Many companies have developed a suite of reporting systems, executive dashboards, and scorecards. Structured analytics is based on a structured or official way of looking at a business and its operations. The structure is based upon the semantic consistency and hierarchical taxonomy of conformed dimensions and is often implemented in an OLAP environment for use in drill-down, roll-up and other point-and-click data navigation.
The strength of structured analytics is the consistent application of key performance indicators (KPIs) and important metrics to the business for understanding important results, both current and as trends over time, of the business and their underlying factors. Structured analytics are well-suited for executives and operational managers who manage business operations and need to act quickly to resolve problems and turn information into action.
Structured analytics are not well suited for thinking outside the square. There are two main types of unstructured analytics: statistical analysis and data discovery. Many companies experience blind spots because they are completely reliant upon the structure analytics. Instead they should use unstructured analytics to identify new insights into their business.
Many companies have gone beyond structured analytics and utilize statistical analysis, often called data mining, for actuarial analysis of insurance and financial products, behavioral analysis of customers, quality analysis of suppliers and many other uses. Statistical analysis applies mathematical models to find statistical correlations between elements of business data. The purpose is to develop a model that predicts an outcome with a measurable degree of certainty so the model may be applied to help create more of a desirable outcome or less of an undesirable one.
The strength of statistical analysis is in its ability to use the power of mathematics to create models that can help increase revenues by optimizing business actions. Potential applications are cross-selling and up-selling products, identifying high-value customers, maximizing supplier quality and so forth. There is one problem, however: statistical analysis requires users to have some background in mathematics or statistics. While statistical analysis tools implement algorithms that don’t require this background, it helps to have an understanding of linear programming, distribution analysis, time-series analysis and other concepts to ensure that the model developed is accurate for the business situation being analyzed.
The use of statistical analysis in business is limited to specialized areas because of this skills requirement. Also, the development of a model takes time: data needs to be gathered, correlations between data elements considered and weighed, and a statistical distribution selected. Nonetheless, statistical analysis is an important capability that should be put to wider use. However, a method of unstructured analytics that does not require as rigorous a skill set is also available.
Data discovery tools promote ease of data analysis and end user self service by not adhering to the traditional BI model of ETL, quality control, data structuring and OLAP delivery of structured analytics. Rather, these tools automate data loading and table joining, typically based on matching column names, to establish de facto relationships between data elements and sets of values. The purpose business intelligence tools is make data access and manipulation seamless and easy.
Data discovery tools are extremely powerful, instead of ETL, these tools appear above application databases, database views, and users’ personal Excel and Access data stores. Further, data discovery technologies facilitate the visualization and analysis of data by providing a user-friendly interface for data manipulation. These tools are perfect complement to any BI solution. This is because well designed and integrated operational data store, it can eliminate data quality, control and audit issues and become a powerful technology for data discovery, complementing structured analytic and statistical analysis capabilities. Discovering a new understanding requires the analyst to understand their business and its data, and so are best suited to power users or data analysts.
Data discovery tools are exploratory, sandbox manipulation providing you with ways to get new insights into your data. Unconstrained by a defined structure or intent to develop a statistical model, data discovery allows a user to observe data values and relationships chosen by the user’s curiosity or interest.
Insights into data discovery can often not be apparent in structured analysis. Additional metrics may evolve, becoming part of the structured analytics. With this type of data evolution, data discovery will be a powerful component in a company’s overall BI capabilities. This will enhance the capabilities provided by statistical analysis and structured analytics.
BCS Technology Business Intelligence can provide additional insight into a companies BI requirements.