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The Importance of Data Quality in this Age of Analytics

In the past, analytics were reserved for big corporations which seemed to have unlimited resources. Today, more than 56% of enterprises are using analytics to better their business. Analytics is an essential aspect of maintaining a successful business. Many of our clients rely on insights gathered by new data to create their marketing strategy.

Businesses use analytics in the best way it suits their business. Some companies use analytics to help with making knowledgeable business decisions, while other businesses use analytics for key strategic initiatives. Some companies use analytics to help with improving their relationship with their customers and their clients. To make analytics work for your business, you need to know how to get the most valuable information from the data you collect.

 

Quality Standards with Data

The good news is companies are not fighting over data because there is more than enough to go around. The challenge comes with it’s time to examine the quality of data that is collected. According to the Harvard Business Review study, data quality has a long way to go before it’s considered acceptable. If your business is like most companies, quality data is becoming more challenging to obtain. Also, in the study conducted by the Harvard Business Review, research reveals only less than 5% of the data scores collected were considered as acceptable.

Many businesses struggle to overcome the hurdles of collecting valuable data. High-quality data is required to provide accurate insights into customers and their current needs and wants. Without valuable data, your business’s performance can plummet due to unused marketing and communication efforts and incomplete prospect and customer data. Other negative effects of low-quality data include less knowledge when making business decisions and increased spending. These four factors alone make collecting valuable data a priority in every business aspect.

One helpful solution to collecting high-quality data is web data integration (WDI). This type of integration is a process you can use to aggregate and normalise data. WDI can also help you make the data you collect from analysis more digestible and easier to understand by using visuals and creating reports. Web data integration is relevant because its process for collecting data is far more advanced than traditional web scraping. Unlike traditional web scraping, web data integration makes the data you collect intuitive. Intuitive data is essential because your business needs it to capitalise on the tremendous amount of data that’s available. Thanks to intuitive data, not only can you collect data from a variety of sources, but you can also break down data in ways that make your client analysis efforts easier. At BizRamp, we use intuitive data from web data integration processes to clean data and make it more presentable to our clients.

WDI is an asset to any business, but before you try to implement this process within your company, you need to determine the goals you have for the data you collect and whether a managed services or an in-house solution best serves your business. Another way you can manipulate data and make it useful for your company is to use machine learning. Machine learning is beneficial because the computer improves and evolves as it continues to take in new data.

 

Assessing Quality Data

Assessing quality data can be a long and drawn-out process, but it doesn’t have to be. To start evaluating the quality of your data, you need to create a list of at least 100 data records you created or used. Once you have your list, you need to focus on a minimum of 10 primary data elements that are essential for the operation of your business. Your management staff and their teams need to go through each data record and look for errors. We suggest using a two-column spreadsheet to examine the data results. One column should be dedicated to the perfect records, and the other column needs to be dedicated to records that contain errors. Look at the spreadsheet and determine whether you have low or high-quality data.

Data Management Tips

  • Standardised data architecture is your best friend. You can move your data to a centralised database.
  • Keeping your employees updated is one of the smartest things you can do. When your employees are aware of the best practices for management, data entry, and compliance and safety, the better they can implement these methods and techniques in the workplace.
  • If you have multiple teams, we suggest creating a data management hierarchy. This hierarchy allows you to keep your teams organised while reducing the risk of a breach.
  • Core data management is one of the essentials of business operations. To ensure the needs of core data are met, you should delegate specific team members to manage this type of data.

 

Choosing the Best Tools

Every business relies on accurate data to thrive and maintain success standards. One of the most valuable business assets is data because it has a long-term effect on the success of your business. Without accurate data, your business doesn’t have much insight about customers and their behaviour. Improving your data accuracy requires the proper tools and technology. We suggest looking for a variety of instances to improve data accuracy, including:

  • Full mobility
  • The use of data normalisation for simple organisation
  • Third-party integration
  • Streamlines communication between team members using sharable dashboards

 

https://hbr.org/When you’re searching for the best data tools, make sure you request a demo of the platform you’re considering using. You need to make sure you understand how to use the platform to ensure it will help provide the data and the results you desire. Make sure to check the interface of the platform to see how easy it is to use and make a note of their features. More features don’t necessarily mean a better platform, so make sure the features that are offered are ones your business can use.

It’s time to unlock your business’s full potential with BizRamp.

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