Data Quality Management : 5 Ways to Optimize Data in 2023

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“All good data relies on heavily cleaned data”

As much as the above statement is true, it is equally challenging to have data quality management systems that match efficiency and cost using the traditional methods of data cleaning. With the advancement in technology, how businesses and individuals communicate has entirely changed the data being captured and stored.

IDC states that the amount of data “created, captured, copied and consumed in the world” will continue to rise at a rapid pace. The amount of data created over the next 3 years will be more than the data created over the last three decades. The world will generate more than 3x the data in the coming five years. This means that there is and will be tons of unstructured and inconsistent data.

But what exactly is the cost of data quality? Does it impact businesses monetarily or does efficiency get affected too? Let’s find out.

The actual cost of poor data quality management

According to Gartner’s Data Quality Market Survey, bad data management costs a business $15 million annually. On the other hand, almost 60% of businesses don’t even measure the financial impact of having poor data hygiene. Another study states that nearly 1/3rd of business analysts spend more than 40% of their time accessing and validating their data before it can be used for strategic decision-making.

Clearly, there exists an underlying data problem that businesses are now finding out to be extremely expensive both in terms of money and human resources. Proper data management is possible with the help of tools which was not the case a few years ago. Now that data cleaning is becoming a very crucial step for most teams working in analytics, it is important to know how exactly data hygiene impacts business performance.

What is Data cleaning?

Data cleaning, also called data scrubbing, is one of the most important processes in the making of intuitive business decisions. The process of cleaning the data includes fixing or removing incorrect, corrupted, unstructured, duplicate, or incomplete data within a dataset. If it is done correctly, it can help businesses reduce costs and human effort in managing them.

In the simplest of forms, good quality data is “data which is suitable for use”. Per Andreas Bitterer, VP, of Gartner Research, said, “Organizations need accurate and trustworthy data to make intelligent business decisions.”

There is a need for data organization and cleanup solutions to help businesses navigate through crucial processes. This will ensure that adequate data quality is managed throughout the organization’s processes.

Because almost all major businesses today are struggling with the bad data problem which not only costs teams a lot of money but also hours of manual labor to fix it. But now, because of AI and technology, there have been evolutionary changes in this spectrum – catering to fixing the ugly data problem.

How can Blox help as a data quality management solution?

Blox fixes the ugly data problem by using Natural Language Processing (NLP) and Computer Vision models. It extracts and enriches data from images, videos, text, and more. Teams can use Blox to organize the enriched data into structured formats for use across business systems, or build and deploy different models on top of it.

Impact businesses have seen with Blox:

85% reduced time in data organization processes
40% increase in conversion rate with enriched data
86% reduction in data errors with AI-powered moderation

Blox empowers businesses to use the power of their data and solve a wide range of business problems from classification & recognition to prediction and forecasting. This means Blox cannot only fix your bad data problem but also help you do more… Click To Tweet

5 ways to fix the data problem

With AI, businesses can now manage and transform their data into actionable business assets. Using Blox, businesses can:

Build extract, enrich, and enhance datasets

Blox.ai’s platform can extract detailed attribute tags and build a structured dataset from them. The data generated by AI takes in inputs from all available sources, is nuanced, and mapped to the hierarchy and data organization structures that the users define.

Customize structures as per business requirements

With Blox, businesses can build customized taxonomy and deploy the models for inference which can be applied to any new content or data inventory coming in and map data at scale. This way businesses can decrease the time it takes to generate tags by scaling custom AI models.

Moderate content at scale

Blox help businesses ensure that the submitted content meets predefined criteria and guidelines, does not contain any inappropriate content, violates existing copyright rules, etc. It helps reduce businesses’ costs of editing and moderating content manually.

Quicken the user-generated content onboarding

With Blox’s AI-enabled APIs, businesses can efficiently detect attributes that can be used to qualify the content quality which includes: checking for the presence of NSFW content, blurred images, other internal guidelines etc.

Refine suitable content searches

Blox.ai uses computer vision and NLP to extract detailed attribute tags and build robust data catalogs. This enables platforms to surface the most suitable result through search and filtering. Now, businesses can deliver relevant and suitable search results across their website or app and also improve the overall content discovery for the users.

Blox.ai is enterprise businesses’ go-to tool for making intelligent cost-effective business decisions.

Learn more about Blox’s Data Cleaning and Organization solution.

Data Quality Management: 5 Ways to Optimize Data in 20235 min read