Business analysis is the means through which operational problems and issues are systematically identified and investigated, different approaches are evaluated, and optimal solutions are determined. Through the practice of business analysis, companies critically examine their processes, technology, and other aspects of their business in order to better understand their needs and priorities and create a roadmap for organizational improvement.
Business analytics software enables business users to easily access, share, and explore data relevant for business analysis—without having to rely on IT or data specialists. Self-service data discovery and analytics platforms like Qlik Sense® provide users with the tools they need to unlock actionable insights from their data and make the sort of discoveries that drive transformation.
Although the terms business analysis and business analytics are sometimes used interchangeably, they differ in meaning. While business analysis refers to the process or practice of analyzing situations, problems, or courses of action, business analytics refers to the use of data analytics to obtain business intelligence. What data analytics is, for those unfamiliar with the term, is the use of technology to examine—transform, organize, and model—and extract meaningful insights from large datasets. Business analytics tools support business analysis by providing business users with powerful technology to explore massive datasets, uncover important patterns and trends, find solutions, make predictions, and support their decisions with data-based insights.
As companies come to rely more on data—and collect more data than ever before on products, services, customers, and operations—the role of business analysts is also changing. Due to the scale and speed of advancements in technology, projects are increasing in complexity, and business models are quickly evolving. Companies must stay agile in the face of rapid change, and business analysis must be carried out faster and more often and take into consideration vast quantities of data when devising recommendations. As a result, business analysts are fast becoming citizen data scientists leveraging sophisticated software to simplify and accelerate business data analysis.
The integration of AI in BI reporting analytics solutions is a critical factor in enabling business users to become citizen data scientists, or at least feel comfortable approaching an analytics program in order to see what the data has to say. By embedding AI in the form of machine learning and natural language processing, the newest generation of BI solutions automates data preparation, algorithm selection, model training, and other complicated parts of the data science lifecycle, allowing a broad range of users to perform both simple and moderately complex analytical tasks including big data analysis. Empowered by AI working in the background, non-data scientists can use self-service BI software for business analysis tasks, or to run predictive models and leverage other advanced analytics capabilities—without having to write code or preselect algorithms. And with conversational analytics, non-technical users can now interact with data using chatbots or digital personal assistants and everyday business language.
A leader in the democratization of advanced analytics tools, Qlik is setting the benchmark for a new generation of self-service BI solutions. Featuring a one-of-a-kind associative analytics engine and sophisticated AI, Qlik Sense accelerates business analysis tasks and supports a wide range of BI use cases including self-service, executive reporting, embedded analytics and more. The Qlik Sense data analytics platform empowers users of all skill levels to combine, load, and explore data from multiple sources, create and manipulate visualizations, and build and deliver stunning reports with drag-and-drop simplicity. With the Qlik Insight Bot®, users can ask questions and uncover valuable insights using natural language. Interactive dashboards make it easy for users to dive in and experiment with data, helping them better understand a situation and make smarter, data-driven decisions.
It might be said that business intelligence is diagnostic and descriptive, or focused on reporting on the past and current events to better manage existing business operations. While business analytics is predictive and prescriptive, or focused on transforming business operations to maximize efficiency, productivity, and value. The distinction between the two is blurring, however, as BI solutions have come to incorporate more advanced analytics capabilities.
You would start by identifying the problems or opportunities a company is facing and the stakeholders involved, defining key business objectives and stakeholder needs, and evaluating possible courses of action. You could use business analytics software to determine the feasibility of a solution by predicting its costs, benefits, risks, and probable outcomes.
Some of the more popular techniques are SWOT (Strengths, Weaknesses, Threats, and Opportunities) Analysis, MOST (Mission, Objective, Strategy, and Tactics) Analysis, PESTLE (Political, Economic, Social, Technological, Legal, Environment, Use) Analysis, mind mapping, business process modeling, and use case modeling.
This refers to software solutions such as Microsoft Excel, Microsoft PowerPoint, Microsoft Access, SQL, Google Analytics, and Qlik Sense—technology that helps business analysts collect and sort data, create and manipulate data visualizations, build and publish reports, and depending on how sophisticated a tool, use advanced analytics for predicting outcomes and finding optimal solutions.