IT professionals analysing data

Data is one of the primary sources of every business to create a competitive advantage

It is now seen that since data is revolutionizing across industries, businesses are facing constant pressure to innovate and increase profitability. Therefore, it is vital to design a framework for data management to reap rewards.

This blog highlights the importance of data quality and why it is an important investment to build a data strategy. It also uncovers tips to identify goals to embed in this framework by leveraging data quality solutions.

What is data quality?

Without an accurate understanding of what quality data looks like, companies, non-profit organisations, or other entities cannot have the highest data quality. There are many ways to define data quality, but all definitions have some things in common.

According to data quality experts, when the data meets the requirements of its intended use, the data is of high quality. In other words, when companies can use this data to communicate effectively with their constituents, determine customer needs, and find effective ways to serve their customer base, they know they have high-quality data.

This definition of data quality is broad enough to help companies with different products, markets, and missions to understand whether their data meets standards.

Why should you invest in Data Quality?

The onset of the unexpected Covid-19 pandemic highlighted the gravity of quality data in modern businesses. With many companies switching to digital formats—from workplace culture to online sales and customer services—data is more critical than ever. Based on Experian’s global data management research study, 72% of businesses say that an accelerated digital transformation has made them more reliant on data and data insights. It is therefore essential to maintain accurate and trustworthy data to avoid dramatic effects on business goals.

Investing in data quality management gives confidence in data validity to support strategic, tactical, and operational decision-making, irrespective of legacy or new data for businesses. The initiative to establish high-quality data can give you a competitive edge and turning point in your industry. For example, you can use your data to customise a marketing plan tailored for your targeted consumers to be ahead of the game. Read the customer case study from Bunches and explore how they attract new customers by delivering better-targeted marketing campaigns.

5 risks of untrustworthy data

Infographic_5 risks of untrustworthy data

A good data quality management plan often involves technology, employee participation, and support from data quality experts. The right resources and tools are vital to realise the best quality of data to run a profitable business that focuses on continued growth by eliminating the below risks:

  1. Wasted time
    Duplicate data is a pressing issue as it negatively impacts business with astronomical costs, low productivity, and ultimately brand image. Manually deleting duplicates is a significant waste of time and effort. These extra steps reduce employees’ productivity rather than encouraging them to focus on rewarding opportunities leading to innovation in business.
  2. Missed opportunities
    Across industries, many businesses leapfrog their competitors by leveraging data to unlock significant value. However, poor data quality due to inconsistencies in formats or error entries undermines business, potentially driving opportunities away to a competitor with advanced data governance process to capitalise.
  3. Compliance issues
    Data issues impose challenges to businesses on financial growth, productivity, and unreliable decision making. With the introduction of regulations like the General Data Protection Regulation (GDPR), organisations must be cautious in data collection, usage, and storage. Non-compliance with GDPR and security breaches impose stiff fines that affect reputation and interruptions to business operations. Whereas good data helps lower the risk of security issues and keeps you compliant with the latest data regulations.
  4. Poor customer experience
    Successful companies invest in providing their customers with a personalised brand experience as part of their core strategy. Inaccurate or outdated data can affect the rate of return on investment as it becomes challenging to analyse consumer behaviour, consumption patterns, and purchase history. It may affect the positioning of the right customers with the right products, leading to a waste of marketing efforts.
  5. Unreliable decision-making
    Poor data can lead to profound consequences. One of the biggest challenges is the inability to make accurate decisions. According to Experian’s research, 55% of business leaders lack confidence in their data assets. Hence, these leaders are reluctant to unlock insights to create value which affects day-to-day business decisions. Improving data quality provides assurance and trust into the insights generated for meeting business goals.
5 risks of untrustworthy data infographic

Download our infographic '5 risks of untrustworthy data'
 

4 tips to start improving your data quality

Accessing the right tools, processes, and methods helps you design a sustainable data quality management plan. This plan requires a comprehensive mix of technology and people to embed the design into your strategy roadmap:

Speak with a data quality expert today about Data Validation for Salesforce and Microsoft Dynamics 365

Speak with a data quality expert today about Data Validation for Salesforce and Microsoft Dynamics 365

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  1. Determine data goals and quality metrics
    A well-formulated data strategy is paramount for a business since it articulates its vision to store, use and manage data. This strategy defines the foundation to craft goals within the context of business and the process to execute them. The ambition to collect and manage a high quality of data from beginning to end will attribute to accurate decision making, regulatory compliance and high customer satisfaction.
  2. Standardise and cleanse databases
    Businesses are prone to lose opportunities when they have inconsistent, inaccurate, and outdated data. Data quality improvements are ideal to standardise and clean the data suitable to meet business objectives. The cleaned data is now the new oil for businesses to drill down to potentially create extraordinary value on products and services.
  3. Fill data gaps
    Data enrichment focuses on adding missing or extra attributes to the cleaned data resulting in rich insights of prospects and customers. The gaps can be further reduced by integrating data enrichment in real-time within CRM or other applications to append business data, location data, and demographics to your contacts. Enrichment supports in building Single Customer View to get an accurate view of your customers.
  4. Use technology and resources for data quality management
    Investing in the data quality journey by leveraging the right technology and resources is essential to gain insights and value on investment. Update your records regularly or automatically verify your customers’ contact data to remove manual tasks that hinder your employees and business.

    • Technology: Select easy to use solutions with data cleansing, real-time validation APIs, data profiling, Single Customer View (SCV), and Identity Resolution Solution (IDR) capabilities to support in ongoing data management process.
    • Expertise: Partner with trusted data quality experts who support your digital transformation initiatives and assist your data quality plan by choosing the right solutions and deploying them into a structured process consistent with your organisation to produce desired outcomes for your business.
    • Employees: Use the right and user-friendly tools and resources that seamlessly integrate into your existing system to help maintain the quality of the work your employees are doing and make their tasks easier.

Get in touch with Experian’s data quality experts to learn how we can help you better manage data quality for your business.