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A PUBLICATION OF RELATIONAL SOLUTIONS
IMPLEMENTING & MANAGING THE
DEMAND SIGNAL
MANAGEMENT PROCESS
THE STEP-BY-STEP GUIDE ON HOW TO IMPLEMENT AND MANAGE THE DSM PROCESS
TABLE OF CONTENTS
3
A Misconception
6
Building Blocks
8
All You Need Is- Time
10
A Single Source of Truth
12
What Else?
14
Choosing Independence
CHAPTER ONE
A MISCONCEPTION
A Misconception
All Data Isn’t Clean Data
One misconception is that retailers send clean data. This simply is not the
case. The cleansing and validation of the data is critical. Invalid data will give you
invalid results. Retailers often send incomplete data. They also send duplicate
data. In addition, they tend to “recast” previously sent data. Thus, how does one
distinguish between duplicate and recast data? How do you identify missing
fields? How is it rectified and re-loaded? What are the business rules? How is it
modeled for reporting that is specific to Point-of-Sale data? Can it be integrated
with internal data such as shipments, forecasts, budgets, etc.? How do you
identify and analyze promotions? How can you predict problems before they
impact your bottom line? Do you have the time and knowledge to shift through all
this data to find only the nuggets that contain actionable information?
Because of the complexity, most CPG manufacturers simply buy
summarized data from third party data providers. But by having an enterprise
demand signal management solution in house or in a hosted environment,
manufacturers can get more timely information and often save money by
purchasing only the data they aren’t already getting from the retailers.
Not all retailers send clean data: Cleansing and validation is critical.
A true demand signal management
solution has pre-defined processes
set up to automate the extraction,
transformation, cleansing,
synchronization, and management
processes.
- Janet Dorenkott, Co-Founder,
Relational Solutions
“
”
CHAPTER TWO
BUILDING BLOCKS
Building Blocks
Starting With IT
Building a demand signal management solution is not an easy task. Most IT
departments of large companies are accustomed to building a data warehouse
from internal data such as orders, shipments and finance. But building a demand
signal management solution for POS data is completely different, although the
fundamental methodologies should be the same.
With POS, you are dealing with 3rd party data that you have little to no
control over. It comes in by the bucket full. Some of its daily, some weekly and
some monthly yielding various levels of granularity. A lot of data from many
retailers and other data sources, in varying formats supporting multiple feature
sets. How do you consolidate it all, automate the process and build it so that it is
both scalable and flexible and in a way that you won’t outgrow it? How do you
align granularity levels and account for unlike calendars?
CHAPTER THREE
ALL YOU NEED IS- TIME
All You Need Is- Time
But At What Cost?
The Data integration process of transferring data from source to target can
be painfully time consuming, tedious, and expensive. Many developers want to
hand-code much of this work through the use of SQL scripts and stored
procedures. This process is time consuming, difficult to maintain and support,
impossible to manage, error prone, and not optimized for performance. What if
something needs to be changed? Is any of it documented?
CHAPTER FOUR
SINGLE SOURCE OF TRUTH
A Single Source of Truth
What Your Solution Should Offer
A true Enterprise demand signal management solution handles the data
integration, cleansing and management processes. A good demand signal
management process flow should be easy to use, have database specific API tie-
ins, make simple tasks easy and complex tasks possible, require little to no coding,
take advantage of in-memory processing for maximum performance and provide
a fast ROI for your POS project.
A true demand signal management solution has pre-defined processes set
up to automate the extraction, transformation, cleansing, synchronization and
management processes. Because every company is different, the demand signal
management solution should be open and allow for customization and
enhancements of these processes.
CHAPTER FIVE
WHAT ELSE?
What Else?
A DSM Checklist
A metadata repository is an important aspect of any demand signal
management project. It contains all your source and target tables and field
definitions. It also contains any re-useable transformation logic.
The demand signal management process should be well documented and should include:
• Job graphics
• Job mappings
• Filters
• Input & output table layouts and table definitions
• SQL Statements
• Transformation & cleansing routines
• Job Parameters
• A flexible data model
• Data Enrichment components
• Business Rules
• Instructions on how to add additional retailer data
• Automated alerts & report distribution
• The ability to create ad-hoc reports
CHAPTER SIX
CHOOSING INDEPENDENCE
Choosing Independence
Solutions That Promote Freedom
Time and time again we see vendors leave their customers completely
dependent on them. If they want to add a new retailer, they need to license a new
module. If they want to add a new data source, they have to license a new data
integration tool and contract services. Since demand signal management is a
“process” and not a “product” companies should have the option to make future
enhancements and management decisions without being forced to go back to the
vendor whenever they need changes.
You should have the control (if you choose) to run processes, stop them,
view the log files, and even monitor the job in real-time. An enterprise solution
should support many databases so as your data grows, you are not throwing away
your old solution. It should also support many business intelligence tools and offer
the option to have the solution hosted or behind your firewall.
ALTERNATIVES?
When it comes to choosing the right solution for your
organization, it is important to weigh the options. To make
things a little easier, we’ve developed a “Demand Signal
Process” checklist to help you weigh the alternatives.
SEND ME A FREE
CHECKLIST

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Implementing & Managing The Demand Signal Managment Process

  • 1. A PUBLICATION OF RELATIONAL SOLUTIONS IMPLEMENTING & MANAGING THE DEMAND SIGNAL MANAGEMENT PROCESS THE STEP-BY-STEP GUIDE ON HOW TO IMPLEMENT AND MANAGE THE DSM PROCESS
  • 2. TABLE OF CONTENTS 3 A Misconception 6 Building Blocks 8 All You Need Is- Time 10 A Single Source of Truth 12 What Else? 14 Choosing Independence
  • 4. A Misconception All Data Isn’t Clean Data One misconception is that retailers send clean data. This simply is not the case. The cleansing and validation of the data is critical. Invalid data will give you invalid results. Retailers often send incomplete data. They also send duplicate data. In addition, they tend to “recast” previously sent data. Thus, how does one distinguish between duplicate and recast data? How do you identify missing fields? How is it rectified and re-loaded? What are the business rules? How is it modeled for reporting that is specific to Point-of-Sale data? Can it be integrated with internal data such as shipments, forecasts, budgets, etc.? How do you identify and analyze promotions? How can you predict problems before they impact your bottom line? Do you have the time and knowledge to shift through all this data to find only the nuggets that contain actionable information? Because of the complexity, most CPG manufacturers simply buy summarized data from third party data providers. But by having an enterprise demand signal management solution in house or in a hosted environment, manufacturers can get more timely information and often save money by purchasing only the data they aren’t already getting from the retailers.
  • 5. Not all retailers send clean data: Cleansing and validation is critical. A true demand signal management solution has pre-defined processes set up to automate the extraction, transformation, cleansing, synchronization, and management processes. - Janet Dorenkott, Co-Founder, Relational Solutions “ ”
  • 7. Building Blocks Starting With IT Building a demand signal management solution is not an easy task. Most IT departments of large companies are accustomed to building a data warehouse from internal data such as orders, shipments and finance. But building a demand signal management solution for POS data is completely different, although the fundamental methodologies should be the same. With POS, you are dealing with 3rd party data that you have little to no control over. It comes in by the bucket full. Some of its daily, some weekly and some monthly yielding various levels of granularity. A lot of data from many retailers and other data sources, in varying formats supporting multiple feature sets. How do you consolidate it all, automate the process and build it so that it is both scalable and flexible and in a way that you won’t outgrow it? How do you align granularity levels and account for unlike calendars?
  • 8. CHAPTER THREE ALL YOU NEED IS- TIME
  • 9. All You Need Is- Time But At What Cost? The Data integration process of transferring data from source to target can be painfully time consuming, tedious, and expensive. Many developers want to hand-code much of this work through the use of SQL scripts and stored procedures. This process is time consuming, difficult to maintain and support, impossible to manage, error prone, and not optimized for performance. What if something needs to be changed? Is any of it documented?
  • 11. A Single Source of Truth What Your Solution Should Offer A true Enterprise demand signal management solution handles the data integration, cleansing and management processes. A good demand signal management process flow should be easy to use, have database specific API tie- ins, make simple tasks easy and complex tasks possible, require little to no coding, take advantage of in-memory processing for maximum performance and provide a fast ROI for your POS project. A true demand signal management solution has pre-defined processes set up to automate the extraction, transformation, cleansing, synchronization and management processes. Because every company is different, the demand signal management solution should be open and allow for customization and enhancements of these processes.
  • 13. What Else? A DSM Checklist A metadata repository is an important aspect of any demand signal management project. It contains all your source and target tables and field definitions. It also contains any re-useable transformation logic. The demand signal management process should be well documented and should include: • Job graphics • Job mappings • Filters • Input & output table layouts and table definitions • SQL Statements • Transformation & cleansing routines • Job Parameters • A flexible data model • Data Enrichment components • Business Rules • Instructions on how to add additional retailer data • Automated alerts & report distribution • The ability to create ad-hoc reports
  • 15. Choosing Independence Solutions That Promote Freedom Time and time again we see vendors leave their customers completely dependent on them. If they want to add a new retailer, they need to license a new module. If they want to add a new data source, they have to license a new data integration tool and contract services. Since demand signal management is a “process” and not a “product” companies should have the option to make future enhancements and management decisions without being forced to go back to the vendor whenever they need changes. You should have the control (if you choose) to run processes, stop them, view the log files, and even monitor the job in real-time. An enterprise solution should support many databases so as your data grows, you are not throwing away your old solution. It should also support many business intelligence tools and offer the option to have the solution hosted or behind your firewall.
  • 16. ALTERNATIVES? When it comes to choosing the right solution for your organization, it is important to weigh the options. To make things a little easier, we’ve developed a “Demand Signal Process” checklist to help you weigh the alternatives. SEND ME A FREE CHECKLIST