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Eliminate Bad Data with Real-Time Data Quality Insights

By Michael Burke posted 24 days ago

  


In business, we must recognize that data is a commodity and that our data quality is crucial since it has a significant monetary value. The absence of clean, interconnected data impedes fast, high-quality company choices, resulting in poor customer service, revenue loss, and inefficiency. Moreover, incorrect data is more than a nuisance; it significantly affects the bottom line. IBM also found that firms in the United States alone lose $3,1 trillion yearly due to poor data quality.

Enterprises can take targeted action based on relevant data points when the data is clean. Hence, the marketing and sales activities are more likely to correspond with the demands of the target consumer the more thorough and precise the data is.

Aiming to deliver an innovative solution to ensure data quality, Reltio provides a real-time data quality dashboard instead of an external tool. This is because, historically, data quality has been a one-time process during the transfer to an MDM. In the past, we have seen clients that pass their data via a data quality tool before importing it into Reltio, at which point quality checks cease.

What is Reltio’s DQ Dashboard?

The Reltio data quality dashboard provides industry-first, continuous, and automated data quality management that enables you to make business decisions based upon timely, trusted data. Enterprises can reduce the amount of time and effort required to locate and clean data. The data quality dashboard delivers aggregate data quality statistics at the entity and attribute level. The real-time data quality dashboard also offers KPIs, metrics, and critical data points to monitor quality in unique ways tailored to your individual business needs.

Monitor, Define, Measure and Improve

Defining Data Quality


How does Reltio define data quality?

While there is no unanimous definition of data quality, Data quality represents the degree to which a data set fulfills its intended function. Data quality measures are based on data quality attributes like precision, exhaustiveness, consistency, validity, originality, and timeliness.

Furthermore, we can extend this definition if we link this to genuine commercial benefit. And a good illustration of this is the marketing campaign example, where even though an email address may be acceptable to a downstream marketer who receives data from MDM, the open rate or actions taken after reading an email may contribute to the quality of that data. Consequently, to really monitor the entire life cycle of business value, we recommend customers monitor both upstream and downstream use cases. 

 

Impact of Bad Data on Marketing and Sales

Ultimately, the effects of inadequate data on the sales and marketing teams might vary from erroneous targeting that limits the production of leads to a stagnant sales funnel that fails to turn prospects into customers.

Moreover, bad data hampers effective automation. Several components of the sales and marketing workflow should be automated to maximize demand-generating outreach. However, since automated email campaigns and auto-dialing sales calls depend on data quality, they might fail if based on inaccurate data.

Benefits of Data Quality Platforms

Overall, one of the primary benefits of real-time data quality platforms is that we can continually check the data quality of our customers. Therefore, any changes to the enterprise’s upstream data or source will be instantly reflected in this data quality dashboard every day. Ideally, one of the things that we will bring to the table in the future that we are very excited about is the ability to monitor and warn of changes to this information automatically.

In this manner, profiles containing inaccurate data are being investigated through a validation process, containing both machine learning and rules associated with each attribute of the pre-established objectives. If numerous rules are imposed, afterward, it would be possible to examine a holistic view of each custom definition and rule associated with each attribute in this entity in this section.


Data by Characteristics

There are techniques to filter for a particular dataset by leveraging data validation functions, which are custom rules, to track specific business needs and customize the meaning of quality for a specific dataset. This is an excellent approach to rapidly identify unique outliers in the case where there are hundreds of attributes connected with a particular entity.

Hence, one of the great things businesses can do to identify bad data is dive into any of these charts and evaluate metrics such as the attribute fill rate to see what is missing or out of place. This is a useful approach to evaluating which attributes  out of place or need to be enriched to improve quality.. The search may then be saved, and data can be exported for future study or remediation.

Another great feature of the Reltio data quality dashboard is that it is free for everyone. We believe that data quality not only provides a better narrative for you to comprehend business objectives but it  also helps us as a kind of guiding process to be able to understand what is going on in your tenant and how we can also support you in making the product function optimally.


Bottom Line

For business decision-making to be accurate and well-informed, having access to high-quality data is essential. Reltio offers a breakthrough avenue for those enterprises willing to step up their data quality processes and further enhance their business prospects.






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