WhereScape New Features and Benefits

July 15, 2015

View Raphael Klebanov's profile on LinkedIn


Many new features are added in every RED and 3D release; it is hard to catch up with fast-paced WhereScape software. This article aims at shedding light on those new features so our customers and prospects have a comprehensible picture as to why they would want to use them.

WhereScape New Features and Benefits


Information Systems Challenges and WhereScape Solution

February 26, 2015

View Raphael Klebanov's profile on LinkedIn


A modern information system serves as an integrated set of components for collecting, storing, and processing data and delivering knowledge. Building an information system – whether it is operational store, data warehouse, or accessing layer et al – has always been a difficult task. WhereScape – as productivity tool – provides a holistic answer for information systems build consisting of Automation Solution, Iterative (Agile) Solution, and Integrated Solution.

IS Challenges WhereScape Solution

Integrate Tableau with your Data Warehouse

January 30, 2015

View Raphael Klebanov's profile on LinkedIn           tableau logo


With WhereScape RED’s Tableau Integration Suite, you can automatically find the impact to your Tableau workbooks of any Data Warehouse change.You can also create Tableau extracts by drag and drop, send Tableau objects to a Tableau Server and refresh any objects on the Tableau Server as part of you data warehouse schedule.

RED Tableau Data Sheet

Seven base reasons why organizations build data warehouses

May 10, 2009

In this short blog, I am bringing to your attention the list of principal causes, which lead companies to implement data warehouses. The reason why I wanted to put these reasons in one blurb is that too much material about data warehouses is dedicated to advantages of using warehouses indirectly. However, the warehouses’ existence, in fact, is the direct consequence of the resolving business problems. Look through for marketing materials available in the Web. You will see that folks are using data warehouses for important yet simple reasons like “to be closer to the customer”, “to transform of the raw data into business intelligence information, “to make decision-making, based on facts, instead of on intuition”, and, possibly, most often mentioned rationale, “to get an edge over the competitors”. Actually, in 99% of projects the data warehouse itself is only one of the steps on a way to achieve the declared goals.

In reality, the main causes to persuading organizations to introduce a data warehouse are:

1. Need to perform analytical inquiries and reports generation utilizing computing resources that are not yet taken by the core information systems

The majority of companies desire that process scheduling to be setup in such way that the probability of the transactions to be completed within a practical time is reasonably high. Reports and inquiries can demand much more processing resources including, among other things, disk and memory. Therefore, running reports and inquiries on the systems that are occupied with transactional processing, severely reduces likelihood of timely completion of such reports and queries. This, in turn, threatens the completion of the business operations.
In other words, performance of analytical inquiries and generation of reports on the servers occupied with transactional systems creates a big problem of processing of transactions in a comprehensible time. The companies conclude that the least expensive and/or organizationally the most simple and fast way of maintaining a high speed of work on the basic systems consists in the introduction of the data warehouse on a separate server with its own disk and memory.

2. The necessity of implementing data models and technologies that accelerate process and increase performance of inquiries and reporting, but not those intended for processing of transactions

There are some ways of designing the structure of the data, which usually accelerates performance of inquiries such as “star” schemas and derivatives of that. However, on the other hand, these structures are not suitable for transactional systems due to reduced speed of processing transactions. There is also a number of technologies which are good at accelerating performance of inquiries and but are not tailored for OLTP (for example, bit indexes) and, on the contrary, applicable only in OLTP (restoration of transactions).

3. Creating environments in which even rudimentary knowledge of RDBMS is enough for creation of inquiries and building reports. It means a reduction of time, cost and risks that the IT personnel demands for support of system

As a rule, OLAP (to be exact the “star” schema and its derivatives) simplifies reports, data warehouse inquiries, and hence, requires less knowledge from the employees working with system. Despite the fact that end users still face problems in preparation of reports and require help from the experts in the IT Department, it is much easier and faster to prepare the necessary reports based on the warehouse data, rather than on transactional database. Notice that the big role in the increase of efficiency of work for the IT Personnel occurs from reduction of the procedural delays arising at the interaction of end users with the IT Department.

4. Creating a source with previously cleared information

The data warehouse gives the possibility of improvement of information quality without changing the data in the transactional system. Clearing of the data is accomplished at the stage prior of loading the warehouse. Moreover, notice that some installations of warehouses allow possibility of updating of the data in the primary sources based on the corrections, which have been carried out at the stage of loading of the data in warehouse.

5. Simplification of the process of report building based on the information from several OLTP systems and/or external sources of the data used exclusively for BI purposes

For organizations which need to prepare reports on several sources of the data (this is the most common case), it is necessary to do an unloading of data from the source, re-sort, “massage” and “cleanse” the data and only after that build the report on the received dataset without using the warehouse. In some cases, it is an adequate strategy. However if the company has great volumes of information required to be mixed, often if the data received from several transactional systems, and it is necessary for generation of reports, and, if the data need to be “clean”, the data warehouse will be most “correct” solution.

6. Constructing the allocated source (dedicated server) when the OLTP systems do not match up to the frequency of data storage required by the business and/or the possibility of needing to prepare reports for certain moments of time in the past (“as was” reporting).

For accelerating the response to inquiries about the data gets removed after a certain time from the transactional systems. For maintaining performance of inquiries and reporting, the historical and current data can be stored in the allocated warehouse that will provide the necessary productivity, both analytical (OLAP) and transaction systems (OLTP).
Building of reports for a certain moment in time (“as was” reporting) is extremely difficult in some cases or even impossible. For example, if you need to a report on salaries of employees with a certain educational level “1234” on some corporate scale for every month of 2005, but you cannot do it, because the only educational levels stored are for the year 2009. For similar problems to be resolved within a company, it is necessary to create data warehouse, which will help by using slowly changing dimensions (SCD).

7. Protection of the end users from being involved in any degree with the underlying structure and logic of how the DB and OS function

Usage and business analysis systems and all mechanisms of processing and storing data allow the hiding of the data warehouse from the end users. A push to more dense analytical work with the information, from outside management and analysts, means an increase in the degree of efficiency by corporate information activities.
So what about the business purposes?

Some the companies create warehouses for the decision of only one of the above-named problems, others face the full list. However, in no event it is impossible to say that the building of a data warehouse solves only particularly technical problems and does not pursue the business purpose.
While you looking at the list, note that the requirement for data warehouse stems from the limitations imposed by transitional system. In certain conditions, these limitations are not apparent; but they are there; one thing is clear: the warehouse of the data, to some extent, is necessary for each company and its introduction – a matter of time.
Revenons à nos moutons, “Let us get back to our sheep”. The company is seeking the best support of decision-making, getting ahead the competitors, wishing to become closer to the customer and for this purpose decided to quickly duct-tape data warehouse can be very surprised by a negative result. For the achievement of these purposes it is required, that the company has understood, usually by trial and error, how to change running the business for the most effective utilization of date stores, data warehouse and data marts. Moreover, it can appear to be more of a challenge than one would anticipate.

A Different Approach to a Data Warehousing Effort in a Shaky Economy Times

February 23, 2009

With the economy decelerating all over, the needs for business intelligence swell; decision-making becomes crucial.  It is not about making mega-bucks any longer but rather about survival of the entire business.


Nobody is questioning the vital necessity for a successful business to constantly monitor a company’s performance, its profitability, its best and worst customers, its best and worst products and its overall health  especially now, when resources are so stretched. The driving force behind such business intelligence functions is a healthy data warehouse.

Nowadays the question is this: “How to achieve the same – and more for less?” How to build such a brawny data warehouse with a constant shortage of money?


Most of the organizations still start with a data warehousing project, using the ol’ traditional waterfall approach. It worked before but does not cut it any longer …

Groundwork for such a project includes taught questions with no good answers:

•          Business users are asked what data they need. But the business generally doesn’t know what it wants. “I’ll tell you what I want when I see it” is their familiar mantra.

•          Then business users are asked what reports they use and what is missing in those reports. But reports are “moving targets” they change swiftly, especially when decisions are about the life and death of the company.

•          Where to find right people? Well, with a continual shortage of the skilled ETL developers, data- modelers and business analysts – good luck. A decent IT chap costs a fortune.

•          How long will the data warehousing project take? Organizations can’t afford the common 6 to 9 months, possibly, longer with painful approvals and rework.

•          How do we keep documentation up to date? What is the risk of such lengthy warehouse project? How to find an enthusiastic sponsor of the business intelligence effort?

•          And many more questions….   Oh stress, the spice of life!


That is the alternative? Not to life it is – to stress!

A different approach that presents a true unique business value for a stressed out business that includes:

•       Built-in methodology that supports the entire data warehouse lifecycle an Iterative approach.  The Ability to stay focused on the goals.

•       Spiral-like rapid “prototyp–>iterate–>deploy” venture. Quick response to changing business needs. The only questions asked: how to make the right business decisions right and what are the measures of the truth?

•       Integrated, metadata-driven data warehouse development environment. All that the developer needs is on his/her fingertips, Auto-generation of procedural code and documentation.

•       A low risk, proven, pragmatic approach to data warehousing. An instrument to get business users and stakeholders involved earlier.

•       Effective use of time & money; quicker ROI.  A process for trimming off 2-4 months a year from the development and support costs; reducing time to deliver.

•       The data warehousing as a process – not a project.  Well-timed accommodation to changing business needs.

•       Leveraging existing, readily available SQL skills in market. Theory and best practices are in the tool, not in developer’s head.

Eight Reasons why Data Warehouse and, Subsequently, Business Intelligence Efforts Fail

January 31, 2009


Please find below some of the deepest pitfalls that you might want to be aware of. In addition, see how WhereScape RED can help you avoid such drawbacks.

1.     Insufficient User’s Involvement

·         IT departments sometimes try to run Data Warehouse/Business Intelligence projects in a vacuum. DW/BI projects are not IT projects! They are rather initiated by and addressed to Business Users.

·         Lack of user involvement typically leads to either solutions that are too hard to use or constructs that do not solve any business problems.

The RED Tips

·         Get Business users involved as early as possible

·         Work with users to help them understand how to formulate the “right” questions. They need to be able to express the precise needs of the business to the IT development team. Create a synergy between IT developers and Business users.

·         Show users frequent updates to make sure you are on the path to solving an identified problem and keep the momentum going.

·         Develop guidelines and best practice principles based on successful prior iterations.

·         Establish a training plan to secure consistency in DW development, tool usage and the organization’s standards.

2.     Enterprise Warehouse at Start

·         Some companies attempt to begin by building an enterprise warehouse.  It is hard to bite, chew and swallow a very risky endeavor. Adding more team-members does not help: Data Warehouse projects do not scale well.

·         Enterprise warehouses have slow ROI, cost a lot of money and, therefore, usually end in disaster.

·         Painful lessons learned are more difficult to implement.

The RED Tips

·         Start by building a Data Mart – the methodology is identical but scope is far more manageable. Data Warehouses need to grow gradually, analysis area by analysis area, building towards the full enterprise Data Warehouse. 

·         Make use of rapid prototyping and speedy development cycles; they give you swift success by

–   solving a real business problem quickly;

–   wining a powerful sponsor of the DW/BI project;

–   helping secure financial support for future projects;

–   gaining experience and outcome at the prior iteration (helps to justify the subsequent ones);

–   establishing a single point of the organization’s business rules and DW and OLAP standards;

·         Be aware that the initial iteration should take no longer than three months (hopefully much shorter).

3.     Poor Data Quality

·         The Data Warehouse, perhaps, is the best way to discover (and report) “noisy” data.

·         Most “bad” data is easy to spot but some can slither into the Data Warehouse.

·         Fixing data in the Data Warehouse does nothing to stop new bad data coming from the source.

The RED Tips

·         Identify the resources that contribute the most into the Data Warehouse and verify its accuracy.

·         Use the Data Warehouse methodology to drive repairs to source systems to prevent bad input.

·         Do not fix data; reject the whole “chunk” or record if invalid data is found.

·         Create “unknown” buckets in the Data Warehouse so data issues get visibility.

·         Assign invalid data to a zero dimensions key; auto-add dimension entries to ensure consistency.

·         Re-process the failed rows once the source issues are resolved.

4.     Deficient Funding

·         Even when starting with a small Data Mart, money is always an issue.

·         Warehousing projects often fail to subsidize all aspects, including hardware and training.

·         Data cleanup always takes longer than expected and will dissolve a significant part of the budget.

The RED Tips

·         Obtain high-level sponsorship from an area of the business.

·         Prepare in advance for adequate hardware and training in addition to the licensing, data and report work.

·         Make sure to schedule delivery of reliable information to the decision-makers.

·         Validate the funding from non-technical points of view:

–   Real  business advantage for the organization is pragmatic, iterative Data Warehouse building;

–   Lower risk  by effective use of time, money and staff;

–   Implementation of red-hot latest DW/BI methodology (Kimball, Inmon, Agile development, etc).

5.     Corporate Jungle

·         Power struggles in the organization may occur over the resources involved in the project or the vision and focus of the warehouse, to name a few.

·         Certain people may feel threatened by the outcome of warehousing projects.

·         Some folks claim they own certain data and no one else is going to use it.

The RED Tips

·         You only have two possibilities here: play more politics or play less politics.

·         Have the organization support of a clear vision on how to solve a specific business problem.

·         Very vigilantly consider opinions and “pain points” from various business groups; hang on their words.

·         Carefully choose your sponsors; this should be the most influential decision-maker that you can recruit.

·         Have your business top dog cut through the maze and drive the project forward.

·         Provide fast offline query performance capability for ad-hoc analysis by business community.

6.     Incorrect or Partial KPIs

·         Even simple cubes may not contain valuable information. E.g., a cube that contains sales information is useless when trying to analyze sales reps’ performance.

·         Warehouses that are built by IT often include just the existing data and not the KPIs needed.

·         A lack of proper KPIs means that the warehouse falls short of solving a business need.

The RED Tips

·         Work with users and business patrons to solve specific business problems; one problem at a time.

·         Remember that KPIs must be focused on solving an explicit problem such as trends, distributions, and empowerments.

·         Adjust the display of KPIs thoroughly to show the organization’s competitive advantage.

·         Understand what metrics are used to calculate bonuses for upper management.

·         Consider delivering the KPIs through a dashboard or scorecard.

7.     Exceedingly Complex Cubes

·         Cube complexity is a major obstacle to the success of the Data Warehouse.

·         The IT department tends to build extremely complex cubes; OLAP’s with many dimensions are very difficult for end users to understand, much less use.

The RED Tips

·         Keep in mind, that there are two very different types of cubes: Analytical and Reporting.

·         Use as few dimensions in an Analytical cube as possible; rule of thumb – maximum of six to eight dimensions.

·         Take advantage of rich OLAP functionality, provided by the DW tool such as hierarchies, calculated members and so on.

·         Build multiple, simple cubes and link them when necessary using the Virtual cube concept. Alternatively, create simplified views of complex cubes.

8.     Limited or Unreachable Access to Information

·         Many companies can build a Data Warehouse but fail to provide users with a simple way to access the data. For example, many companies use Excel as the standard client tool but, in fact, it is one of the weakest tools available.

·         Different users need different ways to interact with the data.

The RED Tips

·         Always remember do not just get the data in but also get the information out; have the right tools for the right users to look at the data:

–   scorecards, dashboards for decision-makers and management;

–   powerful analytical tools for business analysts and SME’s;

–   reporting  with limited analytical functionality for operation workers;

·         Design the Data Warehouse independently from end user tools.

·         Expect a range of functionality from the Data Warehouse, which will mean a variety of front-end tools.

Summing up …

·         Data Warehouse / Business Intelligence efforts do not succeed because of a variety of reasons, many of which are non-technical.

·         What does WhereScape RED put forward for your organization:

–   Offers an integrated Data Warehouse development environment.

–   Promotes a built in methodology that supports the entire Data Warehouse lifecycle.

–   Enables rapid prototype-iterations and deployment of the project

–   Supports a low risk, proven, pragmatic approach to Data Warehousing.

–   Treats Data Warehousing as a process – not a project.

–   Allows for the effective use of time, money and resources.

–   Shaves off two to four months per year from your development and support costs.

–   Leverages existing, readily available SQL skills in market and collaborates.

–   Complies with the Data Warehouse Lifecycle Management principles.

Here are the Endorsements from my Previous Jobs.

December 1, 2008

Please review:

Technical Partner Manager / Tech. Analyst

WhereScape USA, Inc (CURRENT JOB)


“Raphael worked for me as a pre-sales and post sales data warehouse solutions architect. He has a unique blend of both skills, providing pre-sales demo support to prospects but also being able to provide highly technical data warehouse implementations with customers. I highly recommend him for pre-sales or post-sales data warehousing / business intelligence positions.” February 16, 2009

Richard Smith, VP Sales, WhereScape USA Inc.
managed Raphael at WhereScape USA Inc.


“I worked with Raphael at CIBER and have continued to work with him as a client since he moved to Wherescape. Raphael is an intelligent, sharp individual who is able to bring ideas to a level that helps the business. Raphael is easy to work with and is knowledgeable about data warehouse structure and how it can be used to grow or manage business needs. I would work with Raphael again in a minute.” January 2, 2009

Mark Johnson, dba/tech analyst, ciber
worked directly with Raphael at WhereScape USA, Inc


“Raphael is one of the best consultants I’ve worked with. He is articulate, empathetic, highly motivated and extremely technical. He has an effective manner working with people and is the kind of high-caliber person I would put on any project for any client we support. He is a decent human being and I’m glad to know him.” January 3, 2008

Top qualities: Personable, Expert, High Integrity

Thomas Fisher
hired Raphael as a IT Consultant in 2005, and hired Raphael more than once


“Raphael is detail oriented and was involved for the full cycle of Oracle database development at Lipper. His attention to detail and ability to write clear documents was very helpful to our project. He was able to contribute immediately and asked many detailed questions, which really contributed to the client experience of our team. I recommend Raphael to anyone seeking a detail oriented Oracle/ETL database developer.” January 3, 2008

John Adams, Computer Consultant, Lipper
managed Raphael indirectly at WhereScape USA, Inc



Clients / Customers

WhereScape USA


“…We had another very productive week working with Mr. Klebanov of WhereScape building Byram’s Data warehouse. During the previous sessions of our consulting engagement with Raphael we were able to build, the I phase of the Byram data warehouse. During our current week’s session, we have gained momentum with our DW development activity and have resolved the following items…

 Raphael managed to accomplish all goals that were outlined as part of the kick-off meeting on Monday.  The amount of work done and quality of consulting were completely satisfactory.” November 21, 2008

Sumeet Nagrani, DW/BI Manager, Byram Healthcare, White Plains, NY

Worked directly with Raphael as a WhereScape consultant


“…Thank you so much for recommend Mr. Klebanov … His knowledge and expertise has helped our BI program tremendously…

Along with his technical expertise, Mr. Klebanov has good personal skills that allow him to work well with my staff, thus making the engagement much more productive.  …

I look forward in working with your firm and Mr. Klebanov in the future as our paths crossed.” January 16, 2009

Tho V. Dao, IT Manager, City of Shoreline, WA

Worked directly with Raphael as a WhereScape consultant





IT Consultant

WhereScape USA


“To Whom it may Concern: Raphael and I worked together for approximately 1 year… in the time I’ve know him he is one of the most dedicated and loyal employees that you would ever want to meet. He has the heart of a champion and the will to succeed at everything he does. He is probably one of the most knowledgeable that I’ve ever met in the data warehousing world. He really understands the methodologies and follows procedure to a T. I recommend Raphael Klebanov for any position he feels qualified for. Sincerely, Waid Essick Software Sales” November 5, 2008

Waid Essick, ADR, CDC Software
worked directly with Raphael at WhereScape USA


“Raphael is an experienced Data Warehouse technologist, and a very hard worker with a great attitude. He’s always willing to expend the “extra effort” to insure that clients get value from his services. He’s a great communicator, particularly notable since his primary language has been Russian. This was never an issue in dealing with clients, in fact, most found his knowledge to be top-notch, and his communication skills to be great. Would recommend him anytime…..” August 25, 2008

Tim Clark, Account Manager, WhereScape Software USA
worked directly with Raphael at WhereScape USA



Sr. Programmer Analyst / Bus. Associate

INVESCO / AIM Investments


“Raphael Klebanov has been appointed to work in the Houston Data Services group for the last 6+ months. His function includes working on various aspects of enterprise Siebel Analytics Project. Raphael’s previous experience with range of database applications, his well-rounded set of technical skills, especially Informatica, made him a valuable reinforcement to the existing group.

 During the work on the project, Raphael has reveled strong professional skills both technical and interpersonal. He volunteered to take on additional responsibility as project progressed. The scope of the development required extensive learning of new tools and techniques that Raphael achieved with ease.

He has demonstrated strong ability and discipline to support the group’s effort remotely from Denver. However when it was necessary he was reporting to Houston office without the delay. As an individual, Raphael is honest, energetic and enjoys helping people. He has excellent communication skills, is liked by his management, and peers.

I take this opportunity to recommend Raphael Klebanov’ candidature and express my interest in supporting it further, if you desire so.” September 15, 2005

Mindy W. Bredthauer, Sr. Data Services Manager, AIM Management Group Houston

managed Raphael at AIM Management Group

Raphael possesses excellent technical and interpersonal skills necessary to administer assigned projects as well as to analyze, design, and prepare programs and systems.

Raphael demonstrates strong ability in diagnosing and solving information system problems while maintaining professionalism and courtesy in addition to his ability to manage projects, establish priorities, meet deadlines, and concentrate on detailed information in a fast-paced, demanding work environment.

During his five plus years of services with INVESCO/AIM Investments Raphael has proven himself a hard-working, self-motivated individual with strong work ethics and a positive attitude.

I would highly recommend him to any company seeking these qualities in an individual. June 22, 2005

Chris Marlowe, Data Services Manager. INVESCO / AIM Investments

managed Raphael at INVESCO and AIM Management Group