There are quite a few types of “analytics” out there. There’s data analytics, diagnostic analytics, predictive analytics, descriptive analytics, prescriptive analytics, web analytics and business analytics, to name a few. As the BI software industry evolves, more new concepts are getting added, and the problem compounding all of this is that many of them sound incredibly similar. For instance, business analytics vs. data analytics. Is there a difference and how do they compare to each other?
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Business analytics vs data analytics
Data analytics involves collecting data, cleaning and manipulating data to uncover insights that companies use to make business decisions. Data analysts manage data sourcing and its subsequent storage in databases, data lakes and data warehouses, and organize it for exploratory analysis. Data analytics includes disciplines like data science, machine learning and applied statistics.
Business analytics is solution designing and business process optimization through data analytics, statistical analysis and predictive modeling. Data scientists and data analysts perform data analytics, while business analytics is the domain of the management team since it is related to enterprise decision-making.
Data analytics
Often, people use the terms data analysis and data analytics interchangeably.
- Data analytics includes the manipulation and study of data and tools to drive business strategy.
- Data analysis is a subset of the above and is generally performed on a single, pre-prepared data set.
Data analytics is a discipline, data analysis is the act of analyzing a data set.
Data analysts and data scientists provide access to data insights that help companies spot patterns, uncover opportunities, and predict actions, triggers and events. Data analytics covers data ingestion, data management, extraction, transformation and loading (ETL), data analysis and reporting.
Today data analysis is a part of the more extensive data analytics process. Data analysis includes descriptive analysis, predictive analysis and prescriptive analysis, all of which is performed during business analytics as well, though at a much higher level.
Before we take a closer look at business analytics, we should get acquainted with what business intelligence is since it is related to the two terms under discussion.
Business analytics
Business analytics involves statistical analysis of big data insights in tandem with business intelligence to glean meaningful insights. Earlier, it was the domain of highly-trained data scientists combing through vast amounts of unstructured data by hand and manually drawing conclusions.
Today with advancements in software, even non-technical business analysts can perform the same kinds of analyses with ease. Business analytics processes can be designed for a wide range of workflows that include software system development, process improvement, policy development and strategic planning.
A detailed comparison
Let’s take a deeper dive into business analytics vs. data analytics.
Focus
The focus of both these disciplines is different, yet it overlaps in parts. Data analytics is focused on gathering business data and manipulating it through SQL querying and program code to surface operational and previously undiscovered correlations.
Additionally, a data analyst checks data quality and works to improve data management processes through automation, while sharing insights with the business through reports and data dashboards.
Business analytics picks up from where data analytics leaves off. It focuses on identifying data trends and patterns and correlating them with proprietary BI information to uncover meaningful data insights.
Business analytics is also a superset of data analytics. Business analytics leverages data analytics for predictive modeling and forecasting through scenario analysis, regression, decision trees and neural networks.
This is more high-level, with minimal coding and SQL querying.
Data management
Data management incorporates the design and maintenance of your organization’s data architecture. Data scientists design data modeling workflows to manipulate complex data sets and identify trends and patterns that can inform intelligent business decisions.
In contrast, business analytics involves performing a comparative study of data that has already been structured and visualized through data analytics. It leverages this data for benchmarking to see where your business is at, where it is doing well and the improvement areas where your business strategy might need fine-tuning. Business analytics also helps you plan for the future by using business data for predictive modeling.
Data modeling
Data modeling gives a peek into future insights, enabling answers to questions like “what happened,” “what is likely to happen” and “what we can do,” in that order. It is a dynamic process – that is, data scientists leverage the data models not only to predict trends in key metrics but also to tweak those models to identify what the business should be doing now to get the business outcomes that they want. This is called regression analysis.
Building a data model requires the selection of variables, methods and attributes, as well as knowledge of data manipulation techniques like clustering, regression and classification. Interestingly, data modeling is a process that serves both data analytics and business analytics well. It is a great way to view likely future data trends and predict outcomes by analyzing KPI metrics.
Data quality
Data analytics makes available data that is open to interpretation, with a lot of probabilities and theoretical possibilities. It does not offer a business solution, or for that matter, consolidated data insights.
The actual study of data for innovations and problem-solving happens when the business analyst collates the information and analyzes it to present a single version of the truth to the stakeholders.
Differences between a business analyst vs. data analyst
Data analysts and business analysts are similar in that they both work with business data. The difference is in how they use it, which defines the skills needed for each role. Business analysts primarily deal with analytics, whereas data analysts focus on data management. Business analysts use data to enable innovations and enhancements to their products or services and glean out inconsistencies and discrepancies in company processes – as such, they will need higher-level analytical skills. On the other hand, data analysts are more focused on sourcing and studying data to surface trends that enable effective decision-making, so they will need excellent data management skills.
Roles and responsibilities
A business analyst is a vital part of the project management process. Some of their responsibilities are listed below.
- Working on improving business processes in the company.
- Interacting with IT and business stakeholders to identify and communicate business requirements.
- Designing technical solutions as per business requirements and documenting their technical and functional designs.
- Taking up change requests.
- Support during project implementation and user acceptance testing.
The primary role of a data analyst is to gather information from various sources and organize it in a clean, structured manner to enable business analytics.
- Setting up and maintaining automated data processes, and monitoring and auditing data quality.
- Identifying and implementing external tools and services to support data management.
- Manipulating, analyzing and interpreting complex data sets to highlight trends.
- Preparing reports for stakeholders using reporting tools.
- Defining new data management and analytics processes.
Skills needed
To work as a business analyst, you should have a Bachelor’s degree in business or a related domain or an MBA. Additionally, the below skills should be sufficient.
- Communication skills to work with stakeholders and development teams with equal ease.
- Proficiency in collaborative tools like Google Suite.
- Knowledge of task management software like Trello and Jira, and project management tools such as Freshdesk and Gantt charts.
- Expertise in Microsoft applications, including Word and Excel.
- Report and presentation building, with expertise in technical documentation.
- An analytical and problem-solving mindset with time management and organizational skills.
A data analyst needs to have a Bachelor’s or Master’s degree in mathematics, computer science, statistics or economics. In addition, the candidate may be required to have hands-on experience on the below-mentioned skills.
- Basic programming knowledge, mainly in Python, R, SQL and spreadsheets.
- Good knowledge of reporting packages; experience in data modeling is added asset.
- Proficiency in business intelligence and data visualization tools like Tableau, Looker and Microsoft Power BI is an added asset.
- An analytical mind with critical thinking and problem-solving skills, as well as attention to detail.
Final thoughts
The tools and techniques that organizations use during a project life cycle may vary depending on their business requirements and resources. However, data analytics and business analytics are indispensable cogs in the wheel. Both are separate disciplines, yet not mutually exclusive. Data analytics makes available the information and insights needed to make intelligent business decisions. Business analytics tools and technologies help companies prepare better for future developments by aligning their business strategy accordingly.
While in the data analytics vs business analytics discussion, would you like to share your take on analytics based on your personal experience? Let us know in the comments!
