Data Quality – Don’t Ignore This Important Aspect in Your Data Analytics Initiatives

Data Quality – Don’t Ignore This Important Aspect in Your Data Analytics Initiatives

Did you know that businesses lose as much as 20% of revenue due to poor data quality (Source: Kissmetrics)? Moreover, according to DiscoverOrg, marketing and sales departments lose as much as USD 32,000 per sales rep due to bad quality of data.

Although data quality is an old problem, it has acquired urgent new dimensions due to the increasing number of electronic devices that we interact with each day. Data is the new oil and there are understandable reasons why data is such a valuable resource for today’s marketers, publishers, media companies, and tech companies, etc.

However, data quality is an important aspect that organizations, at times, ignore during their analytics initiatives. Bad quality of data is not useful and can even lead companies to make costly mistakes. IBM estimates that poor quality of data can cost the US economy USD 3.1 trillion per year!

Let’s use this article to discuss more about data quality and its importance in your data analytics initiatives.

Understanding data quality

Simply put, data quality is the capability of data to be used effectively, economically, and rapidly to make valuable decisions. It’s the ability of your data to serve its intended purpose in operations, decision making, and planning. The quality of data is determined by the following seven characteristics –

  • Accuracy – Whether the data is correct, precise, and error-free.
  • Availability – Whether the right data is available to the right people in the company.
  • Completeness – Whether your data is complete or has any gaps in crucial information.
  • Granularity – Whether your data can provide the right level of detail to enable effective decision-making.
  • Relevance – Whether your data is really needed for the desired goal.
  • Reliability – Whether your data is unambiguous, precise, and trustworthy.
  • Timelines – Whether your data is up-to-date and available at the right time.

If your data possesses all these key characteristics, then your data is of good quality.

Why is data quality important?

The higher the amount of quality data, the faster a machine learning algorithm can produce results using it.

Data quality is important because the consequences of poor, incomplete, and unreliable data can be quite damaging for your business. High-quality data helps organizations understand their customers. It can offer valuable insights to make decisions about marketing, finance, and product development, etc.

Those organizations that don’t take advantage of data and related technologies risk falling behind their competitors.

Moreover, modern technologies, like artificial intelligence and automation, rely heavily on data. If these technologies are not fed quality data, then the results could be skewed.

Data is also becoming integral to business operations. Some of the most successful companies of today integrate data into everything they do.

Benefits of high data quality

To get a proper understanding of why quality data is important, one needs to look at the benefits it can offer –

Effective marketing campaigns and audience targeting

Marketing is an area that can benefit immensely from quality data. In the past, companies had to market to a broad audience and waste money by targeting people who were unlikely to ever buy any goods or services. But thanks to the availability of demographic and other important data and analytics, marketing these days is tightly focused and more likely to achieve desired results.

High-quality data also enables effective content marketing campaigns. The more you know your audience, the better you can create content that is appealing to them.

Improved customer relations

High-quality data can be important in improving relationships with customers. By gathering quality data from your customers, you can serve them better and offer products/services that are according to their preferences, interests, and needs. This eventually helps build strong relationships with customers.

The goodwill generated by quality product offerings also helps build brand loyalty among customers.

Competitive advantage

Companies can use high-quality data to build a competitive advantage over other players in the same domain. Data is one of the most valuable resources in today’s time and by using high-quality data, companies can discover opportunities before their competitors do. They can anticipate customer needs and capitalize on the opportunities the market offers.

Increased profitability

High-quality data ultimately leads to increased profitability. It can help companies craft effective marketing campaigns and boost sales. Similarly, it decreases wastage of money on irrelevant advertisements and increases cost-efficiency in marketing.

Having insightful data enables organizations to improve their decision-making in terms of time and resources.

Single source of truth

Large organizations that have several entry points for their customers often struggle with data inconsistency and data duplication. This can create a host of problems along with difficulties in reaching key clientele. With high-quality data, the entire business can be on the same page when it comes to analyzing and meeting client needs.

How to ensure data quality?

Upon realizing that the data you have is of poor quality, you need to spend time figuring out the following –

  • How was the data collected within your organization?
  • What was the purpose of the data brought?
  • Who created that data?
  • Who had the access to this data?
  • Where was the data used?

Once you figure out the reason behind your bad data problems, the next step is to fix them. It can be done using the following means –

  • Hire data management experts who can help you in data cleansing, data profiling, data enrichment, data standardization, and data monitoring.
  • Establish a single source of truth for all the data that comes to your organization.
  • Define data quality assurance metrics and perform regular data audits.
  • Make data quality a part of your data governance.
  • Specify roles like data owners, stewards, and custodians, etc. to establish proper processes.
  • Educate employees on data quality.

Conclusion

When working on your data analytics initiatives, you must put a focus on the quality of data. Although it’s not easy to work with the plethora of data available, organizations can bring some order to the chaos by managing data quality at each step.

Want some help with data quality? Grihasoft can help.