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1.Introduction

Corporations having heterogeneous landscapes today are facing the need for a single source of true “data”. The answer comes in the form of MDM 5.5 which offers the capability for “data” consolidation, “data” harmonization and central “data” management etc. It is evident that “Data” lies in the core and hence the integrity and quality of data is of vital importance. Those aware of the concept of Quality Assurance will appreciate that if the process and standards are adhered to; then the delivered products meet the quality requirements. This holds good for data as well. If the process of generation of data( master data or transactional data) follows the standards then quality and integrity of will be ensured. There may be several initiatives or steps an organization may take in order to ensure integrity and quality of data. One such initiative can be “Data Standardization”. The ideal time to undertake “Data Standardization” project is prior to the implementation of any integrated ERP system like R/3. However, this looks a bit impractical because today almost all successful organizations run on one or the other good ERP system. In this situation the way out is to undertake “Data Standardization” project prior to MDM implementation. Obviously this will be like “doing the Home work” or “building a strong foundation” for the prospective MDM implementation.

2.What is Data Standardization?

An example of Data Non-Standardization!!

Sometimes it is said – offense is the best form of defense. On the same lines, I would like to put here a crude example of Data Non-Standardization in an organization; this will give a fair idea what I want to talk about. The Scenario: An organization has three plants at 3 different locations- 001, 0002 and 0003. All these plants use same bearing: Material: Ball Bearing ID: 25mm OD: 47mm, Width: 12mm Catalog # 6005 However, in each plant it is designated as below: image In each plant it has different material code and description. Even the same bearing is having two different material codes and descriptions in one plant. This example may sound too exaggerating in the first look. However, reality may not be too different. If analyzed closely the master data team can filter out many such examples. This is an example of Data Non-Standardization. No unique material code number. No unique or standard way of description. This kind of situation leads to serious issues like lack of visibility of a material or product across the enterprise, higher inventory – same material purchased under different codes and/or descriptions etc. So the bold statement is – Problem exists. But then….what is the way out?

3.Time to go back to Basics!?

Let us quickly revise our basics about Data, Information and Knowledge.

May be the answer lies there only!!

Data, Information and Knowledge:

The classical difference between Data, Information and Knowledge still holds good. Data is factual information, used for analysis or reasoning. Data on its own has no meaning, but becomes information when once it is interpreted. Information is a collection of facts or data. It is this information that gives organizations diagnosis or a health check to enable them make a corrective actions required to align them with business drivers. image We can clearly understand the difference between these three terminologies by defining them with a few examples. Data: Kind of Symbols. Data is raw. It simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself. Example: A spreadsheet generally starts out by holding data. In SAP world, a table in SAP contains data, which does not have meaning of itself. Information: Data that are processed to be useful. Provides answers to “who”, “what”, “where”, and “when” questions. Information is data that has been given meaning by way of relational connection. This “meaning” can be useful, but does not necessarily have to be. Example: A relational database makes information from the data stored within it. In SAP world, an ABAP query gives birth to information by joining various tables containing data. Knowledge: Application of data and information. Answers “how” questions. Knowledge is the appropriate collection of information, such that its intent is to be useful. Knowledge is a deterministic process. Example: “Spend data” gets generated continuously thru Purchase Requisition, Purchase Order, Material master, Invoice verification etc. in SAP. Using this “data”, an ABAP query can provide the “information” in the form of reports for the purchases made for a certain material XYZ during the current financial year on parameters like – 1) from which suppliers, 2) at what rates, 3) for which plants etc. Purchase Manager can come up with “Global Spend Analysis” and use it for Forecasting/Budgeting for next year or to improve cost saving opportunities. This way he is making use of this information by building his “knowledge”. Obviously the data, being the core, is very significant. And it is the quality and integrity of data that makes it significant.

4.Why Data Standardization?

The “Standardization of Data” is imperative to meet the organizational objectives such as: • To provide unique definition of Enterprise entities. • To ensure a single, common, unambiguous identification and description of various entities e.g. materials, vendors, customers, employees etc. across the enterprise. • To provide the data consistency that is required to enable processes and functions to operate in the future Business Framework. • To provide an effective foundation for achieving business flexibility. • To ensure a seamless integration of Business Partners into the Supply Chain of the enterprise through effective support of external data requirements. • To ensure simplified inter-operability between various system platforms. • To achieve common understanding and usage of Data Standards within the enterprise.

5.Benefits of Data Standardization

• It is a way to enable standard and integrated systems to meet its business drivers. • It is a proactive approach which reduces the risk of data duplication, manipulation etc. • It enables the business to respond quickly and effectively to constantly changing client needs • It is a way to ensure – Right information to right people at right time • It provides reliable, accurate, timely data which in turn helps is Cost reduction, reduction in process wastages,

6.How the Data Standardization can be achieved?

This requires a systematic approach to be followed. Typical activities in this approach could be: 1. Planning for the Data Standards 2. Development of Data Standards 3. Implementation of Data Standards 4. Strictly following and adhering to the Data Standards image

7.Next Steps

In the next blog we will take a deep dive into nitty-gritty of each of these activities i.e. 1. What Data should be considered for Standardization and what not? 2. Business rules to define the enterprise data 3. Master Data Vs Global Data 4. Typical business processes and functions supported by Data Standardization We will also walk through those aspects and strategies which go into developing a long term vision about the Data Standardization and putting this vision into action.

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