Enterprise Analytics: A Business Decision

The last three years have seen advances in efficient data mining algorithms, and computing advances that allow software companies to provide powerful analytical tools that were not available to the business community some years ago. Whether we refer to this type of software as data mining, predictive analytics, business intelligence, or analytics (web or business) their purpose is to efficiently detect patterns in large datasets that can lead to increase revenues or lower costs. The purpose of this article is to give a general framework to businesses regarding how to measure the different analytics solutions available in the marketplace.

First, let us make the difference between an analytical tool and an analytical solution. Most companies need an analytical solution instead of an analytical tool. Software vendors are in the business of selling analytical tools (SAS, Microsoft, Oracle, Business Objects, and SPSS). All these companies are using state-of-the-art science and technology to create powerful analytical tools. A hammer, a saw, and nails in the hands of a master carpenter can create a beautiful house. Those same tools in somebody else hands are something that you put in the garage. Therefore, the question is do you need an analytic tool or do you need an analytic solution. If your IT staff has not built an advanced analytical decision support system in the past what you need is a solution. If they have this experience then what you might need is a new tool. One way to tell the difference is whether the sum, or average (or mean) are the most common measurements used in your organization. If this is the case, you need an analytics solution and not a new tool.

Most carpenters will tell you that some people have all the tools needed to build a house in their garage. Therefore, before going out buying new tools you need to know: 1. what do you want to build? 2. What skills do you have in designing and building an analytical system? What tools you already have in your IT department?

Alignment with specific business objectives
What do you want to build? The answer to this question is that you want an analytical solution which is aligned with your organization strategic and operational objectives. This is one of the keystones of an analytical solution: it must match strategic and operational objectives. An enterprise strategic objective will represent the schematics or floor plan of your analytical solution. The enterprise operational objectives will represent the plumbing and the electrical systems of your analytical solution.

An analytical solution must ultimately represent the vision of executive management, while simultaneously be efficient to operate for those responsible for day-to-day operations. We know that a CEO can write his strategic vision even in a napkin and the role of his team should be to translate this vision into a series of strategic and operational goals. Hence, the importance that the overall strategic design of an analytics solution starts at the CEO and executive management team level. The decision to build an enterprise analytics system is an executive decision. A CEO thinks in terms of analytic solutions to business problems, and the IT department tendency is to think in terms of tools.

An experience analytics architect should be able to turn the executives input into an analytics solution prototype with some additional research and input from operations. This prototype should be detailed and must be approved by executive management before proceeding with specific business requirements and technical design. See,http://www.youtube.com/watch?v=bVmLfCajDjI.

Encompass all relevant data
Your data is the equivalent of your raw materials (wood, stone, bricks, pipes, flooring, carpets, and drywall). Since not all your materials are the analytics solutions architect and the lead software developers need to have the proper experience with large, diverse and complex datasets. Not all the houses are the same but the materials used are mostly the same. The same logic applies to the designing and building of an analytical solution. It is important not to confuse the quality of a tool to the quality of the materials. The tools can make a job easier for the builder, but is the quality of the materials in the hands of an expert builder that is going to make an average house an outstanding home.

The skill level of technical personnel varies and is as diverse as the number of home builders in the country. Some IT departments are made of technical staff that can give maintenance to current operations. Other IT departments have a specialized area for development, and others have fully functional project management offices (PMO). Very seldom do IT departments have an integrated analytics expertise (statistician and actuaries) and specialized software analytics developers (data cleansing, OLAP, and interactive visualization) within their departments that would allow the creation of advanced analytics solutions.

Flexibility of use for different business users
A robust analytical solution needs to be flexible for the different business users within an organization, and be adaptable to future analytical needs. Some users are interested in strategic objectives and others in operational objectives. An enterprise analytics solution should meet both strategic and operational demands. A keystone of an analytical solution is that it should allow different business users to have access to it. A business problem may have different potential solutions and analytical solution should take advantage of different perspectives from different users (human interaction) to identify potential solutions.

Also, it should have the capacity to accommodate future growth and changing business needs. The current business analytics requirements of an enterprise may be different from the analytical needs of the future. An analytics solution needs to be design with the capacity to growth, and the flexibility to accommodate unforeseen business issues.

In conclusion, a business analytics solution should cover the fundamentals:
1. Emphasize the alignment of the strategic and operational objectives to the analytic solution instead of the analytical tool;
2. Make sure that you have the correct materials (staff and data) to build your solution;and
3. Design a flexible analytical solution that could be used by strategic and operational users.