What Is Data Quality? 5 Key Features and Best Practices

Although many are diving into emergent technologies with AI integrations and automations, modern businesses are still struggling to find quality data. Why––and where can they find it? We’ll explore that here!

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Data quality impacts critical aspects of the business, including innovation, legal compliance, productivity, marketing, and crisis management. So, why is it that businesses are losing 15 to 20 percent of their revenue due to poor quality data and almost half of new data records have at least one critical error? Their data quality management is suffering.

But before we address what to do about it, it’s important to rethink what quality data even looks like.

What Is Data Quality?

The quality of data is measured by its ability to inform on mission critical topics, products, services or processes. When data is reliable, the risk of the unknown is mitigated, and there is a strong foundation that can further be bolstered by new experiences and understanding. The quality of data is also measured by its ability to evolve and transform to accommodate new information as it becomes available. Adaptability is key in a time when change is constant.

It follows that maintaining the quality of data over time is equally important. And after finding it, one must maintain the integrity of data by auditing and refreshing insight at regular intervals. Consistent monitoring should be a daily occurrence, with updates to messaging and evaluations of product utility happening often––at least every quarter. We’ll explore the timeliness of the data below.

5 Features of Quality Data

Market research standards must be stringent to extract quality data. Here are five features to watch for when undertaking this effort––ensuring that it’s accurate, complete, consistent, relevant and timely.

1. Accurate

Accuracy is a metric every brand must carefully evaluate when selecting a vendor.

How closely does your data represent the facts? Data must be gathered from credible sources using means that guarantee error-free transmission, unclouded by bias. The best researchers can fall prey to bias when manually collecting data. There’s just too much to contend with and they need to pick and choose, and those choices are subjective. Aggregating intel with a research tool is a must.

But the tool employed needs to be up to snuff, able to separate the signal from the noise with finesse––and many just aren’t up to the task.

After collection, analysis is next. Armed with a powerful platform guiding its efforts, companies can extract actionable intel with incredible precision, capturing insight that moves markets and creates categories.

2. Complete

High quality data is complete. In market research, this means all available sources have been captured and consolidated. There are a few major buckets to consider here, as detailed below – consumer, market and your company data. And within each area are subsets to consider that may not be listed – specific websites, forums, business or news data that offer crucial insight.

Taking an inventory of data stores you have on hand, as well as those that your company regularly refers to (or should refer to) is important here. And then finding a tool that can bring it all together for meaningful analysis is next:

data-sources

Storing data is something to keep in mind as well. The quality of data should be maintained by keeping it in a single location rather than distributing it across different silos. The problem with silos is that the data in any one of them is incomplete. Therefore, if you were to try and derive insights from the data in any of those locations, the conclusions would be incomplete and fairly useless. You also wouldn’t know if they’d been updated with any consistency, as we’ll speak to next!

3. Consistent

The quality of data can be called into question if it is not consistent. Consistency in this context is unique. It doesn’t mean that the insight must line up with what is already known, as quality data often reveals entirely new insight. Consistent data means it makes sense in the big picture–that it’s consistent with market trends and consumer understanding, as informed by ongoing research. The “consistency” refers to the ongoing effort, not the consistency of the insight derived from it.

Data collection should always be performed with one eye on existing facts and taken from large sample groups, but it must also remain flexible and plan for the inevitable and ongoing consumer and market shifts.

In sharing and storage, the quality of data can be maintained by having strict guidelines around the introduction of new data sources being added to the business intelligence system, as not all sources are particularly relevant. And this comes next, in our point about relevancy!

4. Relevant

If data is irrelevant, the quality of data is equally so. Companies often have oodles of irrelevant data being captured by manual processes – insight the does little beyond support internal assumptions about products, consumers or the market. For example, many companies invest a lot of time and effort into social media marketing but only track likes and follows, which are irrelevant without context. A social media analysis needs to show much more, and in context of competitors, as we can see below:

rival-IQ-competitive-audit

And every bit of data captured requires processing to ascertain its context and relevancy to a topic:

Sample Tweet with analysis

Before getting to that analysis though, relevant data is found by fine-tuning research results to tease out intel from target customers, market competitors and industry influencers directing important conversations in your category.

Organizations often store research data after they have finished a study – because they may need it in the future and having these historical stores is valuable. Do you know what’s even more valuable though? The ability to quickly capture historical intel, process and analyze it in real-time. Keeping massive quantities of historical intel on hand is often irrelevant as it exists online and can be scraped and uploaded with the right technology. And the more data you have on hand, the harder it is to store and keep in any sort of useful capacity.

When data is kept, it should be well categorized in a business intelligence system optimized for sharing and storage. The storage part is substantial and one to be carefully considered, as today’s efforts are largely informed by and focused on emerging insight, with a significant push toward predictive capabilities and anticipating trends . . .

5. Timely

Finally, there’s the time factor in data quality. Changes in the marketplace that invalidate existing facts are common. Therefore, new data should be collected on a regular basis to keep both these factors in check and ensure that the data pool is always timely. How regularly? Daily is the optimum benchmark, with many companies having ongoing efforts checking multiple times per day.

And as technology advances, there are always better methods and platform updates to stay aware of – things that impact your company’s time to insight.

When it comes to sharing this intel, the window of timeliness can drastically shrink, making intuitive, accessible dashboarding another ‘must’ to add to your growing ‘must have’ data quality list.

These features of quality data all go together. The quality of data is determined by looking at them holistically; even a slight failure in one of them can impact the quality of the whole collection. Thus, it pays to know how to improve the quality of data in your organization––and how to create it to begin with!

How to Improve the Quality of Data

With the five features discussed above, it is clear that improving the quality of data in an organization is a balancing act. Most conspicuous is the balance between the democratization of and restriction of access to the data. On one hand, you don’t want to leave it exposed to tampering and on the other, you want people to always have what they need.

So, how do you achieve proper balance to ensure your data is accurate, complete, consistent, relevant, and timely?

1. Data governance

Data governance is the setting and enforcement of data management standards within the organization. It ensures that data is available, usable, and secure. To achieve this, a team is set up which designs the necessary policies. It may also involve technology such as automation software, communication, collaboration, and workflow management tools.

Data governance is best assigned to one key person such as the chief data officer (CDO). However, they may set up the framework to allow other key people at various points to participate in the process. These include data analysts, heads of departments, and data stewards.

2. Data profiling

Data profiling is the process of determining the quality of data. It ensures that all the data within an organization’s repository has the five quality features. The process involves a range of activities including data description, tagging, and categorization.

This is part of the data governance process that may be assigned to data specialists in the team. Broadly, these professionals look for three things: The structure, to validate the formatting; the content, to eliminate data entry errors; and the correlation, to discover how different components interrelate

3. Data matching

An extension of understanding correlation between different pieces of data is data matching. This is where the data pool is examined to reveal key links between different datasets. It helps identify which elements refer to the same entities, improving security, marketing, and executive decision making. When you’re using the right tool, these redundancies are immediately identified and accounted for in the backend of the tool’s processing capabilities.

Data matching improves the quality of data by identifying and fixing duplication within the data pool. This minimizes the chances of confusion as well as the amount of data storage.

4. Data quality reporting

Data quality reporting refers to the summarizing and presentation of the findings of data quality assessment. This implies that the organization needs to assess the overall quality of its data and present the findings in a data quality report. Aside from policing data quality, the report is a way to keep everyone on the data governance team aware of changes and updates.

The report should answer a range of important questions including those to do with any missing links, inconsistencies, and formatting errors. Again, when everything is captured and fed into a robust business intelligence platform, these changes are quick and painless.

5. Master data management (MDM)

Master data is the foundational data for a particular business operation. It includes customer data (e.g. customer contact details), product data (e.g. product categories), geographical data (e.g. physical premises), and legal data (e.g. licenses). Compared to other data, master data is less volatile (i.e. doesn’t change frequently), more complex, and more valuable. This is why it requires special management through a set of technology and protocols referred to as master data management (MDM).

The purpose of MDM is to establish a single source of truth for the organization’s mission-critical data. It is involved with the creation, storage, sharing, updating, and deleting master data.

6. Data asset management (DAM)

Data assets include the data, systems, services, and other data-related properties of an organization. These can be monetized in one way or another, especially when they are unique and offer a competitive advantage. Hence, modern organizations invest heavily in managing and protecting these assets through data asset management (DAM).

The particular interest paid to data assets consequently improves the quality of data. Conversely, the value goes down if it doesn’t possess those five quality features, and if one doesn’t maintain data quality best practices.

Data Quality Best Practices

Along with a robust structure for improving and maintaining the quality of data are best practices that hold the framework together, including:

1. Prioritize

Data is an important asset for your modern business. You use it to improve your internal operations, get better results out of marketing, and move on opportunities. Thus, it should be treated not as an afterthought, but a priority.

What does prioritizing data quality look like?

  • Ensuring the whole organization is committed to data quality.
  • Investing in training and data management, including robust tools to manage the process.
  • Establishing a data governance program.

2. Monitor

One of the most important things to keep in mind concerning data quality is the constancy of change. Even master data changes from time to time e.g. when a customer changes addresses or products are added or discontinued. Your data should be routinely checked against quality control rules to ensure it keeps up with the standards.

What goes into data quality monitoring?

  • Establishing internal data quality standards and policies.
  • Routinely measuring and evaluating data sources against industry norms.
  • Performing data quality reporting after each assessment and making changes.

3. Protect

Being such an important asset to the organization, there should be measures to restrict access to data, find vulnerabilities, and correct failures. Thus, data should be protected from unauthorized access, tampering, loss, theft, or damage.

What does it mean to protect your data quality?

  • Implementing cyber security/privacy standards at points of collection, storage, and distribution of data.
  • Investigating and fixing data quality failures.
  • Investing in data asset management.

4. Centralize

All business decisions should be based on common data; and all the data in the organization should be common. Apart from creating harmony, trust, and confidence among decision makers, this also makes it easier to maintain the quality of data.

How does centralizing data improve its quality?

  • Establishing a single source of truth for all of the organization’s data.
  • Building protocols for data sharing between individuals and teams.
  • Spreading out the management of data across the organization.

5. Modernize

Finally, you must join us in the 21st century. It is not only convenient to adopt modern data quality management methods and technologies, but also necessary. Data contained in legacy systems should be uploaded onto modern tools where it can more easily be monitored, protected, and used.

What are the modern data quality management practices?

  • Storing data in the secure cloud rather than local corruptible drives.
  • Automating aspects of data collection, analysis, storage, and sharing.
  • Creating fast, secure communication lines.

Start improving the quality of data today by setting high standards for the collection, analysis, storage, and sharing of data in your business. And every time you want to take advantage of an opportunity whether it’s adopting a new technology, addressing an emerging customer need, or giving your competition a run for its money, you’ll be grateful you’ve made the decision to level up your data quality. If you would like to see how NetBase Quid® works to improve the quality of data for leading organizations around the world, reach out for a demo today and we will be happy to show you.

How to Use AI-Enabled Consumer and Market Intelligence to Break Down Data Silos - Read Now

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