June 28, 2009

Data Management and Cloud Computing

LRobison_biopic Blogger: Lyn Robison

The graphic below is from my esteemed Burton Group colleague Dan Blum’s upcoming Catalyst presentation on Cloud computing.

As he explains in a recent blog post,: “As we move from left to right in the diagram and put more and more control in the hands of the service providers, the outlook shifts from fair weather green to ominous red.” 

CloudAndData  

The far-left column shows in green that a traditional enterprise IT department controls the entire technology stack with only the network shared with a service provider (because of the Internet). The next column shows that with server hosting providers, the organization shares control of the server, storage, and network functions.

Dan explains in his blog, “As we move from Infrastructure-as-a-Service (IaaS) with its line of demarcation in the server where the silicon stops, to Platform-as-a-Service (PaaS) where you cross the line after your code and applications are integrated with outside components, to Software-as-a-Service (SaaS) where you abandon all control when you hand over your data I paint the functions these services control an alarming red.”

This graphic illustrates that as cloud computing alters the IT landscape, data is the only thing that organizations maintain any control over. Ironically, most enterprises lack any formal data management function. IT people tend to think that their job is to manage technology and systems, yet data (not technology) is something that enterprises must manage as cloud computing becomes prevelant.

As cloud computing gets adopted, those enterprise IT people who think that their job is merely to manage technology and systems will find themselves no longer working in enterprise IT –- they will be forced to go to work for or to compete against cloud providers.

The Information Management track in the upcoming Catalyst conference will provide guidance for managing enterprise data, which is important because, as this graphic illustrates, data management might become the primary task of enterprise IT in the future.

June 23, 2009

A Data Management Freebie

LRobison_biopic Blogger: Lyn Robison

It’s not often that you get something for nothing, especially something valuable like innovation in silo bridging for large enterprises.

Guidance on overcoming the problem of data silos is particularly valuable because:

  • Data silos are a permanent fixture in modern enterprises -- silos exist because of organizational boundaries and because of the boundaries of information systems, applications, and databases.

  • Data silos prevent businesspeople from getting the information they need to make informed decisions and do their jobs. You can see examples here and here.

  • Efforts at silo busting, where silos are eliminated using SOA or enterprise-wide applications, are risky and expensive and usually don’t succeed.

  • Silo bridging instead of silo busting is the only sensible strategy.

The best way that I know of to bridge silos is to use MODS, the Methodology for Overcoming Data Silos. I am doing a free webcast on MODS. It is not magic, but it is inexpensive, low-risk, and delivers compelling results. You can find out about it here:

You can get the overview of MODS here. You'll need to register to download it, but it's a simple process. I hope that you find this information valuable. Lemme know what you think!

June 22, 2009

Data Integration that can actually Work

LRobison_biopic Blogger: Lyn Robison

Recently, I watched an interesting documentary about Worldport, the worldwide hub for UPS in Louisville, Kentucky. It is obvious that shipping companies such as UPS have conquered the data integration problem, and offer a vital key for the rest of us.

UPS has multiple computer systems at Worldport, multiple computer systems at each of their regional hubs, and handheld computer systems for each of their drivers. These computer systems are silo-ed at UPS, just like computer systems are silo-ed in any other large enterprise, and as a result, each package enters and leaves many data silos on its journey from its origin to its destination. Yet UPS is able to provide an integrated, 360-degree view of each parcel as it moves through UPS’s shipping lifecycle. How does UPS do it?

One thing they do -- and this is a key for any enterprise that is looking to integrate operational data from silos -- is this: they identify each parcel.

That’s it. That is the big secret. They identify each parcel beyond the bounds of any data silo. They don’t waste hundreds of thousands of dollars trying to eliminate silos by doing SOA. They don’t replace all of their little silos with one big silo by implementing a risk-laden and hugely expensive ERP or CRM system. They simply identify each parcel. They give each parcel a tracking number by which it is known within all of the IT applications, information systems, and databases throughout UPS. Because each parcel is known in all information systems by its tracking number, UPS can pull together information about each parcel from all of their data silos, on demand.

Assigning each parcel a unique identifier is no doubt cheaper and a lot more effective than implementing SOA or a CRM system. We ought to do that in enterprise IT. We could give a unique identifier to each thing that we want to keep track of: each customer, each product, each supplier, each policy, each asset, each employee, each project, each decision, each work activity, etc.

If you knew and if everyone in the enterprise knew that every system that had any information about any of these individual things would reveal that information based on that thing’s identifier, data integration could almost be easy. Okay, maybe not easy, but certainly easier.

It turns out that in data integration, which one is almost more important than what kind? Any enterprise that identifies its non-fungible assets with unique identifiers can do silo-bridging instead of silo-busting, and will be better prepared to transition to cloud data management when the time comes.

Identifying important instances of data is one of the pillars of Burton Group’s MODS. Stay tuned for more guidance on MODS at Burton Group’s upcoming Catalyst conference.

BTW, we have a secret discount to Catalyst available to readers of this blog. To get the discount, here's what you do:

1. Go to the Catalyst home page (http://catalyst.burtongroup.com/). Either: click and then drag your mouse off the logo and release the button. OR: roll over the San Diego button but do not click, wait about 20 sec.

2. A message will pop-up stating "Congratulations! You’ve found an exclusive discount code for Catalyst 2009. Use promo code: Easter Egg and get General Sessions for only $999! Register today – this discount is limited to 50 users and could disappear at any time!"

3. Register.

That's it. Hope to see you at Catalyst!

June 17, 2009

“Happy Talk” from an IT Industry Analyst

LRobison_biopic Blogger: Lyn Robison

I’ve never been accused of too much happy talk about the current state of enterprise IT, or about its future. My A Crystal Ball shows the Future of IT, and it is … Detroit?! blog post is a prime example of my lack of happy talk.

Upon closer examination, however, you will realize that I am not all gloom and doom about enterprise IT. My blog post Enterprise IT need not end up like Detroit stikes a positive chord and my post The Economy, Innovation, and the Future of IT is downright rosy in its outlook. I am actually quite bullish on the future of enterprise IT – I am just not bullish about the future of enterprise IT in its current form. Enterprise IT is headed for some big changes in the next five to ten years.

If you wish to keep doing what you’re doing within your role in enterprise IT, mine is probably not the blog you should follow. IT people who hope to ignore the future and continue merrily working inside their cubical will be blindsided by the waves of change that are coming to enterprise IT. On the other hand, if you would like to be forewarned about the changes that the future will bring to the careers of enterprise IT people, I will do my best to tell you about what I see coming.

June 15, 2009

The Economy, Innovation, and the Future of IT

LRobison_biopic Blogger: Lyn Robison

For years, the political leaders of the United States (in both parties) have pandered to the wants of the electorate with reckless abandon, and as a result, the federal government now faces perilous budget deficits. The good news is that, as incredible as it sounds, enterprise IT has actually contributed in the past to solving this problem by significantly increasing the productivity of the U.S. economy. I will explain how in a moment, but first let’s review the bad news. 

A recent article in the New York Times entitled, “America’s Sea of Red Ink Was Years in the Making” quotes Alan Auerbach, an economist at U. C. Berkeley, who says, “Bush behaved incredibly irresponsibly for eight years. On the one hand, it might seem unfair for people to blame Obama for not fixing it. On the other hand, he’s not fixing it. And,” he added, “not fixing it is, in a sense, making it worse.” The article continues, “What, then, will happen? ‘Things will get worse gradually,’ Mr. Auerbach predicts, ‘unless they get worse quickly.’ Either a solution will be put off, or foreign lenders, spooked by the rising debt, will send interest rates higher and create a crisis.” The article also states, “That is the legacy of our trillion-dollar deficits. Erasing them will be one of the great political issues of the coming decade.”

Fortune magazine recently published an article entitled, “The next great crisis: America's debt”. The article states, “Within a decade the average household that pays income tax will owe the equivalent of $155,000 in federal debt, about $90,000 more than last year.” It goes on to say, “It can't go on forever, and it won't. What will shock America into action is the prospect of fiscal collapse, which will grow more vivid each year.” The article paints a bleak future in which big entitlements frustrate any real prospect of reducing the deficits. Our future looks bleak, and there appears to be no way to avoid it.

One hopeful note in this bleak picture of our collective futures is the positive impact that enterprise IT had on the U.S. economy during the 1990s. On April 5, 2000, in an address entitled "Technological innovation and the economy", Alan Greenspan basically gave enterprise IT the credit for the largest economic expansion on record. He asserted that “something profoundly different from the typical postwar business cycle has emerged in recent years. Not only has the expansion reached record length, but it has done so with far stronger-than-expected economic growth ... While there are various competing explanations for an economy that is in many respects without precedent in our annals, the most compelling appears to be the extraordinary surge in technological innovation that developed through the latter decades of the last century.” Mr. Greenspan explains that IT (specifically, ERP systems) succeeded in the 90s in materially reducing “large swaths of inventory safety stocks and worker redundancies”. He observed, “In short, information technology raises output per hour in the total economy principally by reducing hours worked on activities needed to guard productive processes against the unknown and the unanticipated.” During the 1990s, we used enterprise IT to innovate our way to economic prosperity.

That innovation really worked. Federal tax revenue increased during most of the 90s, and the federal government actually ran a surplus in 1999 and 2000. The CBO estimated then that the government would run surpluses of more than $800 billion per year from 2009 to 2012. (Unfortunately, the government will actually run a $1.2 trillion annual deficit instead: a $2 trillion swing in the wrong direction. A little more than a third of that negative $2 trillion came from an economic downturn, a third came from legislation signed by President Bush, and a little less than a third will come from President Obama’s extension of several Bush policies, the stimulus bill, and Mr. Obama’s agenda on health care, education, energy and other areas.)

My point in all of this is that enterprise IT accounted for vast increases in the output and efficiency of the U.S. economy during the 90s. We innovated our way to economic prosperity once -- perhaps we can do it again. Doing so will require another “extraordinary surge in technological innovation”.

Mr. Greenspan referred to a “revolution in information availability” that occurred during the 1990s, which makes me believe that the technological innovation we seek will not come through technology technology, but rather through information technology. IOW, our technological innovation needs to bring about another revolution in information availability.

In my blog post A Crystal Ball shows the Future of IT, and it is … Detroit?!, I predicted that enterprise IT will face the same fate as the American automotive industry if enterprise IT continues to focus on the production of technology instead of the production of information. I stand by that prediction, and Mr. Greenspan’s comments about the 90’s “revolution in information availability” seem to harmonize with my assertion about the importance of information. Now, I hereby predict that enterprise IT can indeed revive the entire U.S. economy and avert a federal budget disaster if enterprise IT will focus on the production and delivery of useful information to the businesspeople who need it to make decisions and do their jobs.

This revolution in information availability needs to be the opposite of the failure in information availability that I delineated in my Smoking Gun blog post, which materially contributed to the subprime mortgage crises, whose ripple effects precipitated the current recession.

The 1990’s revolution in information availability came as a result of the conquest of data silos in the supply chain. The next revolution in information availability will come as a result of the conquest of data silos throughout the enterprise.

The conquest of data silos should be paramount in the minds of enterprise IT leadership and staff. At Burton Group’s Data Management Strategies service, overcoming data silos is paramount in our minds, and we are providing guidance to that effect. We recently published an overview entitled “The Methodology for Overcoming Data Silos (MODS): Using the New XQuery Development Stack” and we will soon publish another overview entitled “Delivering Integrated Information from Data Silos Using MODS”. These overviews point the direction of the innovation that enterprise IT must pursue for its own sake and for the sake of the economy at large.

June 10, 2009

The Seven Top Data Delusions

The world of data is full of delusions - false beliefs or ideas about data. These are fueled by the mountains of data related white papers, articles, blogs, and marketing material. If I "google" any data topic, like master data or BI, millions of hits are returned. As I skim through these, nearly all are regurgitations of the last – thus the data delusions continue to grow. It is interesting how much is assumed to be true if we read it in print.

Below are the seven most popular I continue to see:

Data Delusion One

: If the data is there then it must have been deemed good data. There are not secret data police monitoring the data in most organizations. A large percentage of incorrect data lives within the data stores.

Data Delusion Two

: If it looks right then it must be. Typically, data is considered "poor quality" when it obviously looks incorrect or is known to be incorrect. Often data can "look" right, when it is not. How do you know if the answer returned when you ask a question, using a computer system, is correct - you would not need to ask if you knew the correct answer?

Data Delusion Three

: A new tool/technology will fix the data problems. There continues to be a belief that the tools/technology will auto-magically figure out if the data is correct or belongs together. Unfortunately success is always dependant on the quality of what goes in– garbage in, garbage out is still true.

Data Delusion Four

: Data is a computer phenomenon like software or hardware. Many of the definitions support this, but data has existed for longer than before computers were ever imagined. Data is a representation of the real-world organization, its things, people, locations and events. Computers help to automate the processing of data.

Data Delusion Five

: "Cleaning" the data fixes it. There is always a reason data becomes corrupted. It just does not magically happen. Data errors or poor quality data are a symptom of a problem, rarely the problem itself. Fixing a symptom does not fix the problem - it’s like taking an aspirin for a brain tumor.

Data Delusion Six

: The data meaning can be deduced from its name/definition. Even in the rare case when a data store has been diligently modeled from a business standpoint and implemented accordingly, the data system deteriorates over time. Many of the data stores in our organizations have never been designed / modeled in the first place. The data field names and sparse definitions were often the best guess by the programmer at the time. `

Data Delusion Seven

: Data can be managed/integrate/cleaned at an individual attributes/columns level. The data attributes/ columns are intended for description purposes. They are relative to what they are describing, as well as to the relationships/ dependencies of the things they are describing. When data attributes/columns are taken out of this context and treated indiviually, they can lose much of their meaning, and thus integrity.

June 08, 2009

Increasing the Mileage of Enterprise IT

LRobison_biopic Blogger: Lyn Robison

The best way for an enterprise to improve their IT mileage is to get more from their existing systems, and the best way to get more from their existing systems is to do a better job of managing data.

IOW, to get more out of IT, enterprises don’t need to implement new IT systems; all they need to do is manage data more effectively in their existing systems. Competent data management will breathe new life into old systems and will add polish and shine and power to mainstream systems.

Economic conditions are making it difficult for enterprise IT groups to undertake expensive and risky software development projects. Smart IT groups are looking instead at low-cost projects that offer a large bang for the buck. Data management projects, particularly MODS projects, give the high IT mileage that these lean times demand.

June 04, 2009

Is IT like a $300K watch that can't tell time?

LRobison_biopic Blogger: Lyn Robison

I just found a $300,000 watch that can’t tell time. (See here and here.)

An article about it says, “This $300,000 watch does not tell the time. In fact, all it does is tells you whether it is day or night, a handy feature if you spend countless hours in a windowless room. But the watch makes up for in looks, what it lacks in basic function.”

This will be great for a metaphor in one of my talks in our upcoming Catalyst conference about fancy technology and information systems that don’t deliver any useful information. Information quality metrics are the way to avoid having expensive information systems that don't deliver information.

June 02, 2009

The Business Intelligence Investment: Context

Blogger: Marcus Collins
In my recent post I detailed the critical success factors for a successful analysis initiative namely:

  • Having the right analysis process
  • Having access to the right information
  • Having the right context
  • Having the ability to make decisions at the right time
  • Having the right leadership
  • Having the right team dynamics
  • Having the right people

And finally:

  • Choosing the right problem

In this post I’m going to explore the “right context” in more detail. Whilst it is true that “All men are created equal”, the same cannot be said of analysis problems. It is important to determine the context of the problem as this brings with it a set of constraints and implicit assumptions that the analyst must understand and consider as they perform the analysis.The key elements of decision context are the:

  • scope
  • complexity
  • timeliness

Scope – is it an operational decision for example analyzing customer purchases to determine customers with the potential profit; simulate supply chains to reduce overall inventory levels and order-to-delivery times? Or is it strategic in nature for example deciding whether to move into a new market or embark on a strategic acquisition? Strategic and operational decisions have a different set of characteristics:Strategic Decisions

  • Long Term
  • Historic data
  • Internal and External perspective
  • Enterprise-wide data focus (information politics)
  • Focus on analytics and interpretation/heuristics
  • Poor data/information quality medium/high impact on decision
  • Long term feedback loop – enterprise’s goals/mission statement

Operational Decisions

  • Short Term
  • Real-time data
  • Internal perspective
  • Business unit or function data focus
  • Focus on analytics; less focus on interpretation
  • Poor data/information quality – high impact on decision
  • Short term feedback loop – focus on operational metrics

Complexity – problem can increase in complexity quickly and expand the scope of an analysis that is quantitative and focused on operational data to one that relies on the judgment of the analyst. The key is to be cognizant that the analytics guides the decision making.

Sales Forcast Complexity Take this sales forecast. The data shows that Q1/Q2 has seen steady growth. A simple “interpretation” might assume that Q3 would also show steady growth. But let’s assume that Q3 shows a marked drop in sales in – will Q4 follow Path A (a return to Q1/Q2 growth) or Path B (a continuation of Q3’s falling sales). To answer this question may require data outside that which is normally available from operational systems. For example, if this was a weather dependent product or service you may need weather data to see if there’s a correlation between sales and temperature. A more complex correlation might be an increase in fuel costs leading to a drop in toll road usage. In both examples the decision required an analysis of the operational data and judgment/intuition on the part of the analyst.The final aspect of context is the Timeliness of the data. I explored this aspect in this blog post – analysis when the business needs it. The traditional view of business intelligence is of operational systems feeding a data warehouse through an Extract, Transform and Load (ETL) data pipeline. Little has changed with this model since it was initially proposed in the 1980’s. Increasingly organizations are realizing that the retrospective view of data this model supports is not sufficient to meet the demands of companies that need to function at internet speed. Rather than following this one speed approach organizations should adopt a tiered approach tailored to the organization’s business requirements, competitive environment and customer demands.

In a document to be published in early June and a series of TeleBriefings on June 2 and 3, Burton Group Senior Analyst Marcus Collins will explore the analysis context and each of the other critical success factors in more detail and provide guidance on how organizations can develop a roadmap for the successful deployment of a fact-based decision making culture.

May 28, 2009

The Business Intelligence Investment: The Analysis Process

Blogger: Marcus Collins

In my recent post I detailed the critical success factors for a successful analysis initiative namely:

  • Having the right analysis process
  • Having access to the right information
  • Having the right context
  • Having the ability to make decisions at the right time
  • Having the right leadership
  • Having the right team dynamics
  • Having the right people

And finally:

  • Choosing the right problem

In this post I’m going to explore the “right analysis process” in more detail.

Organizations operate in a high competitive and regulated environment and so efficient and repeatable business processes are integral to an organization’s well being. This applies equally to the analysis process. The analysis process should be tailored to meet the requirements of both the organizational culture and the context of the analysis. For example, a move into or out of a market segment would require a more thorough analysis that a series of experimental what-if scenarios.

The process is shown below:

BI Process + Learning Frame the problem – requires the analyst to have a clear understanding of the problem with the problem boundaries defined and it decomposed into its constituent parts.

Design the analysis – is the selection of the appropriate technique or framework. For example, to identify the most balanced decision amongst candidates the trade study would be used.

Gather the data – identifying data that is relevant to the problem and determining the source of this information and, importantly, determining what information is not available. Data quality metrics are important as they allow the analyst to evaluating the impact of the quality metrics on the analysis output.

Execute and interpret – two different skills are used here. Execute requires an analytic discipline with a focus on quantitative fact and rule-based logic. Interpretation emphasizes human judgment. Both techniques should be used in this step of the process. The output of this step will either be actionable and the process will move onto the implementation stage or not actionable and the process will move back to the start and refine and/or reframe the original problem statement.

Implement – in context of business intelligence this is the implementation of business change. A key recommendation here is that the analysis is not the act of making decisions; rather it is a single factor in the justification for a decision. The actual decision will be a combination of quantitative information, qualitative information and human judgment.

Measure – the traditional IT view of measurement focuses on efficiency and cost. Within the context of the business intelligence initiative the focus should shift to measuring business value.

Continuous learning – the analysis process is iterative and key to the success of this is culture of continuous learning. Organizations should encourage a culture of learning through both successes and failures.

In a document to be published in early June and a series of TeleBriefings on June 2 and 3, Burton Group Senior Analyst Marcus Collins will explore the analysis process and each of the other critical success factors in more detail and provide guidance on how organizations can develop a roadmap for the successful deployment of a fact-based decision making culture.

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