Informatica Perspectives

This is the last of the Data as an Asset series and what better way to wrap up the theme than with a view to the future. As stated by Thomas Redman, author of Data Driven, “Your company's data is a key business asset, and you need to manage it aggressively and professionally.” The future vision then is around Agile Data-Driven Enterprises. [Read more]

Continuing with the Data as Asset series, in this posting I explore the negative side and what can happen when data becomes a liability. Physical assets such as buildings, equipment, or even money, can become a liability if not managed properly; buildings can become unsafe to work in, machinery can be dangerous to operate, and business investments can turn into money-sinks. Similarly, data and information systems can also be assets that provide economic value to the enterprise or they can be liabilities that destroy value or put the business at risk if not managed well. Here are three common scenarios. [Read more]

My last post on this theme A Market-based Approach To Valuing Data, introduced the idea of establishing an internal data economy as a way to value and manage data assets. Here now is a specific scenario for how this could work. I apologize in advance for the length of the posting, so please bear with me. The level of detail is necessary to demonstrate how an internal market can function in a practical manner. [Read more]

As per discussions in prior postings on Managing Data as Assets, yet another method for valuing assets is to use market prices (mark to market). This method works best in a market for a homogeneous product where no individual buyers or sellers can affect those prices by their own actions. Securities such as stocks and bonds fall into this category. If you own 1,000 shares of Informatica stock for example, you know exactly what they are worth at any time since there is a highly liquid market for them – it doesn’t matter what you paid to acquire the securities (except for tax purposes) and you don’t have to sell them to determine their value – you only need to look at what they are trading for at the moment. [Read more]

Continuing from my prior post Valuing Data Using Managerial Accounting Practices, you don’t actually need to put data assets on the public balance sheets to achieve the desired management focus You could use internal management accounting methods such as EVA (Economic Value Add). In EVA, operating costs that have long-run benefits (such as R&D, brand advertising, certain IT investments) are recorded as assets rather than operating expenses. EVA is generally calculated by line-of-business as EVA = NOPAT – (WACC * NOC)

• NOPAT = Net Operating Profit After Tax
• WACC = Working Average Cost of Capital
• NOC = Net Operating Capital [Read more]

Some executives have told me that 40 to 70 percent of their IT budgets are devoted to integration. These are the same ones that still tackle integration on a project-by-project basis, causing unnecessary expense, waste, risk, and delay. They struggle with integration “hairballs”: complex point-to-point information exchanges that are expensive to maintain, difficult to change, and unpredictable in operation.

The solution is two new services that Informatica announced this week; Integration Factory Implementation and Automated Deployment Framework. These services compliment the Informatica platform and are another outcome of the work that went into creating the Lean Integration book which will be available in book stores soon (visit www.integrationfactory.com if you can’t wait). [Read more]

As a follow up to my previous post Data As An Asset Part 1 – Should Data be on your Balance Sheet?, let’s get back to the balance sheet question and ask it differently – how COULD an organization go about formally valuing their information assets?

There is a relatively straight-forward way to establish the initial value of data assets by simply adding up the costs associated with creating the data. In other words, we could quite easily figure out what it costs to initially create the data – the acquisition cost. Once we have the initial value established, we could use standard depreciation models such as straight-line or accelerated depreciation to account for the deterioration in the value over time. [Read more]

You often hear people say “Information is our greatest asset”. If that’s true, why can’t we find “data” assets on the financial balance sheet? Some organizations do indeed capitalize IT investments, but primarily in order to spread the costs over a number of years and not because they are valuing their data assets. Occasionally you might find data on the balance sheet such as the customer lists valued at $14.5B on AT&T’s 2007 financial statements – but again this was a one-time event related to the acquisition of Bell South and not because AT&T systematically values their data. [Read more]

In his whitepaper, Best Practices in Leveraging a Staging Area for SaaS-to-Enterprise Integration, David Linthicum outlines the difference between direct, point-to-point data integration and staging integration. He defines them as follows:

• Direct: “Moving information from one data source and data schema to another, and translating the differences in semantics from the source to the target system.”
• Staged: “A temporary location where the data from the source system or systems is replicated in order to support more complex and valuable data integration operations, including support for many large data sets and data operations that are more complex and of higher value.” [Read more]

Is data architect a role that's interchangeable with enterprise architect? Many observers see the two roles are overlapping to some degree. However, perhaps it’s time that data architecture be recognized for having a distinct role in today's enterprises.

With the rise of service oriented architecture and distributed computing, enterprise architects have been emerging as key players in their organizations – assuring that applications and systems are designed within a coherent framework and follow a roadmap designed with the business in mind. Now, the same discipline needs to apply to an enterprise's data resources. [Read more]