• Pioneering- This is the FIRST and ONLY package of Data Quality Management Frame-work and Tool-Kit being available on the net.
• Cost-Advantage- Acquire a world-class product at a fractional cost (nearly 1/30th) vis-a-vis that from another conceivable source.
• 'Making it Happen' - Run DQ agenda ‘on-the-ground’. Manage hundreds of real-life business situations . You can't get more detailed & Practical.
• Comprehensive Package -
  • TOOL-KIT- 25 work-tools with over 100 work-pages AND 150 page help guide
  • 'Real-life' DQ MANAGEMENT GUIDE- ~60 FAQs AND ~40 TIPs AND 80 page Encyclopedia.. We tell you what you can't learn through books or class-room training.
• Flexible package- Will work irrespective of your size, complexity and the IT technology you are using.
• It is not only an IT framework, but involves 360 Degree factors of Business, Processes, Technology and people aspects.
• Made in a business friendly language, which Business Analysts can understand
• Buying Decision-Support- 40+ pages of product information, email and phone sales support. 50% money back for 15-day cancellation.

	Professional Pack- USD 1150/-
DATA QUALITY MANAGEMENT TOO-KITS
Data Quality Management Tool-Kit Data Governance & Management Kit         Data Quality Program Kit Data Quality Assessment Kit DQ Management Kit- BAU DQ Management Kit -Projects Data Correction and Monitoring Kit	Yes
DATA QUALITY GUIDE AND KNOWLEDGE-BASE
150 Page Help File for Tool-Kit	Yes
Real-life’ Data Quality Management Guide: 60+ FAQ 40+ Field Tips 80 Page Encyclopedia	Yes
SERVICES
ANNUAL SUBSCRIPTION FOR ALL UPGRADES	Yes
LICENSING AND CALCELLATION POLICY
License (Unlimited View)	20 User Edit
Cancellation Refund	50%- 15 days

List of Work-tools in BiPMinstitute.com Data Quality Management Frame-work Tool-Kit

Overall Data Quality Management
Data Quality and System Health Assessment
Data Quality Program
Data Quality Assurance and Gaps management in an Initiative
Data Quality Assurance and Gaps Management in Business as Usual  
Data Monitoring and Data Correction

Here is the brief description for each work-tool in our tool-kit. All work-tools have detailed explanation of each heading and column, along with examples on how to fill it up. Supplementing this ‘within work-tool’ help, you have detailed online help guide along with FAQ and TIPs.

Each of our work-tools in the DQ practice tool-kit has the following features:

• A detailed help-guide to explain each and every column heading, section, sub-section etc.
• FAQ and TIPs on how to use the tool-kit.
• Help guide on how to make changes to the tool-kits to suit them to your needs.
• Sections to track the versioning, approvals related to the document.
• Navigation within the tool.

Overall Data Quality Management                                                              BUY DQ MANAGEMENT PACK   TOP   Data Quality Tool-Kit

These are the foundation elements for pursuing your data quality agenda.

• Data Quality Policy: This tool provides you a template, list of sections sections, which can be in a data quality policy, along with the examples of the text in each of the sections, which you can use as you create the data policy for your organization.
• Data Quality Control Guidelines : This practice-tool enables you to create the data quality control guidelines. It provides you with the flow and also links to our encyclopedia, where all the guidelines are listed in detail.
• Data Management Quality Standards: This is going to be among most time consuming piece. This tool gives you template, guidelines and many examples on how to document your universal data domains & Standards, Business Rules and Data Models.
• Data Management Stake-Holding and Responsibility Matrix : This is a single reference point on all the inter linkages across systems, functions, processes and Data-Groups. As you make any change in your environment, this is a great reference to understand the stakeholders. It also identifies the owners and sponsors for data-groups, processes, functions and systems.
• Data Group Master File: This is the central reference point and universal definition for data-groups. Data-groups are the logically grouped business data, which has to be assigned as business owner.

Data Quality and System Health Assessment                                             BUY DQ MANAGEMENT PACK   TOP   Data Quality Tool-Kit

• Data Mapping and Assessment Management : Data Mapping and Assessment, gives a factual analysis of the current state of your data and its structure. This practice-tool helps you to manage the Data Mapping and Assessment exercise, by capturing the results, categorizing the, defining the gap and also on possible root-causes.
• Data Mapping and Assessment Report : This is the formal output of your Data Mapping and Assessment exercise. This template is filled-up and submitted to the stakeholders for review and sign-off.
• Data Mapping and Assessment WBS : This is the Work Break-Down Structure for Data Mapping and Assessment Exercise. You can use it to develop project plan for DMA exercise.
• System Landscape Data Quality and Management Health Assessment Tool : This is a comprehensive single point capture of your results and analysis, as you assess your system landscape. It summarily captures the results of DMA, but also includes other factors, which determine the health of the system. This includes the level of controls and DQ assurance mechanics in the systems, and the state of overall data governance and management.

Data Quality Program                                                                                 BUY DQ MANAGEMENT PACK   TOP   Data Quality Tool-Kit

Data quality program is a combination of multiple smaller initiatives to address varied gaps, to establish common standards and practices and to create Data quality awareness. Unlike programs related to OLTP transaction based systems, Data Quality program has many more unknowns, as most of the time it is linked to resolving fundamental issues encompassing systems and processes. Therefore we recommend a ‘front-heavy’ data quality program initiation phase, which invests into assessment, analyzing, solution-finding and prioritizing the DQ gaps. The outcome and recommendations coming out of this phase are then funneled into the data quality program planning and execution phase.

• Data Quality Program Initiation Proposal : You can use this template to fill-up DQ program initiation proposal. The data quality program initiation phase required some level of funds, as you do analysis, and solution-finding on Data Quality Gaps. Therefore, having a good DQ initiation phase proposal will help.
• Data Quality Program Initiation completion Report : Based on the findings of DMA, System health assessment and DQ Gaps Management and Tracking tool, you get all what you need to firm-up your findings and proposal.
• Data Quality program proposal and agreement : Once you have got your recommendations agreed and funded, you can submit you DQ program proposal, with all the trappings of a typical program. The TOC includes objectives, deliverables, timelines, resources, communication frame-work, risk management plan etc.
• Data quality Program WBS : This is the list of activities involved in a data quality program. You can use it to create your project plan around a data quality program.

Data Quality Assurance and Gaps management in an Initiative               BUY DQ MANAGEMENT PACK   TOP   Data Quality Tool-Kit

These tools are used to track and manage the DQ assurance in an initiative. The scope is all the objects (input forms, data entry forms, business processes...), where you need to ensure Data Quality.

• Data Quality Assurance Method-Level Tracking tool : This tool tracks the adherence to the DQ Assurance mechanisms at different phases of an initiative. It is the big picture reference for various stakeholders to review, know, sign-off to the level of adherence.
• Data Quality Assurance Specs, Design and Deployment object-level tracking tool : This tool tracks the adherence to the DQ Assurance mechanisms at different phases of an initiative at object level (input forms, data entry forms, business processes...), where you need to ensure Data Quality. This tool feeds data into the Method-Level Tracking tool.
• Data quality Assurance Checklist for an initiative : This is a conscience keeper checklist to see that DQ assurance methods are being incorporated in an initiative.

Data Quality Assurance and Gaps Management in Business as Usual      BUY DQ MANAGEMENT PACK   TOP   Data Quality Tool-Kit                                                                                 

• Data Quality Gap prioritization, Approach finalization, planning and tracking tool : This is a tracking tool, which is used to manage and track ALL gaps, data quality initiatives and data quality programs. This tool enables you to prioritize a gap, document and analyze the alternatives to address the gap, estimate the effort, assign the gap to an initiative and track its closure.
• DQ Assurance Object-inventory Tracking: This tool tracks the adherence to the DQ Assurance mechanisms for all key objects (input forms, data entry forms, business processes, and data entities). If a stakeholder wants to know the state of DQ controls in the environment, this is the central reference.
• Data Quality Risk Assessment Checklist: A lack of data quality does not always mean high risk. Similarly, less than perfect solution to address a data quality issue may still be acceptable given its cost-benefit. Risk assessment is driven by many factors. This checklist enables the user to weight the risk of the DQ issue and its possible solutions on factors like- Volume and Value risk, Speed of deterioration, criticality of data, cascading of DQ gap to external stakeholders, probability of incidence etc...
• Data Quality Gap Impact Assessment Tool: This tool enables you to assess and quantify the business impact of a data quality gap, given its risk. While the Data Quality Risk Assessment checklist is more of a back-end review for the analysts, the output of this tool goes to the business owners and CIO, for final decision. A Data Quality gap may be felt by a single function, but its actual impact could be cross-functional.
  

Data Monitoring and Data Correction                                                         BUY DQ MANAGEMENT PACK   TOP   Data Quality Tool-Kit

• Data Monitoring Checklist : This checklist helps you to ensure a holistic data monitoring. As you conduct Data Monitoring, you can refer while planning, testing and execution stages.
• Data Monitoring Request Form : This is a request form, which one captures all aspects of Data Monitoring.
• Data Correction Checklist : This checklist helps you to ensure a holistic data correction. As you conduct Data correction, you can refer while planning, testing and execution stages.
• Data Correction Request Form : This is a request form, which one captures all aspects of Data Correction.

FAQ AND QUESTIONS ASKED BY OUR CUSTOMERS BEFORE they BOUGHT

Is there any other source to acquire a similar product?
Explain your Cost advantage-
Explain your knowledge and capability advantage-
How can we gain confidence of Audit, Internal Control and compliance authorities, by using this product?
We have got enterprise level Data Quality software packages. Why do we need to use this package, mostly based out of Excel and MS-word?
We already have a detailed methodology related to DQ, with all the templates and processes. Why should we buy the BiPM DQ package?
Please explain your licensing policy in terms of edit vs. view-
How do you enforce this license?
Why do you say that your package is flexible and adaptable?

Is there any other source to acquire a similar product?                            BUY DQ MANAGEMENT PACK   TOP   Your Questions

BiPMinstitute.com has been unique in many ways, and our products follow that spirit. You will not find a DQ Practice + Tool-kit package (along with our extensive Help+Knowledge ecosystem) anywhere on the net. This is first-ever such offering on the net. A similar package is not available in any published source. The only conceivable option, is to engage a premier consulting organization. As per our experience, very few consulting companies have an integrated and packages DQ frame-work.

Our focus is not to build a repository of articles/white-papers and case studies (for which there are many great and respectable online magazines), but to provide an in-depth subject matter expertise and tools, which enable your day-to-day implementation. To our knowledge, there is no similar ‘virtual internet based practice’ OR an ‘online competency centre’, covering a range of subjects related to Data Management, Business Intelligence and Performance Management

Explain your cost advantage-                                                                      BUY DQ MANAGEMENT PACK   TOP  Your Questions

As per our estimates, if you engage a premier consulting organization to acquire or build an integrated DQ management + work-tools, you may easily end-up spending over 40-50 thousand dollars to acquire the intellectual property and knowledge-base alone. This broadly gives you over 30-40 times the cost advantage.

Explain your knowledge and capability advantage ?                                  BUY DQ MANAGEMENT PACK   TOP  Your Questions

Our products and help ecosystem is designed to make our customers self-sufficient. As you use our DQ package, you can start implementing or enhancing your data quality Management from the Day-1. Your teams can learn the concept by relating our knowledgeable and our tools. There are three components to any successful practice implementation-

• Tools with 'how to use' help
• Subject Matter Domain Knowledge-
• Situational Management knowledge- How to adopt a DQ Management frame-work in your unique organizational environment and level of readiness, and how to manage hundreds of different situations that you face.

Our product is unique, as it offers all the three pillars for success. Once your teams are exposed to this holistic input, you will get acceleration not only in 'making it happen' , but also build a significant level of long-term learning and leadership capabilities, within your organization..

How do you feel that by using this product, we will be able to gain confidence from Audit, Internal Control and Compliance authorities?                                                                                

As a disclaimer, Data Quality is one among many other important factors, which builds your overall control and quality environment. Having a holistic frame-work, tools and discipline will help you to avoid most of the data quality issues, and trap the others at early stages. Apart from this, the above-mentioned stakeholders, not only look at 'what you did?', but also ' how you did?', 'Is this sustainable?' and 'are your processes to manage data-quality institutionalized and person-independent?'. A robust DQ Management frame-work, will help you to achieve a higher score on these questions.

We have got enterprise level Data Quality software packages. Why do we need to use this package, mostly based out of Excel and MS-word?                                                                                 

• Our tool-kits do not replace the enterprise software tools like Data Quality Monitoring tools, Data Profiling or any other IT platform. We provide an upstream integration, so that you can use these IT platforms much more effectively, irrespective of their technology, size or complexity. In other words, you need to have our tool-kits to make the best out of your mega investments on your Data Quality related IT systems. At the same time, you can use this package, if you have no established IT platform to support your data quality efforts.
• Most failures of Data Quality and Data Management are our lack of business, process and domain understanding and its specifications. The enterprise IT platform deliver on ‘How you want to do it’?, but they do not help us on:
  • Deciding what we want to do and when?
  • Why we want to do?
  • Prioritizing, planning and tracking our data quality initiatives.
  • Set-up Data Quality Standards.
  • Defining the business and functional specs etc...
• The other reasons for failure in-spite of mega IT investments, is a lack of understanding of success and quality factors, which make Data Quality happen. Ask any DQ consultant and he will tell you that DQ is much more a business challenge than an IT challenge. Our tool-kits help you to do and monitor all aspects of Data Quality and Management. None of the enterprise IT systems, will provide the vehicles to monitor and assure the success factors related to your DQ initiatives.
• More than being a tool-kit, our DQ package helps you clarify and streamline your thinking, processes and practices on how to go about implementing DQ.
• Most of the software packages come along with costly implementation consulting on how to use these platforms. Our tool-kits help you to reduce the need for on-site consultants, build your own DQ competency center. As you are aware, DQ is an ongoing subject, and not one-time initiative. You will need to develop internal capabilities to supplement the external expertise.

We already have a detailed methodology related to DQ, with all the templates and processes. Why should we buy the BiPM DQ package?                                                                                 

We provide lot of details on what we have to offer, along with the samples for you to evaluate, before you buy. We also have a friendly cancellation policy, if you feel that what you have bought, does not add value. We suggest that you evaluate our product, and compare it with what you have to take an informed decision.

The key benefits you can have, if you have your own set of similar frame-work and tools:

• Our package is supported by free encyclopedia of subject matter content.
• You can gain insights into many best practices, concepts and apply it to your framework, even if you do not use all of our work-tools.
• You can also find opportunities to pick and choose a work-tool to supplement your existing frame-work.
• A good proportion of our help guide provides you knowledge and TIPs on implementing a DQ practice, irrespective of which work-tools, that you are using.

Please explain your licensing policy in terms of edit vs. view-                 BUY DQ MANAGEMENT PACK   TOP  Your Questions

Our licensing policy, allow multiple users to be filling the same template in a collaborative manner. We don’t say that for a single user license, only one person can fill or view a template.

Our licensing policy has four components:

• The edit license: This means that a user can create a deliverable using these work-tools. A single license will allow 20 users with edit rights. This is good enough to manage a decent sized DQ initiative.
• The view license: This means that a user can view the output of the work-tools, but cannot change the content. This typically includes higher level signatories or reviewers. A single license will allow unlimited view licenses. In other words, you can place the outputs in your company intranet.
• Access to help guide and knowledge base for year long subscription: This means that a user can view the TIPS, FAQ and Help Guides from our database for one full year. One package will provide you a single license. This means that you will get one ID and Password, for down-loading our work-tools and knowledge-base. This will not be a group ID and password, but will be used by a single individual.

What does it mean for you?

Given the size of your organization, if you feel that you will have large number of people who will need to have edit rights to the content in these work-tools, you may like to go for more than one package. For example, if you feel that you may have nearly 40 people in your organization, who will be creating (and not only viewing) their deliverables using this package, you should go for two packages.

How this license works with a consulting company, who will be using this package to provide DQ consulting to their clients?

A consulting company cannot use this package to provide consulting to their clients, unless they are certified by BiPMinstitute.com. This will be contravention to our Copyright laws. A consulting company can only use this package (like any end-user company) to work on their internal DQ initiatives. 

How do you enforce this license?                                                               BUY DQ MANAGEMENT PACK   TOP  Your Questions

This is an honor license and more driven by the post-facto detection instead of pre-facto enforcement. We have a firm belief that all respectable organizations will honor this licensing policy in letter and spirit. The low cost of our package is a true enabler for license enforcement. This package cannot be freely distributed as it has lot of links to our online helps and knowledge base. It will be a challenge for anyone to use this package in an offline mode. We have registered our content with the US copyright office and we make extensive use of internet based copy protection software to track any misuse, reproduction or selling on the net

Why do you say that your package is flexible and adaptable?                              BUY DQ MANAGEMENT PACK   TOP   Your Questions

A million organizations will be using our package in million different ways. The worst pitfall for any DQ Management package is to enforce its own rigid methodologies. Every organization has different level of skills, readiness and level of evolution. For example a start-up will be using our package in a very different way than a mature organization. Here are the factors, by which our package is flexible to use:

It is a concept to start with:

Our package is extremely detailed on what all and how you need to implement a DQ DQ Management Frame-work. For example, it provides you tricks on

• How can you prioritize your DQ gaps,
• How you should weight between different alternatives and approaches to fix a DQ gap
• How can you can piggy-back on other initiatives
• How do you manage multiple data standards in your environment etc. etc...

Broadly more than half of our embedded value belongs to the knowledge and whole concept of an integrated frame-work. Technically speaking, you can borrow the concepts and design and apply it in your own world, without using a single piece of our work-tools. For example, you may choose to replicate our work-tools or their design to a share-point based work-flow.

It fits with other work-management DQ Management Frame-works

Our package does not enforce on how you have to do your project management, functional and design documentation, issue and risk management. For example, we don’t have a risk memo to highlight a risk related to DQ. We expect this package to work with your own risk management procedures. Our focus is on the Data Quality DQ Management Frame-work and how it hooks to the generic processes within your organization. We have a lot of FAQs and TIPs around this subject.

Changes in the work-tools

Our work tools are based out of Excel and Word (you can do a lot with Excel and MS-Word). This enables a widest level of portability and compatibility. You can add additional columns, rows, conditional formatting etc. Our help guide also tell you on how to manage it without disturbing the core flow of these tools.

Optimum tagging and categorization

Another pitfall, any DQ DQ Management may fall into is to have an over-categorization, scoring and tagging for different objects. This makes any DQ Management package unwieldy. We have used an optimum level of categorizations and tagging to ensure that it does not make it too rigid. High-sophistication and rigidity is good for core systems and process, but not when you are implementing a practice.

HELP guide for data quality practice tool-kit

We take pride in our Help Guide. It provide you can absolute level of detail on how to use our tool-kit. It provides you can overall context of the tool-kit as well as the field and heading level help. You can't get more detailed in terms of understanding 'what-why-who and how' of our tool-kit.

Help Guide on our Work-Tools                                                                                   BUY DQ MANAGEMENT PACK   TOP   

HELP-GUIDE -PART I: As you go to the online page for each work-tool, the first part of Help-Guide is the overall statement on usage of the tool

This overall context includes answers to the questions of:

• A brief on this work-tool
• What this tool is not
• When it gets used
• Who uses it
• Linked Work-Tools

SAMPLE START --------------------------------------------------------------------------------------------------------------------------------------------------------

As a Sample- Here is the piece of text from the Initiative Level DQ Assurance Tracking Tool    

Data Quality assurance methods have both flavors of being a business requirement (the business checks before and after one runs a batch) as well as the technology design (triggers, referential integrity). Refer Data Quality Assurance chapter in BiPM Encyclopedia for the list of methods. Method-Level DQ Assurance Tracking is applicable for any System related initiative with an IT component. We have not covered the data quality assurance methods for purely non-IT initiatives. Please go to end of page to download the template

Purpose of Initiative-Level DQ Assurance Tracking Tool

This tool is a conscience-keeper to have an informed understanding of the extent to which the Quality Assurance Methods are applied. Apart from being conscience-keeper, it also is used as a formal document for doing root-cause analysis, audit review and for future changes in the systems or processes. This tool will have one separate tracking sheet for each different system ( and associated business processes) which is involved in a given project. When you rate 'high' against a specific QA method, it means the you have followed that specific control throughout the system. For example, if you state 'high' in front of 'field level drop down' within 'input controls', it means that all screens in the system are having most of these controls.

When is Initiative -Level DQ Assurance Tracking used?

• Project Analyze phase – The checklist should be filled-up during the detailed business requirements (functional specifications) and modeling Phase. Include this activity as part of the work break down structure of Analyze phase.
• Project Design Phase – Same as above, but to be applied in the design phase. This will include more of file interface, screen design, batch controls etc.
• Project Implementation Phase – This is used for final sign-off on the project.

How is Initiative-Level DQ Assurance Tracking tool used?

Apart from sign-off, this tool should be referred to, while the functional specing (both part of Analyze phase), technical designing and testing & implementation is in progress. Typically this document should be jointly signed off by the project manager as well as the data steward or Head of Information management (Refer Data Quality Program Deliverable- Data Quality Organization ).

This is the broad flow of usage:

• As your functional specs come near completion, the business and IT analysts, will fill-up this tracking sheet and attach it along with the functional specs document as an appendix. This tracking-sheet gets signed-off by the project owner, data steward (if the role exists in the organization) or IT owner and business owner.
• As your Technical and Business Process design (the design phase of a project is not only IT design but also designing the business processes around the IT systems) comes near completion, this checklist will be filled-up, and signed off by the project owner, Data Steward (if the role exists) or IT owner and business owner.
• Once your testing is complete and you are looking for go-ahead for implementation- This checklist will be filled-up depicting if the controls are signed-off and the status of quality assurance methods, finally ready for implementation, are acceptable.
• As and when (through a change control), you add or remove a control, you can go and update the checklist.

Linked Work-Tools

The Initiative-Level DQ Assurance Tracking is closely linked with Object-Level DQ Assurance tracking tool. The Object-Level DQ Assurance tracking tool, is part of your function, design and deployment documents, to define the quality assurance mechanism for each object in the system(s) and the associated business processes linked to the initiative.

SAMPLE-END-----------------------------------------------------------------------------------------------------------------------------------------------------------

HELP-GUIDE PART-II: The overall view is followed by explanation of each and every field in our tools.

• What it means?
• How is it different from a similar sounding column or heading?
• When it should be filled and when it should not? etc...

SAMPLE START --------------------------------------------------------------------------------------------------------------------------------------------------------

As a Sample- This is a small piece of text taken from field-level help guide of Data-Group Stake holding Matrix within Data Management Stake holding and Responsibility Work-Tool.

• Data-Group (DG): A data-group is the logically grouped data, which is typically used or stored together. Some examples of the Data-Groups are Customer Data, Invoice Data, General Ledger data...
• Data Group Code: This is the unique code assigned to the Data-Group or Sub-Data Group .
• Brief Description: A high level description of the Data-Group and/or sub Data-Group. All the description should be more in the business language. It should also highlight on what this data group does not contain. For example, for customer data-group, you may specify that it does not contain the data on potential customer (Sales Leads Data).
  

SAMPLE END --------------------------------------------------------------------------------------------------------------------------------------------------------

Real-Life Data Quality Management Guide

This guide is essentially on how to manage the real-life situations around Data-Quality. The situations may include technical, managarial, operational and political. The guide is in the shape of FAQs and Field-Tips. Most of the content in this guide will not be available in books or class-room training.

FAQs
Field- TIPs
Encyclopedia

FAQs on how to manage 'real-life' situations in data quality Management      BUY DQ MANAGEMENT PACK   TOP   

Next part of our help ecosystem are the FAQs on the following lines:

• Using the tools in specific business and IT scenarios
• Question related to Data Quality domain related to this tools
• Question related to managing 'soft' situations with in the work-environment.

SAMPLE START --------------------------------------------------------------------------------------------------------------------------------------------------------

Here is the list of sample FAQs (out of over 60 FAQs), which are answered in our help ecosystem            

• If a data quality assurance control is missing, does it mean that rating will be low?
• We are a start-up, and we are going through rapid organization changes. Is it possible to assign the business owners for the data-group, because one does need some level of stability in that role?
• Lot of our data in lying in the excel sheets and in the ad-hoc systems developed by business groups. How do we include these systems in our Data-Quality Frame-work? We don't have documentation on many of these systems.
• We have small system initiative. Can we skip the use of object-level data quality tracking and straight-way use the initiative level tracking?
• Can we use these work-tools, only for a single system, instead of doing enterprise wide?
• Do we need to have universal data standards, to ensure a good quality?
• How do we maintain the sanity and version of these work-tools?
• Can we implement same set of work-tools in our work-flow and collaboration system?
• Do we need to have all the components of the Data Quality Policy as mentioned in your Data Quality Policy Template?
• Do we have to implement all work-tools as an integrated piece or can start with some of them?
• We have TiBCO EAI tool installed in our IT and it provides a common bus for data exchange across the systems. There are hardly any point to point interfaces. The interface controls as listed by you look more to be for tradition point to point file exchange. What is the relevance of interface controls in our context?
• Will DQ Gap impact assessment used only for DQ gap management or can also be used in the DQ program initiation phase?
• Does a lack of DQ Assurance method mean a high risk?
• How does business balance between the Data Quality Risk avoidance vs. Revenue initiative?
• Can we have two different sets of universal business rules for the same entity, in two different situations? We have two business units and they are not agreeing on the data management standards for customer and sales person? what should we do?
• Who should be the final decision taker for defining standards for a given data entity?
• Can we have data quality policy, even when we have not created the Data Management standards and have not appointed the data steward?
• How can be assess the adherence to the policy?
• How do we measure the adherence to the Data Quality and Control guidelines?
• Can we have different insert, update and delete rules be different for the same entity (because of valid reasons) in two different systems?
• Can we have one business owner owning multiple data groups?
• Do we need to have all the customer data in a single Data Group?
• Can we have more than one business owner for a data-group?
• We don't have the capacity to map all the locations, where our sales channel data-group resides. This is due to many diverse field systems, with some of them not being owned by IT. The business owner is not ready to take the ownership of this data-group till we complete the mapping. What should we do?
• Which are the top 5-8 data groups we should focus upon, to start with?
• Can we have separate 'enterprise data steward, if we have total disjoint business lines with their own sets of systems and processes?
• IT will take years for the organization of our size to develop a set of universal standards. Does it mean that we are lacking at data governance?
• How do we check if the teams are adhering to the universal data quality assurance guidelines?
• When we rolled out this checklist with the business owners, they have given a conceptual agreement. However we are not able to get the resources to start working on data group master and standards for data entities. What should we do?
  
• Can we have separate 'enterprise data steward, if we have total disjoint business lines with their own sets of systems and processes?
• IT will take years for the organization of our size to develop a set of universal standards. Does it mean that we are lacking at data governance?
• How do we check if the teams are adhering to the universal data quality assurance guidelines?
• When we rolled out this checklist with the business owners, they have given a conceptual agreement. However we are not able to get the resources to start working on data group master and standards for data entities. What should we do?
• Why have we not combined the impact assessment and Risk Assessment?. These two are closely linked.
• Can I do without the Risk assessment and DQ Gap assessment and apply high level judgment?
• How do we ensure that we are not double counting, when doing the impact assessment?
• Doing risk assessment itself is taking too much time, and I am not able to get that band-width from IT. What should I do?
• If there are multiple initiatives in a data quality program and each initiative can have as Assessment
• When we submitted out DQ initiation phase completion report, we did not add any buffer in the estimates. However, as we are creating our project plans, we feel that we should place some safety factor. If we do this, it will create a credibility issue with the stakeholders. What should we do?
• Out of 13 projects within the data quality program, 7 are being done as part of large programs by different IT units. Do we need to track them, as it seems that there is a duplication in managing these projects?
• Are there a minimum or maximum number of projects, which can be in a data quality program?
• We have many critical data quality issues facing us. The stakeholders, want us to just go for addressing them, instead of going through an initiation phase, and spending many weeks before we actually start fixing these issues. What should be our response?

SAMPLE END --------------------------------------------------------------------------------------------------------------------------------------------------------

Field-TIPs on runnning your Data Quality Agenda                    BUY DQ MANAGEMENT PACK   TOP   DQ Management Guide

This is the next pillar of our help ecosystem. It contains TIPs related to implementing overall DQ Management as well as specific work-tools. These TIPS are provided around the following lines:

• How to efficiently and efficiently use these work-tools, so to save your time and effort.
• How to handle the missing information as you are filling-up these work-tools
• How to influence the stakeholders for their support for implementing Data Quality Management.
• How to use your judgment in case you do not have confirmed information. etc..

SAMPLE START --------------------------------------------------------------------------------------------------------------------------------------------------------

Here is the sample list of titles of TIPs (out of over 40 TIPs) in our knowledge-base:

• Making the business owner feel accountable for data quality for his data-group.
• Integrating the data-quality work-tools with your SDLC methodology.
• Avoiding duplication between Data Quality Management and work-done by IT and Business-Groups
• Implementing the DQ Management tools in work-flow system.
• Quantifying the risk assessment of a DQ issue or lack of data quality assurance
• Data Management Standards for Data Entities will be a mix of collaboration and top-down
• Data Management standards for data entities are not for IT systems
• Cascade your standards and guidelines (to business partners and Vendors)
• Involve your key partners in firming up your Data management Standards for data entities.
• Evolve your data quality assurance and control guidelines
• Cascade your standards and guidelines (to business partners and Vendors)
• Data Quality policy should align with the organization readiness.
• The first data quality policy should be a no-brainer.
• Evolve your data quality assurance and control guidelines
• Cascade your standards and guidelines (to business partners and Vendors
• Making a business owner accountable for a data-group
• Do not make Data Steward a business owner for a cross-functional data-group.
• Field Tips Series- 10 simple but highly effective things to do to promote data quality- #1
• Field Tips Series- 10 simple but highly effective things to do to promote data quality- #2
• Field Tips Series- 10 simple but highly effective things to do to promote data quality- #3
• Quantifying the business impact of a DQ Gaps Assessment
• Holistic solution for Data Quality may not be always a best solution
• There is a testing phase for transitioning the business ownership for data quality.
• Data Quality program will survive, if it adapts and is responsive to change.
• Data Quality Program should be aligned with larger data management initiatives.
• Data quality Initiatives should piggy-back on larger funded projects.
• Weigh your risks carefully before asking money. Think business.
• Data Quality is for business and business is not for data quality.
• Not everything related to Data Quality issues need to be resolved through a data quality program
• Don’t create hype on data quality program.

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Data Quality Encyclopedia                                                                           BUY DQ MANAGEMENT PACK   TOP   DQ Management Guide

We have a complete section on Data Quality, whereby we have 5 chapters and nearly 30 topics. This encyclopedia is closely tied to the tool-kit, Field-Tips and Data Quality FAQs. Here is the list of chapters and topics in the Data Quality Encyclopedia:

• Data Quality Overview
  • What is Data Quality
  • Data Quality is Relative
  • Reasons of Bad Data Quality
  • Impact of Bad Data Quality
• Data Quality Assurance and Monitoring
  • Interface Controls
  • Input Controls
  • Domain and Data Standards
  • Data-Model Controls
  • Business Partner Interface Control
  • Business Process Control
  • Batch-Processing Controls
  • Business Rules
• Customer Data Quality
  • Impact of Bad Customer Data Quality
  • Customer Data Quality Challenges
  • Customer Data Searching and Matching
  • Customer Data Correcting Techniques
  • Customer Data Augmentation and Enrichment
• Data Mapping and Assessment
  • Data Mapping and Assessment overview
  • Column Analysis
  • Data Model Analysis
• Data Quality Program
  • Data Quality Program Initiation- Getting OK for Analyze Phase
  • Data Quality Program Analysis- Data Mapping & Assessment
  • Data Quality Gaps- Root Cause Analysis
  • DQ Program Analyze Phase - Data Quality Approach (Possible Options)
  • DQ Program Analyze Phase- Data Quality Approach Considerations
  • Data Quality Program Analyze Phase- Approach Finalization
  • DQ Program Analyze Phase- Business Case & Closure
  • Data Quality Program Deliverable- Data Quality Policy
  • Data Quality Program Deliberable- Data Quality Organization
  • DQ Program Deliberable- Data Quality Procedures