direction. Organizations across all sectors of the economy, including the financial, retail, healthcare, manufacturing, and Digital Startup industries, use the information gathered from their huge data stores to inform the development of their operations or how they deploy technology solutions for their end consumers. However, when speaking about careers or the impact of Data on the Business’s bottom line, many become confused by two very similar terms, Data Analytics vs. Data Science.
While both fields are critical to the success of an organization, influence on the organization is very dependent upon how well the organization understands the strengths and weaknesses of each of these fields. Organizations that recognize the distinctions between Data Analytics and Data Science are better positioned to make better hiring decisions, invest in the technology to effectively leverage their data resources, and create more robust digital strategies. Individuals who understand the differences between Data Analytics and Data Science will have a better opportunity to find the right career opportunity. In this blog post, we take a deep dive into the two disciplines, and relate them to current Business Trends and the role of organizations like SkillsBiz Education in developing the skills of individuals in this rapidly changing environment.
How Data Transformed the Business Landscape
It is critical to have an understanding of the purpose of Data Roles prior to reviewing the definition of Data Analyst and Data Scientist. Historically, companies would utilize their own knowledge and expertise or follow their instincts in terms of whether or not they would make a sale. In this modern day and age, the vast majority of business operations generate Data with every Click, Purchase, Review and Website Visit.
Data, when leveraged appropriately, can assist companies with
- Sales Forecast
- Customer Behavior Patterns
- Operational Efficiency
- Financial Fraud Prevention
The reason Data Analysts and Data Scientists provide unique information for business decision-making is that each one uses Data differently to provide businesses with all the information they need in order to ensure sound strategic business decisions.
What Is Data Analytics? A Business-Driven Approach
Data analytics is the process of using structured datasets to analyze trends in business activity, create regular reports for business leaders and make important business decisions. Data analytics is specific to specific areas of an enterprise including Finance, HR, Marketing, Operations and etc.
Responsibilities of a Data Analyst
- Data Collection and Cleaning
- Developing dashboards for various applications( Power BI, Tableau)
- Preparing reports to support management decisions
- Analysis of data, specifically historical data with the objective of analyzing and improving current performance
- Providing analysis, support and preparation for daily business budgetary and forecasting functions.
Retail chains can leverage data analysis to identify purchasing trends, optimize inventory levels and increase profitability, while finance companies utilize data analysts to increase profitability by reducing overhead costs through the analysis of consumer spending patterns.
Data Analytics and Business Strategy are Intertwined and Open to the General Tech Community.
What Is Data Science?
Data Science provides additional insight into predictive analytics and future predictions of businesses. Just as there is an emphasis on historic data, Data Science shifts the focus to the future and predicts outcomes based on algorithms, machine learning, and advanced statistical modeling. Data Scientists are problem solvers who build models, develop automated systems, and provide companies with intelligence they rely on to make informed long-term business decisions.
Data Scientists will have a variety of main roles and responsibilities.
Among their major responsibilities are:
- Building machine learning models
- Creating predictive and prescriptive analytics
- Working with unstructured data (images, audio, etc.)
- Developing AI solutions for businesses
- Using advanced analytics to optimize business systems
A Data Scientist may build a model that detects fraud for a bank, create a recommendation engine for an e-commerce brand, or develop an AI model that predicts when manufacturing equipment will fail. They perform a significant amount of technical work involving research and innovation.
Key Differences Between Data Analytics and Data Science
1. Purpose and Approach of Data Analytics and Data Science
Data Analytics: Where (what happened?) and how (why did it happen?)
Data Science: What will happen (next) and how can I make that happen? (automatic decision making)
2. Tools and Technologies for Data Analytics and Data Science
Data Analytics: Excel, SQL, Power BI, Tableau
Data Science: Python, R, Tensor Flow, machine learning algorithms
3. Business Impact of Data Analytics vs Data Science
Analysists make short-term decisions.
Data Scientists create ‘Intelligent’ long-term decisions.
4. Skill Requirements for Data Analytics vs Data Science
Data Analytics has a lower skill set (beginner).
Data Science has very high skills (strong math and programming).
5. Output Produced by Data Analysts vs Data Scientists
Analysts provide dashboards and reports.
Data Scientists provide models, predictions, and AI Systems.
Why Modern Businesses Need Both Roles
Businesses must combine prediction and historical analysis to develop prediction intelligence and automated decisions, and by combining Data Analytics with Data Science, businesses will create a full data eco-system.
Some Examples of Business Situations to illustrate this include:
Marketing
Analysts analyze consumer behavior and Data Scientists create Recommendation Engines.
Finance
Analysts monitor business expenses and Data Scientists build models for Credit Risk.
E-commerce
Analysts optimize product performance and Data Scientists use Machine Learning Algorithms to rank Products based on availability.
Manufacturing
Analysts monitor production operations and Data Scientists predict potential machine failure.
As you can see, the combination of these elements results in a Business Advantage, being Stable, Innovative and Growing.
Choosing Between Data Analytics and Data Science as a Career
The path you take will depend on your skills, interests and goals long term.
Consider Data Analytics If You Enjoy About:
- Roles That Are Business Driven
- Visual Dashboard Reports
- Finding Patterns in Data and Identifying Trends
- Close Interaction with Management Teams
Consider Data Science If You Enjoy About:
- Advanced Mathematical Concepts
- The Science of Machine Learning
- Discovering Complex Problem-Solving Techniques
- Creating Predictive Models
Both Fields Have the Ability to Provide High Salary Potential, Strong Job Opportunities, and the Ability to Work Across Many Industries.
SkillsBiz Education and Its Contribution to Data Skill Development
How SkillsBiz Education Helps Learners Build a Strong Data Career
When developing skills that will prepare them for a data-based career, most students seeking to build their capabilities use formalized online courses (i.e. structured online). Skills Biz Education is a company that provides students/learners with relevant industry/market knowledge and skills through formalized training towards certifications in their area, including Data Analytics, Data Science, SAP and Six Sigma. Through Skills Biz, students/learners can gain the practical experience that will enable them to succeed in today’s business environment. Each of Skills Biz’s training programs includes a variety of assignments and case studies to enhance the learning experience and provide students/learners with a thorough introduction to the uses of current tools and technology. Focusing on the practical application to the students’ work experiences, Skills Biz prepares graduates to be job ready for professions related to Data and Business Technology.
The Business Future: Data-Driven and Intelligence-Powered
With the continued adoption of automation and AI across organisations and the ongoing shift towards Digital Transformation , Data Analytics and Data Science will continue to expand as industries undergo change. While some companies are thriving in hyper-competitive environments because of their investment in people and tools, many individuals who continue to improve their skills in these two areas will be able to remain competitive now and throughout their career.
As the industries undergo changes, the ability to distinguish between Data Analytics and Data Science is becoming increasingly important not only for organizations making strategic decisions today but also for learners pursuing careers within those industries.
FAQs
1. What type of career is better—Data Analytics vs. Data Science?
Both are excellent career choices! Many feel that Data Analytics is more of a business-oriented option and an easier start in the areas of analytics. However, others find that Data Science requires a higher degree of technical expertise and involves more in-depth mathematics and programming principles. Choosing your “best fit” for these two areas depends mainly on your interests and your comfort level with mathematics and programming.
2. Are Data Analysts and Data Scientists in demand by businesses?
Absolutely! Data Analysts help to create daily reports, while Data Scientists build statistical models to predict future outcomes and automate data-driven decision-making. Together, Data Analysts and Data Scientists are critical in strengthening a company’s ability to leverage business intelligence.
3. Can someone new to the field make a transition from Data Analytics to a Data Science role?
Definitely! Many people start their professional careers by learning the foundational principles of Data Analytics before transitioning to the Data Science arena, developing their skills in programming and machine learning along the way.
4. Are Data Analytics tools easy to learn?
No, Data Analytics tools are not as difficult to learn as Data Science tools, and they are generally suitable for beginners. Tools like Excel, Power BI, and SQL are examples of Data Analytics tools that are relatively simple to use compared to more complex Data Science tools.
5. Do only technology companies hire Data Scientists?
Wrong! Data Scientists are hired in a wide variety of industries, including finance, healthcare, retail, logistics, manufacturing, education, and even government, to enhance the decision-making process and automate decision-making.
