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HOW TO IMPROVE DATA QUALITY: A COMPLETE PLAN FOR DATA TEAMS


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HOW TO IMPROVE DATA QUALITY: A COMPLETE PLAN FOR DATA TEAMS

How to Improve Data Quality: A Complete Plan for Data Teams
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May 6, 2024 May 21, 2024


In today’s digital age, every interaction, transaction, and customer touchpoint
generates valuable information. Businesses are collecting information at an
unprecedented rate – a trend with no signs of slowing down. IDC forecasts that
by 2025, the global datasphere will reach a staggering 163 zettabytes, a tenfold
increase from 2016. 

But here’s the catch: the sheer volume of data doesn’t guarantee success. It’s
the quality of that data that truly matters.

Research by Gartner indicates that organizations lose an average of $15 million
per year due to poor data quality. This translates to billions of dollars wasted
on misleading insights borne out of bad data leading to missed opportunities.
Beyond day-to-day operations, high-quality data becomes even more critical as
companies embrace AI and automation. These powerful tools are only as good as
the data they learn from. Feeding an AI algorithm with inaccurate or incomplete
data will inevitably lead to unreliable results. 

 


CHALLENGES WITH DATA QUALITY 

While organizations are keen to enhance their data quality, they are usually
hurdled with the following challenges:

 * Increased regulations: The growing number of regulations like GDPR and CCPA
   pose a significant data quality challenge. These regulations dictate how
   personal data is handled, requiring organizations to be accountable for its
   proper management. The challenge lies in incorporating these regulatory
   requirements into existing data architecture to ensure compliance. This adds
   another layer of complexity to data governance and ultimately data quality.
   
 * Lack of resources: It is often difficult to scale data quality programs
   running in one business function across the entire organization due to skill
   gaps. Implementing a robust data quality program requires a skilled team with
   expertise in data governance, data cleansing, and data analysis. However,
   these data professionals are often in high demand and can be expensive to
   hire.
 * Inconsistent data: Data gets compartmentalized within different departments,
   leading to inconsistencies and duplication across the organization. When data
   isn’t standardized and connected, achieving consistent quality becomes a
   major hurdle. For example, customer addresses might be recorded in one format
   by marketing and another by sales. This inconsistency creates confusion and
   hinders effective data analysis.
 * Lack of ownership: Data quality suffers when ownership is unclear. Business
   users might see data as something IT provides on demand, developing a “not my
   job” attitude. However, data quality is a shared responsibility. How business
   users enter, utilize, and manage data directly impacts its quality too.
 * Resistance to change: Shifting established data practices can face internal
   pushback from those accustomed to the status quo. The perceived complexity
   and lack of immediate ROI associated with data quality initiatives can
   further compound this resistance.



These data-related or data management challenges highlight the need for a
proactive approach to data quality. The good news is that there are concrete
steps businesses can take to ensure their data is clean, consistent, and
trustworthy. But focusing on just data quality is only half the battle won.
Without standardized data governance in place, your data quality can quickly go
haywire.

In the following sections, we’ll explore actionable steps to overcome these
common hurdles and get the best out of your data.

 


ACTIONABLE STEPS TO IMPROVE DATA QUALITY

 

STEP 1: DEFINE GOALS AND OBJECTIVES

Before diving into data governance, it’s crucial to understand your
organization’s goals – both the big picture strategic vision and the day-to-day
tactical objectives. Regulations and market shifts can also influence these
goals. A well-defined data strategy considers these factors and identifies the
specific data your organization needs to succeed. This strategy goes beyond data
itself, outlining key performance indicators (KPIs) and stakeholders, and
pinpointing the technology and processes needed to manage your data effectively.
Your data strategy is a living document that should be reviewed and updated as
your business evolves.

Once you have a clear understanding of your goals and data needs, you can set
data governance goals that directly support them. These goals might include
improving data quality for better decision-making, or enhancing data security to
comply with regulations. This will help you focus on the data that has the
biggest impact on your business outcomes, like customer data for improving
customer satisfaction.

 

STEP 2: GET STAKEHOLDERS SUPPORT

Building a successful data governance program requires a team effort. The first
key player is an executive sponsor – a champion who understands the value of
data governance and is willing to invest in its implementation. This sponsor
rallies support and secures resources for the program.

Next comes assembling the data governance team. This cross-functional group
should represent different parts of the organization, including IT staff, data
architects, line-of-business owners, data custodians (those responsible for data
accuracy), and even stakeholders outside your organization, if relevant. Each
member brings their unique expertise to the table.

Effective communication is essential for this team’s success. Everyone involved
needs to understand the program’s goals and the business value it delivers. This
includes establishing a common language around data quality, ensuring everyone
agrees on what “good data” looks like. By working together, the data governance
team can build a program with clear objectives and broad stakeholder buy-in,
ultimately creating a strategic advantage for the organization.

 

STEP 3: ASSIGN ROLES AND RESPONSIBILITIES

Assigning clear roles and responsibilities to everyone involved, from data
collection to usage is vital. This ensures everyone understands their part and
contributes effectively. The structure can vary depending on your organization’s
size. Large companies might have a dedicated data governance team (as discussed
in Step 2), while smaller businesses might integrate these tasks into existing
roles. 

Regardless of the structure, a key element is the data steward. These
individuals collaborate with central data and analytics (D&A) teams to develop
best practices, establish collaboration frameworks, and define data-related
rules and standards. Data stewards are the go-to people for data quality issues,
monitoring data assets, investigating problems, and resolving policy violations.
Their core responsibilities include monitoring data assets, investigating
quality issues, resolving policy violations, and collaborating with stakeholders
to define business rules and monitor compliance. They also play a key role in
setting data quality best practices within their teams and helping collect and
manage metadata, which improves overall data understanding.



STEP 4: ASSESS CURRENT STATE AND SET TARGET STATE

Now that you’ve identified your business goals and assembled your data
governance team, it’s time to assess your current capabilities. Frameworks like
DCAM and CDMA can help you benchmark your data management practices against
industry standards. This evaluation will reveal your strengths and weaknesses,
highlighting areas that need improvement to achieve your objectives. Remember,
you don’t have to tackle everything at once. Focus on the most impactful areas
to deliver quick wins and demonstrate the value of data governance.

An essential step is establishing a baseline for data quality. This initial
“benchmark” will help you measure progress and justify the investment spent thus
far. Remember, data quality requires ongoing effort. Without a sustainable
approach, data quality will quickly deteriorate. Focus on how improved data
quality can positively impact key metrics like financial performance.

 

STEP 5: IMPLEMENT YOUR FRAMEWORK AND PROCESSES

After establishing a data governance framework, the real work begins – putting
it into action. This involves implementing processes and controls to enforce the
framework, like data quality checks or user access restrictions. Setting up data
governance processes early on prevents confusion and issues later. It is good to
integrate data quality processes directly into your business workflows using
data quality tools like DQLabs. Identify frequently occurring data quality
issues and embed solutions into workflows. This might involve using new tools,
changing existing processes, or even adopting new technologies.

Modern data quality tools are essential for scaling these efforts. Dedicated
data quality platforms offer features like automated data assessments, rule
management, data monitoring, and data cleansing. They also leverage AI and
machine learning to provide valuable insights and automate tasks.

Read: You can also read this blog to know how to leverage AI/ML to improve data
quality.

 

STEP 6: EVALUATE AND IMPROVE YOUR APPROACH

Track your program’s effectiveness and adjust your approach as needed to ensure
it meets your evolving needs. A successful data governance program is flexible
and adaptable. For this, consider a trust-based approach to data governance.
Instead of striving for a single “truth,” define different trust levels for your
data based on its criticality as suggested by Gartner. This allows for more
flexibility and efficient management of your data assets.

An iterative approach is also crucial for success. Focus on addressing the most
pressing data quality issues first, even if solutions aren’t perfect. Finally,
think of data governance as a continuous cycle of improvement. Engage
stakeholders to develop improvement plans, analyze root causes of data issues,
and create solutions as regular evaluation helps identify areas for enhancement.

 

STEP 7: CREATE DATA ADVOCACY GROUPS

Now that you have successfully created and implemented your data governance
program, develop a data savvy workforce to sustain this momentum. 

Don’t let data quality become a finger-pointing game! Everyone plays a role.
Break down silos by forming data advocacy groups across departments and IT.
These groups, formal or informal, can be a starting point for anyone facing data
quality issues. Think of them as data champions, working together to share best
practices and identify areas for improvement. Having regular meetings with them
can encourage collaboration and knowledge exchange between teams.

 

STEP 8: PROVIDE DATA QUALITY TRAINING PROGRAMS

Building a culture of data enthusiasm is crucial for successful data governance.
When employees are invested in the idea of leveraging data for better
decision-making, they’re more likely to ensure data quality and share it openly.
However, fostering this excitement can be challenging. Leading organizations
implement change management strategies to cultivate data advocates and address
any skepticism.

This might involve senior leaders acting as role models, recognizing
high-quality data contributions, and showcasing the value of data through
real-world use cases. Additionally, organizations can offer training and career
development opportunities in data science, or even host data-centric events to
generate excitement. Ultimately, the most effective approach depends on the
unique culture of each organization.

 


CONCLUSION

While the sheer volume of data offers incredible potential, its true value lies
in its quality. Poor data quality can lead to skewed insights, hindering
decision-making and hindering business performance. By taking a proactive
approach to data governance, organizations can obtain the true value of their
data assets.

This article has outlined a comprehensive framework for building a data quality
governance program. From defining goals to implementing tools and processes,
each step plays a crucial role in ensuring data accuracy, consistency, and
accessibility. Also, by fostering a culture of data quality and continuously
refining your approach, you can transform your data into a powerful asset that
drives informed decision-making and propels your organization towards success.





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On a mission to help organizations improve data quality  

DQLabs is an automated, modern data quality platform that delivers reliable and
accurate data for better business outcomes. DQLabs automates business quality
checks and resolution using a semantic layer to deliver “fit-for-purpose” data
for consumption across reporting and analytics.

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