Data Management
2
min read

How Data Quality Impacts Effectiveness

Published:
March 28, 2023
Updated:
July 25, 2023

In today's data-driven business landscape, ensuring data quality is not just crucial, but imperative for companies to achieve optimal performance and effectiveness. However, going from big data to clean data is an Everest most data teams aren’t keen on climbing themselves. To ensure data quality, data teams must wade through a morass of entanglements created by data silos, inadequate tools, and a lack of transparency and trust. In this blog, we talk about the importance of data quality, the barriers preventing it, and how marketers can transform their data from garbage to a goldmine.

The downstream effects of data quality

There’s a huge focus in the business world on being “data-driven.” Agencies, clients, and adtech/martech vendors have all opted for pumping out and snarfing up as much available data as possible. Their hope? They’ll be able to do something useful with all that ‘nice-to-have’ data when they finally figure out how to wrangle it best. But if data teams ignore the quality of data they are creating and ingesting, this can create massive speed bumps in their data-driven journeys. Since most agencies don’t lack for raw data, ignoring data quality is an easy pitfall to plunge into. Unfortunately, it can also be massively challenging to climb out of. 

The importance of data quality cannot be overstated, yet many companies struggle with this challenge. In their haste to collect and analyze data quickly, organizations often neglect to establish standards and criteria for data quality assurance. This can result in the use of inaccurate, incomplete, or redundant data, leading to negative consequences. These can include:

  • High costs
  • Wrong decisions
  • Strained relationships with customers

According to a 2016 study from IBM, bad data quality led to $3.1 trillion in losses. In the same year, market research firm, IDC, estimated the size of the big data market at $136 billion. We can all agree that bad data is costly, but these old figures are stunning, and they’ve only increased since then. Current estimates value the big data market at $271 billion. 

A lack of data quality can cause huge mistakes for even the biggest of companies . A lack of data quality helped lead to Coca-Cola’s disastrous rollout of “New Coke” in the 80s, and data conversion errors caused NASA’s $125 million loss of the Mars Orbiter in 1999. 

Nothing is worse than failing a client or customer, but if poor data quality is behind bad decisions or a bad batch of reports, the problem is much more endemic and challenging. That’s why democratization of data and working with a trusted partner to maintain marketing data pipelines might be the best way to remove tension before it becomes an issue. 

Data silos prevent data QA 

A high level of data quality is crucial for advertising and marketing analysis and reporting. Historically, different types of data, such as advertising data, onsite behavioral data, and online/offline purchase data, have been collected and managed separately. This siloed approach was originally perceived as necessary to maintain a competitive advantage in data-driven businesses. However, data silos can lead to inefficiencies, particularly in third-party data. 

There’s also a difference in the way data is approached by adtech and martech vendors. While marketing focuses on optimization and finding the right balance of performance and volume, advertising data is more about weight and may not always know what is working. Therefore, ensuring high-quality data is essential for both advertising and marketing to achieve their goals. As third-party cookies continue to crumble, adtech and martech are beginning to converge, making efficiency a critical factor that differentiates the two. 

If you’re serious about breaking down silos to achieve better data quality, you should:

  • Map out the customer journey
  • ID customer data gaps
  • Create a codified system/taxonomy
  • Invest in data management software
  • Give agents access and context
  • Establish a collaborative culture

In today's data-driven world, the quality of your data can make or break your business. Data silos—where information is isolated and inaccessible to other teams—can hinder growth and cause major problems. To overcome this challenge, it's important to implement effective data management strategies and partner with a sophisticated platform like NinjaCat. By doing so, you can streamline your data management processes and ensure that all teams have access to accurate and up-to-date information, ultimately driving your business forward.

How to ensure better data quality

Along with creating a data dictionary or fundamental strategy for taxonomy, focusing on democratization, accessibility, and purpose-driven decision making, there are seven data quality dimensions that marketers should implement when looking to improve data quality. They are:

  • Timeliness - How current is the data?
  • Completeness - What essential fields have to be filled in?
  • Consistency - Is every iteration of the data the same in every location?
  • Relevance - Is the data collected relevant to the desired goals/outcomes?
  • Transparency - Are the origins and lifecycle of the data clearly defined?
  • Accuracy - Are the consistent values correct and closely reflecting reality?
  • Representativeness - Can conclusions be made about a dataset from a sample?

To ensure access to high-quality data, it is essential to establish clear responsibilities and processes amongst the data team. Creating positions like Data Manager or Data Governance Manager can help ensure data collection and cleansing policies are in place and disseminated across teams. A clear process for handling data organization-wide can also help maintain clean records. 

Combining different data sets  may be a challenge, but working with technology partners and employing data scientists can help turn thousands of data points into actionable steps for your team. It is important to seek a partner with the processing power to deliver these datasets in near real-time to support fast-paced business landscapes.

If you’re serious about data quality and are looking for a platform to help unclog your data pipelines and improve your reporting capabilities, contact us or book a demo to see how NinjaCat can help. 

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