Master Data Management The Lean Way

Transcription

1 Master Data Management The Lean Way Inspired by the spectacular business results enjoyed by Toyota over a sustained period, 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. As Lean thinking continues to spread all over the world, business leaders are also adapting tools and principles beyond the traditional domain of manufacturing, to Supply chain operations, services, retail, healthcare and even Government. Lean awareness is beginning to take root among senior leaders and managers in all sectors today. Applications of Lean concepts can be found in almost all business functions today, spanning sales & marketing, managerial finance & accounting, human resource management, customer service to name a few. However, the Information Technology (IT) function that is entrusted with the responsibility of supporting timely data-driven decision-making for Business Executives has somehow seen tepid off take of Lean concepts. Also, it is common knowledge that many Lean projects do not involve IT in their process improvement initiatives. Why has it come to such a state? The reality of most IT organizations is that they are chronically overburdened and reactive, habituated to fire-fighting and problem solving on the fly. Rarely do they engage in root cause analysis of problems beyond their IT silo, thereby producing piecemeal solutions that are complex, rigid and fragile, breaking at the first change of a given problem context. This further alienates them from the business users of IT applications. We believe that judicious application of Lean concepts to Enterprise Information Management is the right approach to breaking this vicious cycle and transforming IT into a trusted partner for the business. What follows in this document is an effort to throw some light on how key concepts of Lean philosophy map into MDM (Master Data Management) challenges faced by Organizations. We will also see how organizations and their IT teams can apply ideas from Lean to justify investing in appropriate solutions, tools & technologies that can holistically address master data problem. IT in Lean: What is Lean IT There are two distinct ways in which Lean s applicability can be assessed in the IT domain. The application of lean principles to the operations of the IT companies or departments. This is really about efficiency in IT activities how do we make better software, how do we upgrade newer versions faster with minimal disruption to business, how do we better handle threats and challenges to our data and infrastructure assets etc.

2 The effective use of IT in support of Lean Enterprise transformation. What are the information needs of different consumers and how are they related? How can we deliver timely information to decision makers? How do we eliminate redundant data systems? How do we re-architect our systems to become agile to support changing business needs? The latter s scope is the entire Organization and its functions and IT leaders need to especially focus on this aspect of Lean when it comes to IT how to become effective first, efficient later. We propose that the concepts of Lean thinking, especially those around elimination of waste and flow of information, can be applied to some of the most critical functions of IT operations today. We strongly believe that IT leaders (CIOs, CTOs, Enterprise Architects, Chief Data Officers etc) should take a closer look at understanding the application of Lean concepts when it comes to their function. Especially relevant is the subject of managing master data entities, which we think is one of the areas where benefits of lean can be highlighted. Why master data? Master data is at the heart of everyday business communication. Where do we ship this consignment, who makes the payment, what line items of the order can be drop-shipped, which items are nearing their replenishment levels, can we approve this customer order based on credit limit policies, which products generate the most profit margin and why, which regional warehouse had most stock outs the last quarter, when should we launch a new product these are but a few types of operational and analytical questions whose answers revolve around the context of master data entities such as Products, Suppliers, Customers, Assets, Work Centers, Locations, Chart of Accounts etc. Master data is employed not just across the different functions of the organization but also to communicate with partners in the business ecosystem. Given its widespread and pervasive usage, master data can provide a long-term source of competitive advantage when it is mastered effectively. The impact of good master data management can ripple across many information consumers, both upstream and downstream of the supply chain. But master data handling in many organizations is in a state of mess. Due to the siloed nature of many IT systems and applications, master data is stored in multiple places in a redundant manner. This results in disparate data nomenclatures for the same entity, differing data structures and definitions, inconsistent use of rules to enforce business constraints etc. These two factors importance of master data and current poor approaches to governing master data illuminate the urgent need to employ new and better techniques to harmonize master data. Let s now turn our attention to understanding the core lean concept of waste identification and elimination when viewed from the information management standpoint.

3 What is information waste? Central to Lean thinking is the ability to identify and eliminate waste from 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, but in an IT environment, waste is often intangible and difficult to spot. In our view, the following types of waste are applicable for processes that manage information. i. Over production: This manifests in the form of duplicate records that relate to a single real-world entity. A common example being recording a customer with more than one Identifier, or creating a Product / Item multiple times with different unique identifiers or keys. The result is that the data store contains more records than the number of real world entities that the organization truly operates with. Another aspect of over production is the redundant storage of master data spread across multiple backend systems, instances and business units. ii. iii. iv. Defects: This refers to creation of master data records with erroneous values for attributes, missing information for some elements, partial / incomplete data for certain attributes. Examples include storing a customer s address that turns out to be undeliverable, or missing ZIP/Postal codes or creating a new item but without assigning it to any known product classification etc. When data entry systems do not have sufficient validations and business rules to enforce at the point of data creation, it often manifests in the form of defective data records. This renders the master records useless in the consumption of business transactions or reporting, further downstream. Over processing: In the absence of usable information, data needs to be massaged and transformed by performing extra operations and consuming additional resources. This would typically take the shape of costly Extract-Transform-Load (ETL) processes that require sophisticated knowledge of special tools. Motion: This refers to the additional movement of employees and equipment, required to accommodate inefficient plant layout, defects, reprocessing, excess inventory etc. From an information management point of view, a good example of Motion waste is the navigation of tens of User Interface (UI) screens to create a single Product record in a backend ERP system. Accessing and closing each screen causes multiple context switches (and lots of mouse clicks) which makes data entry extremely unproductive and error-prone. v. Waiting: Business users and Analysts typically wait for data to be batch processed from the operational systems before they can access data for analysis and business intelligence purposes. As is evident in the flow diagram that follows, poorly designed processes for data governance & administration cause delays in generating right type(quality) of information in a timely manner resulting in unnecessary Waiting.

4 When master data is inaccurate, incomplete, duplicated and unusable, business processes magnify and propagate these errors further into other parts of the Enterprise. From a Lean perspective, an organization that does not manage master data well ends up with inefficient processes represented as follows The waste outlined in the above process adds latency to the key goal 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 add non value-added stages into the process rather than eliminating the root cause that created the wasteful steps to begin with. And because the underlying causes of data errors are not eliminated/corrected, the wasteful steps often recur, forcing IT to spend significant amount of time and resources fighting fires regularly. Making a case for mastering data management: The Lean view By applying concepts of Lean framework to the challenges of data management, IT leaders will realize that a major percentage of their current activities aimed at data are non valueadded and a waste of time. Realizing that mastering data management is really about eliminating waste and making information flow to the consumers (wherever possible), will bring a whole new perspective to IT leaders who are looking for new solutions to their master data problems. It will help them evaluate IT solutions from vendors in a new light - judging products based on their ability to support lean concepts rather than just by fanciful lists of product features & functions quoted by the vendors. They understand that an effective toolkit for master data management will be one that: Helps eliminate various wasteful information through strong data quality capabilities Builds a Process perspective into managing data through incorporation of robust workflow features that facilitate collaborative authoring of master data using

5 extremely productive, role-based User interfaces, while also managing approvals, notifications, escalations etc Fosters setup and implementation of a strong data governance system supported by operational data stewardship Provides on-demand insight into the quality of master data through measurement of user-defined key quality indicators Delivers strong Reporting capabilities around Process performance indicators to measure the effectiveness of current practices. This process performance data is especially important for Process Owners and Management and helps in prioritizing continuous improvement initiatives. For example, by providing insight into the fact that process cycle times for on boarding new suppliers have shown an increasing trend over the past 6 months, it becomes clear to the IT team that this is an area where process needs to be improved. A Lean view of Information Management can really go a long way in helping business and IT leaders to make a strong case for investing in tools that support master data management. MDM as a tool vs MDM implemented with Lean principles This is an interesting question for organizations who have already implemented an MDM tool. Does the fact that they have implemented an MDM solution automatically make them LEAN from a master data perspective? The answer is no. If MDM the tool were implemented with LEAN principles, only then the answer would be yes. Early MDM implementations were necessitated to fulfill requirements of data warehousing and supply chain planning as an overall part of an ETL strategy. If that is the case, then it hinders rather than enables Lean. No wonder Gartner (Market clock for MDM 2015) predicts the obsolescence of such MDM hubs. There is an additional challenge for IT organizations that have implemented MDM for DWH and SCP to make the business case for either a reimplementation of the existing tool, or acquiring new tools as the tools implemented may not be designed to support Lean concepts. Recommendations for Lean Implementations of MDM i. When typical Lean improvement projects such as setup time reduction at a large work center or layout changes in a manufacturing cell are being undertaken, IT leaders would do well to view those projects as opportunities to join forces with the business project teams in order to harmonize the underlying master data pertaining to machines, products, work centers, locations etc. Because such projects usually involve collection of accurate and detailed data about current and target conditions for the operations, they present a good opportunity to assess the state of data in their IT systems and see how it maps to the reality of the shop floor. Once the assessment reveals opportunities for corrections of master data, a data cleaning exercise followed by the institution of proper data governance rules will offer IT a more structured approach to master their data management practices. Both as a beneficiary and as an enabler, IT can thus play an important role in Leandriven operational improvements.

6 ii. When creating a business case for justifying investments in MDM systems, IT leaders should complement their financial arguments with insights related to applying Lean Thinking to Information Management in general, and master data management in particular. They should present their case in terms of how an MDM project would help establish & strengthen the core Lean tenets of waste reduction, process standardization, information flow to provide highly trusted source of master data to the Organization. For Organizations that are already on the Lean transformation journey, this would be a perfect opportunity to incorporate Lean ideas into a function which has seen very little enthusiasm for lean. For those that are not yet into Lean, the arguments would expose decision-makers to new ways of thinking about Information Management practices and help justify investments in tools supporting MDM. Summary Lean practitioners understand the need to extend Lean philosophy beyond the shop floor, inventory management, and supply chain functions. They understand that to build a truly Lean organization across the entire spectrum of the organizational functions, it is necessary to transform the core support function of IT as a Lean enabler. However, Lean programs so far have made very little headway in bringing IT around as a Lean facilitator. On the other hand, CIOs, CTOs, Enterprise Architects etc who are responsible for aligning IT with business strategy have had very little opportunity and awareness of how they can complement the efforts of Business teams in a Lean-driven business transformation. Shared corporate information entities such as Master data about Products, Customers, Suppliers, Assets, Locations, etc provide a perfect opportunity for bringing together Business and IT teams. By applying a Lean lens, we can understand how the current practices of information management have built in a lot of process inefficiencies with many non valueadded activities that add cost and complexity to the whole aspect of information lifecycle management, thus greatly reducing the utility value of information being stored and accessed by Enterprise Information consumers. By identifying these wasteful activities and working towards eliminating them through the use of well-designed IT products custom-built to support Lean operations, Organizations gain following advantages: Significantly reduce their costs and efforts to maintain data processes (and consequently, enhance resource allocation towards strategic IT initiatives), Facilitate business teams to accelerate their transformation into data-driven, Zero Latency Enterprise (ZLE). Accelerate ROI on downstream applications such as CRM, ERP, SCM and BI Rapidly integrate M&A s and realize economies of scale. Respond quickly to changing market conditions. By delivering such compelling value proposition to both the sides of the business, Lean Master Data Management approaches have the potential to truly align IT with the business side.