Do you want to know the difference between big data analytics, business intelligence and data science? If YES, here is a detailed explanation and how each can help your small business.
One unique thing that came with this age and also advanced our way of life is data. In fact, the amount of digital data that exists in our world is rising every day at a rapid rate. Reports have it that more than 2.7 zettabytes of data exist in today’s digital universe, and that is projected to grow to 180 zettabytes in 2025.
This big data and all the possibilities it contains have only recently begun to be analyzed to provide insights that can help organizations grow their businesses. This is why a lot of enterprises are seriously searching for professionals who can understand and express data in a simpler form.
What is Analytics?
First you need to know that Analytics is an interdisciplinary field that incorporates training in four main areas: advanced mathematics and statistics; computer science and programming; database technologies; and enterprise decision management. Broadly speaking, analytics involves learning how to use the big data processing capabilities of modern IT systems to store, sort, and analyze relevant data.
The goal is to provide scientifically based, quantitative solutions to a range of complex problems. Research has shown that individuals or rather professionals who know how to squeeze commercially useful information out of data are in high demand.
Organizations in all business industries are constantly depending on business intelligence analysts and data scientists to give them a massive competitive advantage and help boost profits. Data analysts, business analysts and data scientists all work together to turn raw data into useful information. These experts fill different, but related, roles.
Why Understanding the Relationship Between Data Analytics, Business Intelligence and Data Science is Important?
Business Intelligence, Data Analytics, and Data Science programs we believe, discuss and strive to understand three connected but overlapping specializations within the diverse field of analytics. Their difference is not farfetched as the program specializations are distinguished by differences in their curricular focus.
Data Analytics programs are grounded in the foundational elements of analytics, including advanced mathematics and statistics, and data mining. Data Science programs delve into the more technical aspects of computer science, computer programming, and computer engineering. Business Intelligence programs are defined by their focus on IT systems that process analytics data. To properly explain and analyze their differences, let’s begin by explaining the various systems.
What is Data Analytics
Data analytics (DA) is the process of understanding data sets in order to draw conclusions about the information they contain, with the help of specialized systems and software.
Research has shown that data analytics technologies and techniques are widely used in commercial industries to give organizations the opportunity to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.
It’s very important to state that data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing (OLAP) to many other forms of advanced analytics.
How Does Data Analytics Help Businesses?
Have it in mind that data analytics is quite similar in nature to business analytics, another umbrella term for approaches to analysing data — with the difference that the latter is oriented to business uses, while data analytics has a broader focus.
It aids businesses grow revenues, increase operational efficiency, improve marketing campaigns and customer service efforts, respond faster to emerging market trends and have a competitive advantage. Simply put, it helps in boosting business performance. Based on the particular application, we believe that the data that’s analyzed can be made up of either historical records or new information that has been processed for real-time analytics uses.
What is Business Intelligence?
Business Intelligence (BI) simply means the technologies, applications and practices for the collection, integration, analysis, and presentation of business information. Business Intelligence helps and supports better business decision making.
They are sometimes used interchangeably with briefing books, reports and query tools and executive information systems. These systems help provide historical, current, and predictive views of business operations, most often using data that has been organized into a data warehouse or a data mart and occasionally working from operational data.
Note that software elements support reporting, interactive “slice-and-dice” pivot-table analyses, visualization, and statistical data mining. Its application tackles sales, production, financial, and many other sources of business data especially for business performance management.
In this modern age, businesses are beginning to understand that data and content should not be seen as separate aspects of information management, but instead should be understood and managed in an integrated enterprise approach.
Have it in mind that Enterprise information management brings Business Intelligence and Enterprise Content Management together. Reports have shown that businesses are currently moving towards Operational Business Intelligence which is currently under served and uncontested by vendors.
What is Data Science and How Does It Help Businesses?
Data science is simply a system used to extract knowledge or insights from data in various forms, either structured or unstructured. Data science makes use of techniques and theories drawn from a lot of fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization.
Data science is a discipline that uses a set of techniques and methodologies to build business applications from various sources of structured or unstructured data. Have it in mind that businesses in this modern age need data science skills to gain knowledge from their data. The diversity of the business applications impacted by the use of data science is very large. Data science helps in making results achievable and their benefits may include…
- Energy providers mould their production to the predicted demand
- Banks and financial actors understand risks better and detect frauds
- Insurance companies bid the right offer to the right customer at the right life moment
- Marketers work more efficiently on customers loyalty
- E-commerce players are able to raise their conversion rates
- Big businesses match an unsatisfied collaborator to a vacant job which will be her dream job
- Industries prevent breakdowns before they happen
- Sentiment analysis for brands by gathering Facebook and Twitter conversations
- The supply chain can optimize their stocks and deliver times
- Telco or similar subscription businesses who want to work on churn prevention
- B2C companies evaluate customer lifetime value to focus their best efforts
- Retailers predict their future sales by analyzing customer behaviour
Data Analytics vs. Business Intelligence vs. Data Science programs – What’s the Difference?
You need to understand that the field of analytics is divided into three primary types of degree programs: Data Analytics, Data Science, and Business Intelligence. Even though it is useful to sort programs into these categories, there is considerable overlap between the three different program types. We believe there are no standardized naming convention or curricula standards for degree programs in analytics.
- Diversity and Broadness
The best way to determine each program’s focus is to carefully evaluate its curriculum, including required and elective courses. It’s very important to note that Data Analytics programs are the largest and most diverse of the three analytics degree programs, with courses in applied mathematics, statistics, computer science, and IT systems.
But then Data Science programs cover these areas as well, they typically need more advanced coursework in computer science, programming, and engineering. Business Intelligence programs are the most IT focused of the analytics program specializations, with coursework in data warehousing, database management, and dash boarding technologies.
Indeed knowing and understanding the right analytics program to choose can be very hard. Analytics programs exist on a continuum, with Data Analytics in the middle, and Data Science and Business Intelligence on each side. Have it in mind that Data Analytics programs are typically the broadest with Data Science and Business Intelligence programs being more specialized.
But then Science programs need more computer science related coursework, while Business Intelligence programs require more IT related coursework. We believe that a lot of programs have set requirements that more explicitly explain them as Data Analytics, Data Science, or Business Intelligence programs. But, these programs may also offer electives that allow students to combine elements of the three different programs.
Data Analytics programs are made up of the foundational curriculum in analytics, the curriculum in which other analytics specializations are grounded. These programs have a bigger scope than other analytics specializations. Note that students in Data Analytics programs are taught to use advanced mathematics and computer programming to test hypotheses, identify trends, and answer questions quantitatively.
We believe that this involves making use of spreadsheets and databases to aggregate and sort unstructured data, and using statistical modelling techniques, probability matrixes, and algorithms to analyse different kinds of data. While Data Science programs are in some aspects more technical than Data Analytics programs, with more coursework dedicated to computer science, computer programming, and computer engineering.
But then Data Science students learn to make use of the same tools used in Data Analytics, especially statistical modelling, advanced mathematics, algorithmic programming, and big data systems.
Data Science curricula also discusses a lot of the same chief concerns as Data Analytics curricula, including the many processes for collecting, sorting, and interpreting empirical data in order to identify meaningful trends, correlations, and causations. But have it in mind that Data Science programs delve into experimental analytics applications that go beyond what is typical in a Data Analytics program.
This includes learning how to work with the largest, most complex datasets, and writing computer code that can handle the unique challenges of structuring and analyzing these datasets. Business Intelligence (BI) curriculum is made up of a clearly explained focus on the IT systems that are used to enhance the flow of analytics information within an organization.
These include advanced databases, data warehousing technologies, and executive dashboard platforms, which are tools that provide non-technical members of an organization access to metrics generated by data analysts.
Data analysts, Data scientists and BI analysts share great job prospects, especially since a lot of businesses are striving to fill positions in these fields. Reports have it that the U.S. currently labours under a shortage of 140,000 to 190,000 professionals with specific analytical expertise. Other reports also state that 89 percent of data scientists on LinkedIn were contacted about job opportunities monthly, and 25 percent received weekly job offers through the site, showing the high demand of these professionals in this modern age. Experts believe that this demand or situation will become far more serious over the next few years. It is believed that the U.S. industries will add four million more big data positions, and those positions will all need to be filled with professions who possess quantitative and analytical skills in the year 2018.
It’s also expected that the country will experience a shortfall of 1.5 million analysts and managers who can analyze big data and make sound business decisions based on that analysis. To build a career in this field, you will need an advanced degree in mathematics; statistics, computer science, engineering, or business intelligence, desirable job candidates must have good business sense and strong communication skills. Have it in mind that data job prospects are already great even if you’re a new graduate just entering the field, but with experience in the field comes more opportunities.
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