Part one of this article discussed the difference between traditional BI and embedded/operational BI and some of the business considerations to identify when looking to implement BI. Part 2 explores the technical and business considerations to take into account within both business and IT departments.
The key differences between traditional BI and embedded or operational BI is the way it is applied within the organization. The general structure of BI is the same, with data being captured and analyzed more often to enable decision making on a regular basis using daily data updates. These slight differences may affect an organization’s IT infrastructure. Therefore, organizations should identify potential technical considerations to discover the best way to manage BI within the organization while taking into account future expansions of both applications as well as users within the organization.
Technical considerations for BI differences – between traditional and OBI
Once an organization has a general understanding of BI’s main components and how it works, it becomes possible to decipher what technical differences exist based on the deployment of BI when general architecture may be similar or even the same. This means that although the same infrastructure may exist, the technical considerations required to adequately maintain a BI infrastructure can differ based on the way business intelligence is deployed within the organization. For the purposes of this article, extract transform and load (ETL), server space, and the frequency of transactions will be discussed.
The first step to delivering valuable results through business intelligence is to identify the data needed to make informed decisions. By looking at the business problem and working backwards to see which data is required to provide answers to business pains or to current gaps in knowledge, organizations can identify the data sources required. Addressing business issues might require a subset of information from five or ten different data sources. Within ETL processes, this information can be identified and loaded into the data warehouse in the form required for analysis. This means that raw operational data is transformed into an analytical tool that when added to front end analytical applications and reports can be used as an aid to solve the business issues affecting the organization.
Generally, organizations should be aware of more than just what information they require. In some cases, information used in general reporting is consolidated, have algorithms associated with output, etc. that make it almost impossible to create duplicate output without knowing the intricacies of how the data interrelates and how the original information was created. One example regularly used to illustrate this problem is when executives have planning meetings but the information they are coming to the table with differs from one another and creates a gap between what each executive sees as the organization’s reality. This example highlights the importance of having a resource that is familiar with the business rules associated with the data itself. Once information is added to the data warehouse a centralized source of reporting and analytics can be distributed to executives and other decision makers within the organization.
The freedom of BI enables end users to slice and dice data to create valuable information views and helps with the decision making process. However, if different versions of reports still exist within the organization, it becomes more difficult to identify which version is the most accurate.
Overall, ETL processes enable organizations to identify, to capture and to load relevant data into the BI infrastructure to use for more in-depth analysis. Organizations should spend the appropriate amount of time identifying the right data sources, what subset of information they require and the additional transformation requirements, such as data cleansing and ongoing data quality initiatives. Because data integration activities may encompass the bulk of initial BI efforts, strong processes should be put in place to ensure the ability to maintain as well as to change data input. In addition, as technologies evolve, the ability to access operational data stores and to interact with the data warehouse creates a different dynamic regarding how data is captured and managed. Consequently, as ETL needs are being identified, the various ways in which organizations can interact with their data should be identified to consider the most appropriate data capture and data management for the organization.