The growth and operations of a business are determined by the quality of the decisions it makes as an entity. The dire need for quality decision-making by companies has never been more pronounced. This is because the adoption of technology has tremendously raised the bar in terms of service delivery and customer experience leading to cutthroat competition. In the middle of these transformations are two pivotal fields; business analytics and data science.
As businesses transition into data-driven decision-making, the demand for data scientists and business analysts is on an upward trajectory. The good news is that most learning institutions now offer a business analytics or Data Science course online.
It comes not as a surprise, therefore, that the data science market is currently worth about US$45 billion while that of business analytics stands at US$70 billion and is expected to grow to US$140 billion and US$100 billion respectively by 2025. Interestingly, despite the growth in demand and relevance of data analytics and data science, some still use these two terms interchangeably. This article will elaborate on the two and hopefully provide readers with a better understanding of these two fields.
Business analytics
Business analytics is a subset of business intelligence that employs statistical methodologies and tools to gain new insights from mostly structured historical data, with an aim of making smarter, data-driven decisions.
Data is a business’s greatest resource in that it is factual feedback of the reality of the business’s performance. Thus for businesses to operate effectively and achieve their objectives, they need to employ data-driven decisions and strategies and it is the domain of business analytics that possesses the capability to translate data into the same. A business that operates minus the input of business analytics can be compared to a traveler without an accurate map since business analytics gives the business direction based on historical data. This greatly enhances efficiency and effectiveness to help the business remain competitive and profitable.
What is the role of a business analyst?
The overall role of a business analyst is to act as the link between the IT department and business executives in charge of decision-making. They do this by assessing structured data; determining the need and requirements for changes in processes, products, or services; evaluating the feasibility for these changes based on the business’s capability, and drafting them into sound business recommendations based on their (the analysts) expertise in both business and technology.
What are the qualifications of a business analyst?
To be considered for the role of a business analyst most employers will require a degree in either business, IT, computer science, economics, accounting, finance, or any related field.
An alternative route is to undertake a business analyst degree apprenticeship program or gain professional training and certifications from bonafide institutes. This can be done before applying for the job or as part of career development.
What are the core business analytics skills?
Just like other valuable professions, business analytics requires a balanced combination of technical and soft. A business analyst should possess:
- Statistical analysis skills
- Negotiation skills
- Communication skills
- Documentation skills
- Critical thinking skills
- Time management skills
Unlike soft skills which are closely tied to one’s personality and character, technical skills require one to be conversant with some important tools that are necessary for business analytics. A business analyst should therefore be well-versed in:
- Tableau
- Programming skills in Python and R
- Data mining and visualization
- Microsoft Excel, Access, and PowerPoint
- SQL
- Axure
- SWOT analysis
The salary prospects of a business analyst
Business analytics is a lucrative career. According to the US Bureau of Labor and Statistics (BLS), the median annual salary for management analysts (which includes business analysts) was $87,660 as of May 2020 and the demand for these professionals was projected to register an 11% increase between 2019 and 2029.
Data Science
Data science can be defined as an interdisciplinary field that involves the extraction of actionable insights from structured, semi-structured, and/or unstructured data using scientific processes, methods, and systems. In a business scenario, this is the process that precedes business analytics since it is the data scientist that hands over already structured and modeled data to the business analyst.
Embed Youtube Video URL here: https://www.youtube.com/embed/X3paOmcrTjQ
Considering the amount of data being generated in the world every second and the importance of data to any competitive company or business, it is nearly impossible for a layman to convert such vast amounts of data into useful insights. It takes the intervention of data science as a specialized domain to handle this process. Without data science, therefore, the business analyst will have nothing to analyze.
What is the role of a data scientist?
A data scientist’s fundamental role to play in a company as a custodian and regulator of data. He/she is responsible for designing the methods to be used to capture, store, process, and analyze data. He’s responsible for identifying, collecting, and organizing data as well as building models and algorithms that make it possible to analyze the data before handing it over to the business analysts for a more business-centered analysis.
What are the qualifications of a data scientist?
For the data scientist role, employers may require a degree certificate in computer science, modeling, maths, statistics, economics or analytics, IT, and other related fields. At least three years of working experience should boost their employment chances.
Core data science skills
Data scientists require both soft and technical skills to excel at their job. These include
- Communication skills
- Problem-solving skills
- Critical thinking skills
- Documentation
- Attention to detail skills
Technical skills required for a data scientist include:
- Programming languages e.g. SQL, R, and python
- Practical knowledge of distributed data computing tools like MapReduce, Hadoop, and spark
- Data mining, cleaning, and warehousing
- Statistical analysis using machine learning algorithms
Unlike business analytics, data science is a bit more complex technically and involves a lot of coding and deep statistics.
Data scientist salary prospect
According to the US Bureau of Labor Statistics, the average annual data scientist salary is $100,560 which places them slightly above business analysts.
Conclusion
While business analytics and data science are distinct in many ways, they are similar in several ways and also complement each other. For instance, both data science and data analytics require backgrounds in computer science, IT, statistics, maths, and domain knowledge. They both heavily rely on analytical and statistical tools to gain insights.
Finally, both these fields are useful to any business as they use data to enhance strategic decision-making which in turn helps to minimize and manage risks, maximize profits through well-crafted and customized service delivery, and ultimately enhance business growth.
