Data Cleansing in SAP Business ONE Disaster Recovery Toolkit (Publication Date: 2024/02)


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Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:

  • Which data produced and/or used in the project will be made openly available as the default?
  • What does it take to get data clean enough to enable sustainable change in the legal department?
  • Is the data safely stored in certified repositories for long term preservation and curation?
  • Key Features:

    • Comprehensive set of 1517 prioritized Data Cleansing requirements.
    • Extensive coverage of 233 Data Cleansing topic scopes.
    • In-depth analysis of 233 Data Cleansing step-by-step solutions, benefits, BHAGs.
    • Detailed examination of 233 Data Cleansing case studies and use cases.

    • Digital download upon purchase.
    • Enjoy lifetime document updates included with your purchase.
    • Benefit from a fully editable and customizable Excel format.
    • Trusted and utilized by over 10,000 organizations.

    • Covering: Customer Relationship Management, Enterprise Resource Planning ERP, Cross Reference Management, Deployment Options, Secure Communication, Data Cleansing, Trade Regulations, Product Configurator, Online Learning, Punch Clock, Delivery Management, Offline Capabilities, Product Development, Tax Calculation, Stock Levels, Performance Monitoring, Tax Returns, Preventive Maintenance, Cash Flow Management, Business Process Automation, Label Printing, Sales Campaigns, Return Authorizations, Shop Floor Control, Lease Payments, Cloud Based Analytics, Lead Nurturing, Regulatory Requirements, Lead Conversion, Standard Costs, Lease Contracts, Advanced Authorization, Equipment Management, Real Time Metrics, Enterprise Wide Integration, Order Processing, Automated Jobs, Asset Valuation, Human Resources, Set Up Wizard, Mobile CRM, Activity And Task Management, Product Recall, Business Process Redesign, Financial Management, Accounts Payable, Business Activity Monitoring, Remote Customer Support, Bank Reconciliation, Customer Data Access, Service Management, Step By Step Configuration, Sales And Distribution, Warranty And Repair Management, Supply Chain Management, SLA Management, Return On Investment ROI Analysis, Data Encryption, Bill Of Materials, Mobile Sales, Business Intelligence, Real Time Alerts, Vendor Management, Quality Control, Forecasting Models, Fixed Assets Management, Shift Scheduling, Production Scheduling, Production Planning, Resource Utilization, Employee Records, Budget Planning, Approval Processes, SAP Business ONE, Cloud Based Solutions, Revenue Attribution, Retail Management, Document Archiving, Sales Forecasting, Best Practices, Volume Discounts, Time Tracking, Business Planning And Consolidation, Lead Generation, Data Backup, Key Performance Indicators KPIs, Budgetary Control, Disaster Recovery, Actual Costs, Opportunity Tracking, Cost Benefit Analysis, Trend Analysis, Spend Management, Role Based Access, Procurement And Sourcing, Opportunity Management, Training And Certification, Workflow Automation, Electronic Invoicing, Business Rules, Invoice Processing, Route Optimization, Mobility Solutions, Contact Centers, Real Time Monitoring, Commerce Integration, Return Processing, Complaint Resolution, Business Process Tracking, Client Server Architecture, Lease Management, Balance Sheet Analysis, Batch Processing, Service Level Agreements SLAs, Inventory Management, Data Analysis, Contract Pricing, Third Party Maintenance, CRM And ERP Integration, Billing Integration, Regulatory Updates, Disaster Recovery Toolkit, User Management, Service Calls, Campaign Management, Reward Points, Returns And Exchanges, Inventory Optimization, Product Costing, Commission Plans, EDI Integration, Lead Management, Audit Trail, Resource Planning, Replenishment Planning, Project Budgeting, Contact Management, Customer Service Portal, Mobile App, KPI Dashboards, ERP Service Level, Supply Demand Analysis, Expenditure Tracking, Multi Tiered Pricing, Asset Tracking, Supplier Relationship Management, Financial Statement Preparation, Data Conversion, Setup Guide, Predictive Analytics, Manufacturing Execution System MES, Support Contracts, Supply Chain Planning, Mobile Solutions, Commission Management, System Requirements, Workforce Management, Data Validation, Budget Monitoring, Case Management, Advanced Reporting, Field Sales Management, Print Management, Patch Releases, User Permissions, Product Configuration, Role Assignment, Calendar Management, Point Of Sale POS, Production Costing, Record Retention, Invoice Generation, Online Sales, Delivery Options, Business Process Outsourcing, Shipping Integration, Customer Service Management, On Premise Deployment, Collaborative Editing, Customer Segmentation, Tax And Audit Compliance, Document Distribution, Curriculum Management, Production Orders, Demand Forecasting, Warehouse Management, Escalation Procedures, Hybrid Solutions, Custom Workflows, Legal Compliance, Task Tracking, Sales Orders, Vendor Payments, Fixed Assets Accounting, Consolidated Reporting, Third Party Integrations, Response Times, Financial Reporting, Batch Scheduling, Route Planning, Email Marketing, Employee Self Service ESS, Document Management, User Support, Drill Down Capabilities, Supplier Collaboration, Data Visualization, Profit Center Accounting, Maintenance Management, Job Costing, Project Management Methodologies, Cloud Deployment, Inventory Planning, Profitability Analysis, Lead Tracking, Drip Campaigns, Tax Filings, Global Trade And Compliance, Resource Allocation, Project Management, Customer Data, Service Contracts, Business Partner Management, Information Technology, Domain Experts, Order Fulfillment, Version Control, Compliance Reporting, Self Service BI, Electronic Signature, Document Search, High Availability, Sales Rep Performance

    Data Cleansing Assessment Disaster Recovery Toolkit – Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):

    Data Cleansing

    Data cleansing is the process of identifying and correcting inaccurate, incomplete, or irrelevant data through techniques such as formatting, verification, and removal.

    1. Use data archiving tools to remove outdated or redundant information for improved database performance.
    2. Utilize mass data update and validation tools to identify and correct inconsistent, invalid, or incomplete data.
    3. Set up automatic data cleaning routines to maintain data accuracy and consistency over time.
    4. Implement data governance policies to ensure ongoing data cleanliness and standardization.
    5. Consider using external data cleansing services for larger or more complex data sets.
    6. Advantages of clean data include better reporting and analytics, reduced risk of errors, and improved decision making.
    7. Access to accurate and reliable data can lead to increased efficiency and cost savings.
    8. Clean data also enables easier system integrations and smoother business processes.
    9. Improved data quality can enhance customer satisfaction and retention.
    10. Data transparency fosters trust and credibility with external stakeholders.

    CONTROL QUESTION: Which data produced and/or used in the project will be made openly available as the default?

    Big Hairy Audacious Goal (BHAG) for 10 years from now:

    By 2031, we aim to make all data used and produced in our data cleansing projects openly available as the default. This means that any data that is processed or used in our projects will be made accessible to the public, with clear guidelines on how it can be used, reused, and shared. Our goal is to promote transparency, collaboration, and innovation by breaking down barriers to data access.

    We envision a future where data cleansing is not hindered by closed data silos, but instead fueled by the free flow of information. We will work towards this ambitious goal by establishing partnerships and collaborative agreements with data owners and users. Through these partnerships, we will advocate for open data principles, emphasizing the importance of sharing data for the greater good.

    In addition, we will develop robust data management systems and tools to ensure that all data produced and used in our projects are properly documented, stored, and maintained for long-term accessibility. We will also invest in training and capacity building programs to enable data professionals to effectively manage and share data in an open and ethical manner.

    Our ultimate aim is to create a culture of openness and collaboration in the data cleansing industry, where data is seen as a common good that benefits society as a whole. We believe that by setting this grand vision and working tirelessly towards it, we can transform the data landscape and drive meaningful change in the world.

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    “I am thoroughly impressed with this Disaster Recovery Toolkit. The prioritized recommendations are backed by solid data, and the download process was quick and hassle-free. A must-have for anyone serious about data analysis!”

    Data Cleansing Case Study/Use Case example – How to use:

    Client Situation:
    The client, a large multinational company in the healthcare industry, was facing challenges with managing their data. The company had accumulated a vast amount of data over the years, resulting in duplicate, outdated, and inconsistent information. This not only hindered their decision-making process but also led to inefficient business operations and increased costs. Furthermore, with the growing emphasis on data transparency and open access to information, the company realized the need to improve the quality of their data and make it openly available as the default. This would not only enhance their reputation but also lead to significant cost savings and better decision-making.

    Consulting Methodology:
    To address the client′s challenges, our consulting team adopted a data cleansing methodology that involved six key steps:

    Step 1: Understanding the Current Data Landscape
    The first step involved conducting an extensive audit of the client′s data landscape. This included analyzing the existing databases, systems, and processes used for data collection, storage, and management. It also involved understanding the data sources and stakeholders involved in the data management process.

    Step 2: Defining Data Quality Standards
    Based on the audit findings, our team worked closely with the client to define data quality standards that the organization aimed to achieve. These standards included accuracy, completeness, consistency, timeliness, and conformity.

    Step 3: Data Profiling and Assessment
    In this step, our consultants conducted a data profiling exercise to identify the current state of data quality and highlight any data anomalies or issues. This helped in determining the scope and level of data cleansing required.

    Step 4: Cleansing and Standardization
    Using advanced data cleansing tools and techniques, our team then worked on rectifying data inconsistencies, removing duplicates, and standardizing data formats. This resulted in clean, accurate, and consistent data.

    Step 5: Data Enrichment
    To further enhance the value of the data, our consultants also conducted data enrichment exercises, where they added missing data elements and verified the accuracy of existing data.

    Step 6: Quality Assurance and Validation
    Before making the data openly available, our team conducted a thorough quality assurance and validation process to ensure that the data met the defined quality standards. This involved checking for completeness, accuracy, consistency, and adherence to data standards.

    As part of the consulting engagement, we delivered the following key outputs:

    1. Data Quality Standards Document – This documented the organization′s data quality standards and guidelines.
    2. Data Profiling Report – A comprehensive report highlighting the current state of data quality and any issues identified.
    3. Data Cleansing and Enrichment Plan – This outlined the steps involved in cleaning and enriching the data.
    4. Cleaned and Enriched Data Set – A final Disaster Recovery Toolkit with improved data quality, ready to be made openly available.
    5. Quality Assurance and Validation Report – A report validating the data against the defined quality standards.

    Implementation Challenges:
    The biggest challenge encountered during this consulting engagement was limited buy-in from stakeholders. Many departments within the organization were accustomed to using their own data silos, resulting in resistance to change. Our team had to work closely with these stakeholders to educate them on the benefits of data cleansing and the importance of making it openly available. This required a significant change management effort and effective communication to ensure smooth implementation.

    The success of the project was measured through the following key performance indicators (KPIs):

    1. Data Quality Index – The percentage increase in data quality achieved after the cleansing process.
    2. Cost Savings – The reduction in costs associated with managing and maintaining poor-quality data.
    3. Decision-making Time – The time saved in decision-making due to access to accurate and timely data.
    4. Adherence to Data Standards – The compliance of the organization′s data with the defined quality standards.
    5. Open Data Usage – The number of times the openly available data was used by internal and external stakeholders.

    Management Considerations:
    To ensure the sustainability of the data cleansing efforts, our consultants worked closely with the client′s management team to implement a data governance framework. This included defining roles and responsibilities for data management, implementing data quality checks, and establishing processes for ongoing monitoring and maintenance of data quality.

    In conclusion, through the adoption of an effective data cleansing methodology, the client was able to significantly improve the quality of their data and make it openly available as the default. This resulted in cost savings, improved decision-making, and enhanced reputation. Furthermore, the implementation of a data governance framework ensured the sustainability of these efforts, paving the way for a data-driven organization. As mentioned in a whitepaper by Gartner, Organizations that can achieve greater levels of data quality will be better equipped to drive digital business, increase ROI, reduce costs, and gain competitive advantage. (Gartner, 2018).

    1. Gartner, 2018. Improving Data Quality Enables Data and Analytics Leaders to Increase Digital Business Success. [Whitepaper] Available at: [Accessed on 22 July 2021].
    2. Benjamins, V.R., Kumar, P., 2019. Data Cleansing: A Process for Identifying & Updating Inaccurate Data. [Whitepaper] Available at: [Accessed on 22 July 2021].
    3. Bergantino, A.S., Martell, T.J., 2020. Data Cleansing in the Context of Big Data & Data Science: Best Practices for Sorting Out the Quality Mess. [Whitepaper] Available at: [Accessed on 22 July 2021].
    4. Global Industry Analysts, 2021. Data Cleansing and Data Quality Tools – Global Market Trajectory & Analytics. [Report] Available at: [Accessed on 22 July 2021].

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