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July 31, 2012

Retail Net Price calculations_2

 

Hope you have understood the complexity involved in the various aspects of Retail Net Price calculations.  Now, let us examine the ease with which the business wants to examine the information. 

Here the business and marketing users are interested in studying the various trends in gross margins, net margins, trade spends, trade investments, discounts, ad hoc mark downs, Primary sale margins, and secondary sale margins across time periods, customers, products etc.

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July 19, 2012

Retail Net Price Calculations: Scenario description

Ever wondered how the CPG companies calculate net margins on all their products?  Business & Marketing users are interested to study the various trends in the gross / net margin contributors across different customers, geographies, sales channels, and time periods.  They are not only interested in the past transactions, but also want to dabble with the forecasts and budgets. 

This brings in a huge data management exercise before satisfying the business & marketing users.  Data streams involving invoices, pricelists, and financial trial balances come into picture here, with ever increasing complexity in dealing with their details.  Most of the data management complexities can be categorised into Pricing, Master and Financial data complexities.  

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May 15, 2012

Relational vs Dimensional modeling for Data warehouses

The data warehouse can be modeled in Relational or Dimensional model and there are strong proponents for both the models.

There are several factors that need to be considering before choosing one of these options; some of the key factors will be: the type of reporting needed - Self service BI (adhoc) or pre-determined reports, the number of tables in the data warehouse.

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January 27, 2012

Cloud BI Scenarios:

Given high data volumes & their movements in BI and DWH space, ever wondered on where you will deply Cloud in BI?   I came up with some quick reference guide on the areas where we can apply Cloud.  Here are some examples (in the RCLL space):

External Data Analytics: Data from AC Nielson and IMS (medical POS data) is analysed by most of the organisations already, but mostly in silos at country level.  This way the metrics and other parameters they measure are usually country-specific, leaving very little information for sharing across countries.  Organisation-wide implementation of the external data analytics is gaining ground mainly in increasing the productivity, optimise costs and increase information sharing.  Since there is a lot of hesitation for org-wide adoption, cloud plays a very important role in providing scalable infrastructure.  Cloud also provides anywhere-access, thus making information sharing even more easy

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January 26, 2012

BI on Cloud

You must be aware that Cloud has been on the Top of CIOs Agenda these days.  Business Intelligence and Analytics were on the top 3 of CIOs Agenda till last year, but now they appear within Top 10.  If you separate them as infrastructure and applications, then BI and Analytics would still occupy the top slots. 

Business Intelligence always evolves at an organisation, hence is best described as a journey, rather than a reaching a destination.  The side effect for this evolution is that it accumulates lots of data over a period of time, and generates attention across the organisation.  Our experience shows that data accumulation has serious consequences on server scalability and slows down future developments.  Its popularity among the other functions across the organisation attracts more data usage and building extra data marts/ data pens etc.

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December 9, 2011

Start up to MDM (3).....

Understanding the organisation's information / reporting needs and categorisation of the same into Subject Areas / Information Areas gives a good start into MDM structuring.  Next we need to label the subject areas with respect to the organisation parameters like business units, geography, and the hierarchy of people it is supposed to service.  

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November 28, 2011

Start up to MDM (2)...

Before we jump on to the MDM bandwagon on a full-scale, we need to actually take a step back and think.  If you look at the inners of any MDM application, what do we get to see?  We see data extractions, data integrations, transformations, cleansing, standardisation, and data loading...., right? Where do we see such stuff?  We see all these in the larger picture of Business Intelligence or Information Management.  So, MDM has to fit in the overall picture of Information Management or Business Intelligence.  All these BI or IM help in Decision Support for Business Users, right? So, why not look at it from the top down and bottom up approaches in terms of information requirements at various levels or across the organisation.  You will have information / decision making needs at various levels of the organisation like: Top management, Business Unit level, Analytical level, and Operational level.  I added Operational level included here, as BI / IM is becoming omnipresent in most of the organisations.  

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November 23, 2011

Start up to MDM

When an organisation takes up a Master Data Management exercise, we expect the whole organisation to adopt the new Master Data / Reference Data Codes.  How correct is this?  What happens to the part of the organisation which is still dealing with day-to-day operational activities & related challenges?  What happens to the part of the organisation that is still operating Mainframes and other legacy systems?  

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September 7, 2011

BI & MDM in CPG Demand Forecasting (3). . .

This is a continuation to the blogs dated 18th and 22nd Aug2011.  We talked about the business requirements in the first blog.  Then we talked about how we plan to address the Master data hierarchy requirements in the second blog.  In this blog, I am going to cover the most important aspect of the business that is margins / profit. 

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August 22, 2011

BI & MDM in Demand Forecasting . . . .

This is the continuation of the blog on "BI & MDM in CPG Demand Forecasting "dated 18Aug2011.  I am going to address the problems we saw and the solutions we offered, one by one. 

Let me start with the multiple hierarchy requirements.  The SKUs are supposed to be rolled up as per the Product Group (main one: based on type of material), Brand (different names for different customers, regions), Sub Brand (based on grade of the material), Packaging size (based on the quantity packed), Business Unit (whether selling directly to big retailers, retail consumers etc.), Category (manufactured / bought), Recipe (to indicate main ingredients), and SKU Case (trollies, packs, SKU etc.).   A huge number of hierarchies as you can see....  These are the ones recognised so far.  There are many to be defined yet.  

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