Data Enhancement What are the Benefits and Why Businesses Should Use AI for It

Data Enhancement: What are the Benefits and Why Businesses Should Use AI for It

We live in a data-driven world. The more data you have, the more chances you have to make informed business decisions and facilitate growth. However, the quantity of data does not always define success. What really matters is its quality, and that’s where data enhancement comes to the rescue.

With this instrument, you can add more accuracy to your information and minimize the risks in your business. Add to this equation artificial intelligence (AI), and no matter what industry you are in – commerce, tech, or banking — you will inevitably prosper. Some of the biggest companies, like Netflix, Spotify, and Amazon, already use artificial intelligence in data enhancement, so what are you waiting for?

This article will clue you in on the essence of data enhancement, its benefits, and how artificial intelligence (AI) can facilitate this process for your business.

What is data enhancement?

Data enhancement, otherwise known as data enrichment or data append, is the process of improving raw data by adding contextual information from third-party sources. The enhancement might include phone numbers and emails, addresses, sex, birthdate, employment data, customer surveys, income, or financial standing.

For example, let’s say you want to get insights into customer behavior. You can enrich your customer base with information from their social media profiles and use the insights to devise an effective business strategy. Another example is incorporating customer information, such as location, birthday, gender, for a more refined, targeted, and personalized offer that fits their demographics.

What are the benefits of data enhancement?

There are many advantages of focusing on the use of data enhancement in your business:

  • Improved data structure and quality  Data enhancement contributes to cleaning, standardizing, and validating the information. The first process involves identifying, correcting, and updating data to eliminate all the errors.
  • Cost-effectiveness — Data enhancement helps create error-free databases and eliminate irrelevant information, and it can significantly reduce the costs that businesses allocate for these processes. For comparison, in the US alone, businesses lose around $3.1 trillion due to the poor quality of data.
  • Competitiveness — Data enhancement is a critical tool for businesses that want to stay ahead. By using advanced analytics and tools, companies can gain a deeper understanding of their markets, customers, and competitors and develop innovative solutions that meet their customers’ changing needs.
  • Improved decision-making — Accurate and comprehensive data leads to better insights. These insights can then be used to develop effective strategies to handle business matters better, improve overall efficiency, and optimize marketing campaigns.
  • Personalized experience — By analyzing accurate user data, companies can customize the products and services they provide to cater to the needs of every user. It leads to higher customer satisfaction and loyalty.
  • Regulatory compliances — Businesses are often required to adhere to regulatory compliance, such as Know Your Customer (KYC) verification. When businesses have enhanced data of customers using third-party tools, they can cross-verify customers’ identity to ensure that they are who they say they are. Mismatch or inconsistent information calls for a deeper verification.

How can AI contribute to data enhancement?

Inception and improvement of AI has opened new doors to a more streamlined, refined, and faster business operation. One of the use-cases is that Artificial intelligence simplifies data enhancement by streamlining processes such as cleaning and correcting errors, inconsistencies, and duplicates in databases. AI models can identify duplication and standardize data formats, ensuring data is accurate and consistent.

In addition to that, AI utilizes Natural Language Processing (NLP) to comprehend human language and provide better decisions and insights. NLP enables AI-powered assistants to analyze and interpret unstructured data from sources like social media, emails, and customer feedback, allowing businesses to better understand their customers’ needs and preferences.

If you are hesitant about where to start with implementing AI in your data enhancement process, here are some crucial steps you should consider taking:

  1. Choose the most suitable data sources that would enrich your existing databases. Make sure that the third-party data company provides an accurate and reliable source of information.
  2. Make sure your data complies with government and privacy regulations. Determine who owns the information you want to use and ensure that you are not violating consumer protection laws.
  3. Choose the most optimal AI model for data enhancement. Ensure it corresponds to the specific needs of your company.
  4. Check how trained your AI models are. The algorithms should be trained on high-quality, error-free information. Otherwise, they will produce faulty and flawed output.
  5. Secure your data. As AI models have to deal with a lot of sensitive information from different sources, ensure your company has introduced all the necessary security measures to protect the personal details of your users.
  6. Constantly test your AI models and compare their output to the source material to ensure they provide the same high-quality output.
  7. Monitor all the time. Any minor anomaly might lead to severe output errors, so ensure you track the progress.
  8. Draw conclusions. After using AI models in data enhancement, evaluate the processes and how they influence your business. Weigh all the pros and cons of continuing working with artificial intelligence.

The challenges of artificial intelligence in data enhancement

Artificial intelligence is very efficient for businesses, but it is still not perfect. There are certain drawbacks of AI you should consider when drawing insights from it:

  • Bias output — AI algorithms are trained on datasets. If the data is distorted or one-sided, it will result in biased production for the data enhancement. The biases might touch upon information about gender, race, or social status.  Always challenge the AI with queries and prompts for more insights.
  • Data security issues— AI systems can become the target of attacks, namely data poisoning and adversarial examples. Data poisoning happens when cybercriminals introduce false information to the datasets, influencing the training process. Adversarial examples are the types of attacks that force neural systems to make mistakes when classifying objects.
  • Too many resources needed — Implementing artificial intelligence into data enhancement takes a lot of resources and time, namely for setting up the infrastructure and training specialists. Additionally, AI has to handle large amounts of data, requiring a lot of data storage.
  • The necessity for change and adaptability — As new trends and datasets emerge, AI systems must be constantly monitored and updated to match the current situation.
  • Interpretability issues — There is a need for a deep understanding of how AI works and how it arrives at certain decisions, as some AI models might struggle to explain their reasoning. There should be tools that could help visualize and explain their decision-making processes. Interpretability is crucial for compliance and accountability. In case the AI model makes a biased or discriminatory decision, figure out how it arrived at that decision to identify and address the root cause of the problem.

The use cases of AI in data enhancement

Multiple AI-powered tools can bring those aforementioned benefits to life. Here are some of them:

  • will help you find and merge all duplicates across multiple CRM platforms and accounts. It has advanced filtering features and a user-friendly interface.
  • DemandTools from Validity can help you handle your CRM data quickly and easily, saving you time and effort. This powerful tool ensures that your data is accurate and ready for reports, allowing you to make better-informed business decisions and improve your marketing, sales, and customer service strategies.
  • Trifacta is a tool for cleaning and prepping data before analysis. Its main features include predictive transformation suggestions powered by machine learning algorithms and intelligent data quality assessment.
  • MonkeyLearn utilizes machine learning and natural language processing to analyze text and draw valuable information, such as customer feedback and review analysis. It can also visualize textual insights and turn them into comprehensive charts. You can choose machine learning models from the ones available or create your own tailored to your preferences.
  • Clearbit is a data enrichment tool that can add more information to primary sales and marketing databases, such as CRMs, CDPs, or marketing automation platforms. With this software, you can identify missing information, update customer records, and correct mistakes. It will help companies better understand their customers, create more precise segments and reports and enhance workflows.
  • OpenRefine is another tool for data cleaning and preparation. It allows you to easily handle tasks like basic data formatting, filtering, and sorting, as well as advanced data cleaning and connecting across heterogeneous sources. Its intuitive interface is suitable even for people without experience working with data.
  • Clarifai uses AI technologies to analyze image and video content to derive valuable insights, including object detection, image classification, and facial recognition.

Final note

Implementing artificial intelligence in your data enhancement process is an absolute necessity for companies like you who want to achieve unprecedented overall success. Using artificial intelligence can streamline all business processes, from data cleaning to predictive analysis, and improve the accuracy and quality of the data.

AI-powered data enhancement not only enables companies to gain fresh insights but also helps them to reevaluate their approach to customer satisfaction and business intelligence.

So, waste no more time — embrace the future of data management and invest in AI!