7 Reasons to Build Your Data Quality Business Case - A Step-By-Step Guide to Better Data - Adastra
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7 Reasons to Build
Your Data Quality
Business Case
A Step-By-Step Guide to
Better Data
adastracorp.com
An unprecedented volume of data to explore, international market competition, new
consumer behaviors, and the need for corporative transparency are some examples
of how data asset has been driving numerous discussions within companies. While
high-end technology adoption is part of the strategy to derive value from this re-
source, initiatives to enforce data quality must be considered when exploring data
potential in business development. Challenges such as unstandardized, misplaced,
incorrect, or inconsistent data can lead to many efficiency problems and may even
invalidate business opportunities. According to Gartner, poor data quality can cost an
organization $9.7 million annually and result in a 20% decrease in worker productivity
and explain why 40% of business initiatives fail to achieve set goals.
Data Quality is no longer just about having accurate data to make better decisions. It
is about being able to have accurate, complete, consistent, and unique information
that can be shared across the organization to drive all initiatives with the end-user
focus in mind. The following are 7 reasons why investing in Data Quality is vital for
your business’ success:
1. Getting Ahead of Regulatory Compliance
Governments of many countries are working hard to create personal
information protection regulations. By avoiding the implementation of data quality
initiatives, the organization risk facing penalties for non-compliance. As companies
were scrambling to comply with GDPR, the California Consumer Privacy Act (CCPA) in
the US was emerging.
The CCPA states that consumers have the right to access their personal data,
request a business to delete it, to be informed about all other businesses with
whom their data has been shared, and to have the business to disclose what, how
and why personal information is being used.
Suppose a customer requests the deletion of their personal information from the
company database, and the company has not verified duplication or replication in
other systems or databases. Then immediately, they would be out of compliance
and potentially subjected to hefty fines. Data quality allows for more accurate
matching and linking of consumer records across various systems. Getting ahead
of regulatory compliance not only ensures unforeseen spending but allows for a
system of governance of data within the organization.
2. Challenges Around Ethical AI
According to the Committee on Civil Liberties, Justice and Home Affairs of the Euro-
pean Parliament:
”The use of low-quality, outdated, incomplete, or incorrect data at different stages of
data processing may lead to poor predictions and assessments and, in turn, to bias,
which can eventually result in infringements of the fundamental rights of individuals
or purely incorrect conclusions or false outcomes.”
The European concern represents a world-wide discussion around the ethical use of
Artificial Intelligence models because organizations are finding it difficult to explain
the reasons behind the outcomes produced by AI and how the generated analy-
ses and decisions occur. Monitoring the data against data quality dimensions such
as completeness, conformity, consistency, validity, accuracy, timeless, relevance,uniqueness, and integrity, guarantee that the analyzed samples, will represent a fair image of the entire picture. Assume that the government wants to use AI to auto- mate investment decisions in public-health based on patients’ previous attendance to health care facilities. The population of this data needs to consider peculiarities, such as disabilities, financial conditions, age, etc. If an inaccurate or incomplete data sample is chosen, the AI model may learn from a set of inputs that delivers biased outcomes, hence favoring incorrect groups. Data quality initiatives can certainly play a crucial role in preventing this type of problem by detecting variations and enhanc- ing data. 3. Enabling Data Democratization Data democratization can be summarized as broader access to data inside an or- ganization. In this scenario, data is ready to be explored by various users inside the company without IT dependence or intervention. While data consumers are looking forward to having this freedom, processes for data reliability must be put in place before the democratic state becomes a reality. In other words, users must understand and consequently have confidence in the available data. Data quality is a powerful initiative to address this pre-requisite because, among other benefits, it guarantees a system of accountability and trust company-wide. For example, creating standard formats of phone numbers, dates, and postal codes ensures a common data language. It also enables mechanisms to allow for missed in- formation to be completed when possible, and that duplicated data can be effective- ly consolidated for use. In addition, pre-defined data quality rules monitor updates and deliver continual data excellence. Data quality processes bring peace of mind when implementing data democratization because the data undergoes a rigorous validation and enrichment process before it becomes available. Thus, delivering data 4. Self-service BI Success By leveraging the quality of data during self-service BI enablement, companies are improving productivity and lowering decision risks. Self-service BI users have data largely available to produce their reports; however, if the level of data quality is not up to par, then they are reporting on inaccurate and incomplete information. For example, in a pharmaceutical industry, it is imperative to control the expiry date of medication. If the expiry date is January 12th, 2021, and any date format is accepted upon entry, then this date can be written as 12/01/2021 or 01-12-2021 or 01/12/2021. Since the data format rule or standard is not clear, it can be decoded as either “December 1st, 2021” or “January 12th, 2021,” which drastically changes the in- formation conveyed. These inaccuracies lead to wrong metrics and mistaken actions such as the disposal of drugs before or after the expiry date. The imprecisions are converted into company loss or significant health risks for consumers. If data quality standards and rules are put in place, data discrepancies can be detected, reported, and fixed, prior to any mistrust or poor decisions with the data. 5. Data Analytics Efficiency Data quality is an important step that supports data analytics because it ensures the data’s standard of quality can produce assertive outcomes and prevent com- pany losses. Collecting data from source systems, transforming this data, and then ingesting it in a data lake, data warehouse, or even a spreadsheet is a well-
known process used to make data ready to be further explored. Yet, this process
is insufficient in guaranteeing that the data is prepared for consumption. For
example, suppose a data scientist in a health insurance company wants to build
a predictive model to foresee future medication expenses based on the most
common diseases. What if that the same disease has different descriptions? For
example, hepatitis B is also identified as ‘HBV,’ ‘Human hepatitis B virus,’ ‘hepatitis
B virus,’ ‘HB virus.’ The prediction could skip some records or identify the wrong
disease, resulting in over or underestimated costs.
6. Cloud Data Migration Optimization
When migrating data to the cloud, it is imperative to make sure that data is
unique and clean, as moving unnecessary data assets substantially increases
cloud services costs. When creating a cloud data migration plan, not only do data
types, field content, data transformations, readiness assessments need to be put
in place, but the characteristic of consumption costs must also be considered.
A dataset with duplicated data will incur a higher cost for storage and retrieval.
The table below is a simple example of costs incurred as it relates to storage and
transactions. It shows that if 30% of your data and workload is increased because
of poor data quality, the monthly costs will rise by 23%.
Data Quality Data stored Data stored Infrequent
Provisioned Cost
State in Standard in Infrequent Access
Throughput Incurred
storage Access storage requests
Clean and Unified 10.000 GB 1 000 GB 100 GB 1 000 6,026 USD
7,833 USD
Duplicated 13.000 GB 1 300 GB 130 GB 1 300
(23% more)
Understanding, measuring, monitoring, and improving data before cloud data mi-
grations will not only guarantee data migration success but also prevent unneed-
ed costs increasing.
7. Client Focus
Bettering client relationships lead to customer retention and reduction in churn.
Data quality can help get an accurate view of your client’s profile, thus enabling
you to develop a closer relationship with them. Accurate information about salary,
age, other investments, marital status, etc., allows for more assertive outcomes.
Think about how much a bank would benefit by offering the right product for the
right customer. If the wrong or irrelevant product is offered, not only will mar-
keting expenses increase, as ads may not reach desired audiences the way it was
intended, but the trust and affinity of the customer as well as brand reputation
will be damaged. Accurate data for market segmentation categorization is key to
creating that 1-on-1 targeted marketing experience that will truly resonate with
the correct audience.Author:
Angela Paiva
Data Management Consultant,
Adastra
About Adastra
Adastra Corporation transforms businesses into digital leaders. Since 2000, Adas-
tra has been helping global organizations accelerate innovation, improve oper-
ational excellence, and create unforgettable customer experiences, all with the
power of their data. By providing cutting-edge Artificial Intelligence, Big Data,
Cloud, Digital and Governance services and solutions, Adastra helps enterprises
leverage data that they can control and trust, connecting them to their customers
– and their customers to the world.
Adastra has been helping companies for the past 20 years, across various indus-
tries in multiple lines of business realize value in their data, with our award-win-
ning expertise, proven methodologies, and highly qualified team. Let Adastra
help your company achieve data quality excellence.
Contact
hello@adastragrp.com to schedule
a free discovery session.
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