Bad Data: Solving Enterprises’ Biggest Problem With AI

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The rate of AI adoption has skyrocketed in the recent past. Organizations are turning to AI to improve operational agility and efficiency, deliver superior customer experiences, inform decision-making, and ultimately secure a competitive edge over the others. Today, over 74% of companies are currently exploring or deploying AI in their businesses. However, one of the biggest challenges that stand in the way of realizing the full impact of their AI investments is inefficient data management and bad data.

What Is Bad Data?

Bad data is essentially inaccurate information. These could be incomplete, inconsistent, or unstructured records. It also includes everything from missing data, duplicate data, and wrong information to non-conforming entries. With the volume of data exponentially growing every day, the amount of unstructured data is also growing at unprecedented rates. Today, over 90% of the world’s data is unstructured and inaccessible for business operations. 

Why Should This Matter To Organizations Like Yours?

Every year, poor data quality costs organizations an average of $12.9 million.  Apart from the immediate impact it has on revenue, poor quality data increases the complexity of data ecosystems. It also leads to poor decision-making in the long term.

“Data quality is directly linked to the quality of decision-making. Good quality data provides better leads, a better understanding of customers, and better customer relationships. Data quality is a competitive advantage that D&A leaders need to improve upon continuously.”  – Melody Chien, Senior Director Analyst, Gartner.

With the majority of enterprise data being either inaccurate or unavailable, organizations are failing to deliver on their data-backed promises. 95% of businesses cite the need to manage unstructured data as a problem for their business. In this article, we will explore how AI can help organizations with their management.

Here’s Why Businesses Should Fix Their Data:

With AI, businesses can not only clean and organize their data but ensure appropriate data management as well. The benefits of which are multi-fold.

1. Improved Productivity

Organizations spend 80% of their time looking for data and only 20% of the time utilizing it.

Bad data slows down an organization and its pipelines. Businesses collect over 75,000 data points for a single customer. Up to 30% of this data becomes inaccurate every year. As a result, data science teams spend hours on time-consuming legacy processes and manually execute mundane tasks such as looking, cleaning, normalizing, and organizing data. This is time that could be better spent on extracting clear insights that could determine critical business decisions.  

2. Enhanced Customer Experience

78% of organizations say that the data they collect helps them increase customer acquisitions and lead conversions.

Today, consumer data typically comes from so many sources that it is often siloed, inconsistent, and lacks a single source of truth. As a result, insights drawn from these disparate datasets are inaccurate as well.
By consolidating transactional and behavioral signals acrossvarious channels and standardizing them in formats that are compatible with internal workflows, businesses can feed actionable insights to downstream processes such as prediction, personalization, and targeting and deliver stellar experiences that keep their customers coming back for more. 

3. Superior Decision Making

Data-driven companies are 23X  more likely to acquire customers and 6X more likely to retain them.

The adage “garbage in, garbage out” holds especially true when it comes to AI. With good data, business leaders can stop shooting in the dark and work with precision. They can draw inferences and predict demand for products and services based on historical data as well as identify trends, create accurate forecasts from past patterns and situations, and devise data-backed strategies to figure out the optimal solution to a situation. Businesses can also leverage these insights to make informed decisions, efficiently meet demand, optimize operations and maximize profitability.

4. More Efficient Processes

Fortune 1000 companies stand to gain $65 million in additional income with even a 10% increase in data visibility.

With the right data, businesses can uncover inefficiencies, optimize operations for profitability and even cut down on unnecessary spending. For instance, most businesses have fixed guidelines around how content, text, images, audio, or video, should appear on their platforms. As a result, they invest a significant amount of time, money, and resources to ensure that all content onboarded onto their platforms adheres to their internal guidelines. With AI, businesses can moderate content at scale and ensure that the submitted content meets preset criteria, thus reducing the cost associated with manual moderation.

The Vue.ai Intervention

At Vue.ai, we understand that an organization’s AI strategy is only as good as the data they work with. As a result, we’ve developed an intelligent system to extract, enrich, organize, and manage enterprise data and transform it into a strategic asset.

Raw data consisting of images, numerical, structured, and unstructured text is enriched with attributes to build rich metadata and structured data catalogs. This is done using a variety of AI-enabled APIs that can detect, recognize, and extract data across all incoming media sources such as text, image, audio.

The structured data will be labeled according to standard or custom taxonomy that is relevant to your segment, business, and use case. In fact,our tool gives enterprise users complete control over their data by enabling them to visually label or classify data points based on their custom attributes on an intuitive, no-code, fully customizable data organization platform.

The labeled data is then classified into normalized data structures that various teams across the organization can use to build and maintain AI models that solve for their KPIs.

We fix the ugly parts of enterprise data by breaking down silos, bringing the data together, converting raw data into meaningful attributes, and organizing it into structured formats so that your teams can seamlessly integrate it into their systems and uncover data-backed insights that drive competitive value in the long term.

Use your data to build a strategic moat around your business so that you can focus on scale and growth.