Tag Archives: connectivity

How to Ace Application Migration & Consolidation (Hint: Data Management)

Myth Vs Reality: Application Migration & Consolidation

Myth Vs Reality: Application Migration & Consolidation (No, it’s not about dating)

Will your application consolidation or migration go live on time and on budget?  According to Gartner, “through 2019, more than 50% of data migration projects will exceed budget and/or result in some form of business disruption due to flawed execution.”1  That is a scary number by any measure. A colleague of mine put it well: ‘I wouldn’t get on a plane that had 50% chance of failure’. So should you be losing sleep over your migration or consolidation project? Well that depends.  Are you the former CIO of Levi Strauss? Who, according to Harvard Business Review, was forced to resign due to a botched SAP migration project and a $192.5 million earnings write-off?2  If so, perhaps you would feel a bit apprehensive. Otherwise, I say you can be cautiously optimistic, if you go into it with a healthy dose of reality. Please ensure you have a good understanding of the potential pitfalls and how to address them.  You need an appreciation for the myths and realities of application consolidation and migration.

First off, let me get one thing off my chest.  If you don’t pay close attention to your data, throughout the application consolidation or migration process, you are almost guaranteed delays and budget overruns. Data consolidation and migration is at least 30%-40% of the application go-live effort. We have learned this by helping customers deliver over 1500 projects of this type.  What’s worse, if you are not super meticulous about your data, you can be assured to encounter unhappy business stakeholders at the end of this treacherous journey. The users of your new application expect all their business-critical data to be there at the end of the road. All the bells and whistles in your new application will matter naught if the data falls apart.  Imagine if you will, students’ transcripts gone missing, or your frequent-flyer balance a 100,000 miles short!  Need I say more?  Now, you may already be guessing where I am going with this.  That’s right, we are talking about the myths and realities related to your data!   Let’s explore a few of these.

Myth #1: All my data is there.

Reality #1: It may be there… But can you get it? if you want to find, access and move out all the data from your legacy systems, you must have a good set of connectivity tools to easily and automatically find, access and extract the data from your source systems. You don’t want to hand-code this for each source.  Ouch!

Myth #2: I can just move my data from point A to point B.

Reality #2: You can try that approach if you want.  However you might not be happy with the results.  Reality is that there can be significant gaps and format mismatches between the data in your legacy system and the data required by your new application. Additionally you will likely need to assemble data from disparate systems. You need sophisticated tools to profile, assemble and transform your legacy data so that it is purpose-fit for your new application.

Myth #3: All my data is clean.

Reality #3:  It’s not. And here is a tip:  better profile, scrub and cleanse your data before you migrate it. You don’t want to put a shiny new application on top of questionable data . In other words let’s get a fresh start on the data in your new application!

Myth #4: All my data will move over as expected

Reality #4: It will not.  Any time you move and transform large sets of data, there is room for logical or operational errors and surprises.  The best way to avoid this is to automatically validate that your data has moved over as intended.

Myth #5: It’s a one-time effort.

Reality #5: ‘Load and explode’ is formula for disaster.  Our proven methodology recommends you first prototype your migration path and identify a small subset of the data to move over. Then test it, tweak your model, try it again and gradually expand.  More importantly, your application architecture should not be a one-time effort.  It is work in progress and really an ongoing journey.  Regardless of where you are on this journey, we recommend paying close attention to managing your application’s data foundation.

As you can see, there is a multitude of data issues that can plague an application consolidation or migration project and lead to its doom.  These potential challenges are not always recognized and understood early on.  This perception gap is a root-cause of project failure. This is why we are excited to host Philip Russom, of TDWI, in our upcoming webinar to discuss data management best practices and methodologies for application consolidation and migration. If you are undertaking any IT modernization or rationalization project, such as consolidating applications or migrating legacy applications to the cloud or to ‘on-prem’ application, such as SAP, this webinar is a must-see.

So what’s your reality going to be like?  Will your project run like a dream or will it escalate into a scary nightmare? Here’s hoping for the former.  And also hoping you can join us for this upcoming webinar to learn more:

Webinar with TDWI:
Successful Application Consolidation & Migration: Data Management Best Practices.

Date: Tuesday March 10, 10 am PT / 1 pm ET

Don’t miss out, Register Today!

1) Gartner report titled “Best Practices Mitigate Data Migration Risks and Challenges” published on December 9, 2014

2) Harvard Business Review: ‘Why your IT project may be riskier than you think’.

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Posted in Data Integration, Data Migration, Data Quality, Enterprise Data Management | Tagged , , , , , , , , , , , , , | 2 Comments

Garbage In, Garbage Out? Don’t Take Data for Granted in Analytics Initiatives!

Cant trust data_1The verdict is in. Data is now broadly perceived as a source of competitive advantage. We all feel the heat to deliver good data. It is no wonder organizations view Analytics initiatives as highly strategic. But the big question is, can you really trust your data? Or are you just creating pretty visualizations on top of bad data?

We also know there is a shift towards self-service Analytics. But did you know that according to Gartner, “through 2016, less than 10% of self-service BI initiatives will be governed sufficiently to prevent inconsistencies that adversely affect the business”?1 This means that you may actually show up at your next big meeting and have data that contradicts your colleague’s data.  Perhaps you are not working off of the same version of the truth. Maybe you have siloed data on different systems and they are not working in concert? Or is your definition of ‘revenue’ or ‘leads’ different from that of your colleague’s?

So are we taking our data for granted? Are we just assuming that it’s all available, clean, complete, integrated and consistent?  As we work with organizations to support their Analytics journey, we often find that the harsh realities of data are quite different from perceptions. Let’s further investigate this perception gap.

For one, people may assume they can easily access all data. In reality, if data connectivity is not managed effectively, we often need to beg borrow and steal to get the right data from the right person. If we are lucky. In less fortunate scenarios, we may need to settle for partial data or a cheap substitute for the data we really wanted. And you know what they say, the only thing worse than no data is bad data. Right?

Another common misperception is: “Our data is clean. We have no data quality issues”.  Wrong again.  When we work with organizations to profile their data, they are often quite surprised to learn that their data is full of errors and gaps.  One company recently discovered within one minute of starting their data profiling exercise, that millions of their customer records contained the company’s own address instead of the customers’ addresses… Oops.

Another myth is that all data is integrated.  In reality, your data may reside in multiple locations: in the cloud, on premise, in Hadoop and on mainframe and anything in between. Integrating data from all these disparate and heterogeneous data sources is not a trivial task, unless you have the right tools.

And here is one more consideration to mull over. Do you find yourself manually hunting down and combining data to reproduce the same ad hoc report over and over again? Perhaps you often find yourself doing this in the wee hours of the night? Why reinvent the wheel? It would be more productive to automate the process of data ingestion and integration for reusable and shareable reports and Analytics.

Simply put, you need great data for great Analytics. We are excited to host Philip Russom of TDWI in a webinar to discuss how data management best practices can enable successful Analytics initiatives. 

And how about you?  Can you trust your data?  Please join us for this webinar to learn more about building a trust-relationship with your data!

  1. Gartner Report, ‘Predicts 2015: Power Shift in Business Intelligence and Analytics Will Fuel Disruption’; Authors: Josh Parenteau, Neil Chandler, Rita L. Sallam, Douglas Laney, Alan D. Duncan; Nov 21 2014
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Posted in Architects, Business/IT Collaboration, Data Governance, Data Integration, Data Warehousing | Tagged , , , , , , | 1 Comment

Is The Cloud Real?

Many of us read reports from industry pundits as well as the latest industry rags and hear the “experts” describe the latest trends. A lot of these for the most part, don’t come to fruition. The most unfortunate part is that our bosses and internal customers read these reports as gospel and we spend our time debunking a lot of the myths.

An area that is much hyped is the Cloud. The question is whether or not this is fact or fiction. Today, I will focus on SaaS in particular, as even the word Cloud conjures up conflicting images. In my world, SaaS is real. The most compelling metric is that in 2010, the number of Cloud apps exceeded our on-premise apps, and the impact on our business and IT has been profound. Let me repeat, SaaS exceeds on-premise apps! (more…)

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Posted in CIO, Cloud Computing, Governance, Risk and Compliance, SaaS | Tagged , , , , , | 1 Comment