Master Data Management – The Lean Way

Triniti Lean ManagementKey Challenges

Despite making significant IT investments in various applications to run their operations, companies today still face a key challenge when it comes to managing the quality of their master data. How do we spend less time maintaining data and more time managing our operations? You could be increasing your inventory turns or shaping the demand or improving your demand fulfillment instead of fixing errors in product or customer data that typically result in bad planning and poor execution.

Key Outcomes

Visionary organizations have realized that managing master data is a business issue, not an IT problem. By applying the concepts of Lean business philosophy to master data, many wasteful processes such as fixing data errors in the information supply chain can be identified and eliminated. As a result:

  • Cost of managing data is significantly reduced

  • Error-free and high-quality data is produced in real time

  • Improved work standards for Master Data Management(MDM) are enforced

  • Information flows much faster across enterprise applications to support decision-making at all levels

Our Customers can create and use BI reports instantaneously!


The Lean Way - An Introduction to its Concepts

Inspired by the spectacular business results enjoyed by Toyota over a sustained period, leading academics and American business leaders studied the Toyota Production System and its inner workings. The insights and lessons from those exercises were incorporated into what is currently now a well-known business framework called Lean. As a framework, Lean provides fundamental concepts, strategic philosophical guidance, as well as operational tools and best practices that can be applied throughout the Enterprise to build a World-class organization.

There are many cornerstone principles underpinning the Lean philosophy that strengthen the process, people, and strategy dimensions of an Organization. It is not our intention to cover all of them in this paper. We will focus on the Business Process perspective, which is the area of focus for most Business leaders. Lean thinking exhorts companies to embrace the notion of “Continuous Process Improvement” – a never-ending journey towards excellence. Some of the key concepts that support Continuous Process Improvement include:

  • Identification and Elimination of waste (as in wasteful processes)

  • Focusing on end-to-end value stream

  • Standardization of processes

  • Assuring quality at source (No defects passed forward)

Due to their timeless appeal and universal applicability, it would make a good case to apply some of these key tenets to the task of Information Management – a critical organizational support function that enables both operational and analytical decision-making. 


Process Improvements Begin with Data

A business process operates within the context of a given operating environment, which in turn is described by data that defines its various entities (both real world and abstract). And these entities interact as part of the transaction. Clearly, Data is king and is the enabling language construct that allows communication about a business process amongst multiple participants.
 
Business executives realize that ascertaining the current “As Is” state of the business is the first step in any process improvement program. Accurate and complete data has to be present in a form that is easy to use for business executives to get a clear picture of their current performance benchmarks. When an enabling data infrastructure is missing, the need to focus on addressing the data issues first becomes imperative. This is another simple reason why executives leading Lean transformation programs will have to focus on getting the data right from the get-go.


Significance of Master Data in Process Improvement

Within the realm of Data or Information Management, master data domains have a unique character in that they are shared across the enterprise and have to be used consistently at all levels. Following are the three reasons why managing master data becomes a focus area for application of lean ideas:

  • Master Data is the language of the business – and it is in the best interest of the Organization to speak in a single, well-understood language when it comes to communicating with its customers and supply chain partners. Failure to do so would impact the company’s bottom line negatively.

  • Master data management (MDM) processes in today’s organizations are mainly driven based on what is allowed by applications such as ERP, CRM, SCM etc., that run most of the business operations. They provide rudimentary support for handling master data involving tedious, labor-intensive tasks using poorly-designed data entry interfaces. Without automation of data quality rules and lacking a process-centric approach, master data produced by such systems is of questionable quality and highly variable. The impact of poor master data ripples into the backend applications causing business process execution to be broken, requiring extensive and costly manual intervention.

  • Unlike the considerable attention and effort spent on managing their physical assets (inventories, work centers, supply chains etc.,), Business executives have not placed enough emphasis (so far) in managing information as a shared enterprise asset. As a result, many companies’ information systems have grown sporadically, operate as independent silos, or do not share information with each other in a timely manner - leading to sub-optimal decision-making.

As enunciated in the introduction, while there are many key principles of Lean, we focus primarily on understanding the lean concept of waste identification and elimination as viewed from the information management standpoint, in this paper. Refer to our similar whitepapers that take a holistic approach to MDM with respect to other concepts (ZLE, ADM, and MDM).
 

Forms of Waste in Master Data Processing

Central to Lean thinking is the ability to identify and eliminate wastes from the value-creating activities of the value chain. The classic seven different kinds of waste (Inventory, Overproduction, Transport, Defects, Motion, over processing, Waiting) are not too difficult to spot in physical work environments such as shop floors, however, in a business environment, waste is often intangible and difficult to spot.
 
The following table highlights different types of waste in the manufacturing discipline and its counterpart in data management.

Type of Waste Manufacturing Data Management Examples
Over Production Excess Inventory
  • Duplicate records
  • Errored data that is not fixed but recreated
  • Same data created in multiple systems
  • Same customer created with different names
  • Same part number created by different departments or systems
Defects Not complying with manufacturing specifications Data records created with erroneous or missing values in attributes
  • Customer’s address records created without postal code
  • Product record created with incorrect lead time to procure
Over Processing Rework
  • Mismatch between data created in two systems causing extra work to be done downstream
  • Erroneous data created while transacting; causing downstream action of fixing and or adding data
  • Incorrect account being entered or derived in sub-ledger causing manual overriding in the General ledger
  • Incorrect part number being entered in the shipping transaction causing inventory inaccuracy and adjustment transactions in inventory
Motion Inefficient layout on the shop floor for people and or resources
  • Complex UI (User Interface)
  • Multiple screens to maintain data that makes a data set complete
21 Screens in Oracle ERP to enter master data related to product (Item, BOM, Sourcing Rule/BOD, Assignment Sets, MPN’s, Cross-references, Categories, Routings, Resources, Catalogs)
Waiting Queue time Latency between master and transaction data being created and being consumed by downstream transactions and reporting applications
  • Data scrubbing
  • Data massaging
  • Complex ETL for BI Reporting
 

As you can see, all of the wasteful steps induce latency of varying degrees into the process, inhibiting data from being consumed in downstream operations such as transactions and reporting in a timely and reliable manner – for the simple reason that the data is not only delayed but also inaccurate and incomplete.


Processes will manifest themselves in the following form:

 
 

The waste outlined in the above example (process) adds latency to the key point of getting actionable reports. The waste manifests itself as expensive ETL development and maintenance costs of BI solutions. These wasteful steps permanently leave rework and non-value added stages in the process rather than eliminating the root cause. And when the root causes of data errors are not eliminated, they often keep recurring, forcing IT to spend a significant amount of its time and resources fighting fires on a regular basis.
 
Thus, what passes off as “Master Data Management” in organizations today is actually waste in various forms, when viewed from the Lean standpoint. It can be seen that a vast majority (upwards of 80%) of daily activities pertaining to master data issues are simply waste, from a customer’s point of view.
 

Making a case for “Mastering” Data Management: The Lean approach

By applying a Lean lens to their current data management practices, companies will recognize that a majority of their current activities are of non-value added nature. This awareness and focusing on improving master data used in the business will be the starting points for business leaders to begin their Lean projects.

A systematic lean approach to MDM incorporates integration of other key lean concepts that were discussed in the introduction to this paper. Such a system advocates:

  • Elimination of different forms of waste in activities performed over the lifecycle of master data. All with a built-in quality assurance of data at the point of creation

  • Incorporation of standard work processes that ensure master data of consistent quality is created and delivered

  • Taking the Value Stream process model to implement workflow and assigning clear roles & responsibilities. Resulting in metrics & measures for monitoring data quality that will provide new insights into opportunities for further process improvement.

 The benefits of such an MDM include:

  • Creation of high-quality master data at a fraction of cost and time, compared to current alternatives

  • Nearly 100% defect elimination in master data, ensuring no bad data flows to consuming member applications

  • Vast increase in master data reuse across current and future applications

  • Incorporation of Quality benchmarks through Data Quality scorecards


Summary

Business executives, tasked with leading Lean-based Organizational Transformation Initiatives, have to realize the primacy of putting the right Master Data Management systems in place, as a key enabler for their business process improvement activities.
 
For companies still struggling with master data in spite of investments in IT, the Lean concepts such as waste elimination, quality at the source, process standardization offer unique insights to design a simpler, cost-effective approach to manage these critical data resources.

Triniti's master data management is designed with the above mentioned Lean principles. You can avail yourself of the benefits of managing your master data in a Lean way and achieve the business benefits mentioned in this paper.

Call 866-531-9587 / Fill out the contact form for a free consultation on how Triniti MDM can be leveraged with Lean priciples for your master data management.