Besides the small introduction, subscribers and consulting clients within this management domain have access to:
Learn about what causes data quality issues, how to measure data quality, what makes a good data quality practice in relation to your data and business environments.
Determine your business unit priorities to create data quality improvement projects.
Revisit the root causes of data quality issues and identify the relevant root causes to the highest priority business unit, then determine a strategy for fixing those issues.
Identify strategies for continuously monitoring and improving data quality at the organization.
Workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn't enough, we offer low-cost delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.
Evaluate the maturity of the existing data quality practice and activities.
Assess how data quality is embedded into related data management practices.
Envision a target state for the data quality practice.
Understanding of the current data quality landscape
Gaps, inefficiencies, and opportunities in the data quality practice are identified
Target state for the data quality practice is defined
1.1 Explain approach and value proposition
1.2 Detail business vision, objectives, and drivers
1.3 Discuss data quality barriers, needs, and principles
1.4 Assess current enterprise-wide data quality capabilities
1.5 Identify data quality practice future state
1.6 Analyze gaps in data quality practice
Data Quality Management Primer
Business Capability Map Template
Data Culture Diagnostic
Data Quality Diagnostic
Data Quality Problem Statement Template
Define improvement initiatives
Define a data quality improvement strategy and roadmap
Improvement initiatives are defined
Improvement initiatives are evaluated and prioritized to develop an improvement strategy
A roadmap is defined to depict when and how to tackle the improvement initiatives
2.1 Create business unit prioritization roadmap
2.2 Develop subject areas project scope
2.3 By subject area 1 data lineage analysis, root cause analysis, impact assessment, and business analysis
Business Unit Prioritization Roadmap
Subject area scope
Data Lineage Diagram
Define improvement initiatives
Define a data quality improvement strategy and roadmap
Improvement initiatives are defined
Improvement initiatives are evaluated and prioritized to develop an improvement strategy
A roadmap is defined to depict when and how to tackle the improvement initiatives
3.1 Understand how data quality management fits in with the organization’s data governance and data management programs
3.2 By subject area 2 data lineage analysis, root cause analysis, impact assessment, and business analysis
Data Lineage Diagram
Root Cause Analysis
Impact Analysis
Determine a strategy for fixing data quality issues for the highest priority business unit
Strategy defined for fixing data quality issues for highest priority business unit
4.1 Formulate strategies and actions to achieve data quality practice future state
4.2 Formulate a data quality resolution plan for the defined subject area
4.3 By subject area 3 data lineage analysis, root cause analysis, impact assessment, and business analysis
Data Quality Improvement Plan
Data Lineage Diagram
Plan for continuous improvement in data quality
Incorporate data quality management into the organization’s existing data management and governance programs
Sustained and communicated data quality program
5.1 Formulate metrics for continuous tracking of data quality and monitoring the success of the data quality improvement initiative
5.2 Workshop Debrief with Project Sponsor
5.3 Meet with project sponsor/manager to discuss results and action items
5.4 Wrap up outstanding items from the workshop, deliverables expectations, GIs
Data Quality Practice Improvement Roadmap
Data Quality Improvement Plan (for defined subject areas)
Regardless of the driving business strategy or focus, organizations are turning to data to leverage key insights and help improve the organization’s ability to realize its vision, key goals, and objectives.
Poor quality data, however, can negatively affect time-to-insight and can undermine an organization’s customer experience efforts, product or service innovation, operational efficiency, or risk and compliance management. If you are looking to draw insights from your data for decision making, the quality of those insights is only as good as the quality of the data feeding or fueling them.
Improving data quality means having a data quality management practice that is sustainably successful and appropriate to the use of the data, while evolving to keep pace with or get ahead of changing business and data landscapes. It is not a matter of fixing one data set at a time, which is resource and time intensive, but instead identifying where data quality consistently goes off the rails, and creating a program to improve the data processes at the source.
Crystal Singh
Research Director, Data and Analytics
Info-Tech Research Group
Your organization is experiencing the pitfalls of poor data quality, including:
Poor data quality hinders successful decision making.
Not understanding the purpose and execution of data quality causes some disorientation with your data.
Organizations tend to adopt a project mentality when it comes to data quality instead of taking the strategic approach that would be all-around more beneficial in the long term.
Address the root causes of your data quality issues by forming a viable data quality program.
It is important to sustain best practices and grow your data quality program.
Info-Tech Insight
Fix data quality issues as close as possible to the source of data while understanding that business use cases will each have different requirements and expectations from data quality.
Reliable data is needed to facilitate data consumers at all levels of the enterprise.
Insights, knowledge, and information are needed to inform operational, tactical, and strategic decision-making processes. Data and information are needed to manage the business and empower business processes such as billing, customer touchpoints, and fulfillment.
Data should be at the foundation of your organization’s evolution. The transformational insights that executives are constantly seeking can be uncovered with a data quality practice that makes high-quality, trustworthy information readily available to the business users who need it.
98% of companies use data to improve customer experience. (Experian Data Quality, 2019)
Info-Tech Insight
As data is ingested, integrated, and maintained in the various streams of the organization's system and application architecture, there are multiple points where the quality of the data can degrade.
Insight:
Proper application of data quality dimensions throughout the data pipeline will result in superior business decisions.
Data quality issues can occur at any stage of the data flow.
Therefore, if there are problems with the organization’s underlying data, this can have a domino effect on many downstream business functions.
Let’s use an example to illustrate the domino effect of poor data quality.
Organization X is looking to migrate their data to a single platform, System Y. After the migration, it has become apparent that reports generated from this platform are inconsistent and often seem wrong. What is the effect of this?
30% Poor data quality
30% Method of interaction changing
30% Legacy systems or lack of new technology
95% Of organizations indicated that poor data quality undermines business performance.
(Source: Experian Data Quality, 2019)
Business decisions should be made with a strong rationale. Data can provide insight into key business questions, such as, “How can I provide better customer satisfaction?”
89% Of CIOs surveyed say lack of quality data is an obstacle to good decision making. (Larry Dignan, CIOs juggling digital transformation pace, bad data, cloud lock0in and business alignment, 2020)
Improve marketing and the customer experience by using the right data from the system of record to analyze complete customer views of transactions, sentiments, and interactions.
94% Percentage of senior IT leaders who say that poor data quality impinges business outcomes. (Clint Boulton, Disconnect between CIOs and LOB managers weakens data quality, 2016)
Gain insights on your products, services, usage trends, industry directions, and competitor results to support decisions on innovations, new products, services, and pricing.
20% Businesses lose as much as 20% of revenue due to poor data quality. (RingLead Data Management Solutions, 10 Stats About Data Quality I Bet You Didn’t Know)
Make sure the right solution is delivered rapidly and consistently to the right parties for the right price and cost structure. Automate processes by using the right data to drive process improvements.
10-20% The implementation of data quality initiatives can lead to reductions in corporate budget of up to 20%. (HaloBI, 2015)
Info-Tech Insight
Data quality suffers most at the point of entry. This is one of the causes of the domino effect of data quality – and can be one of the most costly forms of data quality errors due to the error propagation. In other words, fix data ingestion, whether through improving your application and database design or improving your data ingestion policy, and you will fix a large majority of data quality issues.
(Source: DAMA International)
Build a Robust and Comprehensive Data Strategy
Create a Data Management Roadmap
Phase Steps | 1. Define Your Organization’s Data Environment and Business Landscape | 2. Analyze Your Priorities for Data Quality Fixes | 3. Establish Your Organization’s Data Quality Program | 4. Grow and Sustain Your Data Quality Practice |
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Phase Outcomes | This step identifies the foundational understanding of your data and business landscape, the essential concepts around data quality, as well as the core capabilities and competencies that IT needs to effectively improve data quality. | To begin addressing specific, business-driven data quality projects, you must identify and prioritize the data-driven business units. This will ensure that data improvement initiatives are aligned to business goals and priorities. | After determining whose data is going to be fixed based on priority, determine the specific problems that they are facing with data quality, and implement an improvement plan to fix it. | Now that you have put an improvement plan into action, make sure that the data quality issues don’t keep cropping up. Integrate data quality management with data governance practices into your organization and look to grow your organization’s overall data maturity. |
Info-Tech Insight
“Data Quality is in the eyes of the beholder.”– Igor Ikonnikov, Research Director
Data from Info-Tech’s CIO Business Vision Diagnostic, which represents over 400 business stakeholders, shows that data quality is very important when satisfaction with data quality is low.
However, when data quality satisfaction hit a threshold, it became less important.
Respondents were asked “How satisfied are you with the quality, reliability, and effectiveness of the data you use to manage your group?” as well as to rank how important data quality was to their organization.
When the business satisfaction of data quality reached a threshold value of 71-80%, the rated importance reached its lowest value.
Info-Tech Insight
Data needs to be good, but truly spectacular data may go unnoticed.
Provide the right level of data quality, with the appropriate effort, for the correct usage. This blueprint will help you to determine what “the right level of data quality” means, as well as create a plan to achieve that goal for the business.
Data Strategy Data Strategy should contain Data Quality as a standard component. ← Data Quality issues can occur throughout at any stage of the data flow → |
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DQ Dimensions Timeliness – Representation – Usability – Consistency – Completeness – Uniqueness – Entry Quality – Validity – Confidence – Importance |
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Source System Layer
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Data Transformation Layer
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Consumption Layer
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Data Creation → | [SLA] Data Ingestion [ QA] | →Data Accumulation & Engineering → | [SLA] Data Delivery [QA] | →Reporting & Analytics |
Fix Data Quality root causes here… | → | to prevent expensive cures here. |
Industry: Healthcare
Source: Primary Info-Tech Research
A healthcare insurance agency faced data quality issues in which a key business use case was impacted negatively. Business rules were not well defined, and default values instead of real value caused a concern. When dealing with multiple addresses, data was coming from different source systems.
The challenge was to identify the most accurate address, as some were incomplete, and some lacked currency and were not up to date. This especially challenged a key business unit, marketing, to derive business value in performing key activities by being unable to reach out to existing customers to advertise any additional products.
For this initiative, this insurance agency took an economic approach by addressing those data quality issues using internal resources.
Without having any MDM tools or having a master record or any specific technology relating to data quality, this insurance agency used in-house development to tackle those particular issues at the source system. Data quality capabilities such as data profiling were used to uncover those issues and address them.
“Data quality is subjective; you have to be selective in terms of targeting the data that matters the most. When getting business tools right, most issues will be fixed and lead to achieving the most value.” – Asif Mumtaz, Data & Solution Architect
"Our team has already made this critical project a priority, and we have the time and capability, but some guidance along the way would be helpful."
"Our team knows that we need to fix a process, but we need assistance to determine where to focus. Some check-ins along the way would help keep us on track."
"We need to hit the ground running and get this project kicked off immediately. Our team has the ability to take this over once we get a framework and strategy in place."
"Our team does not have the time or the knowledge to take this project on. We need assistance through the entirety of this project."
Phase 1 | Phase 2 | Phase 3 | Phase 4 |
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A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.
A typical GI is between eight to twelve calls over the course of four to six months.
Contact your account representative for more information. workshops@infotech.com 1-888-670-8889
Day 1 | Day 2 | Day 3 | Day 4 | Day 5 | |
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Define Your Organization’s Data Environment and Business Landscape | Create a Strategy for Data Quality Project 1 | Create a Strategy for Data Quality Project 2 | Create a Strategy for Data Quality Project 3 | Create a Plan for Sustaining Data Quality | |
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A comprehensive data quality practice includes appropriate business requirements gathering, planning, governance, and oversight capabilities, as well as empowering technologies for properly trained staff, and ongoing development processes.
Some common examples of appropriate data management methodologies for data quality are:
Effective data quality practices coordinate with other overarching data disciplines, related data practices, and strategic business objectives.
“You don’t solve data quality with a Band-Aid; you solve it with a methodology.” – Diraj Goel, Growth Advisor, BC Tech
Similar to measuring the acidity of a substance with a litmus test, the quality of your data can be measured using a simple indicator test. As you learn about common root causes of data quality problems in the following slides, think about these four quality indicators to assess the quality of your data:
Info-Tech Insight
Quality is a relative term. Data quality is measured in terms of tolerance. Perfect data quality is both impossible and a waste of time and effort.
Follow these steps to convince leadership of the value of data quality:
“You have to level with people, you cannot just start talking with the language of data and expect them to understand when the other language is money and numbers.” – Izabela Edmunds, Information Architect at Mott MacDonald
1. Data Culture Diagnostic
Use this report to understand where your organization lies across areas relating to data culture.
While the Quality & Trust area of the report might be most prevalent to this blueprint, this diagnostic may point out other areas demanding more attention.
Please speak to your account manager for access
2. Business Capability Map Template
Perform this process to understand the capabilities that enable specific value streams. The output of this deliverable is a high-level view of your organization’s defined business capabilities.
Info-Tech Insight
Understanding your data culture and business capabilities are foundational to starting the journey of data quality improvement.
Key deliverable:
3. Data Quality Diagnostic
The Data Quality Report is designed to help you understand, assess, and improve key organizational data quality issues. This is where respondents across various areas in the organization can assess Data Quality across various dimensions.
If there are data elements that are considered of high importance and low confidence, then they must be prioritized.
After you get to know the properties of good quality data, understand the underlying causes of why those indicators can point to poor data quality.
If you notice that the usability, completeness, timeliness, or accessibility of the organization’s data is suffering, one or more of the following root causes are likely plaguing your data:
Common root causes of poor data quality, through the lens of Info-Tech’s Five-Tier Data Architecture:
These root causes of poor data quality are difficult to avoid, not only because they are often generated at an organization’s beginning stages, but also because change can be difficult. This means that the root causes are often propagated through stale or outdated business processes.
Application design plays one of the largest roles in the quality of the organization’s data. The proper design of applications can prevent data quality issues that can snowball into larger issues downstream.
Proper ingestion is 90% of the battle. An ounce of prevention is worth a pound of cure. This is true in many different topics, and data quality is one of them. Designing an application so that data gets entered properly, whether by internal staff or external customers, is the single most effective way to prevent data quality issues.
Some common causes of data quality problems at the application/system level include:
Database design also affects data quality. How a database is designed to handle incoming data, including the schema and key identification, can impact the integrity of the data used for reporting and analytics.
The most common type of database is the relational database. Therefore, we will focus on this type of database.
When working with and designing relational databases, there are some important concepts that must be considered.
Referential integrity is a term that is important for the design of relational database schema, and indicates that table relationships must always be consistent.
For table relationships to be consistent, primary keys (unique value for each row) must uniquely identify entities in columns of the table. Foreign keys (field that is defined in a second table but refers to the primary key in the first table) must agree with the primary key that is referenced by the foreign key. To maintain referential integrity, any updates must be propagated to the primary parent key.
Info-Tech Insight
Other types of databases, including databases with unstructured data, need data quality consideration. However, unstructured data may have different levels of quality tolerance.
Databases and People:
Even though database design is a technology issue, don’t forget about the people.
A lack of training employees on database permissions for updating/entering data into the physical databases is a common problem for data quality.
Data ingestion is another category of data-quality-issue root causes. When moving data in Tier 2, whether it is through ETL, ESB, point-to-point integration, etc., the integrity of the data during movement and/or transformation needs to be maintained.
Tier 2 (the data ingestion layer) serves to move data for one of two main purposes:
This ensures the data is pristine throughout the process and improves trustworthiness of outcomes and speed to task completion.
Data policies and procedures are necessary for establishing standards around data and represent another category of data-quality-issue root causes. This issue spans across all five of the 5 Tier Architecture.
Data policies are short statements that seek to manage the creation, acquisition, integrity, security, compliance, and quality of data. These policies vary amongst organizations, depending on your specific data needs.
Business processes can impact data quality. How data is entered into systems, as well as employee training and knowledge about the correct data definitions, can impact the quality of your organization’s data.
These problematic business process root causes can lead to:
Duplicate records
Incomplete data
Improper use of data
Wrong data entered into fields
These data quality issues will result in costly and inefficient manual fixes, wasting valuable time and resources.
1. Data Quality Understanding
2. Phase 0 Deliverables
Introduced foundational tools to help you throughout this blueprint:
3. Common Root Causes
Addressed where multiple root causes can occur throughout the flow of your data.
Analyzed the following common root causes of data quality:
Business Vision
Business Goals
Business Drivers
Business Differentiators
Understanding where data lives can be challenging as it is often in motion and rarely resides in one place. There are multiple benefits that come from taking the time to create a data flow diagram.
Info-Tech’s Four-Column Model of Data will help you to identify the essential aspects of your data:
Business Use Case →Used by→Business Unit →Housed in→Systems→Used for→Usage of the Data
To prioritize your business units for data quality improvement projects, you must analyze the relative importance of the data they use to the business. The more important the data is to the business, the higher the priority is of fixing that data. There are two measures for determining the importance of data: business value and business impact.
Business value of data can be evaluated by thinking about its ties to revenue generation for the organization, as well as how it is used for productivity and operations at the organization.
The business value of data is assessed by asking what would happen to the following parameters if the data is not usable (due to poor quality, for example):
Business impact of data should take into account the effects of poor data on both internal and external parties.
The business impact of data is assessed by asking what the impact would be of bad data on the following parameters:
Value + Impact = Data Priority Score
Before you can identify a solution, you must identify the problem with the business unit’s data.
Use Info-Tech’s Data Quality Problem Statement Template to identify the symptoms of poor data quality and articulate the problem.
Info-Tech’s Data Quality Problem Statement Template will walk you through a step-by-step approach to identifying and describing the problems that the business unit feels regarding its data quality.
Before articulating the problem, it helps to identify the symptoms of the problem. The following W’s will help you to describe the symptoms of the data quality issues:
What
Define the symptoms and feelings produced by poor data quality in the business unit.
Where
Define the location of the data that are causing data quality issues.
When
Define how severe the data quality issues are in frequency and duration.
Who
Define who is affected by the data quality problems and who works with the data.
Info-Tech Best Practice
Symptoms vs. Problems. Often, people will identify a list of symptoms of a problem and mistake those for the problem. Identifying the symptoms helps to define the problem, but symptoms do not help to identify the solution. The problem statement helps you to create solutions.
1 hour
A defined problem helps you to create clear goals, as well as lead your thinking to determine solutions to the problem.
A problem statement consists of one or two sentences that summarize a condition or issue that a quality improvement team is meant to address. For the improvement team to fix the problem, the problem statement therefore has to be specific and concise.
Instructions
MathWorks
Industry
Software Development
Source
Primary Info-Tech Research
As part of moving to a formalized data quality practice, MathWorks leveraged an incremental approach that took its time investigating business cases to support improvement actions. Establishing realistic goals for improvement in the form of a roadmap was a central component for gaining executive approval to push the project forward.
Roadmap Creation
In constructing a comprehensive roadmap that incorporated findings from business process and data analyses, MathWorks opted to document five-year and three-year overall goals, with one-year objectives that supported each goal. This approach ensured that the tactical actions taken were directed by long-term strategic objectives.
In presenting their roadmap for executive approval, MathWorks placed emphasis on communicating the progression and impact of their initiatives in terms that would engage business users. They focused on maintaining continual lines of communication with business stakeholders to demonstrate the value of the initiatives and also to gradually shift the corporate culture to one that is invested in an effective data quality practice.
“Don’t jump at the first opportunity, because you may be putting out a fire with a cup of water where a fire truck is needed.” – Executive Advisor, IT Research and Advisory Firm
Assess IT’s capabilities and competencies around data quality and plan to build these as the organization’s data quality practice develops. Before you can fix data quality, make sure you have the necessary skills and abilities to fix data quality correctly.
The following IT capabilities are developed on an ongoing basis and are necessary for standardizing and structuring a data quality practice:
Data Handling and Remediation Competencies:
After these capabilities and competencies are assessed for a current and desired target state, the Data Quality Practice Assessment and Project Planning Tool will suggest improvement actions that should be followed in order to build your data quality practice. In addition, a roadmap will be generated after target dates are set to create your data quality practice development strategy.
1 hour
Use the Data Quality Practice Assessment and Project Planning Tool to evaluate the baseline and target capabilities of your practice in terms of how data quality is approached and executed.
Instructions
These results will set the baseline against which you will monitor performance progress and keep track of improvements over time.
Info-Tech Insight
Focus on early alignment. Assessing capabilities within specific people’s job functions can naturally result in disagreement or debate, especially between business and IT people. Remind everyone that data quality should ultimately serve business needs wherever possible.
To enable deeper analysis on the results of your practice assessment, Tab 3: Data Quality Practice Scorecard in the Data Quality Practice Assessment and Project Planning Tool creates visualizations of the gaps identified in each of your practice capabilities and related data management practices. These diagrams serve as analysis summaries.
Gap assessment of “Meeting Business Needs” capabilities
Visualization of gap assessment of data quality practice capabilities
This means that before engaging IT in data quality projects to fix the business units’ data in Phase 2, IT must assess feasibility of the data quality improvement plan. A feasibility analysis is typically used to review the strengths and weaknesses of the projects, as well as the availability of required skills and technologies needed to complete them. Use the following workflow to guide you in performing a feasibility analysis:
Project evaluation process:
Present capabilities
Info-Tech Best Practice
While the PMO identifies and coordinates projects, IT must determine how long and for how much.
1 hour
Instructions
1 hour
Generating Your Roadmap
Use the Practice Roadmap to plan and improve data quality capabilities
Info-Tech Best Practice
To help get you started, Info-Tech has provided an extensive list of data quality improvement initiatives that are commonly undertaken by organizations looking to improve their data quality.
2 hours
Create practice-level metrics to monitor your data quality practice.
Instructions:
Metric | Current | Goal |
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Usage (% of trained users using the data warehouse) | ||
Performance (response time) | ||
Performance (response time) | ||
Resource utilization (memory usage, number of machine cycles) | ||
User satisfaction (quarterly user surveys) | ||
Data quality (% values outside valid values, % fields missing, wrong data type, data outside acceptable range, data that violates business rules. Some aspects of data quality can be automatically tracked and reported) | ||
Costs (initial installation and ongoing, Total Cost of Ownership including servers, software licenses, support staff) | ||
Security (security violations detected, where violations are coming from, breaches) | ||
Patterns that are used | ||
Reduction in time to market for the data | ||
Completeness of data that is available | ||
How many "standard" data models are being used | ||
What is the extra business value from the data governance program? | ||
How much time is spent for data prep by BI & analytics team? |
As you improve your data quality practice and move from reactive to stable, don’t rest and assume that you can let data quality keep going by itself. Rapidly changing consumer requirements or other pains will catch up to your organization and you will fall behind again. By moving to the proactive and predictive end of the maturity scale, you can stay ahead of the curve. By following the methodology laid out in Phase 1, the data quality practices at your organization will improve over time, leading to the following results:
Before Data Quality Practice Improvements
Year 1
Year 2
Year 3
(Global Data Excellence, Data Excellence Maturity Model)
It is important to understand the various data that exist in the business unit, as well as which data are essential to business function and require the highest degree of quality efforts.
Visualize your databases and the flow of data. A data lineage diagram can help you and the Data Quality Improvement Team visualize where data issues lie. Keeping the five-tier architecture in mind, build your data lineage diagram.
Reminder: Five-Tier Architecture
Use the following icons to represent your various data systems and databases.
2 hours
Map the flow and location of data within a business unit by creating a system context diagram.
Gain an accurate view of data locations and uses: Engage business users and representatives with a wide breadth of knowledge-related business processes and the use of data by related business operations.
Sample Data Lineage Diagram
1 hour
Develop goals and align them with specific objectives to set the framework for your data quality initiatives.
In the context of achieving business vision, mission, goals, and objectives and sustaining differentiators and key drivers, think about where and how data quality is a barrier. Then brainstorm data quality improvement objectives that map to these barriers. Document your list of objectives in Tab 5. Prioritize business units of the Data Quality Practice Assessment and Project Planning Tool.
Establishing Business Context Example Healthcare Industry |
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Vision | To improve member services and make service provider experience more effective through improving data quality and data collection, aggregation, and accessibility for all the members. |
Goals | Establish meaningful metrics that guide to the improvement of healthcare for member effectiveness of health care providers:
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Differentiator | Connect service consumers with service providers, that comply with established regulations by delivering data that is accurate, trusted, timely, and easy to understand to connect service providers and eliminate bureaucracy and save money and time. |
Key Driver | Seamlessly provide a healthcare for members. |
30 minutes
Instructions
To prioritize your business units for data quality improvement projects, you must analyze the relative importance of the data they use to the business. The more important the data is to the business, the higher the priority is of fixing that data. There are two measures for determining the importance of data: business value and business impact.
Business value of data can be evaluated by thinking about its ties to revenue generation for the organization, as well as how it is used for productivity and operations at the organization.
The business value of data is assessed by asking what would happen to the following parameters if the data is not usable (due to poor quality, for example):
Business impact of data should take into account the effects of poor data on both internal and external parties.
The business impact of data is assessed by asking what the impact would be of bad data on the following parameters:
Value + Impact = Data Priority Score
2 hours
Instructions
Instructions In Tab 5: Prioritize Business Units of the Data Quality Practice Assessment and Project Planning Tool, assess business value and business impact of the data within each documented business unit.
Use the ratings High, Medium, and Low to measure the financial, productivity, and efficiency value and impact of each business unit’s data.
In addition to these ratings, assess the number of help desk tickets that are submitted to IT regarding data quality issues. This parameter is an indicator that the business unit’s data is high priority for data quality fixes.
1 hour
Instructions
After assessing the business units for the business value and business impact of their data, the Data Quality Practice Assessment and Project Planning Tool automatically assesses the prioritization of the business units based on your ratings. These prioritizations are then summarized in a roadmap on Tab 6: Data Quality Project Roadmap. The following is an example of a project roadmap:
On Tab 6, insert the timeline for your data quality improvement projects, as well as the starting date of your first data quality project. The roadmap will automatically update with the chosen timing and dates.
As you improve the data quality for specific business units, measuring the benefits of data quality improvements will help you demonstrate the value of the projects to the business.
Use the following table to guide you in creating business-aligned metrics:
Business Unit | Driver | Metrics | Goal |
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Sales | Customer Intimacy | Accuracy of customer data. Percent of missing or incomplete records. | 10% decrease in customer record errors. |
Marketing |
Customer Intimacy | Accuracy of customer data. Percent of missing or incomplete records. | 10% decrease in customer record errors. |
Finance | Operational Excellence | Relevance of financial reports. | Decrease in report inaccuracy complaints. |
HR | Risk Management | Accuracy of employee data. | 10% decrease in employee record errors. |
Shipping | Operational Excellence | Timeliness of invoice data. | 10% decrease in time to report. |
Info-Tech Insight
Relating data governance success metrics to overall business benefits keeps executive management and executive sponsors engaged because they are seeing actionable results. Review metrics on an ongoing basis with those data owners/stewards who are accountable, the data governance steering committee, and the executive sponsors.
Industry: Government
Source: Environment Development of Canada (EDC)
Environment Development Canada (EDC) would initially identify data elements that are important to the business purely based on their business instinct.
Leadership attempted to tackle the enterprise’s data issues by bringing a set of different tools into the organization.
It didn’t work out because the fundamental foundational layer, which is the data and infrastructure, was not right – they didn't have the foundational capabilities to enable those tools.
Leadership listened to the need for one single team to be responsible for the data persistence.
Therefore, the data platform team was granted that mandate to extensively execute the data quality program across the enterprise.
A data quality team was formed under the Data & Analytics COE. They had the mandate to profile the data and to understand what quality of data needed to be achieved. They worked constantly with the business to build the data quality rules.
EDC tackled the source of their data quality issues through initially performing a data quality management assessment with business stakeholders.
From then on, EDC was able to establish their data quality program and carry out other key initiatives that prove the ROI on data quality.
Now that you have a prioritized list for your data quality improvement projects, identify the highest priority business unit. This is the business unit you will work through Phase 3 with to fix their data quality issues.
Once you have initiated and identified solutions for the first business unit, tackle data quality for the next business unit in the prioritized list.
1 hour
The Data Quality Improvement Plan is a concise document that should be created for each data quality project (i.e. for each business unit) to keep track of the project.
Instructions
Team role | Assigned to |
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Data Owner | [Name] |
Project Manager | [Name] |
Business Analyst/BRM | [Name] |
Data Steward | [Name] |
Data Analyst | [Name] |
1 hour
Data quality initiatives have to be relevant to the business, and the business context will be used to provide inputs to the data improvement strategy. The context can then be used to determine exactly where the root causes of data quality issues are, which will inform your solutions.
Instructions
The business context of the data quality improvement plan includes documenting from previous activities:
Info-Tech Best Practice
While many organizations adopt data quality principles, not all organizations express them along the same terms. Have multiple perspectives within your organization outline principles that fit your unique data quality agenda. Anyone interested in resolving the day-to-day data quality issues that they face can be helpful for creating the context around the project.
You previously fleshed out the problem with data quality present in the business unit chosen as highest priority. Now it is time to figure out what is causing those problems.
In the table below, you will find some of the common categories of causes of data quality issues, as well as some specific root causes.
Category | Description |
---|---|
1. System/Application Design | Ineffective, insufficient, or even incorrect system/application design accepts incorrect and missing data elements to the source applications and databases. The data records in those source systems may propagate into systems in tiers 2, 3, 4, and 5 of the 5-tier architecture, creating domino and ripple effects. |
2. Database design | Database is created and modeled in an incorrect manner so that the management of the data records is incorrect, resulting in duplicated and orphaned records, and records that are missing data elements or records that contain incorrect data elements. Poor operational data in databases often leads to issues in tiers 2, 3, 4, and 5. |
3. Enterprise Integration | Data or information is improperly integrated, transformed, masked, and aggregated in tier 2. In addition, some data integration tasks might not be timely, resulting in out-of-date data or even data that contradicts with other data. Enterprise integration is a precursor of loading a data warehouse and data marts. Issues in this layer affect tier 3, 4 and 5 on the 5-tier architecture. |
4. Policies and Procedures | Policies and procedures are not effectively used to reinforce data quality. In some situations, policy gaps are found. In others, policies are overlapped and duplicated. Policies may also be out-of-date or too complex, affecting the users’ ability to interpret the policy objectives. Policies affect all tiers in the 5-tier architecture. |
5. Business Processes | Improper business process design introduces poor data into the data systems. Failure to create processes around approving data changes, failure to document key data elements, and failure to train employees on the proper uses of data make data quality a burning problem. |
A root cause analysis is a systematic approach to decompose a problem into its components. Use fishbone diagrams to help reveal the root causes of data issues.
Info-Tech recommends five root cause categories for assessing data quality issues:
Application Design. Is the issue caused by human error at the application level? Consider internal employees, external partners/suppliers, and customers.
Database Design. Is the issue caused by a particular database and stems from inadequacies in its design?
Integration. Data integration tools may not be fully leveraged, or data matching rules may be poorly designed.
Policies and Procedures. Do the issues take place because of lack of governance?
Business Processes. Do the issues take place due to insufficient processes?
For Example:
When performing a deeper analysis of your data issues related to the accuracy of the business unit’s data, you would perform a root cause analysis by assessing the contribution of each of the five categories of data quality problem root causes:
Including all attributes of the key subject area in your data profiling activities may produce too much information to make sense of. Conduct data profiling primarily at the table level and undergo attribute profiling only if you are able to narrow down your scope sufficiently.
Data profiling extracts a sample of the target data set and runs it through multiple levels of analysis. The end result is a detailed report of statistics about a variety of data quality criteria (duplicate data, incomplete data, stale data, etc.).
Many data profiling tools have built-in templates and reports to help you uncover data issues. In addition, they quantify the occurrences of the data issues.
This supplements a profiling tool. For Example, use a BI tool to create a custom grouping of all the invalid states (e.g. “CAL,” “AZN,” etc.) and visualize the percentage of invalid states compared to all states.
This supplements a profiling tool. For example, use a SQL statement to group the customer data by customer segment and then by state to identify which segment–state combinations contain poor data.
2 hours
Instructions
Example:
1 hour
Now that you have data quality issues classified according to the data quality attributes, map these issues onto four fishbone diagrams.
Suboptimal system/application design provides entry points for bad data.
Business Process | |||||
---|---|---|---|---|---|
Usually found in → | Tier 1 | Tier 2 | Tier 3 | Tier 4 | Tier 5 |
Issue | Root Causes | Usability | Completeness | Timeliness | Accessibility |
---|---|---|---|---|---|
Insufficient data mask | No data mask is defined for a free-form text field in a user interface. E.g. North American phone number should have 4 masks – country code (1-digit), area code (3-digit), and local number (7-digit). | X | X | ||
Too many free-form text fields | Incorrect use of free-form text fields (fields that accept a variety of inputs). E.g. Use a free-form text field for zip code instead of a backend look up. | X | X | ||
Lack of value lookup | Reference data is not looked up from a reference list. E.g. State abbreviation is entered instead of being looked up from a standard list of states. | X | X | ||
Lack of mandatory field definitions | Mandatory fields are not identified and reinforced. Resulting data records with many missing data elements. E.g. Some users may fill up 2 or 3 fields in a UI that has 20 non-mandatory fields. | X |
Improper database design allows incorrect data to be stored and propagated.
Business Process | |||||
---|---|---|---|---|---|
Usually found in → | Tier 1 | Tier 2 | Tier 3 | Tier 4 | Tier 5 |
Issue | Root Causes | Usability | Completeness | Timeliness | Accessibility |
---|---|---|---|---|---|
Incorrect referential integrity | Referential integrity constraints are absent or incorrectly implemented, resulting in child records without parent records, or related records are updated or deleted in a cascading manner. E.g. An invoice line item is created before an invoice is created. | X | X | ||
Lack of unique keys | Lack of unique keys creating scenarios where record uniqueness cannot be guaranteed. E.g. Customer records with the same customer_ID. | X | X | ||
Data range | Fail to define a data range for incoming data, resulting in data values that are out of range. E.g. The age field is able to store an age of 999. | X | X | ||
Incorrect data type | Incorrect data types are used to store data fields. E.g. A string field is used to store zip codes. Some users use that to store phone numbers, birthdays, etc. | X | X |
Improper data integration or synchronization may create poor analytical data.
Business Process | |||||
---|---|---|---|---|---|
Usually found in → | Tier 1 | Tier 2 | Tier 3 | Tier 4 | Tier 5 |
Issue | Root Causes | Usability | Completeness | Timeliness | Accessibility |
---|---|---|---|---|---|
Incorrect transformation | Transformation is done incorrectly. A wrong formula may have been used, transformation is done at the wrong data granularity, or aggregation logic is incorrect. E.g. Aggregation is done for all customers instead of just active customers. | X | X | ||
Data refresh is out of sync | Data is synchronized at different intervals, resulting in a data warehouse where data domains are out of sync. E.g. Customer transactions are refreshed to reflect the latest activities but the account balance is not yet refreshed. | X | X | ||
Data is matched incorrectly | Fail to match records from disparate systems, resulting in duplications and unmatched records. E.g. Unable to match customers from different systems because they have different cust_ID. | X | X | ||
Incorrect data mapping | Fields from source systems are not properly matched with data warehouse fields. E.g. Status fields from different systems are mixed into one field. | X | X |
Suboptimal policies and procedures undermine the effect of best practices.
Business Process | |||||
---|---|---|---|---|---|
Usually found in → | Tier 1 | Tier 2 | Tier 3 | Tier 4 | Tier 5 |
Issue | Root Causes | Usability | Completeness | Timeliness | Accessibility |
---|---|---|---|---|---|
Policy Gaps | There are gaps in the policy landscape in terms of some missing key policies or policies that are not refreshed to reflect the latest changes. E.g. A data entry policy is absent, leading to inconsistent data entry practices. | X | X | ||
Policy Communications | Policies are in place but the policies are not communicated effectively to the organization, resulting in misinterpretation of policies and under-enforcement of policies. E.g. The data standard is created but very few developers are aware of its existence. | X | X | ||
Policy Enforcement | Policies are in place but not proactively re-enforced and that leads to inconsistent application of policies and policy adoption. E.g. Policy adoption is dropping over time due to lack of reinforcement. | X | X | ||
Policy Quality | Policies are written by untrained authors and they do not communicate the messages. E.g. A non-technical data user may find a policy that is loaded with technical terms confusing. | X | X |
Ineffective and inefficient business processes create entry points for poor data.
Business Process | |||||
---|---|---|---|---|---|
Usually found in → | Tier 1 | Tier 2 | Tier 3 | Tier 4 | Tier 5 |
Issue | Root Causes | Usability | Completeness | Timeliness | Accessibility |
---|---|---|---|---|---|
Lack of training | Key data personnel and business analysts are not trained in data quality and data governance, leading to lack of accountability. E.g. A data steward is not aware of downstream impact of a duplicated financial statement. | X | X | ||
Ineffective business process | The same piece of information is entered into data systems two or more times. Or a piece of data is stalled in a data system for too long. E.g. A paper form is scanned multiple times to extract data into different data systems. | X | X | ||
Lack of documentation | Fail to document the work flows of the key business processes. A lack of work flow results in sub-optimal use of data. E.g. Data is modeled incorrectly due to undocumented business logic. | X | X | ||
Lack of integration between business silos | Business silos hold on to their own datasets resulting in data silos in which data is not shared and/or data is transferred with errors. E.g. Data from a unit is extracted as a data file and stored in a shared drive with little access. | X | X |
As you worked through the previous step, you identified the root causes of your data quality problems within the business unit. Now, it is time to identify solutions.
The following slides provide an overview of the solutions to common data quality issues. As you identify solutions that apply to the business unit being addressed, insert the solution tables in Section 4: Proposed Solutions of the Data Quality Improvement Plan Template.
All data quality solutions have two components to them:
For the next five data quality solution slides, look for the slider for the contributions of each category to the solution. Use this scale to guide you in creating solutions.
When designing solutions, keep in mind that solutions to data quality problems are not mutually exclusive. In other words, an identified root cause may have multiple solutions that apply to it.
For example, if an application is plagued with inaccurate data, the application design may be suboptimal, but also the process that leads to data being entered may need fixing.
Restrict field length – Capture only the characters you need for your application.
Leverage data masks – Use data masks in standardized fields like zip code and phone number.
Restrict the use of open text fields and use reference tables – Only present open text fields when there is a need. Use reference tables to limit data values.
Provide options – Use radio buttons, drop-down lists, and multi-select instead of using open text fields.
Validate data before committing – Use simple validation to ensure the data entered is not random numbers and letters.
Track history – Keep track of who entered what fields.
Cannot submit twice – Only design for one-time submission.
Data-entry training – Training that is related to data entry, creating, or updating data records.
Data resolution training – Training data stewards or other dedicated data personnel on how to resolve data records that are not entered properly.
Standards – Develop application design principles and standards.
Field testing – Field data entry with a few people to look for abnormalities and discrepancies.
Detection and resolution – Abnormal data records should be isolated and resolved ASAP.
Thorough testing – Application design is your first line of defence against poor data. Test to ensure bad data is kept out of the systems.
HMS
Industry: Healthcare
Source: Informatica
Healthcare Management Systems (HMS) provides cost containment services for healthcare sponsors and payers, and coordinates benefits services. This is to ensure that healthcare claims are paid correctly to both government agencies and individuals. To do so, HMS relies on data, and this data needs to be of high quality to ensure the correct decisions are made, the right people get the correct claims, and the appropriate parties pay out.
To improve the integrity of HMS’s customer data, HMS put in place a framework that helped to standardize the collection of high volume and highly variable data.
Working with a data quality platform vendor to establish a framework for data standardization, HMS was able to streamline data analysis and reduce new customer implementations from months to weeks.
Before improving data quality processes | After improving data quality processes |
Data Ingestion | Data Ingestion |
Many standards of ingestion. | Standardized data ingestion |
Data Storage | Data Storage |
Lack of ability to match data, creating data quality errors. | |
Data Analysis | Data Analysis |
= | = |
Slow Customer Implementation Time | 50% Reduction in Customer Implementation Time |
Referential integrity – Ensure parent/child relationships are maintained in terms of cascade creation, update, and deletion.
Primary key definition – Ensure there is at least one key to guarantee the uniqueness of the data records, and primary key should not allow null.
Validate data domain – Create triggers to check the data values entered in the database fields.
Field type and length – Define the most suitable data type and length to hold field values.
Explore solutions – Where to fix the data issues? Is there a case to fix the issues?
Running profiling tools to catch errors – Run scans on the database with defined criteria to identify occurrences of questionable data.
Fix a sample before fixing all records – Use a proof-of-concept approach to explore fix options and evaluate impacts before fixing the full set.
Perform key tasks in pairs – Take a pair approach to perform key tasks so that validation and cross-check can happen.
Skilled DBAs – DBAs should be certified and accredited.
Competence – Assess DBA competency on an ongoing basis.
Preparedness – Develop drills to stimulate data issues and train DBAs.
Cross train – Cross train team members so that one DBA can cover another DBA.
Info-Tech’s 5-Tier Architecture – When doing transformations, it is good practice to persist the integration results in tier 3 before the data is further refined and presented in tier 4.
Timing, timing, and timing – Think of the sequence of events. You may need to perform some ETL tasks before other tasks to achieve synchronization and consistence.
Historical changes – Ensure your tier 3 is robust enough to include historical data. You need to enable type 2 slowly, changing dimension to recreate the data at a point in time.
Standardize – Leverage data standardization to standardize name and address fields to improve matching and integration.
Fuzzy matching – When there are no common keys between datasets. The datasets can only be matched by fuzzy matching. Fuzzy matching is not hard science; define a confidence level and think about a mechanism to deal with the unmatched.
Business data glossary and data lineage – Define a business data glossary to enhance findability of key data elements. Document data mappings and ETL logics.
Create data quality reports – Many ETL platforms provide canned data quality reports. Leverage those quality reports to monitor the data health.
Create data quality reports – Many ETL platforms provide canned data quality reports. Leverage those quality reports to monitor the data health.
ARB (architectural review board) – All ETL codes should be approved by the architectural review board to ensure alignment with the overall integration strategy.
Data quality reports – Leverage canned data quality reports from the ETL platforms to monitor data quality on an on-going basis. When abnormalities are found, provoke the right policies to deal with the issues.
Store policies in a central location that is well known and easy to find and access. A key way that technology can help communicate policies is by having them published on a centralized website.
Make the repository searchable and easily navigable. myPolicies helps you do all this and more.
myPolicies helps you do all this and more.
Policy review – Create a schedule for reviewing policies on a regular basis – invite professional writers to ensure polices are understandable.
Policy training – Policies are often unread and misread. Training users and stakeholders on policies is an effective way to make sure those users and stakeholders understand the rationale of the policies. It is also a good practice to include a few scenarios that are handled by the policies.
Policy hotline/mailbox – To avoid misinterpretation of the policies, a policy hotline/mailbox should be set up to answer any data policy questions from the end users/stakeholders.
Simplified communications – Create handy one-pagers and infographic posters to communicate the key messages of the polices.
Policy briefing – Whenever a new data project is initiated, a briefing of data policies should be given to ensure the project team follows the policies from the very beginning.
Data Lineage – Leverage a metadata management tool to construct and document data lineage for future reference.
Documentations Repository – It is a best practice to document key project information and share that knowledge across the project team and with the stakeholder. An improvement understanding of the project helps to identify data quality issues early on in the project.
“Automating creation of data would help data quality most. You have to look at existing processes and create data signatures. You can then derive data off those data codes.” – Patrick Bossey, Manager of Business Intelligence, Crawford and Company
Info-Tech’s 4-Column Model – The datasets may exist but the business units do not have an effective way of communicating the quality needs. Use our four-column model and the eleven supporting questions to better understand the quality needs. See subsequent slides.
I don’t know what the data means so I think the quality is poor – It is not uncommon to see that the right data presented to the business but the business does not trust the data. They also do not understand the business logic done on the data. See our Business Data Glossary in subsequent slides.
Understand the business workflow – Know the business workflow to understand the manual steps associated with the workflow. You may find steps in which data is entered, manipulated, or consumed inappropriately.
“Do a shadow data exercise where you identify the human workflows of how data gets entered, and then you can identify where data entry can be automated.” – Diraj Goel, Growth Advisor, BC Tech
4 hours
After walking through the best-practice solutions to data quality issues, propose solutions to fix your identified issues.
Instructions
Solution Approaches |
---|
Technology Approach |
People Approach |
X crossover with
Problematic Areas |
---|
Application/System Design |
Database Design |
Data Integration and Synchronization |
Policies and Procedures |
Business Processes |
Quality data is the ultimate outcome of data governance and data quality management. Data governance enables data quality by providing the necessary oversight and controls for business processes in order to maintain data quality. There are three primary groups (at right) that are involved in a mature governance practice. Data quality should be tightly integrated with all of them.
Define an effective data governance strategy and ensure the strategy integrates well with data quality with Info-Tech’s Establish Data Governance blueprint.
This council establishes data management practices that span across the organization. This should be comprised of senior management or C-suite executives that can represent the various departments and lines of business within the organization. The data governance council can help to promote the value of data governance, facilitate a culture that nurtures data quality, and ensure that the goals of the data governance program are well aligned with business objectives.
Identifying the data owner role within an organization helps to create a greater degree of accountability for data issues. They often oversee how the data is being generated as well as how it is being consumed. Data owners come from the business side and have legal rights and defined control over a data set. They ensure data is available to the right people within the organization.
Conflict can occur within an organization’s data governance program when a data steward’s role is confused with that of the steering committee’s role. Data stewards exist to enforce decisions made about data governance and data management. Data stewards are often business analysts or power users of a particular system/dataset. Where a data owner is primarily responsible for access, a data steward is responsible for the quality of a dataset.
Ongoing and regular data quality management is the responsibility of the data governance bodies of the organization.
The oversight of ongoing data quality activities rests on the shoulders of the data governance committees that exist in the organization.
There is no one-size-fits-all data governance structure. However, most organizations follow a similar pattern when establishing committees, councils, and cross-functional groups. They strive to identify roles and responsibilities at a strategic, tactical, and operational level:
2 hours
A crucial aspect of data quality and governance is the Business Data Glossary. The Business Data Glossary helps to align the terminology of the business with the organization’s data assets. It allows the people who interact with the data to quickly identify the applications, processes, and stewardship associated with it, which will enhance the accuracy and efficiency of searches for organization data definitions and attributes, enabling better access to the data. This will, in turn, enhance the quality of the organization’s data because it will be more accurate, relevant, and accessible.
Use the Business Data Glossary Template to document key aspects of the data, such as:
Data Element
Info-Tech Insight
The Business Data Glossary ensures that the crucial data that has key business use by key business systems and users is appropriately owned and defined. It also establishes rules that lead to proper data management and quality to be enforced by the data owners.
Integrating your data quality strategy into the organization’s data governance program requires passing the strategy over to members of the data governance program. The data steward role is responsible for data quality at the business unit level, and should have been involved with the creation and implementation of the data quality improvement project. After the data quality repairs have been made, it is the responsibility of the data steward to regularly monitor the quality of the business unit’s data.
Create Improvement Plan ↓ |
|
Implement Improvement Plan ↓ |
|
Sustain Improvement Plan |
|
See Info-Tech’s Data Steward Job Description Template for a detailed understanding of the roles and responsibilities of the data steward.
Responsible for sustaining
One tool that the data steward can take advantage of is the data quality dashboard. Initiatives that are implemented to address data quality must have metrics defined by business objectives in order to demonstrate the value of the data quality improvement projects. In addition, the data steward should have tools for tracking data quality in the business unit to report issues to the data owner and data governance steering committee.
Notes on chart:
General improvement in billing address quality
Sudden drop in touchpoint accuracy may prompt business to ask for explanations
Data quality is a program that requires continual care:
→Maintain→Good Data →
Data quality management is a long-term commitment that shifts how an organization views, manages, and utilizes its corporate data assets. Long-term buy-in from all involved is critical.
“Data quality is a process. We are trying to constantly improve the quality over time. It is not a one-time fix.” – Akin Akinwumi, Manager of Data Governance, Startech.com
2 hours
As a data steward, you are responsible for ongoing data quality checks of the business unit’s data. Define an improvement agenda to organize the improvement activities. Organize the activities yearly and quarterly to ensure improvement is done year-round.
Info-Tech Insight
Do data quality diagnostic at the beginning of any improvement plan, then recheck health with the diagnostic at regular intervals to see if symptoms are coming back. This should be a monitoring activity, not a data quality fixing activity. If symptoms are bad enough, repeat the improvement plan process.
Consider… “Garbage in, garbage out.”
Lay a solid foundation by addressing your data quality issues prior to investing heavily in an AI solution.
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Izabela Edmunds
Information Architect Mott MacDonald
Akin Akinwumi
Manager of Data Governance Startech.com
Diraj Goel
Growth Advisor BC Tech
Sujay Deb
Director of Data Analytics Technology and Platforms Export Development Canada
Asif Mumtaz
Data & Solution Architect Blue Cross Blue Shield Association
Patrick Bossey
Manager of Business Intelligence Crawford and Company
Anonymous Contributors
Ibrahim Abdel-Kader
Research Specialist Info-Tech Research Group
Ibrahim is a Research Specialist at Info-Tech Research Group. In his career to date he has assisted many clients using his knowledge in process design, knowledge management, SharePoint for ECM, and more. He is expanding his familiarity in many areas such as data and analytics, enterprise architecture, and CIO-related topics.
Reddy Doddipalli
Senior Workshop Director Info-Tech Research Group
Reddy is a Senior Workshop Director at Info-Tech Research Group, focused on data management and specialized analytics applications. He has over 25 years of strong industry experience in IT leading and managing analytics suite of solutions, enterprise data management, enterprise architecture, and artificial intelligence–based complex expert systems.
Andy Neill
Practice Lead, Data & Analytics and Enterprise Architecture Info-Tech Research Group
Andy leads the data and analytics and enterprise architecture practices at ITRG. He has over 15 years of experience in managing technical teams, information architecture, data modeling, and enterprise data strategy. He is an expert in enterprise data architecture, data integration, data standards, data strategy, big data, and development of industry standard data models.
Crystal Singh
Research Director, Data & Analytics Info-Tech Research Group
Crystal is a Research Director at Info-Tech Research Group. She brings a diverse and global perspective to her role, drawing from her professional experiences in various industries and locations. Prior to joining Info-Tech, Crystal led the Enterprise Data Services function at Rogers Communications, one of Canada’s leading telecommunications companies.
Igor Ikonnikov
Research Director, Data & Analytics Info-Tech Research Group
Igor is a Research Director at Info-Tech Research Group. He has extensive experience in strategy formation and execution in the information management domain, including master data management, data governance, knowledge management, enterprise content management, big data, and analytics.
Andrea Malick
Research Director, Data & Analytics Info-Tech Research Group
Andrea Malick is a Research Director at Info-Tech Research Group, focused on building best practices knowledge in the enterprise information management domain, with corporate and consulting leadership in enterprise architecture and content management (ECM).
Natalia Modjeska
Research Director, Data & Analytics Info-Tech Research Group
Natalia Modjeska is a Research Director at Info-Tech Research Group. She advises members on topics related to AI, machine learning, advanced analytics, and data science, including ethics and governance. Natalia has over 15 years of experience in developing, selling, and implementing analytical solutions.
Rajesh Parab
Research Director, Data & Analytics Info-Tech Research Group
Rajesh Parab is a Research Director at Info-Tech Research Group. He has over 20 years of global experience and brings a unique mix of technology and business acumen. He has worked on many data-driven business applications. In his previous architecture roles, Rajesh created a number of product roadmaps, technology strategies, and models.
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