Build Your Data Quality Program

Quality Data Drives Quality Business Decisions

Executive Brief

Analyst Perspective

Get ahead of the data curve by conquering data quality challenges.

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

Executive Summary

Your Challenge

Your organization is experiencing the pitfalls of poor data quality, including:

• Unreliable data and unfavorable output.
• Inefficiencies and costly remedies.
• Dissatisfied stakeholders.

Poor data quality hinders successful decision making.

Common Obstacles

Not understanding the purpose and execution of data quality causes some disorientation with your data.

• Failure to realize the importance/value of data quality.
• Unsure of where to start with data quality.
• Lack of investment in data quality.

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.

Info-Tech’s Approach

Address the root causes of your data quality issues by forming a viable data quality program.

• Be familiar with your organization’s data environment and business landscape.
• Prioritize business use cases for data quality fixes.
• Fixing data quality issues at the root cause to ensure a proper foundation for your data to flow.

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.

Data is the foundation of your organization’s knowledge

Data enables your organization to make decisions.

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.

Raw Data

Business Information

Actionable Insights

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)

High-Level Data Architecture

Build Your Data Quality Program

1. Data Quality & Data Culture Diagnostics Business Landscape Exercise
2. Business Strategy & Use Cases
3. Prioritize Use Cases With Poor Quality

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.

1. Understand the organization's data culture and data quality environment across the business landscape.
2. Prioritize business use cases with poor data quality.
3. For each use case, identify data quality issues and requirements throughout the data pipeline.
4. Fix data quality issues at the root cause.
5. As data flow through quality assurance monitoring checkpoints, monitor data to ensure good quality output.

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.

Prevent the domino effect of poor data quality

Data is the foundation of decisions made at data-driven organizations.

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?

1. Time must be spent on identifying the data quality issues, and often manual data quality fixes are employed. This will extend the time to deliver the project that depends on system Y by X months.
2. To repair these issues, the business needs to contract two additional resources to complete the unforeseen work. The new resources cost $X each, as well as additional infrastructure and hardware costs.
3. Now, the strategic objectives of the business are at risk and there is a feeling of mistrust in the new system Y.

Three key challenges impacting the ability to deliver excellent customer experience

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)

Maintaining quality data will support more informed decisions and strategic insight

Improving your organization’s data quality will help the business realize the following benefits:

Data-Driven Decision Making

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)

Customer Intimacy

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)

Innovation Leadership

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)

Operational Excellence

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)

However, maintaining data quality is difficult

Avoid these pitfalls to get the true value out of your data.

1. Data debt drags down ROI – a high degree of data debt will hinder you from attaining the ROI you’re expecting.
2. Lack of trust means lack of usage – a lack of confidence in data results in a lack of data usage in your organization, which negatively effects strategic planning, KPIs, and business outcomes.
3. Strategic assets become a liability – bad data puts your business at risk of failing compliance standards, which could result in you paying millions in fines.
4. Increased costs and inefficiency – time spent fixing bad data means less workload capacity for your important initiatives and the inability to make data-based decisions.
5. Barrier to adopting data-driven tech – emerging technologies, such as predictive analytics and artificial intelligence, rely on quality data. Inaccurate, incomplete, or irrelevant data will result in delays or a lack of ROI.
6. Bad customer experience – Running your business on bad data can hinder your ability to deliver to your customers, growing their frustration, which negatively impacts your ability to maintain your customer base.

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.

Follow Our Data & Analytics Journey

Data Quality is laced into Data Strategy, Data Management, and Data Governance.

• Data Strategy
  • Data Management
    • Data Quality
    • Data Governance
      • Data Architecture
        • MDM
        • Data Integration
        • Enterprise Content Management
        • Information Lifecycle Management
          • Data Warehouse/Lake/Lakehouse
            • Reporting and Analytics
            • AI

Data quality is rooted in data management

Extract Maximum Benefit Out of Your Data Quality Management.

• Data management is the planning, execution, and oversight of policies, practices, and projects that acquire, control, protect, deliver, and enhance the value of data and information assets (DAMA, 2009).
• In other words, getting the right information, to the right people, at the right time.
• Data quality management exists within each of the data practices, information dimensions, business resources, and subject areas that comprise the data management framework.
• Within this framework, an effective data quality practice will replace ad hoc processes with standardized practices.
• An effective data quality practice cannot succeed without proper alignment and collaboration across this framework.
• Alignment ensures that the data quality practice is fit for purpose to the business.

The DAMA DMBOK2 Data Management Framework

• Data Governance
  • Data Quality
  • Data Architecture
  • Data Modeling & Design
  • Data Storage & Operations
  • Data Security
  • Data Integration & Interoperability
  • Documents & Content
  • Reference & Master Data
  • Data Warehousing & Business Intelligence
  • Meta-data

(Source: DAMA International)

Related Info-Tech Research

Build a Robust and Comprehensive Data Strategy

• People often think that the main problems they need to fix first are related to data quality when the issues transpire at a much larger level. This blueprint is the key to building and fostering a data-driven culture.

Create a Data Management Roadmap

• Refer to this blueprint to understand data quality in the context of data disciplines and methods for improving your data management capabilities.

Establish Data Governance

• Define an effective data governance strategy and ensure the strategy integrates well with data quality with this blueprint.

Info-Tech’s methodology for Data Quality

Info-Tech Insight

“Data Quality is in the eyes of the beholder.”– Igor Ikonnikov, Research Director

Data quality means tolerance, not perfection

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 Roles and Responsibilities

Data quality occurs through three main layers across the data lifecycle

Executive Brief Case Study

Industry: Healthcare

Source: Primary Info-Tech Research

Align source systems to maximize business output.

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.

Results

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

Info-Tech offers various levels of support to best suit your needs

DIY Toolkit

"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."

Guided Implementation

"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."

Workshop

"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."

Consulting

"Our team does not have the time or the knowledge to take this project on. We need assistance through the entirety of this project."

Diagnostic and consistent frameworks are used throughout all four options.

Guided Implementation

What does a typical GI on this topic look like?

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.

Workshop Overview

Contact your account representative for more information. workshops@infotech.com 1-888-670-8889