Visionary organizations have realized that managing master data is a business issue, not an IT problem. Eliminate wasteful processes such as fixing data errors in the information supply chain by applying Lean business philosophy concepts to master data. As a result:
Reduce the cost of managing data
Produce Error-free and high-quality Data in real-time
Enforce work standards for Master Data Management (MDM)
Flow information much faster across enterprise applications to support decision-making at all levels
Despite making significant IT investments in various applications to run their operations, companies today still face a key challenge in managing their master data quality. 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 inadequate planning and poor execution.
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Introduction to Lean Concepts
Inspired by Toyota's spectacular business results 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 are now a well-known business framework called Lean. As a framework, Lean provides fundamental concepts, strategic philosophical guidance, and operational tools and best practices that can be applied throughout the Enterprise to build a World-class organization.
Many cornerstone principles underpinning the Lean philosophy 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 for business leaders. Lean thinking urges companies to embrace the notion of "Continuous Process Improvement" – an endless 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 an excellent case to apply some of these critical tenets to the task of Information Management – an essential 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 is described by data that defines its various entities (both real-world and abstract). These entities interact as part of the transaction. Data is king and the enabling language construct that allows communication about multiple participants' business processes.
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 must 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. It is another simple reason why executives leading Lean transformation programs should focus on getting the data right from the start.
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 entire organization and have to be used consistently at all levels. Following are the three reasons why managing master data becomes a focus area for the application of lean ideas:
Master Data is the business's language – and it is in the organization's best interest 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, and HCM that run most of the business operations. These systems 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 questionable and highly variable. It impacts the downstream application modules that consume it. It breaks business process execution, requiring extensive and costly manual intervention
Unlike the considerable attention and effort spent on managing their physical assets (Examples include inventories, work centers, and supply chains), Business executives have not placed enough emphasis (so far) on governing 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 promptly - leading to suboptimal decision-making
As enunciated in the introduction, while there are many fundamental principles of Lean, we focus primarily on understanding the lean concept of waste identification and elimination. Refer to our similar whitepapers that take a holistic approach to MDM concerning other ideas (ZLE, ADM, and MDM).
Forms of Waste in Master Data Processing
Central to Lean thinking is identifying and eliminating waste from the value-creating activities of the value chain. Unlike manufacturing, it is difficult to spot the classic seven different Wastes (Inventory, Overproduction, Transport, Defects, Motion, over-processing, Waiting) in a business environment. It 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||
|Defects||Not complying with manufacturing specifications||Data records created with erroneous or missing values in attributes||
|Motion||Inefficient layout on the shop floor for people and or resources||
||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||
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 vital 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. Eliminate recurring root causes of data errors. They force IT to spend a significant amount of its time and resources fighting fires regularly.
Thus, what passes off as "Master Data Management" in organizations today is waste in various forms when viewed from the Lean standpoint. A vast majority (upwards of 80%) of daily activities about master data issues are waste, from a customer's perspective.
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 most of their everyday activities are non-value-added. 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 other critical lean concepts 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 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
Value Stream process model to implement workflow and assigning clear roles & responsibilities. It results in metrics & measures for monitoring data quality that will provide new insights into further process improvement opportunities
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
Triniti's Master Data Management embodies the above mentioned Lean principles. You can avail the benefits of managing your master data in a Lean way and achieve the business benefits mentioned in this paper.