Business Analytics

Although businesses have always crunched the numbers, “business analytics” refers to a more rigorous approach that applies statistical analysis and other quantitative methods to solve real-world business problems.

Whereas traditional sales reports are retrospective, this form of data analysis is more forward-looking and better serves strategic decisions.

The difference between business analytics, data analytics, data science, and business intelligence

How well companies harness their data determines their long-term success. Four types of analysis can contribute to that success when used appropriately.

Business analytics vs. data analytics

Data analytics is an umbrella term for the application of statistical methods. It encompasses operational dashboards, daily reports, and any other analyses.

Business analytics is a subset of data analytics focused on the organization’s strategic goals or key performance indicators (KPIs).

Business analytics vs. data science

Data scientists use big data analytics tools like machine learning and artificial intelligence algorithms to explore large data sets. Their projects will have a purpose, but the raw data itself guides this exploration.

Business analytics is more constrained in service to a specific business need.

Business analytics vs. business intelligence

Business intelligence reveals how the past led to the present. It is most helpful to people managing the company’s daily operations.

Business analytics reveals how the past may shape future outcomes. It is more concerned with identifying trends and patterns in the data that yield predictive insights about larger business goals.

Roles and responsibilities in business analytics

Building future-forward perspectives requires a team of people whose skills span the fundamentals of information technology, quantitative analysis, and business acumen.

Data analyst

Data analysts live and breathe data. They have quantitative bachelor’s or master’s degrees and experience with advanced statistics, programming, and data visualization. In addition, their communication skills let them share their results with a range of stakeholders.

Data analysis teams provide a shared resource to the larger organization. Departments can request a data analyst’s expertise to provide advanced business analysis.

On any given project, data analysts may be responsible for exploring and integrating diverse data sets. They will clean and preprocess the data ahead of the analysis. Data analysts may also convert the results of their investigations into the final deliverable’s reports or dashboards.

Business intelligence analyst

While business intelligence analysts also have strong quantitative skills, they typically have business degrees and focus more on analyzing data to support short-term and long-term decision-making.

Business analysts work within business groups where they develop a deeper understanding of the group’s operations and business goals.

The business intelligence analyst’s responsibilities may include developing dashboards and reports to support the business group’s KPIs. In addition, they may analyze historical data to inform ad hoc business decisions.

Data engineer

Data engineers have academic and professional backgrounds in information technology, programming, and database design. Their expertise lies in understanding the organization’s information systems, storage infrastructure, and data supply chains.

Typically part of the data management team, data engineers develop the pipelines that ingest, move, copy, and transform data to make it useful and accessible.

Since analysts and data scientists rarely understand the mechanics of data management, they usually request data engineers’ help to compile and prepare large data sets for analysis.

Data scientist

Analyzing data at petabyte scales is fundamentally different from traditional business analysis. Data scientists with Ph.D.’s in big data analytics bring specialized skills in statistics, machine learning, and database analysis.

While data scientists may contribute to business analytics efforts, they aim to help large businesses address the most complex challenges by unlocking insights hidden in multiple complex data sources.

How business analytics works

Although the following workflow appears linear, business analytics processes require constant communication, iteration, and feedback.

1. Assign business goals

A business analytics project is not a fishing expedition. Every aspect of the project serves a business goal.

2. Create a plan

Analysts work with business and data management stakeholders to define the project’s scope, schedule, and budget.

3. Explore and discover data

With help from data engineers, analysts explore data sources across the enterprise to find data relevant to the project.

4. Clean and integrate data

Data engineers handle the data collection process by developing pipelines to clean, preprocess, and integrate the project’s data in a database or data warehouse.

5. Analyze the data

Analysts use statistical methods and advanced techniques, such as machine learning algorithms, to reveal trends and patterns in the data.

6. Recommend actions

Statistical analysis, visualizations, simulations, and models help decision-makers take appropriate measures to achieve the business goal.

What are the 4 types of business analytics?

The kind of analysis used will vary from project to project. However, typical projects will use some combination of these four types of business analytics techniques.

Descriptive analytics

Despite all the advanced techniques analysts can apply, a straightforward analysis of historical data still adds value. Descriptive analytics explain how things got to be where they are.

Although primarily used to report on the state of the business, descriptive analytics often provides baselines for business analytics projects.

Diagnostic analytics

Often used in process management and quality control, diagnostic analytics focuses on identifying the root causes of an event. With its historical perspective, diagnostic analytics is a subset of descriptive analytics.

Predictive analytics

Predictive analytics uses modeling and other statistical techniques to assess the probability of future events. These forecasts are far more advanced than historical trend analysis. They incorporate hundreds of variables to create reliable and testable predictions.

Prescriptive analytics

Scenario planning is not about forecasting a single future. Instead, analysts develop scenarios for many possible futures. Simulations and statistical models let analysts test decisions to recommend the actions needed to achieve decision-makers’ preferred future.

Business analytics tools

Analysts can choose from various tools based on their project’s size, scope, and complexity. Excel has its place, for example, but business intelligence software like Tableau can present more sophisticated statistical analyses with elegant visualizations. Python is invaluable for general programming needs, while the statistical programming language R is better suited to processing large data sets. Although many applications include SQL capabilities, analysts may use the open-source SQL query engine Trino for querying large distributed data sets.

3 Common challenges of business analytics

Teasing insights from data requires consistent investments in expertise and resources. Qualified analysts and engineers are highly sought after and don’t come cheap. Staffing is a constant headache, but the greatest challenges of any business analytics initiative stem from the complexity of modern enterprise data management.

Data complexity

Modern analytics must turn structured, unstructured, and real-time data into meaningful business insights. Integrating different types of data stored in disparate data sources is one of the biggest challenges in analytics.

Traditionally, the solution is to ingest everything into a data warehouse or other central repository. However, the pipeline development time takes so long that the consolidated data becomes obsolete before analysts can get to it.

Data storage limitations

Since analytics projects require copying vast amounts of data into dedicated warehouses, the logistics and storage costs are not insignificant. As warehouses multiply, information technology teams look to data lakes as repositories of unstructured data.

However, data lakes cannot centralize all data. Analysts will always need data stored in other systems and regions.

Data Integration

Since business data is inherently complex and distributed, the challenge of data integration is omnipresent. Without strong data governance practices, domains develop their own metadata, formatting, structuring, and quality standards.

Data engineers must develop pipelines that bring consistency to this complexity. That takes time that analysts rarely have.

How Starburst can help with business analytics

Starburst’s modern data lakes analytics platform tackles these challenges by unifying an enterprise’s disparate data sources. Turning the single source of truth concept on its head, Starburst accepts that centralizing enterprise data is impossible. Instead, Starburst abstracts each data source within an enterprise-wide virtual access layer. Any authorized user can analyze data stored anywhere in the company from a single point of access.

Complexity: Data complexity no longer interferes with business analytics since analysts can query structured, unstructured, and real-time data without requiring new pipeline development.

Integration: Starburst also simplifies metadata management and allows companies to implement strong data governance practices. Analysts can easily explore, discover, and integrate data when they see consistent formatting across data sets.

Storage: Starburst leaves data at the source, decoupling storage from compute. Queries pull data from each source at runtime, so there’s no need to over-invest in storage infrastructure.

Unifying an enterprise’s disparate data sources within a single point of access streamlines the business analytics process, reducing time to insight, and empowering companies through forward-looking, data-driven, decision-making.

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