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RESOURCES CATEGORY: Enterprise Performance Management (EPM)

Quick Delivery Kit for Oracle’s EBS Extension to Endeca


PREDICTif is excited to announce a Quick Delivery Kit (QDK) offering for Oracle’s EBS Extension to Endeca (EEE) solution. Many customers are leveraging Oracle e-Business Suite (EBS) as its core ERP solution to manage financial and operational processes. The myriad of options are available to EBS customers include: OBIEE, OBIA and EEE. PREDICTif’s unique capabilities allow us to identify the right solution for you! Our extensive experience across Business Intelligence will provide you with reassurance that our recommended approach ensures the lowest total cost of ownership without compromising on functionality.

EBS Extensions for Endeca, being built upon Endeca’s information discovery capabilities, offers significant flexibility for ad-hoc reporting and analytics while still providing seamless access to EBS for EBS users. Already equipped with over a dozen of pre-built modules for EBS including AR, AP, Order Management, Inventory, Project Accounting, EEE is easy to extend and customize for any existing EBS custom views and tables.

PREDICTif’s EEE QDK offering is a 12-week program that will enable customers to gain reporting and analytics capabilities for two of EBS modules.

  • Conduct a discovery and planning process to document the high-level business requirements and complete a plan for implementation
  • Establish the solution architecture and complete the installation of the initial 2 EEE modules
  • Complete the required EEE customizations and configurations to meet the business requirements
  • Perform knowledge transfer to customers’ resources to enable them to be self-sufficient for future enhancements
  • Enter into a support arrangement post production to ensure that customers will continue to enjoy support from PREDICTif

EEE QDK will rapidly deliver sophisticated reporting and analytics capabilities out of EBS for our customers who are using EBS as their core ERP systems. Please contact us for more information

To download a Power Point regarding EBS and Endeca, please fill out the form blow:

EBS Power Point Picture

Quick Delivery Kit for Oracle’s Big Data Discovery


PREDICTif is excited to announce a Quick Delivery Kit (QDK) offering for Oracle’s Big Data Discovery (BDD) solution. Oracle has furthered its leadership position in the space of information discovery by extending its market-leading information discovery tool, Oracle Endeca Information Discovery (OEID) and releasing BDD. BDD is designed to empower business users such as data scientists and business analysts to explore, transform and discover within terabytes of structured and unstructured data and gain critical insights that transform today’s business. Being a thought leader in the big data and information discovery space, PREDICTif has formed a strong partnership with Oracle to bring the values of BDD to our joint customers. To this end, PREDICTif has enhanced its OEID QDK and begun to offer this unique BDD Quick Delivery Kit.

PREDICTif’s BDD QDK offering is a 12-week program that will enable customers to leverage the marketing leading big data discovery tool and gain unparalleled capabilities to enable competitive advantages from their mountains of data.

  • Conduct a discovery and planning process to identify high value use cases and complete a plan for implementation
  • Establish the solution architecture and complete the development of the initial use case(s) through an iterative development process
  • Enable customer self-sufficiency by performing knowledge transfer throughout our engagement
  • Develop a post-production support arrangement to ensure that customers will continue to enjoy support from PREDICTif

BDD QDK is a highly valuable offering to our customers who plan to and have established a big data store in Hadoop and are in need of exploring and discovering the big data repository. Please contact us for more information.

PREDICTif’s Discoverer Replacement


PREDICTif is excited to announce an offering to replace Discoverer and upgrade customers’ EBS reporting capabilities. As you are no doubt aware, Oracle is sunsetting Discoverer and will soon cease support on this valuable tool. At PREDICTif, we have worked with many companies to identify a suitable replacement for this functionality. We will work with you to determine the most appropriate solution that provides your end users with a seamless experience to continue to perform reporting and analytics across your critical financial and operational data.

PREDICTif’s Discoverer Replacement offering is a 12-week program that will enable customers to leverage the best reporting tool and gain insight from its financial and operation data.

  • Conduct a discovery and planning process to identify a best-fit tool and complete a plan for implementation
  • Establish the solution architecture and complete the development of the initial set of reports
  • Perform knowledge transfer to customers’ resources to empower them to develop additional reports
  • Enter into a support arrangement post production to ensure that customers will continue to enjoy support from PREDICTif

PREDICTif believes this is a valuable offering to our customers who are currently using Discoverer to report out of EBS. Please contact us for more information.

PREDICTif delivers a Benefits Compensation Management Solution for a leading communications company


PREDICTif delivers a Benefits Compensation Management Solution for a leading communications company


A leading provider of communications, high-speed Internet and entertainment services in small-to-mid-size markets delivering advanced communications services through its broadband and fiber optics network in more than 33 states.


  • Rapid data loads
  • Rapid data retrieval
  • Centralized security
  • Improved user interface


The customer’s work force consists of approximately 6,000 employees of which, over 1,000 are eligible for bonus compensation beyond their annual salaries. The human resources department is required to maintain detailed information for each employee. They are also charged with providing the supervising managers with processes to set goals for and ultimately evaluate direct reports. The bonus goal-setting and evaluation processes must be accurate and secure.

  • The legacy system was incapable of synchronizing data with the SAP system;
  • Salary adjustments and bonus compensation processing required extensive manual input and maintenance;
  • The existing legacy GUI interface was unpopular with users;
  • The legacy system utilized a proprietary programming language, which few staff members were trained to use;
  • No system reporting tool existed to provide analysis driven reporting.
  • The legacy solution did not allow for querying by specific criteria;
  • Business rules and logic existed in multiple locations, posing significant risk to accuracy; and
  • The existing system was inflexible, un-scalable and could not accommodate company growth.


The Benefits Compensation Management solution used IBM Cognos TM1 as the backbone technology and delivered functionality that is easy to use with a flexible and scalable architecture that is highly interactive.

  • Integrated with the SAP system to retrieve and update human resource and workforce information;
  • Provided a user friendly interface that required minimal training and a mild learning curve;
  • Produced a solution that the HR department can support and upgrade independent of IT support;
  • Offered sophisticated reporting functionality for analysis and process enhancements;
  • Created a single version of truth for all data, business logic and rules; and
  • Provided initial product training to power users and post-production solution specific training to end users.


In late 2007, the customer evaluated a number of business intelligence and performance management systems. IBM’s Cognos TM1 was selected for its ease of deployment, scalability, flexibility, and MOLAP (Multi Dimensional Online Analytical Processing) architecture.

The customer’s initial deployment of Cognos TM1 centered on its Bonus Awards Management

System. Teaming with PREDICTif, the customer arrived at a solution that combined Microsoft Excel templates with TM1 functionality. Once employee data was loaded in the designated cubes, calculations and updates were performed and saved in a single location providing “single version of the truth” processing and reporting.

  • Delivered a Benefits Compensation Management Application to process and report from its employee bonus payout program;
  • Supervising managers could review employee bonus forms easily and quickly. Any updates made by supervisors were saved to the TM1 server;
  • The Excel interface coupled with TM1 Perspectives permitted deployment with minimal training requirements;
  • Group security was provided to the element level using a TM1 security scheme; and
  • Calculations occurred on the TM1 Server preserving integrity.


The solution delivered significant results and met all business objectives as defined by the customer. Delivery increased the productivity and efficiency of the HR department as well as enhanced the employees’ satisfaction and morale.

  • A “single version of the truth” was attained;
  • Analysis based reporting and querying was readily available;
  • Effort required to complete annual bonus processing was greatly reduced;
  • Overall manual effort was significantly decreased;
  • Employee and organization hierarchy changes were synchronized with SAP using TM1 Turbo Integrator processing; and

Security settings were available for groups to the element level.

Scalable financial reporting solution for healthcare management


PREDICTif delivered a scalable financial reporting solution for a leading health management services and equipment company


A leading health management and diagnostic services and equipment company.


  • Financial consolidation and reporting
  • Interactive analysis
  • Forecasting and Budgeting
  • Cognos TM1 Web


As one of the leading construction multimedia companies, this customer is faced with the following challenges: inefficient central financial reports, reliance on manual inputs, and cumbersome Excel intensive reporting tool. With financial data coming from 8 different data sources, a central reporting tool to consolidate all the reports is needed. The business is presented in six platforms and each platform gets the data from its own data source.

The chart of accounts is compiled from a string with over 20 characters.  The accounts dimension hierarchy was set up in such a way as to hinder reporting leading to miss interpretation of data and errors in reports.  Each of the six platforms has their own chart of accounts and reporting method making it difficult to consolidate in a single tool. Therefore, a lot of manual methods are involved in creating reports.   Heavy reliance on Microsoft Excel to input budgeting data is present.  As a Solution, PREDICTif Solutions proposes an end to end solution to centralize the financial reporting system to provide budgeting and forecasting for the organization.


  • The objective of the project was to deliver a solution that leveraged TM1’s in-memory OLAP capabilities and simplified the monthly financial consolidation and reporting process.
  • Centralized, efficient and ease of use access application which can reduce preparation time. Instead of spending time on preparing reports, users can now focus on analyzing data.
  • Instill ‘one version of the truth’ consolidation reporting system that can be applied to six platforms
  • Provide a proficient budgeting and forecasting web based process that allows users to input data at the line item level.
  • Give ability to end users to build and analyze reports without reliance on IT or the Accounting department


After evaluating several world class solutions, IBM Cognos TM1 was selected for a number of reasons – one of them being that IBM Cognos TM1 provides a real-time approach to consolidating, viewing, and editing enormous volumes of multidimensional data.

PREDICTif was engaged to provide an end-to-end solution delivering 25 Cubes with dimensions ranging from 5 to 15 in order to provide Financials and Assumptions models. Changes in financial forecasts, assumption metrics and indirect expenses were made via customizable Excel templates as well as directly through the TM1 interface.   Reports created both in TM1 and Excel were readily accessible through the web with no additional effort.

  • PREDICTif Solutions was able to create a set of multidimensional cubes utilizing OLAP technology that can dynamically slice and dice through data sources.
  • Design a user-friendly excel input templates which can access through TM1 web. The cubes support consolidation and reporting of both budgeting and forecasting data as well as allocation.
  • An automated data extraction using by TI processes (ETL) tool that can update multi dimensional cubes from 8 different data sources.
  • Users can use dimension editor as primary method to maintain or update dimensions that are not from the source system
  • Setting up different instances for security purposes and separate the finance consolidation modules, budgeting applications and compensation modules will be given access by corporate controller.
  • Substantially reduce time spend on creating monthly financial reports. Reports are easily access and produce with little or no reliance on the accounting and IT department.
  • Provide a real-time consolidation reports and offer an automated tool for producing annual budget and monthly forecast process.
  • Eliminate manual effort and errors of gathering data from multiple reports, spreadsheet and data sources
  • Provide power users training and knowledge transfer to the client so they will be able to build additional reports, budget templates and forecasting procedures out of the cubes.


PREDICTif solutions delivered measurable results to the client.  Now the client is able to provide “one version of the truth” in their reporting system.  Customers can now use the centralized financial reporting cube to analyze data across six platforms and they now have an automated ETL process to update dimensions and cubes from 8 different data sources.  The TM1 web based input budgeting and forecasting system also allows users across the enterprise system to input data at the line item level.  The application significantly speeds up the budgeting and forecasting process and empowers users to create or modify input templates without IT department influence.

  • With the deployment of the TM1 solution, the number of hours spent generating consolidated financial statements were reduced considerably, allowing more time for analysis;
  • Multiple disparate business units were brought together under one streamlined reporting process with a standardized company-wide output;
  • The finance group supported and upgraded the solution and the accompanying infrastructure through its own staff, without dependence on the IT support; and
  • Consolidated financial reports have obtained further reach as a result of the use of TM1 web.

IBM Cognos TM1 Feeders


What Are Feeders?

In a sparse cube with relatively few values compared to the number of cells in the cube, it can cause a severe drag on system performance to require the cube to calculate every cell when only a few values will result from those calculations. To combat this, TM1 allows you to use the SKIPCHECK function to tell the rules to only calculate values for cells that will result in non-zero values.

To put it briefly, feeders are merely markers signifying to the rules which values are to be calculated and which are to be ignored. Think of it in the context of a cubeview. The feeders essentially mark each cell that needs to be calculated. The rules then come behind that and actually perform the calculations on only the marked cells.

Feeder Misconceptions (Or, How To Overfeed)

One of the most common misconceptions – especially when you’re writing your first rules – is to feed all parts of a calculation. Take the following rule – calculating an employee’s monthly bonus in a generic headcount cube – as an example:

[‘Monthly Bonus’] = N: ([‘Annual Salary’] * DB(‘Calendar’, !Year, !Period, ‘Monthly’, ‘Percent of Year’) ) * [‘Bonus %’];

I’ve found that many developers will assume they need to feed all parts of the calculation. So, for the above example, they will feed “Monthly Bonus” with “Annual Salary” and “Bonus %” from the same cube, and “Percent of Year” from the “Calendar” cube. However, all they’re doing is “marking” the cell for calculation three separate times when all they had to do was mark it once. This is adding three times the amount of processing time per cell. Extrapolate that out to the number of cells in your cube and you’re talking about a serious performance issue in your model.

So the cell only needs to be fed once, and to do this in the most efficient way possible you feed it with the part of the calculation that will most likely signify that the cell will result in a value of zero.

To elaborate on this point a bit: what universal logic can we grasp that will help us in determining the best possible feeder? Well, first you have to separate these logical truths into two categories: multiplication/division calculations, and addition/subtraction calculations.

Feeders for Multiplication and Division Calculations

Multiplication and division calculations are our best friends. In these calculations, we truly need only one feeder. Take a simple example of calculating a monthly FICA amount at the most basic level – Monthly Salary * FICA %. If either of these amounts equals zero, the FICA amount is going to be zero.

So we take this logic and conclude that, in fact, we only need one feeder – the one most likely to be zero. The FICA percentage is a single number (a lot of companies take them as two – Social Security and Medicare, but for simplicity’s sake we’ll assume that it’s one percentage), provided by the Social Security Administration every year. This percentage will never be zero, so we can rule it out as a possible feeder. The Monthly Salary, however, is a good feeder because unless our company has an employee who loves his job so much that he’s willing to work for free, it should be a pretty good indicator that we need to calculate that employee’s FICA %.

This same logic can be applied to division calculations. If the numerator is zero, the calculation must result in zero. If the denominator is zero, the calculation is illogical because you cannot divide anything by zero, and this is handled by TM1 as a zero.

Feeders for Addition and Subtraction Calculations

Feeders for addition and subtraction calculations, while not trickier, are slightly more involved. Take FICA for instance, and now assume that we do prefer to split FICA into its’ two parts – Social Security and Medicare. If we didn’t want to include these as children of a consolidation, we would calculate “FICA” as:

[‘FICA’] = N: [‘Social Security’] + [‘Medicare’];

However, if either “Social Security” or “Medicare” were to be zero, this would not guarantee that “FICA” will be zero for the obvious reason that x + 0 = x. The solution to this, unfortunately for us, is to feed both “Social Security” and “Medicare”. The same can be said for subtraction calculations, as x – 0 = x. With this in mind, let’s examine each of the parts of the above rule to see which we should use to feed “Monthly Bonus”:

  • Annual Salary: every employee should have a salary, so if we run across a “Monthly Bonus” cell belonging to an employee slot that does not have a salary, we can safely assume they will have no monthly bonus since any value multiplied by a zero will result in a zero. But this does not necessarily mean they will have a Monthly Bonus since not all employees receive a bonus, so let’s continue to the other parts of the calculation.
  • Percent of Year: we can instantly eliminate this as an option because there will never be a non-zero “Percent of Year” amount. If we were to use this as a feeder every cell would be fed, negating the entire advantage of feeders.
  • Bonus %: assuming every employee who receives a bonus has their own “Bonus %” populated, this is a great indication that “Monthly Bonus” for a given employee will need to be calculated. We should use “Bonus %” to feed “Monthly Bonus”.

[‘Bonus %’] => [‘Monthly Bonus’];

However, another misconception is that a feeder even needs to be a part of the fed calculation. This is not at all true. Remember, you’re just “marking” a fed cell, in the easiest and most efficient way possible.

Say that we need to derive an hourly pay rate from an annual salary:

[‘Hourly Rate’] = N: [‘Annual Salary’] \ DB(‘Calendar’, !Year, !Period, ‘Annual’, ‘Hours’);

Based on our prior analysis of “Monthly Bonus”, we can eliminate “Hours” in the Calendar cube as a contender to feed “Hourly Rate”. We could definitely use “Annual Salary” to feed these cells.

However, say we have another element called “FTE”. This is basically used to signify a valid employee with a value of 1, and could also be used to calculate part-time employees with a value less than 1 – say, 0.5. But no matter what specific value it is, if it’s greater than zero it signifies a valid employee. And since any valid employee will have some value in “Annual Salary”, “FTE” would also be a great feeder for “Hourly Rate”:

[‘FTE’] => [‘Hourly Rate’];

At this point you might be asking yourself, “Why wouldn’t I just use ‘Annual Salary’?” Well, in the instance of a single rule such as “Hourly Rate”, yes it might not make much sense to confuse the situation by using a value outside of the components of the calculation you’re feeding. However, consider the situation (a very likely one) where you have a number of these elements that you can assume every valid employee will have in all situations. Instead of feeding them with parts of their calculations, which will all be different and obviously dependent upon how they’re calculated, you can potentially feed them all with “FTE”:

[‘FTE’] => [‘Hourly Rate’],
[‘Social Security’],

This example is a purely aesthetic one, but it could potentially help performance in the event you are pulling amounts from another cube, one-for-one with no calculation on the amount. We’ll discuss this further in the following section on inter-cube feeders.

Inter-Cube Feeders and How to Avoid Them

In my experience inter-cube feeders are the biggest drag on performance, and they should be avoided if possible. Continuing our example with “FTE”, we can use this value to aid us in pulling amounts from another cube. Let’s say our originating cube (“PositionInfo”) is just a snapshot in time of employees (i.e. it does not have time dimensions) and we need to pull those values into our “Headcount” cube and apply them to Period and Year. We can feed “FTE” in the “Headcount” cube like this:

[‘FTE’] => DB(‘Headcount’, ‘Fcst Years’, ‘Year’, !Scenario, !Company, !CostCenter, !Location, !Employee, !EmpInfo);

It (hopefully) goes without saying that when you’re dealing with inter-cube feeders you’re going to run into situations where your dimensions do not match and you’re forced to feed to consolidations, as we do in this example when we feed to all forecast years in the “Year” dimension and all months in the “Period” dimension. Feeding to consolidations is probably the single biggest reason as to why you should avoid inter-cube feeders as much as possible. When you’re feeding to a consolidation such as “Fcst Years”, you’re actually creating multiple feeders, equal to the amount of elements in the consolidation. To illustrate this, imagine we actually have time dimensions in our originating cube (“PositionInfo”); we would feed the “Headcount” cube with one feeder:

[‘FTE’] => DB(‘Headcount’, !Year, !Period, !Scenario, !Company, !CostCenter, !Location, !Employee, !EmpInfo);

However, when we feed to “Fcst Years”, we’re essentially creating the following feeders:

[‘FTE’] =>

DB(‘Headcount’, ‘2011’, !Period, !Scenario, !Company, !CostCenter, !Location, !Employee, !EmpInfo),
DB(‘Headcount’, ‘2012’, !Period, !Scenario, !Company, !CostCenter, !Location, !Employee, !EmpInfo),
DB(‘Headcount’, ‘2013’, !Period, !Scenario, !Company, !CostCenter, !Location, !Employee, !EmpInfo),
DB(‘Headcount’, ‘2014’, !Period, !Scenario, !Company, !CostCenter, !Location, !Employee, !EmpInfo);

As with feeding each component of a calculation, this creates a huge strain on performance and should be avoided. But it cannot always be avoided, and so you should take actions that minimize the adverse affects. So let us operate under the assumption that if we can feed our great “universal” feeder (“FTE”) from the “PositionInfo” cube to the “Headcount” cube, we can then create all our other feeders inside the “Headcount” cube. Here would be our feeder from the “FTE” element in the “PositionInfo” cube to the “FTE” element in the “Headcount” cube:

[‘FTE’] => DB(‘Headcount’, ‘Fcst Years’, ‘Year’, !Scenario, !Company, !CostCenter, !Location, !Employee, ‘FTE’);

We would then be able to use that newly-fed “FTE” element in the “Headcount” cube to feed all of the elements for which we otherwise would have written inter-cube feeders:

[‘FTE’] => [‘Hourly Rate’],
[‘Social Security’],


Hopefully this can help some of you to improve your model’s performance through making your feeders more efficient. We’re never going to completely eliminate the headaches that come with feeders, but we can at least minimize the heartburn that comes with a poorly-performing system.

EPM Success for Geophysical Survey Company


PREDICTif  successfully assists major geophysical survey company to select the right Enterprise Performance Management product for its financial reporting and analysis needs


A leading seismic data-acquisition imaging and software systems company who assists petroleum exploration companies to identify and measure subsurface geological structures.


  • Financial consolidation and reporting
  • Interactive analysis
  • Forecasting and Budgeting
  • Oracle Hyperion Planning, Essbase, Financial Reporting, Financial Data Management


The Customer used an out-dated solution that was under-utilized and deficient in terms of data mining and planning automation. As a result, the business was heavily dependent upon IT for both support of its existing solution and development of new functionality when the enterprise required. The software deployed was also 2 major versions behind that of the publicly released version and had run out of vendor support. Significant growth in its Solutions business units served as a catalyst to replace its manual, error-prone, labor intensive financial reporting and planning solution with a more automated, streamlined and efficient one.

The company, comprised of multiple legal entities that span the globe, was challenged with having to manage all of its financial operations in a single, multicurrency environment. It also had needs to transform individual financial plans from a group of entities into a single, consolidated corporate level plan.  All this made the existing solution severely limiting in terms of the ability to predict its revenue and costs for each of its business units.


Needing objective and unbiased professional advice on various EPM solutions on the market, the Customer engaged with PREDICTif who led a rigorous product selection process to ensure that they procured the right EPM product for its current and future needs. The overall goal was to assist the Customer in identifying an EPM product that met its current and future business needs:

  • Integrate seamlessly with its main ERP solution (Infor’s Syteline), its existing Microsoft SQL Server based data warehouse and miscellaneous data sources;
  • Upgrade its antiquated and unsupported EPM architecture to a modern and sustainable one;
  • Automate and streamline its financial reporting and planning processes eliminating manual, redundant and time-consuming steps;
  • Enable drill-downs and what-if analysis for gaining insight and better financial plans;
  • Establish a single version of the truth and generate accurate/ up-to-date reports;
  • Leverage both web and Excel as available interfaces for ad-hoc analysis, interactive report authoring and viewing;
  • Offer an easy-to-learn and easy-to use tool that enables the business to achieve self-sufficiency; and
  • Ensure long term, advantageous total cost of ownership.



PREDICTif led a systematic product selection process leveraging its extensive experience with EPM solutions, vertical specific evaluation criteria and its own, proprietary production selection templates to ensure a thorough evaluation was undertaken. PREDICTif led the preparation of the PoC, the coordination of the vendors and finally, the short-listing and evaluation/selection of vendors who best addressed Customer’s business requirements.

Over the course of the evaluation, PREDICTif:

  • Assisted the Customer in its identification of the appropriate use cases that addressed all critical business needs;
  • Prepared the use case documentation and accompanying data;
  • Worked with Customer personnel in order to facilitate software vendors’ involvement in the PoC;
  • Supported the Customer as needed throughout their participation in the evaluation process;
  • Administered the different software vendors’ PoC’s and validated their results;
  • Incorporated PREDICTif’s proprietary software evaluation process and compared/contrasted its weighted scoring criteria against that of the differing, competing vendor technologies;
  • Led vendor’ demos and meetings in an effort to contribute towards the information gathering process;
  • Provided cost estimates for license and implementation costs; and
  • Produced a high level roadmap and long term implementation plan for each technology so that the Customer understood not only the current state, but also the future vision of the chosen technology.

The PoC was conducted in parallel by three vendors over the course of a two-week period.  We aggregated all the gathered information and then, used PREDICTif’s weighted software selection criteria to contrast that against the Customer’s business objectives to render an option on a best of breed solution.

After evaluating several leading vendors, Oracle Hyperion/Essbase was selected for a number of reasons:

  • Oracle Hyperion Enterprise Performance Management (EPM) software meets the key business requirements, enables the automation of planning and reporting processes and powers the finance team to perform data mining and ad-hoc analysis;
  • Oracle Hyperion is easy to use, easy to implement and easy to support/maintain;
  • It is a cost effective, high return investment, conducive to quick wins, resulting in lower total cost of ownership;
  • It establishes a solid platform for future reporting and analysis needs; and
  • Oracle utilizes updated and modern technologies, presents a strategic product development roadmap and enjoys both financial as well as organizational stability.


Post product selection, the Customer negotiated with Oracle and ultimately procured the Hyperion product stack including Essbase, Hyperion Financial Reporting, Hyperion Planning and Financial Data Manager (FDM). Utilizing the acquired Oracle stack of products, the PREDICTif team successfully implemented and replaced the customer’s outdated solution with that of a new one fully automating countless functions and in the process, eliminating nearly half of the Excel worksheets it previously utilized resulting in increased productivity. Additionally, PREDICTif:

  • Upgraded the customer’s financial reporting and analysis architecture to a modern and fully supported technology;
  • Increased the architectures performance resulting in a more robust and stable environment;
  • Eliminated a multitude of manual, error-prone and time-consuming steps resulting in a more efficient and accurate financial analysis and reporting process;
  • Delivered to the business users the ability to do ad-hoc analysis and create their own reports;
  • Delivered a solution to the business that the customer personnel were able to support and maintain without significant IT support or involvement; and
  • Provided the business a solution that resulted in a significant return on investment.

Shanghai East Hospital Enhances Patient Care by Speeding Data Exchange and Analysis


Shanghai East Hospital Enhances Patient Care by Speeding Data Exchange and Analysis



Located in the Lujiazui area, Shanghai East Hospital is a comprehensive health institution providing medical treatment, disease prevention, professional education, and research. The 850-bed hospital is one of the largest healthcare providers in the Pudong New District, employing 22 PhD tutors, 65 masters degree supervisors, and other senior healthcare professionals. The hospital provides up to 1.4 million outpatient services, 25,000 inpatient services, and 100,000 emergency treatments a year.


“Oracle’s business solutions streamlined the integration of our hospital’s management systems, lowered overall complexity and costs, enabled intersystem data exchange and connections, and made it easier to-share patient data”

Liu Bo, Director, Information Center, Shanghai East Hospital


Integrate hospital management systems to enable information such as doctor and technician schedules to be shared between different clinical and technical departments

Enable staff to improve the use of hospital resources by analyzing doctor and technician workloads and the use of medical equipment

Lower system development and maintenance costs


Engaged Oracle Gold Partner Shanghai Yanhua Smartech to integrate disparate systems using Oracle Data Integrator and Oracle Service Bus, and to build an analysis system using Oracle Business Intelligence and Oracle Essbase

Facilitated a smooth information exchange between clinical and technical departments using the Oracle platform, facilitating up to 100 system queries per second

Enabled hospital management to analyze doctor and technician workloads, allowing accurate schedules that ensure a health professional is always available to treat patients

Optimized the use of medical equipment and avoided scheduling conflicts

Cut system development workload by 50%

Expect to explore the use of Oracle Business Intelligence Enterprise Edition and Oracle Essbase to analyze patients’ prescription rates for preventing drug misuse and analyzing prescription trends

How to consolidate data in TM1


When loading data into a TM1 cube, the Turbo Integrator command used is CellPutN.  This sends a numeric value to a specific cell in your cube.   This command does not consolidate values, if the cells you are loading to have a value, it will be over written by the new value you send.  If you are loading two or more records to the same cell, each value will be over written by the next only the value from the last record from data load file will be retained.

For example, below is the data you want to send to the cube.

10050 C1 MKT USD NYC 2011 JAN 1000
10050 C1 MKT USD NYC 2011 JAN 500


Note that all cells (dimensions) for these two rows are the same but the data is different. By using the syntax CellPutN, the result for Account 10050 will show as 500 not the desired consolidated value of 1500.  To accumulate data as you load, you need to add the existing value from the cube to the data you are loading before loading the data to the cube.  Using the TI function CELLGETN to retrieve numeric data from a cell in the cube and then use CellPutN to send the consolidated value back to the cube.

TI Solution in two steps:



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