300% ROI in Data Migration Project Using Zendeux Data Integration Services

Often, in every IT project, there are hidden costs that are not accounted for that creep up and cost businesses a significant amounts of money.  During my previous roles as an executive at several Fortune companies.I realized that one must correctly account for all costs associated with an IT project to measure true ROI.

When we established Zendeux years ago, our basic premise and philosophy in doing business was to bring tangible value to our clients.  This is mainly because I had played the role of the client to so many vendors and consulting companies that tried to sell me benefits only, leaving me to guess the value!  That is why we always begin our engagements with understanding the true business objective and tangible value while measuring the direct and indirect costs of the project.

Zendeux Fast Track Data Migration Services

 

To this end, our data migration and integration services are designed to save our clients money related to resourcing, project time, and indirect costs.  Once we measure those costs, we then come up with the most effective approach to ensure positive ROI in their project due to costs saving and/or tangle business benefits.

I like to illustrate the success of this approach using a recent project we did for a large national real estate investment company with offices in all major US cities.  Before becoming our client, they were planning  to hire one or two in-house full time resources to augment their IT  team.  These new employees were intended to help migrate data from seven different legacy systems to Salesforce.com and then from there into their brand new in-house custom CRM system.  These systems were scattered across 25 locations nationally.

To untrained eyes, hiring in-house resources are certainly more cost effective than hiring an outside vendor to do the job.  However,  working together with the client,

our joint analysis of their costs and timeline estimate using the in-house approach VS our fact track services showed them how we could reduce the project timeline by 29 months and save them $408,000 in Salesforce subscription fees alone!  Overall, we showed a saving of about $570,000 and 300% ROI.

We gave them two fast track options, one with us doing the entire data migration project, or a hybrid model with a lower cost to work with their existing resources to complete the job in 6 months instead of 35 months!

We finished  the job on time and on budget!  By reducing the project timeline we managed to reduce the significant cost associated with interim license fees and project resource costs.  Our project management also significantly reduced non-tangible business costs by avoiding  a lengthy deployment of a new CRM system.

This approach is something we offer to all of our prospective clients. The presale analysis and ROI analysis is completely free of cost.  If we cannot show you an ROI, then we will at least leave you with a great costs analysis!

To learn more about our data migration and integration services, please feel free to contact us directly at info@zendeux.com or visit our site www.zendeux.com.

What Does Document Data Management Have to do with Big Data?

Document Data Management is the discipline that consists of processes, tools, and techniques that are used to define, model, discover, extract, integrate, standardize, normalize, report, and govern the data embedded within documents.  This should not be confused with Document Management which is mainly used to manage the actual documents within an organization.  Document management is more interested in the original document and preservation of the document while being able to locate or classify it as needed.

To that end, document management uses metadata to describe documents using several simple document attributes that help users classify and find it easily.  This approach is similar to how books are cataloged in a library.  However, Document Data Management is more interested in the content or data within the documents than the classification or archiving.

It is said that 70 to 80 percentage of data inside an organization is actually unstructured.  That means this type of data is not usually stored in tables or even spreadsheets and cannot be abstracted into attributes and fields.  Unstructured data is deeply embedded in the texts of many documents types such as invoices, purchase orders, sales contracts, maintenance narrative, and many more.  Sometimes these documents are actually stored within databases as long unstructured texts such as notes, comments, support case narratives, doctors’ notes, and others.

With emergence of big data and the availability of techniques to store, search, and mine unstructured data using big data tools and techniques, there has been a great increase in demand for discovering and extracting this type of data from documents.  Companies are now trying to extract valuable data from huge volumes of call center cases to understand customer sentiment, product defects, fraud detection, and many more powerful insights.

However, so far all such efforts have been quite organic and limited to data mining rather that data management.  The main reason for this limitation is that there has been no well defined disciplines or methodologies that describe how unstructured data within documents should be managed.

Document Data Management as a discipline tries to address this void by providing the methods, techniques, processes, and in short the science of managing document data. I started my work with document data since the late 1990’s when I was building my first form processing software called FormBase which extracted key data points from printed forms and stored them in a database.  It also could identify the type of form among many different types and archive it in the correct location inside a document management software.

Today we are dealing with large volumes of fully unstructured data such as real estate county records, medical records, oil and gas maintenance records, and other exciting but challenging unstructured data to manage.  But this time around, we are trying to build the disciple, methods, processes, and tools to allow us to define, model, extract, integrate, report, and govern document data.  The goal is to turn unstructured document data into a structured form where it can be fully integrated into the rest of the enterprise data which can then be used in operations and business intelligence.  Imagine enhancing the information breadth, depth, and insight of an organization by adding data that was till now untapped.

In my next blog post I will write more about what each aspect of document data management entails and how organizations can incorporate this discipline into their overall data management strategy.

Why Should Project Managers Know Data Management?

It is not surprising that most data management professionals at some point in time in their career have either played the role of an IT project manager or have worked as one in an official capacity.

In IT most technical changes including data related changes are implemented through a project in one form or another.  Now if we accept the notation that change is a constant reality in today’s businesses, then that means most technical changes in an organization may be implemented through a project.

Most if not all IT projects have some data impact or are impacted by data.  That is, whether they are rolling out a new software solution, hardware solution, reporting, integration, migration, and others, there are elements of data tasks involved.  These projects often require a delicate balance between complex tasks in data management domain.  For example, a software implementation project would need data architecture, migration, integration, quality, and governance.  This translate to many tasks and many resources as well as dependencies.

A project manager must manage all these tasks and understand their impact on resourcing, budget, timeline, solution, and much more!  Most IT projects can easily fail to meet these demands when one of these moving parts is out of order.

Project managers who understand these data management principles can clearly understand what is required in each phase and what to look out for.  They can manage expectation and impact with project sponsors and stakeholders to ensure a successful and smooth project delivery.

It is because of this that we recently developed a very specific data management training for IT project managers to train them in the fundamentals of data management as well as specific types of projects that they will be dealing with.  They can now use the same tools and techniques we use in data management to ensure a successful delivery of data solutions in their IT projects.

Last week we delivered the first of these training sessions in Indianapolis and it was attended by a number of seasoned project managers who found it to be extremely valuable (according to their survey and interactive feedback).  We are offering another one in Newport Beach, CA in September.  The goal is to have at least one per month in different cities.

If you are interested in learning more about these classes, you can go to our training page.

http://www.zendeux.org/training-courses/applied-data-project-management-training/

Till next time!

Majd Izadian

Copyright 2013, Zendeux Business Data Solutions

Data Project Management OC2

Big Data, New Hype, or New Reality?

IT industry is not immune to the “New Shining Object Syndrome” that impacts other industries as well as consumers.  Every once in a while we get a new technology or idea that grabs the attention of IT professionals and CIOs alike.

There have been many examples of these new ideas, some of which have endured the test of time and some that have not.  There was once a huge excitement around Object Oriented Programming (OOP), SOA (Service Oriented Architecture), BI (Business Intelligence), ASP (Application Service Provider), Master Data Management (MDM), and now Big Data!  Some of these ideas like OOP have been observed into the way we write our software where we no longer call it that.  ASP (not the same as Active Server Pages) has evolved into SAAS (Software As a Service) and hardly anyone remembers about ASP anymore.

Big Data however, has created a huge buzz lately and the excitement is very contagious and seems to have gotten the attention of the media much more than the previous trends.  The reason may be that unlike those other technical breakthroughs in the IT industry, this one has a strong business and even consumer impact.  Though many do not clearly understand what Big Data means, they have by now a vague notion that it somehow involves or impacts them.

News such as the NSA’s (National Security Agency) tapping into the metadata of domestic and intentional phone communications to Google, Amazon, and other companies profiling consumer’s every click on the web has awaken fear and excitement in normal consumers.  For this reason alone, it is no longer easy to ignore Big Data as a mere hype or “new shining object”, but perhaps a new reality which we are all forced to be reckoned with.

Big data itself and its potential is more analogous to the internet at its early days where only few understood its immediate potential and perhaps none could forecast its future potentials.  Like the internet, the early days of Big Data is only accessible to few and far in between, and like the internet, unless it is readily available to the common man, it will not fulfill its full potential and depth.

Therefore, if I were a betting man, I would bet on the Big Data.  But one should be weary of the hype, as like the internet, Big Data could find many .COM victims who may be blinded by the “new shining object” without knowing its risks and potentials of what makes it shine!

Copyright, 2013, Majd Izadian, CEO Zendeux Business Data Solutions

Big Data, Hype or Reality?