Business Uses and Applications for OLAP Software

What are some of the key business uses and applications for OLAP software that companies might want to look at and are there certain industries where analytics is more applicable than it is in others?

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Let me give a couple of examples of the types of analyses that go beyond basic reporting, and they cover a lot of different industries. A few industries are perhaps more well-known for the use of advanced analytics.

Lets start with the finance function. This cuts across industries. In the finance department, you see more use of planning and simulation types of business intelligence capabilities. Much of it starts out in a spreadsheet where people create formulas that take data and project into the future. The organizations cash flow and budgeting needs and income statement and balance sheet are projected out into the future using formulas that relate to different pieces of data.

You don't see as much of that in the deployment of business intelligence in other domains outside of finance, but there are certainly many opportunities to do such forecasting. The next most common area where we see advanced OLAP use is in the operations function. This is particularly true in manufacturing organizations because they have a lot of well known dependencies in the manufacturing process. You know how many things go into the manufacturing process, and how many things come out. You can express that relationship mathematically.

For an example of data mining let's look at the telecommunications industry. One of the big issues in telecommunications is predicting which people will leave your service. Its much more expensive to acquire new customers than it is to retain a customer so if you can spend some time and effort retaining your existing customers, that's a worthwhile activity because it's going to cost you a lot more to replace those customers.

Data mining is applied to look at the calling patterns of people using voice and data services and predict from their calling patterns and other demographic data if and when they might be likely to leave your service. This way you can make them an offer, some discounts, some free minutes the next month, something like that that would keep them on your rolls as a customer.

Given those examples we find that there are really only slight differences in the maturity of different industries. Perhaps the technology industry is a little more advanced. Since the technology industry has been generating some of these capabilities perhaps it is a little more disposed to using it. Government is a little bit behind. But there are not any huge differences separating industries at what you would call the innovative level.

Surprisingly finance, insurance and real estate have the fewest organizations at the innovative level, but again there are not huge differences. We see analytics being used widely across all segments, and in order of prevalence, it's first customer oriented analysis, second finance, then sales and marketing and then IT. It is widely used across the different industries.

How do you sell an analytics project internally before it gets off the ground? How do you go about building a business case for investing in data analytics?

The first question is where do you sit in the organization. If you're in the IT organization you absolutely need to get the business users involved and determine the business benefits and the return on investment. The return on investment and cost tend to be the biggest factors driving an investment decision.

If you're on the business side you need to gather data from within your organization and research that shows what other similar organizations of your industry and your size are doing. With this information you can then develop a clearly defined set of business benefits and the cost justification including what might happen from a competitive standpoint if you don't make this investment and your competitors do. Business and IT need to get aligned in order to successfully move these projects forward.

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Posted in Software Post Date 10/17/2016






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