What are the differences between data analysis, data analytics, business analysis, and business analytics?

Gaining a thorough understanding of business terms is a great asset for any businessperson. It enables them to make more informed decisions and operate their business more effectively. However, there are some terms that are quite similar, which can lead to some confusion. In this blog post, we will attempt to clarify these terms by providing examples.

1. Data Analysis

A.  Definition of Data Analysis

Data analysis is the process of examining, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. It involves using techniques such as statistical analysis, data mining, machine learning, and visualization to explore and uncover patterns and trends in data sets.

B.  Types of Data Analysis

There are four types of Data Analysis. They are-

I. Descriptive Analysis

Descriptive analysis is the foundation of data insight and the most common use of data in business today. It answers the “what happened” by summarizing past data, such as in the form of dashboards. Descriptive analysis is used to track Key Performance Indicators (KPIs), which describe the performance of a business in terms of chosen benchmarks. Businesses commonly use descriptive analysis to create KPI dashboards, monthly revenue reports, and sales lead overviews.

  II. Diagnostic Analysis

Once the main question of “what happened” has been asked, the next step is to delve deeper and ask why it happened. This is the aim of diagnostic analysis, which takes the insights from descriptive analytics and investigates why those outcomes occurred. Companies make use of this type of analytics to build connections between data and identify patterns of behavior. A key part of the diagnostic analysis is creating comprehensive data. If a new issue arises, it is possible to have already gathered data related to the problem. Having the data available prevents unnecessary repetition of work and allows problems to be interconnected. Examples of business applications of diagnostic analysis include a freight company seeking to find the cause of delayed shipments in a specific region, and a SaaS company investigating which marketing strategies led to increased trials.

III. Predictive Analysis

Predictive analysis is a type of analytics that attempts to answer the question of “what is likely to happen”. It uses past data to make predictions about potential outcomes and requires the use of statistical modeling, which needs added technology and workforce to forecast. It is important to note that forecasting is just an estimation; the accuracy of the predictions will depend on the quality and detailed data. Despite the fact that descriptive and diagnostic analysis is often used in business, predictive analysis can be a challenge for some companies. They may not have the required manpower to conduct predictive analysis in every area they would like or be willing to invest in analysis teams in each department or to educate current teams. Predictive analysis can be applied in different business areas, such as Risk Assessment, Sales Forecasting, customer segmentation to identify leads that have the best chance of converting, Predictive analytics in customer success teams to find the cause of delayed shipments in a specific region, and for SaaS companies to investigate which marketing strategies led to increased trials.

IV. Prescriptive Analysis

Prescriptive analysis is the most sought-after type of data analysis, although few organizations are equipped to perform it. Combining the insights from previous analyses determines the course of action to take for a current problem or decision. Using state-of-the-art technology and data practices requires a large organizational commitment of resources and effort. Artificial Intelligence (AI) is an example of prescriptive analytics which uses data to make informed decisions and even put them into action. Companies such as Apple, Facebook, and Netflix are already utilizing prescriptive analytics and AI for improved decision-making. However, for other organizations, the jump to predictive and prescriptive analytics can be difficult. As technology progresses and more professionals are educated in data, more companies will start to enter the data-driven realm.

C.  Data Analysis Tools

When selecting a data analysis tool, it is important to consider the type of analysis you will be performing and the type of data you will be working with. Below is a list of some popular tools for data analysis:

1.    Excel

Excel is a powerful and versatile tool for data analysis, boasting numerous compelling features. Its capabilities can be expanded even further by installing additional plugins, allowing it to process an immense amount of data. Therefore, if your data is not particularly large, Excel is a great choice for data analysis.

2.    Tableau

Tableau is a BI Tool specifically designed for data analysis. It offers powerful features such as Pivot Tables and Pivot Charts, which allow users to easily visualize and analyze their data. Tableau also includes a data-cleaning feature and a wide range of analytical functions.

3.    Power BI

Power BI began as a plugin for Microsoft Excel, but eventually became its own stand-alone data analytics tool. It is offered in three versions: Free, Pro, and Premium. This tool allows users to use PowerPivot and the DAX language to perform complex data analysis tasks, similar to writing formulas in Excel.

4.    Fine Report

Fine Report offers a convenient drag-and-drop interface for creating a variety of reports and constructing a data analysis system. It can connect to all kinds of databases, and its format is similar to Excel. This software also includes an array of dashboard templates and customized visual plug-in libraries.

5.    R & Python

R is an incredibly powerful and flexible programming language, particularly well-suited to statistical analysis. It can be used to perform normal distribution, cluster classification algorithms, and regression analysis, as well as individual predictive analyses such as customer behavior, spending, and items preferred by customers based on their browsing history. Additionally, R is also well-suited for use in machine learning and artificial intelligence applications.

6.    SAS

SAS is a powerful programming language used for data analytics and manipulation. It provides users with easy access to data from any source, as well as a range of customer profiling products for web, social media, and marketing analytics. Using these tools, SAS can help users predict customer behaviors, manage, and optimize their communications.

 

2. Data Analytics

A.  Definition of Data Analytics

Data analytics involves taking raw data and utilizing various techniques and processes to draw conclusions from it. Many of these processes have been automated and transformed into algorithms that can be used to interpret the data for human understanding.

A.  Types of Data Analytics

Data analytics can be classified into four main types: Descriptive, Diagnostic, Predictive, and Prescriptive.

I. Descriptive analytics involves examining data from a given period of time to determine what has happened, such as whether views have increased and if sales are stronger than usual.

II. Diagnostic analytics delves into why something happened by utilizing multiple data sources and forming hypotheses. For example, it might explore if beer sales were affected by the weather or a marketing campaign.

III. Predictive analytics focuses on what is likely to happen in the near future. It assesses past data and models to estimate the outcome, such as how sales reacted to a hot summer in the past and what the probabilities are of a hot summer in the present.

IV. Prescriptive analytics provides a course of action. It might suggest increasing output by adding a night shift to the brewery and renting an extra tank if the likelihood of a hot summer exceeds a certain threshold.

B.  Data Analytics Techniques

Data analysts have several analytical methods and techniques at their disposal to process and extract information from data.

• Regression analysis examines the relationship between dependent variables to determine the effect of a change from one to another.

• Factor analysis reduces a large data set to a smaller set, allowing for the detection of trends that may have otherwise been overlooked.

• Cohort analysis divides data into groups according to a customer demographic, allowing for further analysis of the numbers pertaining to a specific subset of data.

• Monte Carlo simulations are used for risk mitigation and forecasting by modeling the probability of certain outcomes based on multiple variables and values.

• Lastly, time series analysis tracks data points over time to look for cyclical patterns or devise financial forecasts.

C.  Data Analytics tools

Data Analytics has long been associated with spreadsheets and Microsoft Excel. Now, however, data analysts have access to a wide range of software tools to acquire, store, process, and report data. These tools include open-source languages such as Python and R, which can be used to transform and manipulate databases. Additionally, data analysts can use visualization and analysis tools such as Tableau and Power BI to compile information, perform analytics, and distribute results through dashboards and reports. Furthermore, emerging tools like SAS and Apache Spark provide data analysts with the capability to work with data mining and large datasets, respectively. With these tools, data analysts have the ability to deliver increased value to their company.

3. Business Analysis

A.  Definition of Business Analysis

Business analysis is a practice that involves utilizing data and particular techniques to gain insight, identify the needs of an organization, and suggest changes and solutions that bring value to the stakeholders. Solutions can involve anything from software and digital data-based components to organizational changes such as improved processes, new policies, and strategic planning.

B.  Techniques Used in Business Analysis

Here are the most popular 10 business analysis techniques-

1. Business Process Modeling (BPM) is a technique used to analyze and understand the gap between a current business process and a desired future process. This method consists of four tasks: strategic planning, business model analysis, defining and designing the process, and technical analysis for complex business solutions. BPM is widely used in the IT industry as it is a straightforward way to demonstrate the steps of an execution process and its operation in different roles.
2. Brainstorming: The tried-and-true technique of brainstorming is an invaluable tool for business professionals. It can be used to uncover innovative ideas, get to the bottom of a problem, and come up with effective solutions for complex business matters. This method of group discussion is often used in conjunction with other analytical approaches, such as conducting a PESTLE or SWOT analysis.

1. CATWOE is a tool used by business analysts to understand the impacts of any proposed action on the different stakeholders. The acronym stands for: Customers, Actors, Transformation Process, World View, Owner, and Environmental Constraints. This technique collects the perceptions of various stakeholders and unifies them on one platform, allowing analysts to identify who the leading players and beneficiaries are. By analyzing each element of CATWOE, analysts are able to gain an understanding of how their proposed action will affect the system and its stakeholders.

1. MoSCoW is a prioritization framework used to evaluate the relative importance of requirements. It forces you to question the necessity of each demand, by distinguishing between those that are ‘Must’ and ‘Should’ have, and those that are ‘Could’ and ‘Would’ have. This process helps to identify which requirements are essential to the product, and which could be implemented in the future.

• The MOST Analysis is a powerful tool for analyzing an organization’s mission, objectives, strategies, and tactics. This framework provides an in-depth look at an organization’s goals and the best methods to achieve them. The acronym MOST stands for Mission, Objectives, Strategies, and Tactics. The mission is the organization’s purpose for existing. Objectives are the key goals that allow the mission to be accomplished. Strategies are the various options available for achieving the objectives. Tactics are the methods the organization will use to implement the strategies.

• PESTLE: Business analysts use the PESTLE model to assess the external environment and gain a better understanding of the influences that will affect their business. These influences include political factors such as financial support, government initiatives, and policies; economic factors like labor and energy costs, inflation, and interest rates; sociological elements like education, culture, media, and population; technological developments such as new information and communication systems; legal regulations and employment standards; and environmental matters such as waste, recycling, pollution, and weather. With knowledge of these factors, analysts can develop strategies to address them and make informed business decisions.

• SWOT Analysis is a popular technique in the industry that helps identify the strengths and weaknesses within a corporate structure and then presents them as either opportunities or threats. This knowledge aids analysts in making more informed decisions on resource allocation and suggestions for organizational improvement. The four components of SWOT are Strengths, Weaknesses, Opportunities, and Threats. Strengths are the qualities of the project or business that give it an advantage over its competition. Weaknesses are the characteristics that put the project or organization at a disadvantage when compared to its competition or other projects. Opportunities are elements present in the environment that the project or business can leverage. Lastly, Threats are the elements in the environment that could harm the project or business. SWOT is a simple and versatile tool that can be utilized for either a quick or in-depth analysis for any size organization. It is also beneficial for assessing other topics such as groups, functions, or individuals.

• Six thinking hats: The six thinking hats approach encourages a group of people to think in a structured way by inviting them to consider different perspectives. The six hats are White, Red, Black, Yellow, Green, and Blue. White encourages a focus on data and logic, while Red encourages the use of intuition and emotions. Black helps to anticipate potential negative results, while Yellow helps to maintain an optimistic outlook. Green encourages creativity and Blue helps to take the big picture into account, allowing for better process control. This technique is often combined with brainstorming to create an environment that encourages the consideration of diverse views.

1. The 5 Whys is a technique that is found in both Six Sigma and business analysis circles. It is based on the journalism “Five W’s” (Who, What, When, Where, and Why), but instead just uses the “Why” in a series of questions. This is done in order to identify the root cause of a problem. For example, if the issue is that a client refuses to accept the delivery of some 3-D printers, one might ask why this is. The answer could be that the wrong models were shipped. This could be due to the product information in the database being incorrect, which could be caused by insufficient resources allocated to modernizing the database software. This may be because managers didn’t think the matter had priority, and no one was aware of how often this problem occurred. To rectify this, measures such as improving incident reporting, making sure managers read reports, and allocating budget funds for modernizing database software could be put into place.

• Non-Functional Requirement Analysis is a technique used to define and capture the characteristics needed for a new or modified system. This is typically done when a technology solution is replaced, changed, or built up from scratch. The analysis usually covers logging, performance, reliability, and security. This technique is usually implemented during the Analysis phase of a project and put into action during the Design phase.

4. Business Analytics

A.  Definition of Business Analytics

Business analytics is a powerful tool for any company looking to make informed decisions. By leveraging data-driven insights, organizations can make better use of their resources and optimize their processes for better results. With the right combination of technical and communication skills, businesses can use business analytics to make sound decisions and stay competitive.

 

Business Analysis	Business Analytics
The main emphasis should be on processes, techniques, and functions.	The main emphasis should be on data and statistical analysis.
This tool can be utilized to address intricate business challenges and provide effective solutions	This technique can be utilized to anticipate potential outcomes and inform business choices.
In order to analyze business requirements, one must possess knowledge of the function, operations, and context of the business.	Business analytics requires statistical, mathematical, and programming knowledge in order to be performed.
Creating unified, consistent processes across all business units of a company in order to ensure efficiency and effectiveness.	Creating a dashboard to monitor key performance metrics, utilizing statistical techniques to forecast future sales according to historic data.

Even though those key terms have differences they have similarities as well. But overall all of those terms are used to improve the business performance and betterment of the business to increase business revenue and profit. To know more about key business terms keep reading our blogs.