Melissa AU Team | Australia, Data Quality | data analysis, data profiling, data qualiyt

Today organizations source their data from websites, social media, IoT devices, clickstream analysis, custom applications and more. Each source provides different types of data in independent formats.

For example, a retailer may collect a customer’s name and email address from a web form, their date of birth, gender and demographic profile from social media platforms and budget preferences from responses to digital marketing initiatives.

In its raw form, the insights revealed are limited. What’s more, depending on this data could lead to decisions that have unintended results. However, when it is processed and enriched to meet data quality standards, you get dependable insights that can be used to make better business decisions. A survey found that 34% of the respondents felt that improving data quality could improve revenue by up to 10%.

The question is, how can you ensure that the data you have is ready to be analyzed for business decisions? There are three basic steps that must be completed.  

1. Data Profiling

The first step to making data useful is profiling the different data sources and the data available to you. This can be a daunting task given the variety of sources and software systems in use. Today’s businesses depend not only on static spreadsheets on local servers but also data from cloud-based CRM and ERP systems. The resulting data set is vast and varied.

When profiling data and data sources, you need to be able to assess what information is being gathered at each point, the format being followed and so on. You will also need to make connections between data sets and find patterns to identify dependencies.

Data from all sources is brought to a central database where the initial data-cleaning processes can be completed. Centralizing data storage is important to build complete customer profiles and keep information from being siloed. Doing so includes unifying structures but standardizing formats, identifying anomalies, and highlighting duplicates.

For example, you have two records with the mobile number entered as +91 1234567890 and 1234567890. When this format is standardized to remove the country code, you’ll find that they are duplicates.  Having duplicates may not sound like a big problem but it influences reporting, increases storage costs and could have a negative impact on ROI for marketing activities.

2. Quality Analysis

Next, you need to assess the data set to evaluate the existing data quality. Data must be checked against a third-party database to assess whether it is accurate, complete, current and valid. For example, addresses must contain an apartment number, building name, street name, city, state and pin code. Data quality analysis can look at addresses to see if they are missing information in any of these fields and to ensure that they are linked to the relevant individual.

Any records identified as inaccurate, incomplete or invalid must be highlighted so that appropriate action can be taken. Similarly, records identified as obsolete or invalid must also be flagged.

Data quality analysis is a step that involves not only the IT team but also the data users. The latter may not have the technical knowledge to deal with quality analysis, but they can provide context into what kind of information is required and what details need to be prioritized. For example, the billing department collecting billing addresses may not require pin codes but this may be a critical detail for logistics managers handling online order deliveries.

3. Raising Data Quality Standards

Comparing data in your dataset to a reliable third-party database can also help correct errors and fill in the gaps to complete records. For example, if the address entered doesn’t have a district name, the same can be inferred from the pin code and entered to complete the address. Similarly, let’s say data analysis highlighted an address as invalid since the city had changed the street name. In such cases, the information can be automatically updated to ensure that the address remains deliverable.

This stage of preparing data to derive business insights also involves merging records identified as duplicates. How this is done varies depending on your data governance rules. While some organizations choose to maintain the latest entry and eliminate older duplicate files, some may choose to keep the record with maximum details.

4. Scaling The Operation With Automation

When you have 1 or 3 or 10 records, you can complete the above steps manually. But, manually checking thousands of records can be virtually impossible. Over 2.5 quintillion bytes of data is collected by businesses every day. This is the main challenge with maintaining data quality.

At this point, it is also important to note that data needs to be checked not only when it enters the database but also on a regular basis to keep decayed data from settling in your database. For example, a customer may change his/her phone number and create a second account. Regular data checks can identify such duplicate files and eliminate the decayed record.

Hence, the system needs to be automated.

Implementing the right data verification and enrichment tools can streamline the processes and keep you in control over your dataset. Modern cloud platforms that can unify data form varied sources, standardize formats, analyze and clean data could be just what you need to gain an advantage over your competition. With these tools, you can assess data quality in real time with minimal inconvenience to your customers.

You also need to develop a comprehensive data governance strategy. Data quality does not exist in a vacuum. This involves assigning responsibilities and making people accountable for the way data is sourced, processed and used so as to improve data quality standards.

In Conclusion

The benefits of data-driven decision-making have been extolled by many. From empowering retail agencies to make personalized product recommendations to helping insurance agencies evaluate claims data has the power to push businesses closer to success. That said, only those businesses that invest in data quality can reap these benefits. This is an ongoing process and it’s never too late to start.