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In our previous blog posts, we’ve looked at agile software development and some of its benefits, as well as explored some examples of how agile development was being deployed in the real world. In this blog post, we change gears and turn our attention to data – specifically data quality.

We look at why the quality of your data is important and why you should be paying attention to it. We also look at what can happen when systems use substandard data.

Download our checklist to help you improve the quality of your data.

What is Data Quality and Why is it Important?

Before we move onto the importance of quality data, we should start with a definition of “data quality.”

Data quality is a perception or an assessment of data’s fitness to serve it’s purpose in a given context. As data becomes more intricately linked with the operations or organizations, the emphasis on data quality has gained greater attention. 

Quality data tends to have the following attributes:

• Completeness – the proportion of stored data against the potential of “100% complete”
• Uniqueness – nothing is recorded more than once based upon how that thing is identified
• Timeliness – the data represents reality from the required point in time
• Validity – the data is valid and conforms to the syntax
• Accuracy – the data is correct, relevant and represents what it should
• Consistency – the same rules found in all data sets

The core to great analytics is great data Click To Tweet

Benefits of using Quality Data

Now that we have a definition of what “data quality” is, let’s look at some of the benefits that quality data can bring to your business or organization.

Decision Making

Data quality is important because people in any given domain need accurate, dependable and timely information. Having quality data can help businesses improve decision-making abilities, reduce risk and increase efficiency, which leads to the next important point:

Productivity

When teams or professionals use quality data, productivity can be improved dramatically. Having all the required information in one or more quality datasets means that teams don’t have to waste time, cobbling together information to get the “full picture” or plug gaps in the data. Think about the number of times you have had to access multiple systems or even worse, multiple spreadsheets and wondered why some of the data was missing or didn’t seem correct.

Marketing

It’s often said that “data is the new oil,” and this couldn’t be more true for marketing. For example, having quality datasets in terms of your customer profile lets you better segment and target your customers based on their online habits and behaviors, thereby improving the likelihood of the uptake of your products or services. Decisions powered by quality and more granular data can lead to higher revenue numbers.

Product Development

If you’re in the software development industry or launching a new product, being able to track and audit KPIs, such as engagement with your product, is something you likely want to do. Auditing data points such as button clicks and page views can help surface actionable insights. This can help inform your product development team in terms of the next set of features to develop, which features to remove and even help determine product viability.

Download our checklist to help you improve the quality of your data.

Business Performance

Quality data can provide insights that will allow you to better understand customer and market behaviors which can lead to new product opportunities. A review of your data as an asset rather than just information can yield new monetization paths, new partnerships and innovative revenue models. A deeper analysis of employee data can lead to higher levels of engagement and productivity. Examining data and processes on the operational side of the business equation can results in significant cost savings for the business.

These are just some of the benefits that show the importance of data quality. Other benefits can include but are not limited to:

• Support teams and other customer-facing workers can have access to contact records they can trust with detailed customer preferences and complete interaction history
• Marketing teams have a better chance of improving lead to customer conversion rates with better data regarding hit rates on marketing campaigns
• Sales teams can identify higher quality sales leads and better target sales messaging
• You can reduce operational costs for your business through analysis of each process in the business

And these are just some of the additional benefits that quality data will bring!

What can happen when you don’t use quality data

So, we’ve looked at some examples of the benefits of using quality data in your projects. Now, let’s look at what can happen if you’re working with sub-standard data.

Inefficiency

If your business processes rely on data (and most of them do), poor quality data increases the likelihood of slowing down the velocity of your business. Without high-quality data, there is no confidence. Without confidence, there is no trust. According to a Harvard Business Review article, studies show that knowledge workers waste up to 50% of their time hunting for data, finding and correcting errors and searching for confirmatory sources for data they don’t trust.

Damage to brands

Poor quality data can negatively affect customer experience and damage the brand. The strength of a brand can’t be underestimated, and in today’s interconnected world, it doesn’t take much to erode the public image of a brand. Consumers are quick to react on social media platforms like Twitter and Facebook. For instance, the Apple Maps rollout back in 2012 received harsh criticisms on social networks. During the release of Apple Maps, it became clear very quickly that a lot of the required underlying data was inaccurate, and in some cases even missing. As a result, Apple shares lost 4.5% in value, which translates to $30 billion dollars in market cap.

What happens when systems use substandard data? Inefficiencies, damage to brands, lost revenue and missed opportunities. Click To Tweet

Missed Opportunities

If rival businesses are gaining more actionable insights about consumers from data than you, this could translate into missed opportunities. That might include new ideas for products or services or extra features you could add to existing software applications. For example, when Blockbuster had the opportunity to partner with Netflix, they dismissed this idea because Blockbuster failed to see the value in Netflix’s vision for innovating consumer content consumption. Netflix believed in leveraging consumer behavior data to gather market insights, including a trend towards on-demand streaming. This missed opportunity eventually led to Blockbuster’s bankruptcy in 2010.

Lost Revenue

A direct consequence of missed opportunities can be an impact to your business’s bottom line. Unreliable data can negatively impact business decisions – and produce negative financial results. In 2016, IBM estimated that the yearly cost of poor quality data resulted in the loss of $3.1 trillion in the U.S. alone.

Summary

In this article, we’ve introduced what data quality is and why it’s important along with some of the attributes that quality data tends to have. We’ve also looked at the benefits quality data can bring to your business or organization. We closed out by looking at what can potentially happen if you find yourself working with substandard datasets and how these have impacted businesses in the real world.

Here at Growth Acceleration Partners, we have extensive expertise in helping companies achieve the data quality needed to achieve their business objectives. The growth of your business depends on having high-quality data. Our integrated nearshore and onshore business model enables you to gain significant cost savings, and build a team dedicated to improving your data quality.

Our Centers of Engineering Excellence in Latin America focus on combining business acumen with development expertise to help your business. We can provide your organization with resources in the following areas:

• Software development for cloud and mobile applications
• Data analytics and data science
• Information systems (DevOps)
• Machine learning and artificial intelligence
• Predictive modeling
• QA and QA Automation

If you’d like to find out more, then visit our website here.  Or if you’d prefer, why not arrange a call with us?