This is a question that seems to come up in one way or another quite frequently. And it makes sense. Being fairly new (on the main stage), these terms are not well defined in the public consciousness (or sometimes even in the heads of those in the field) and so there can be a lot of confusion. There are some out there that think that both are simply creating pretty charts somehow relating to data in some way and there are others that know that the rabbit hole goes far, far deeper…
Business Intelligence and Business Analtyics are both disciplines inside of a wider field of being a Data Professional. Both Business Intelligence and Business Analytics deal with deriving meaning from data. The differences between the two tend to stem from the types of questions each attempts to answer and the tools and processes used to answer those questions.
Business Intelligence is the infrastructure. Business Intelligence includes the gathering, storing, and summarizing of data as well as making it available in the forms of reports, dashboards, KPIs, raw queries, and other visualizations to answer questions about the past and present which are commonly used in decision making. Some of these questions might be: What happened? When did it happen? What did it happen to? How much did it happen? Business Intelligence catalogs the past and makes it available for future insight.
Business Analtyics is the crystal ball. Business Analytics makes use of existing infrastructure (such as data gathered in Business Intelligence) and aims for deeper insight into the data, frequently dealing with predictive analytics about the future. Why did it happen? Will it happen again? What will happen if we make a change? What trends can be expected for the future? Business Analytics makes use of data to provide predictive capabilities.
Traditional Business Intelligence Components and Tools
A fully mature, enterprise Business Intelligence solution can have a large number of components. TDWI, among other organizations, has developed a model which organizations tend to follow as they enter the Business Intelligence arena and then grow it to an increasingly valuable facet of the business. As can be expected, there is a direct relationship between the Business Intelligence Maturity of an organization and the tools and components used within the Business Intelligence architecture. TDWI’s BI Maturity Model is as follows:
- Stage 1: Prenatal – Executive perception is that of a cost-center, which primarily churns out static reports for management operational reporting. It is also the stage which costs the most.
- Stage 2: Infant – The BI function’s role is to inform executives, with several reports leading to “spreadmarts”
A ‘Gulf‘ separates Stage 2 and Stage 3.
- Stage 3: Child – The BI function’s role is perceived to empower workers, and this is the first evolution into an analytical system where OLAP and ad-hoc reports are used off data marts.
- Stage 4: Teenager – The BI function has evolved into a performance monitoring system by now, using Dashboards and Scorecards, supported by data warehouses.
A ‘Chasm‘ separates Stage 4 and Stage 5.
- Stage 5: Adult – This is where the ROI from the BI function shoots up, with predictive analytics answering what-if questions making the BI a strategic utility. The TDWI thinks that organizations’ BI architecture has evolved to have enterprise DW by now, with BI becoming a ‘Drive the Business’ function.
- Stage 6: Sage – The BI function at this stage has the highest ROI and decreasing costs based off Analytic Services (SOA) with pervasive BI (e.g. embedded BI) making it ‘Drive the market’
How does the above maturity model translate into a fully developed Business Intelligence solution architecture?
A typical organization is likely to first have data, in one or more data sources. This data may be the transactional database which powers a line-of-business application (such as CRM software) or it may be a collection of spreadsheets or other files. The organization has data (in some form) and is interested in using that data. The common entry point is to jump to the Presentation layer to create static reports using SQL Server Reporting Services, Excel/Power Pivot, or another presentation tool to access the data in its native form.
As the Business Intelligence needs grow, additional data sources may be added in which case other layers enter the picture. A Data Warehouse to hold the data from these various forms, in a format friendly for the presentation layer. Getting data into the Data Warehouse requires some form of ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) — which frequently is powered by SQL Server Integration Services (SSIS).
Of course, now that multiple data sources are being used, duplicate or somewhat duplicate data may be in play, so Data Quality checking via Data Quality Services or Master Data Management may be used to ensure data is high quality before being placed in the Data Warehouse. Additionally, OLAP Semantic Models (frequently using SQL Server Analysis Services (SSAS) in the form of multidimensional (MDX) or tabular (DAX) data models) enter the picture as a way to consolidate business logic in a single place and greatly enhance the speed of reporting, ability to slice and dice data, and use ad-hoc reporting. With all of these components and tools in place, the traditional enterprise Business Intelligence solution is complete and fully mature.