Customer Experience and Design

Intelligent Taxonomies

In my past, I’ve had to perform Data Warehouse (DW) Assessments where we need to assess an organization’s DW maturity. In doing so, it’s important to categorize the things that you’re measuring to give it more context. Here is a two part blog where I provide a simple definition of BI and what categories are important to the organization. Given you agree on my categories, I will discuss how we might assess our IT strategy to determine how we align\mis-align to the objectives of business. Remember, we want our assessment to be objective, responsive to the business (quick turnaround) and accurate regarding the facts. So, let’s start by defining the terms of what we want to measure.

What is Business Intelligence?

Business Intelligence (BI) is a user-centric effort to provide access, exploration and the means to analyze raw data to improve a user’s insight and to develop a better understanding of the business. BI enables the organization to harvest the information from its legacy systems, to integrate data across the enterprise and to empower business users in becoming information self-sufficient.

I categorize BI into 3 distinct types.

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The Future of Big Data

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Type I – Reporting and Query tool sets. Type I is generally characterized by predefined or ad hoc queries. Technologists (Developers or Power Users) will set up reports based on business requirements or management’s communication on what needs to be explored based on time or some other criteria. The reports can made up of a set of charts, graphs, formatted layout and so forth. The data sources can be any Database, Multiple Disparate Sources (Oracle and MS SQL Server), or Multiple Homogenous Databases (2 or more Oracle RDB), a Web Service and any combination as necessary. It makes no difference as long as the users have the proper authority for access. These canned reports represent a moment in time. The underlying queries are known, and the data is mostly summarized and presented quickly. Type I reporting is something all organization will do in its initial DSS stages. Type I will answer strategic questions such as “What was my revenue by customer ABC for last quarter?” or “Who are my current Customers today?” Type I BI allows the user to get to know their data. Sometimes you’ll hear Type I referred to as ‘hindsight reporting.’

Type II – Analytic tool sets. Type II is generally where we take the raw data and begin treating it as a Corporate Asset. At this stage, we may want to transform the information in a Data Warehouse or a Data Mart into an OLAP cube. Today, some of the BI Visualization tools will dynamically create your visual BI maps based on the available Data Sources. By the way, OLAP is short for Online Analytical Processing, which is a set of software tools that provides the means for analysis of data (stored or otherwise). OLAP will segment data and analyze information by 1 or more dimensions (subject area of interest) such as by customer against a metric like revenue. OLAP tools enable users to analyze large volumes of data to gain better insight into your business. At a large, established manufacturer we built an OLAP application for the Business Community where we went from a 7 point market share and drove it to 14 within a 2 year time period. The stock price quadruple and the CEO attributed the significant gains by putting Type II toolsets into the hands of his front line warriors.

An OLAP cube has 2 primary components: dimensions and measures. A dimension is a subject area of interest to the business. Typical dimensions include TIME, PRODUCT, GEOG, and possible RETAIL (Trade Channels and Distributors). Also, the business will want facts in supporting of the dimensions. Facts, also known as measures\metrics will include counts and amounts such as revenue, volumes, physical inventory of product counts and so forth. Think of it as anything our customer wants to measure. This information is stored in an OLAP cube. OLAP cubes have various characteristics. MOLAP is built for fast access, least CPU intensive, and where the information needs to be updated on a periodic basis. ROLAP is built for access to ever changing data and its very CPU intensive. It will generally be pointed to a Data Mart (RDB). HOLAP allows for a combination of MOLAP and ROLAP. HOLAP cubes require more time and thought in their creation and maintenance. And some vendors will not offer an actual OLAP engine where the cube is considered a Virtual OLAP Cube (VOLAP), aka in-memory analytics. VOLAP is good for light OLAP needs. The difference between OLTP systems and OLAP is that OLTP systems help users capture the transaction information necessary to run their business operations, and OLAP systems analyze transaction information at an aggregate level to improve the decision-making process.

Type III – Predictive Modeling tool sets. Predictive modeling allows us to forecast where we think our business may be going. It creates a better means for managing “what if” scenarios so decision makers can identify critical facts and review the results against multiple of economy models. We want to deploy sophisticated statistical analysis, identify patterns and trends, and before our competition does. Users are generally more pro-active in this stage. Data Mining is used to determine where hidden patterns may exist to predict future behavior.

Now that you have a good understanding of BI categories, you might be asking, “How can we use it to improve our IT Services and garner a bigger year-end bonus for ourselves?” I like how you think. 🙂

My next blog will be on the importance of aligning our IT Strategy to our Business Strategy. And if no IT Strategy exists, how you can map it out like Lewis and Clark where you’re the early explorer mapping the terrain to determine the best route to the Gold Country. Also, I’ll address how to improve your BI Strategy in making it more effective and cost efficient.

About the Author

Michael has more than twenty years of Data Warehousing experience on multiple hardware platforms for various industries. Enterprise Architect for Data Warehouses and Marts at Fortune 500 Companies. He has extensive experience with ETL, Business Intelligence\DSS, SOA, and Data Quality related issues.

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