I, Business.

A common enough question, one that I myself found a little confusing to begin with. What are the differences between transaction databases and data warehouses?

In effect, a transaction database contains data that handles current operations. This data changes regularly, with the addition/cancellation of new operations (or transactions). A data warehouswe, on the other hand, is a respository for storing and analyzing numberical information - that can be updated as and when required (even regularly - hourly/daily/weekly, etc.)

Transaction databases will provide information about activities, whereas data warehouses will provide you information to plan out future activities. For example, a book store may have a transaction database to collect information about books that have been borrowed or bought. A data warehouse would consist of data that would explain the trends in borrowing books, as per duration, time of year, genre, etc.

A transaction database will consist of data for only one application, whereas a data warehouse will look to combine various transaction databases to provide a base for analysis of all the data.

While data warehouses deal with aggregate values, a transaction database will consist of specifics. Hence, DW will deal with quick retreival of aggregate data, whereas TDB will will deal with retreival of to-the-point data.

It is near impossible to define all the terms in Information Systems - new words and terminology come up every year. Couple this with the newer technologies booming, and it is easy to realize that IS is a vast field.

Computer-based information systems are developed and maintained continuously, under a field I shall henceforth pile under Systems Analysis and Design. Systems analysis contains many possible methods, and what a company uses depends upon the complexity of the IS, the company policies, time limits and other constraints.

The primary responsibility of analysing an IS falls on the shoulders of a Systems Analyst. I shall discuss this role in detail in another post, down the line. A systems analyst helps the system users and end users define their business requirements, hence tailoring the system to their needs.

There are many types of Information Systems - but they can be loosely classified into the following types.

1. Transaction Processing Systems

2. Management Information Systems

3. Desicion Supposrt Systems

Although the names are self-explanatory, i shall discuss these in slightly more detail in another post.

I shall also discuss the Systems Development Life Cycle (SDLC), and the various types of SDLC - from the primitive Waterfall model to the new and improved EXtreme Programming, Agile Technologies, among others. For starters, though, the SDLC is a common methodology followed by msot businesses today, to handle projects - right from inception and planning to execution and feedback from end users.

Source: Information Week

Author: Charles Babcock

Main article here.

Summary:

Came across this very interesting article, about the commons issues with a business' approach towards cloud projects.

The author discusses seven issues here.

1. Using the Cloud forces different functions in an organization to work together to define the end-result of data integration on the users.

2. It is difficult (nay, highly difficult) to integrate all of the enterprise's current applications onto the Cloud at once. Especially the current mainframe apps.

3. The cloud imposes simplification on what heretofore has been a self-reinforcing march of complexity in the data center.

4. The bigger your commitment to the cloud, the more you will be involved in Vendor Management, sometimes taking months to negotiate with Cloud providers. This may even derails the project, and is not a step to be contemplated lightly.

5. There is no sense in believing a vendor all the time, especially when he can promise a solution as simple as a plugin to enable an enterprise on the Cloud.

6. A project planner should be able to predict the costs involved in using more of the Cloud, which is not as easy as it looks: these costs may be dependant on a number of factors including the amounts of data, number of html requests, etc.

7. Adhering to strict policy making, and rigorous application of the standards implied are vital to ensure a smooth transition of the project from a traditional x86 machine onto the Cloud.

Well, this post was always going to be the first one, logically speaking. So let us take a look at this tag phrase, "Business Intelligence". that's been doing the rounds lately.

Lately? Well, maybe that's exactly true.

Accroding to Wikipedia, the term Business Intelligence (henceforth referred to as BI) was first coined back in the late 1950's.

To put it simply, intelligence that is acquired through reliable data, information that can be used to analyze market trends, customer behavior, sales forecasts - all can be coined under BI.

Notice that I use the word "reliable" in front of data. 

With the boom in Information Technology, we are seeing vast amounts of data floating around, both online and off. It is becoming easier (less expensive) to carry increasingly more data, in increasingly smaller hardware devices. Add to that the explosion of Cloud services, and we witness, one of the most exciting (and volatile) eras of information management ever.

So why did I say "reliable"? When we have so much data lying around, it only makes sense to realize that not all of it is useful. Nor is all of it true. Data Governance and Data Quality management are the new keywords that business want to associate with. Everyone wants to make sure that the best information is obtained, that can be obtained by the best analysts, to prepare for the most optimal business strategies. 

Information Security is another hot trend. Though it's always been around, the highly publicized (and probably chastised) Wikileaks is probably the best example, of millions of sensitive documents getting leaked without a trace. 2011 may well be regarded the stepping stone to better security measures. Firms are increasingly more secretive, with utmost care being taken of, when it comes to sensitive information.

We shall look into all these trends (and more!), along with an eye on the current happenings in the Information field, about Data Warehousing and Data Mining, and Cloud Computing.