1. What is Data Science? Business Analytics? What are the differences between these terms? What skills set are required for each?

Introduction

Data science and business analytics almost go hand-in-hand, with their similarity being in the sense that they are needed to collect data, model and interpret it, and to make projections.

Data science is the science of extracting knowledge from data. It involves the use of automated methods to analyze massive data amounts and to extract knowledge from them. It seeks to accomplish this by defining and implementing methods and procedures that extract knowledge and information from sets of data (Earnshaw, Dill & Kasik, 2019). Data science analyzes data using the scientific method, and is mostly concerned with the rigorous data analytics-related work.

On the other hand, business analytics, which refers to the psychoanalysis of data using arithmetical concepts to draw conclusions and to get insights and solutions, is about simplifying data upon the solving of a data problem and making it more accessible to deliver insights. By business analytics, there is the focus on business using analytics. The first step in the understanding of what business analytics is all about is the definition of business objectives.

Once there are objectives, data can be collected, then analyzed and finally visualized. Analytics creates insights when applied to data. Business analytics as a part of analytics but in business circles is concerned with taking the insights from analytics and using them to create value (Liebowitz, 2013).

Data science and business analytics are closely-related in terms of their functions, mostly because both need to collect data, the model and interpret it to create solutions and to make projections. There are certain similarities between the two which explains why there are instances during which the two are used interchangeably. The differences between them make them two different domains, especially in professional circles. It is business analytics which leverages knowledge to provide the right data which is a natural input for data science. Business analytics navigates and addresses the organizational challenges that arise in the adoption and use of data science. It means that business analytics in itself is a set of data science. Business analytics also is the end-product of data science and the two are related because business analysts and data scientists use big data to inform decision-makers and organizational stakeholders for optimal results (Provost & Fawcett, 2013). However, there are major differences between them.

Business analytics involves the evaluation of collected data, from which actionable insights are developed, insights which are also solutions to specific problems and roadblocks for businesses. On the other hand, data science mainly uses algorithms and statistics plus technology to give actionable insights on structured as well as unstructured data, thereby solving issues like customer behavior, which are on a broader perspective. While business analytics uses structured data mostly, data science uses both structured and unstructured data. Structured data is the highly organized information which can be located within a defined file or record, for example, point of sales, financial, and customer data. It is usually contained in relational spreadsheets and databases, and compiling and preparing, then storing it for purposes such as analysis is relatively easy (Marr, 2015). Business analytics mainly uses this kind of data. Unstructured data on the other hand is the data that does not work so well in formats such as those of databases and spreadsheets, especially because it cannot be easily slotted into columns, fields, and rows. Text heavy data and text files like PDFs and social media posts, graphic images and photos, videos, and websites, and even PowerPoint presentations are examples of the unstructured data that together with structured data are analyzed using algorithms, statistics, and technology under data science (Marr, 2015).

Business analytics leans more towards statistics, which is why the majority of the analysis is based on traditional to some digital statistical concepts, unlike data science which requires the data scientist to combine traditional analytics practices with proper computer knowledge and skills such as coding. More so, statistics in the latter follows coding and algorithm building. Another major difference between the two is that while business analytics works on specific business issues and problems, data science studies and works on trends and patterns. It explains why data scientists should be curious, result-oriented, and possess industry- specific knowledge plus communication skills for them to explain the highly technical results to some non-technical counterparts as the business analysts.

The data scientists need a strong quantitative background in such fields as statistics and linear algebra as do business analysts, but the former also need a background in programming knowledge, in particular skills in data mining, warehousing, and modeling (Zhu & Xiong, 2015). These skills enable them to build and analyze complex models and quantitative algorithms that help organize and synthesize big data and information from where questions regarding how to drive strategies by solving business problems can be answered. In addition to modeling and coding skills, data scientists are required to be of great mathematical understanding and aptitude, conversant with data visualization, and that possess significant business knowledge. This allows them to easily translate data into a particular, understandable narrative, data and results they can use to tell a story and show decision-makers and stakeholders just how much the evidence that is provided by the analyzed data is important.

Business analysts should mainly be able to define business requirements using analytics and problem-solving skills. They also need to be effective communicators, and they should have developed process modeling skills that are necessary for roles and responsibilities like forecasting, pricing, budgeting, financial analysis, and the passing of regulations and reporting requirements to decision-makers and stakeholders (Aston University Online, 2019). The analytical strengths, business acumen, and efficiency business analysts need are necessary for them to properly and efficiently discern insights and to help companies operate at peak efficiency through their analysis and description of important guidelines as customer bases and purchasing habits.

2. How can Data Science and/or Business Analytics help a business? How can it enhance a business’s key performance indicators? Discuss briefly.

Data Science and Business Analytics in Business

The descriptive analytics, predictive, and prescriptive techniques that data scientist and business analyst professionals hold and that make them fit for these or closely-related positions in a business are needed because businesses need to collect, analyze, and understand data about their customers, the market, and their industries to make predictions that improve business performance. Data science plus business analytics equal to business intelligence, which businesses need to make smarter decisions. In the decision-making process, for example, data science ensures that the problems after being understood, and data quantified can be solved using the relevant tools implemented by data scientists and that translate the data into insights for better understanding of the business processes and teams. Companies analyze customer reviews using the analytical tools operated by data scientists from where they find the best fit for their products. It is a function that helps businesses understand and analyze the current market trends, thereby devising products for the right masses using data science tools. Considering the extent to which businesses today are data rich, they need data science to unearth the patterns hidden inside that data, from where they can predict events and make meaningful analysis. For instance, it is possible to use the raw data that is turned into cooked data by data scientists to predict the success rate of their business strategies, and all they need is key metrics identified by data science and business analytics. Predictive analytics that are carried out using tools like IBM SPSS fall under data science, and they help with customer segmentation, market analysis, sales forecasting, and risk assessment purposes.

Business analytics helps businesses leverage data they then use to make calculated and data-driven decisions. Business analytics is primarily concerned with statistical analysis from where actionable recommendations result. Business analytics ensure that the data collected by businesses is centralized and cleaned to steer of problems that may result in poor decision- making as duplication. Business analytics tools filter the collected data to remove any instances of incomplete, inaccurate, or inaccurate data. As a category of business analytics, business intelligence analyzes historical data to gain insight into how a team, department, individual employee, or the whole company has performed over a certain time period (Bichler, Heinzl & van der Aalst, 2017). Statistical analysis works closely with business intelligence as the other category through predictive analysis that is carried out using statistical algorithms and tools to make predictions about that business’ future performance. The predictive analysis is based on the historical data collected. Descriptive analytics too fall under business analytics and involve the tracking of key performance indicators to know where business performance currently stands (Evans & Lindner, 2012). Prescriptive analytics on the other hand are those that use past performance to recommend how the business can handle similar situations should they arise in the future. Company data is used to boost process and cost efficiency, to monitor and improve financial performance, and to drive strategy and change, and all these happen when companies use business analytics and data science correctly to make informed decision-making. They improve overall operational efficiency and may make higher revenue in the long-run because of embracing data and analytics-related initiatives (Gavin, 2019).

3. Illustrate with ONE example how data analytics have been used to help a business.

Data Analytics in Use

Companies today depend on data they collect from mobile devices, applications, websites, and social media platforms. Companies like Airbnb and Netflix among other streaming platforms use data analytics, in particular data science to improve their service delivery. They collect the data generated by users on the apps and websites and on social media platforms, then process and analyze it to address requirements. The analytics they use help provide premium services to their customers henceforth. They mainly use machine learning, one of the applications of data analytics and data mining to build specific solutions. Airbnb’s Dataportal, for example, captures guests’ and hosts’ metadata information in a graph that shows resources like users, teams, reports, data tables, dashboards and business outcomes. The manner in which they’re shown to be connected reflects their consumption, production, and association relationships (Rodriguez, 2019). Data analytics has helped Airbnb develop workflow management systems to avoid writing scripts regularly and to have scripts call other scripts. They take advantage of big data and data analytics to predict what consumers may like and the market information, from where their decisions on the products and services they should offer result.

Major

4. State your major. Pick the best or top 3 data science or business analytics software relevant to your major and briefly discuss why you think they are relevant?

Data science and business analytics are now part of core business activities that their software apply in human resources, my major. More companies now use data to support their evidence-based decision making in human resources. They use the software applications for planning, forecasting, recruitment, development, and the retention of members of staff. Python, a programming language, can now be used for people analytics. Companies now have teams dedicate to people analytics, and are using Python, an open source programming language that is relevant for its wide variety of developers and support, to improve HR functions as collaboration among employees. Python is readable, almost better than Excel, because things on it are laid out clearly (Kohli, 2018). HR in a company can easily carry out analysis and reuse scripts they have saved using it. Python makes it possible to create predictive models and to deriver insights from data. It is scalable and people can analyze large datasets in it. Companies that need to predict employee churn can use Python.

Tableau, another modern software for HR analytics, can be used to make hiring, retention, and investment decisions. It is an especially important tool for companies that need to visualize the relationship between HR functions like hours, productivity, and tasks. Visualization helps optimize schedules and resources with greater precision. Its relevance in HR comes from its ability to bring together human resources data in a sleek visual interface, which can best help drive insights. Companies like Walmart have noted that Tableau has provided them with efficient people analytics (Diez, Busssin & Lee, 2019).

SAP SuccessFactors, the other business analytics software is used as a talent management suite and is now a major human resources technology component in companies. It works as a software as a service (SaaS) software for human capital management and is best suited for functions of talent management as recruiting, performance management, learning and development, and compensation management (Chang, 2015). It is relevant to business analytics for its functionality in people analytics, for workforce planning, and as a time and attendance software with its hubs like Employee Central serving as human resources systems of record and data repository can store employee information like their addresses, social security numbers, and salary and benefits enrolments. Its workforce analytics uses accurate workforce intelligence to make HR decisions (Yang, Smith & Churin, 2018).

References

• Aston University Online. (2019). Data Science: Business Analytics and Big Data. Retrieved from https://studyonline.aston.ac.uk/news/2019/10/25/data-science-business-analytics-and-big-data
• Bichler, M., Heinzl, A., & van der Aalst, W. M. (2017). Business analytics and data science: once again?
• Chang, V. (Ed.). (2015). Delivery and adoption of cloud computing Services in Contemporary Organizations. IGI Global.
• Diez, F., Bussin, M., & Lee, V. (2019). Fundamentals of HR Analytics: A Manual on Becoming HR Analytical. Bingley: Emerald Publishing Limited.
• Earnshaw, R. A., Dill, J., & Kasik, D. (2019). Data science and visual computing.
• Evans, J. R., & Lindner, C. H. (2012). Business analytics: the next frontier for decision sciences. Decision Line, 43(2), 4-6.
• Gavin, M. (2019). Business Analytics: What it is & why it is important. Retrieved from https://online.hbs.edu/blog/post/importance-of-business-analytics
• Kohli, S. (2018). Innovative applications of big data in the railway industry.
• Liebowitz, J. (Ed.). (2013). Business analytics: An introduction. CRC Press.
• Marr, B. (2015). Big data: Using smart big data, analytics and metrics to make better decisions and improve performance. Chichester: Wiley.