Collaborate 2012-accelerated-business-data-validation-and-managemet


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Collaborate 2012-accelerated-business-data-validation-and-managemet

  1. 1. Accelerated Business Data Validation and Management for a GlobalEnergy Services CompanyObjective of this PaperGood data is worth more than gold (even at today’s prices). During any conversion, a data validationstrategy is an often overlooked, high-risk activity. For large multi-national organizations, this entireprocess will be repeated numerous times and a complete data management strategy is required toensure data is not only initially converted, but systematically managed through its lifecycle of beingmaintained and quickly re-organized. This paper provides a systematic approach for acceleratedconversion, a compelling data management process and strategies for long-term data re-engineeringapplicable for any business.Intended Audiences:(i) Individual contributor (ii) Project team member (iii) Project ManagerHigh Level OverviewDuring an initial data conversion, the strategy is typically to pack data from the old system, move to thenew system and unpack the same old data. However, to truly manage a large data conversion, enabledata validation or have effective data management strategies, it is key to acknowledge that data, itself,has a life cycle. Understanding data, its lifecycle and how to manage it from beginning to end is thestarting process to have a successful initial conversion, on-going management process and future re-organization approach. While data quality problems may be caused by human, process, or systemissues, both the project and business users must work together to systematically manage their data anddevelop quality processes for data management.What is data?What is data and how is it related to information? Information is not just data like strings of numbers,customer addresses or reporting data stored in a computer. Information is the resulting product ofbusiness processes and is used repeatedly in the system--sometimes within the same business processand other times from one business flow to another. But understanding this is one of the keys to learninghow to manage data. First let us define the types of data:Master Data: Master data describes the people, places and things that are involved in an organization’sbusiness e.g.: People (Customer, employees, vendors), places (locations, sales territories, organizations),and things (accounts, products, assets, document sets).Reference Data: Reference data are sets of values or classification schemas that are referred to bysystems, applications, data stores, processes and reports as well as by transactional and master recordse.g.: Customer type in Customer Master data, Item type in Item master data.COLLABORATE 12-OAUG Forum Copyright ©2012 by Chain-Sys Page 1
  2. 2. Transactional Data: Transactional data describes an internal or external event or transaction that takesplace as an organization conducts its business e.g,: sales order, invoices, purchase orders, trips,deliveries, cash receipts, payments, inventory transactions etc.Metadata: Metadata literally means “data about data”. It shows all the characteristics of the tables andfields within them such as: Field name, Constraints, Data Type etc.The Data Life CycleFrom the definition of data types, next we need to understand that data has a life cycle. Just like anyother element which goes through any changes, data is the same. Awareness of this key point is the firststep in understanding how data can impact the business process. For example, simple errors in themaster data will lead to inconsistencies in the transactional data. Not having business rules whichvalidate against the reference data could impact not only the current business process but relatedbusiness flows where the output of one is the input to the next. Visualizing this allows us to see how asimple error in one cycle allows the propagation of errors to the next. Extrapolating with many sets ofdata, this can be an expensive cycle to stop without a clear strategy for data validation, maintenance andre-organization. Zooming from a micro to a more macro view, we now can see now the importance ofmanaging data in a business flow as an organization begins, changes merges, de-merges and re-organizes. This cycle may be repeated many times.Having an effective solution to manage this cycle has become a necessity for every large globalcompany. It may seem pre-mature to think about a data management strategy during conversion;however, a successful and on-time engagement is achieved as result of not only early and effective datacleansing but on-going data management strategies synergizing both the project and the business.The ToolNow, we understand that data has a life cycle and requires a strategy to manage from the beginning to itsend. Having the right tools is the next key to ensuring that your organization can gain efficiency postconversion. What are important aspects of the right tool? It should be able to do the following:Perform Conversion:Eliminate development of programsHandle large data sets quicklyHave configurable rules to validate dataAllow the business to validate and cleanse dataHave minimal impact to cut-over timelinesHave enterprise features like scheduling and reportingManage changes to Data:Have repeatable process for maintenance and roll-outsUpdate rules/validations quicklyCOLLABORATE 12-OAUG Forum Copyright ©2012 by Chain-Sys Page 2
  3. 3. Make updates to data quickly (on-going maintenance and mass data changes)Provide audit to track changesAllow for data re-engineeringAllow for data re-organization:Re-engineer data for business process changesRe-organize data for business re-organization (merger, de-merger, re-organizations)These simple requirements will allow for utilization of ideally one or many tools to achieve the goal of datamanagement. These aspects should address the various life cycle stages of data while creating aprocess of iterative data improvement. This allows the project teams to choose the steps that thebusiness requires and can repeat these steps as many times as needed to improve the data quality,perform mass data maintenance, make repeatable project roll outs and reorganize entities with large setsof data (related to mergers, de-mergers and re-organizations.) Along with a strategy, having a tool thateffectively manages data across all of its life cycle phases will reduce risks and ensure a greater chanceof success to the organization.Managing Data to Eliminate RisksWhile much is made to eliminate risks related to networking, hardware and software, extensive money isspend to build redundancy into the infrastructure of an organization. However, typically little is done toidentify the risks of a data migration project until it is usually too late. As every successful enterprise EBSsystem is given attention to defining business processes, so should data uses be included with allprocess designs and reviews as early as possible. From a data migration perspective, enterprises shouldbe assigning data owners of corporate data the authority to define and require compliance to corporatestandards for not only processes but also data definitions. This should apply not only at the initial onsetof data conversion, but later during data maintenance and further into data re-organization. Bysynergistically engaging the project and the business early in the process of data management, enforcingadherence to standard or corporate definitions can be achieved.Once the initial conversion is accomplished, a similar initiative must happen to manage and makerepeatable the subsequent roll-outs for releases of the data into other parts of the organization. Goingforward, a simple “Get Clean, Stay Clean” approach will provide much-needed framework to ensuring thatany new data into the system follows a systematic approach to validation, cleansing and updating.Further, any new source data from legacy or other systems will follow this same path, ensuring that thedata will adhere to the established business processes.As the organization grows globally and changes their business processes, systems and data can becomede-centralized resulting in the same previous unmanaged data risk as before the initial conversion.Having the ability to quickly and effectively re-organize your data to handle mergers, de-mergers, and re-organizations will provide the global organization a strategic benefit.COLLABORATE 12-OAUG Forum Copyright ©2012 by Chain-Sys Page 3
  4. 4. ConclusionFrom this paper you have learned several key concepts regarding data management from the simpledefinition of data to a basic understanding of its life cycle. First, have strategies to handle the differentparts of your project from initial conversion and on-going data maintenance to data re-organization:  Figure, Configure: Document mapping and validation during conversion process; be able to configure tools quickly to meet changing business validation needs  Get Clean, Stay Clean: Understand your “Data Life Cycle” and utilize the business to achieve your strategy for data management  Rinse, Repeat: Have a repeatable data management strategy for roll-outs and re-organizations. Data is continually updated and re-organized as an organization grows, merges, de-merges and organizes.Planning for operational efficiency by understanding and validating your data will result in fewer data riskswith each release or iteration of organizational growth. Next, have a concept of “Get Clean” and “StayClean” as an essential strategy for on-going data maintenance. Finally, have the right automated tools, arepeatable process and the early synergy of the project and business team members. These are the keysuccess factors in managing your data.COLLABORATE 12-OAUG Forum Copyright ©2012 by Chain-Sys Page 4