Companies are under constant pressure to improve efficiency and drive innovation and growth, and insight-driven businesses have a distinct advantage in competitive markets. Insights are important discoveries made about a business and obtained using business data analysis. Using business intelligence software, firms can leverage their vast stores of data to solve complex business problems and develop new strategies and initiatives. They can better identify and take advantage of critical opportunities, anticipate and manage risk, and stay agile in rapidly changing markets. Business data analysis is how organizations unlock the value of their data, extracting the actionable intelligence they need to lead.
Business data analysis is the means through which firms can harness the power of their data to improve their products and services and optimize processes throughout the organization. Data analytics* can be used to evaluate processes, find solutions to problems, and provide executives and other stakeholders with data-driven recommendations and reports. Through business data analysis, decision-makers get the information they need to better understand situations, evaluate courses of action, refine their strategies, and act with confidence knowing that their decisions are based on the facts. Employees can be more productive while companies become more efficient, profitable, and competitive.
*What is data analytics? It refers to the process of analyzing data, typically through the use of software applications, in order to identify meaningful relationships, patterns, and trends and discover valuable insights.
Business data analysis is used to obtain insights around all aspects of a business—customers, products, services, sales, marketing initiatives, supply chains, partners, and more. Here are just a few ways business data analysis helps companies improve their overall performance:
One of the most powerful ways data analytics is used in the enterprise is through embedded business data analysis. By embedding analytics into business processes and operational applications, modern business analysis tools can provide users with insights in the context of their workflows, supplying them with the answers they need when and where they need them. By integrating self-service data exploration and analytics into existing business applications, they enable users to quickly and easily find data-supported insights that can help them make the right decision. Business users can take insights-driven action without having to use a separate tool or rely on analysts or data scientists. They can leverage powerful data analytics capabilities to see what the data says whenever they want to better understand a situation or solve a complex problem.
Featuring a unique associative analytics engine that indexes and understands all relationships in your data, Qlik Sense is a game-changing data analytics platform for the enterprise. Qlik Sense incorporates AI for augmented business data analysis, empowering everyone in your organization to take on analytical challenges. It supports any BI use case you can think of—from executive reporting to big data analysis—putting powerful analytics in the hands of both power users and non-technical business users. No matter their skill level, every user can easily combine and explore data, create smart visualizations, and start making data-driven decisions. With Qlik Sense, you can embed fully interactive analytics solutions in the applications your workforce uses every day, letting them be more productive, impactful, and inspired by your data.
Some of the most common tools include Microsoft Excel, the leading spreadsheet program; SAS, a statistical analysis software suite; and, R and Python, which are programming languages popular among data scientists. Data analysis and visualizations tools like QlikView®, a guided analytics solution, and Qlik Sense, a self-service data discovery and analytics platform, are popular among business users.
It is used to combine and analyze datasets in order to uncover meaningful trends, patterns, and connections and extract actionable insights. These insights are useful for better understanding customer behavior, assessing the impact of marketing campaigns, improving customer service efforts, identifying emerging market trends, mitigating risk, and guiding decision making.
Some of the most common techniques include: cluster analysis, which is used for market research and audience segmentation; decision tree analysis, which is used in the healthcare industry; factor analysis; regression analysis, which is used for risk assessment; multivariate analysis; multivariate regression; segmentation analysis; sentiment analysis; simulation analysis; and time series analysis, which is used in the airline industry.