In today’s fast-paced business landscape, gaining a deeper understanding of your customers is crucial for staying ahead of the competition. With the sheer volume of customer data available, it can be overwhelming to sift through and extract meaningful insights. However, with the right techniques and tools, you can unlock the full potential of your customer data and supercharge your customer insights. According to recent research, the global market for AI in data enrichment, a key component of data enrichment automation, is projected to reach $5 billion by 2025, up from $2.5 billion in 2020, highlighting the significant impact of AI on data enrichment processes.
This rapid growth is driven by the increasing need for accurate and comprehensive data insights. In fact, companies that use data enrichment automation techniques are more likely to see an increase in customer satisfaction and loyalty. With the ability to automatically collect, process, and analyze large amounts of customer data, businesses can gain a more nuanced understanding of their customers’ needs, preferences, and behaviors. This, in turn, enables them to make data-driven decisions, improve customer experiences, and drive business growth.
Why Advanced Data Enrichment Automation Techniques Matter
The importance of advanced data enrichment automation techniques cannot be overstated. By leveraging these techniques, businesses can enhance their customer insights, improve data quality, and increase efficiency. Some of the key benefits of advanced data enrichment automation techniques include:
- Improved data accuracy and completeness
- Enhanced customer segmentation and targeting
- Increased efficiency and reduced manual data processing
- Better decision-making and strategic planning
In this blog post, we will explore the world of advanced data enrichment automation techniques and provide a comprehensive guide on how to supercharge your customer insights. We will delve into the latest trends and technologies, including AI and machine learning, and discuss real-world case studies and implementations. By the end of this post, you will have a clear understanding of how to leverage advanced data enrichment automation techniques to drive business growth and improve customer satisfaction. So, let’s dive in and discover the power of advanced data enrichment automation techniques for yourself.
Introduction to Data Enrichment Automation
Data enrichment automation is a rapidly growing field, driven by the increasing need for accurate and comprehensive data insights. The global market for AI in data enrichment, a key component of data enrichment automation, is projected to reach $5 billion by 2025, up from $2.5 billion in 2020. This almost twofold increase highlights the significant impact of AI on data enrichment processes. Companies like Salesforce, Microsoft, and Oracle are already leveraging data enrichment automation to enhance their customer insights and improve business outcomes.
According to a report by MarketsandMarkets, the data enrichment market is expected to grow at a Compound Annual Growth Rate (CAGR) of 12.3% from 2020 to 2025. This growth is driven by the increasing demand for data-driven decision-making and the need for accurate and comprehensive data insights. Data enrichment automation tools like RingLead, InsideView, and Clearbit are helping companies to automate the process of data enrichment, reducing manual errors and improving data quality.
Data Enrichment Automation Process
The data enrichment automation process involves several steps, including data collection, data cleansing, data matching, and data enrichment. Companies like Salesforce and HubSpot are using data enrichment automation tools to automate these processes and improve their customer insights. For example, Salesforce uses its Einstein Analytics tool to automate the process of data enrichment, providing its customers with real-time insights and recommendations.
Some of the key benefits of data enrichment automation include improved data quality, reduced manual errors, and enhanced customer insights. Companies like Amazon and Netflix are using data enrichment automation to personalize their customer experiences and improve their business outcomes. According to a report by Forrester, companies that use data enrichment automation are seeing a significant improvement in their customer satisfaction rates, with some companies seeing an increase of up to 25%.
Here are some of the key features of data enrichment automation tools:
- Automated data collection and cleansing
- Data matching and merging
- Data enrichment and append
- Real-time data insights and recommendations
- Machine learning and AI-based algorithms
Some of the popular data enrichment automation tools include:
| Tool | Features | Pricing |
|---|---|---|
| RingLead | Automated data collection and cleansing, data matching and merging, data enrichment and append | Custom pricing |
| InsideView | Data enrichment and append, real-time data insights and recommendations, machine learning and AI-based algorithms | $99 per user per month |
| Clearbit | Automated data collection and cleansing, data matching and merging, data enrichment and append | $99 per month |
In conclusion, data enrichment automation is a rapidly growing field, driven by the increasing need for accurate and comprehensive data insights. Companies like Salesforce, Microsoft, and Oracle are already leveraging data enrichment automation to enhance their customer insights and improve business outcomes. By automating the process of data enrichment, companies can reduce manual errors, improve data quality, and enhance customer insights.
According to Gartner, the use of data enrichment automation is expected to increase significantly in the next few years, with up to 80% of companies using data enrichment automation by 2025. As the market continues to grow, we can expect to see new and innovative solutions emerge, helping companies to automate the process of data enrichment and improve their customer insights. With the help of data enrichment automation tools like RingLead, InsideView, and Clearbit, companies can unlock the full potential of their data and drive business success.
Market Trends and Statistics
The market for data enrichment automation is rapidly growing, driven by the increasing need for accurate and comprehensive data insights. According to recent research, the global market for AI in data enrichment, a key component of data enrichment automation, is projected to reach $5 billion by 2025, up from $2.5 billion in 2020. This almost twofold increase highlights the significant impact of AI on data enrichment processes. Companies such as Google, Amazon, and Microsoft are investing heavily in AI-powered data enrichment tools, such as Google’s Data Enrichment and Amazon’s Data Enrichment services.
One of the key trends driving the growth of the data enrichment automation market is the increasing demand for real-time data insights. With the rise of social media, IoT devices, and other digital channels, companies are generating vast amounts of data that need to be processed and analyzed in real-time. Data enrichment automation tools, such as Talend and Informatica, are helping companies to automate the data enrichment process, reducing the time and cost associated with manual data processing. For example, a study by Forrester found that companies that use data enrichment automation tools can reduce their data processing time by up to 70% and improve their data quality by up to 90%.
Statistics and Market Data
The data enrichment automation market is expected to continue growing in the coming years, driven by the increasing demand for AI-powered data insights. Some key statistics and market data include:
- The global market for AI in data enrichment is projected to grow at a compound annual growth rate (CAGR) of 25% from 2020 to 2025.
- The market for data enrichment automation is expected to reach $10 billion by 2027, up from $2.5 billion in 2020.
- Over 70% of companies are using data enrichment automation tools to improve their data quality and reduce their data processing time.
- The use of AI-powered data enrichment tools is expected to increase by 50% in the next two years, driven by the growing demand for real-time data insights.
Companies such as Salesforce and Oracle are also investing in data enrichment automation tools, with Salesforce acquiring MuleSoft in 2018 to enhance its data integration and enrichment capabilities. Oracle has also launched a range of data enrichment automation tools, including its Data Cloud service.
Case Studies and Real-World Implementations
There are many real-world examples of companies using data enrichment automation tools to improve their data insights and business decision-making. For example, Walmart uses data enrichment automation tools to analyze customer data and improve its marketing and customer service efforts. Amazon also uses data enrichment automation tools to personalize its customer recommendations and improve its supply chain management.
| Company | Data Enrichment Automation Tool | Benefits |
|---|---|---|
| Walmart | Talend | Improved customer insights, increased marketing effectiveness |
| Amazon | Informatica | Personalized customer recommendations, improved supply chain management |
Data enrichment automation is a rapidly growing field that is expected to continue growing in the coming years, driven by the increasing demand for AI-powered data insights. Companies such as Google, Amazon, and Microsoft are investing heavily in data enrichment automation tools, and there are many real-world examples of companies using these tools to improve their data insights and business decision-making.
As the demand for data enrichment automation continues to grow, it is likely that we will see more companies investing in AI-powered data enrichment tools and more real-world examples of companies using these tools to improve their business performance. Gartner predicts that by 2025, over 80% of companies will be using data enrichment automation tools to improve their data insights and business decision-making.
Case Studies and Real-World Examples
Now that we’ve explored the concept of data enrichment automation and its market trends, let’s dive into some real-world examples and case studies. These examples will help illustrate the practical applications of data enrichment automation and provide insights into the benefits and challenges of implementing such solutions. According to a report by MarketsandMarkets, the global market for AI in data enrichment is projected to reach $5 billion by 2025, up from $2.5 billion in 2020. This growth highlights the increasing demand for accurate and comprehensive data insights.
Case Studies of Data Enrichment Automation
Several companies have successfully implemented data enrichment automation solutions to improve their customer insights and drive business growth. For example, IBM has developed an AI-powered data enrichment platform that helps businesses automate the process of collecting, integrating, and analyzing customer data. This platform has been used by companies such as Walmart and Target to gain a better understanding of their customers’ behavior and preferences.
Another example is Salesforce, which has developed a data enrichment tool called Einstein Analytics. This tool uses AI and machine learning algorithms to analyze customer data and provide insights into customer behavior and preferences. Salesforce has reported that companies using Einstein Analytics have seen an average increase of 25% in sales revenue and a 30% reduction in customer churn.
Benefits of Data Enrichment Automation
The benefits of data enrichment automation are numerous. Some of the key benefits include:
- Improved accuracy and completeness of customer data
- Enhanced customer insights and personalized marketing
- Increased efficiency and reduced manual errors
- Better decision-making and strategic planning
- Improved customer experience and loyalty
According to a study by Gartner, companies that have implemented data enrichment automation solutions have seen an average increase of 15% in customer satisfaction and a 20% reduction in customer complaints.
In addition to these benefits, data enrichment automation can also help companies comply with data regulations such as GDPR and CCPA. By automating the process of data collection and analysis, companies can ensure that they are meeting the requirements of these regulations and avoiding potential fines and penalties.
Challenges and Limitations of Data Enrichment Automation
While data enrichment automation offers many benefits, it also presents some challenges and limitations. Some of the key challenges include:
- Data quality and accuracy issues
- Integration with existing systems and tools
- Security and compliance concerns
- Cost and ROI justification
- Change management and training
According to a report by Forrester, the majority of companies (60%) have reported that data quality and accuracy issues are the biggest challenge they face when implementing data enrichment automation solutions.
| Company | Solution | Benefits |
|---|---|---|
| IBM | AI-powered data enrichment platform | Improved customer insights, increased efficiency |
| Salesforce | Einstein Analytics | Increased sales revenue, reduced customer churn |
As the demand for accurate and comprehensive data insights continues to grow, companies are turning to data enrichment automation solutions to improve their customer insights and drive business growth. By understanding the benefits and challenges of data enrichment automation, companies can make informed decisions about how to implement these solutions and achieve their business goals.
According to Forrester, the key to successful data enrichment automation is to focus on the quality and accuracy of the data, as well as the integration with existing systems and tools. By doing so, companies can ensure that they are getting the most out of their data enrichment automation solutions and achieving the desired business outcomes.
Building on the case studies and real-world examples discussed in this section, the next section will explore the various tools and software available for data enrichment automation, including their features, pricing, and user experiences.
Tools and Software for Data Enrichment
Data enrichment automation is a rapidly growing field, driven by the increasing need for accurate and comprehensive data insights. To capitalize on this trend, businesses can leverage a variety of tools and software. The following table provides an overview of some of the key data enrichment tools available, including their key features, pricing, and best use cases.
| Tool | Key Features | Pricing | Best For | Rating |
|---|---|---|---|---|
| Salesforce Sales Cloud | Contact management, lead tracking, sales forecasting | $25-$300 per user per month | Large enterprises | 4.5/5 |
| HubSpot CRM | Contact management, sales tracking, marketing automation | Free-$50 per user per month | Small to medium-sized businesses | 4.5/5 |
| InsideView | Company and contact data, sales intelligence, marketing automation | Custom pricing | Large enterprises | 4.2/5 |
The tools listed above offer a range of features and pricing options, making them suitable for different types of businesses. For example, Salesforce Sales Cloud is a popular choice among large enterprises, thanks to its robust feature set and scalability. On the other hand, HubSpot CRM is a good option for small to medium-sized businesses, as it offers a free version and affordable pricing plans.
1. Salesforce Sales Cloud
Salesforce Sales Cloud is a comprehensive sales platform that offers a range of features, including contact management, lead tracking, and sales forecasting. It is widely used by large enterprises, including Apple and Amazon.
Key Features:
- Contact management
- Lead tracking
- Sales forecasting
- Marketing automation
Pros:
- Robust feature set
- Scalable
- Good customer support
Cons:
- Expensive
- Steep learning curve
- Customization options can be overwhelming
Best For:
Large enterprises with complex sales processes.
Pricing:
$25-$300 per user per month, depending on the edition and features required.
2. HubSpot CRM
HubSpot CRM is a popular CRM platform that offers a range of features, including contact management, sales tracking, and marketing automation. It is widely used by small to medium-sized businesses, including HubSpot itself.
Key Features:
- Contact management
- Sales tracking
- Marketing automation
- Lead generation
Pros:
- Affordable pricing plans
- Easy to use
- Good customer support
Cons:
- Limited customization options
- Not suitable for large enterprises
- Free version has limited features
Best For:
Small to medium-sized businesses with simple sales processes.
Pricing:
Free-$50 per user per month, depending on the edition and features required.
In conclusion, the choice of data enrichment tool depends on the specific needs of the business. By considering factors such as feature set, pricing, and scalability, businesses can choose the tool that best fits their requirements. InsideView, for example, is a good option for large enterprises that require advanced sales intelligence and marketing automation features. On the other hand, HubSpot CRM is a good option for small to medium-sized businesses that require a simple and affordable CRM solution.
Expert Insights and Industry Quotes
To gain a deeper understanding of data enrichment automation, it’s essential to hear from experts in the field. According to a report by Marketsandmarkets, the global market for AI in data enrichment, a key component of data enrichment automation, is projected to reach $5 billion by 2025, up from $2.5 billion in 2020. This significant growth highlights the increasing demand for accurate and comprehensive data insights.
Experts like Forrester analyst, Fiona Swerdlow, emphasize the importance of data enrichment automation in today’s data-driven world. In a recent report, Swerdlow noted that “data enrichment automation is no longer a nice-to-have, but a must-have for organizations that want to stay competitive.” This sentiment is echoed by other industry leaders, including Gartner, which predicts that by 2025, 70% of organizations will have implemented some form of data enrichment automation.
Expert Insights on Data Enrichment Automation Tools
When it comes to data enrichment automation tools, experts recommend using platforms like ZoomInfo and Clearbit. These tools offer advanced features like data validation, cleansing, and enrichment, which can help organizations improve the accuracy and completeness of their data. For example, ZoomInfo offers a range of data enrichment services, including company and contact data, as well as intent data and job change data.
In addition to these tools, experts also recommend using Apache Beam and Apache NiFi for data integration and processing. These open-source platforms offer a range of features and functionalities that can help organizations streamline their data enrichment processes and improve the overall quality of their data.
Best Practices for Implementing Data Enrichment Automation
When implementing data enrichment automation, experts recommend following best practices like:
- Defining clear goals and objectives for data enrichment automation
- Assessing the quality and completeness of existing data
- Choosing the right data enrichment automation tools and platforms
- Developing a comprehensive data governance strategy
- Monitoring and evaluating the effectiveness of data enrichment automation
By following these best practices, organizations can ensure that their data enrichment automation initiatives are successful and deliver the desired outcomes. As noted by IDC, organizations that implement data enrichment automation can expect to see significant improvements in data quality, as well as increased efficiency and productivity.
In terms of specific statistics, a study by Experian found that organizations that use data enrichment automation experience an average increase of 25% in data quality, as well as a 30% reduction in data-related costs. These findings highlight the significant benefits of data enrichment automation and the importance of implementing this technology in today’s data-driven world.
Future Trends and Developments in Data Enrichment Automation
Looking ahead, experts predict that data enrichment automation will continue to evolve and improve, with advancements in technologies like AI and machine learning. According to a report by ResearchAndMarkets, the global market for AI in data enrichment is expected to grow at a compound annual growth rate (CAGR) of 21.5% from 2020 to 2025.
In addition to these technological advancements, experts also predict that there will be increased focus on data governance and regulation, with the implementation of laws and regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). As a result, organizations will need to prioritize data quality and compliance, and ensure that their data enrichment automation initiatives are aligned with these regulatory requirements.
| Company | Data Enrichment Automation Tool | Features and Functionalities |
|---|---|---|
| ZoomInfo | ZoomInfo Platform | Company and contact data, intent data, job change data |
| Clearbit | Clearbit Platform | Company and contact data, firmographic data, technographic data |
By staying informed about the latest trends and developments in data enrichment automation, organizations can ensure that they are using the most effective and efficient technologies to achieve their data-driven goals. Whether it’s improving data quality, increasing efficiency, or driving business growth, data enrichment automation is a critical component of any successful data strategy.
Best Practices and Methodologies
When it comes to supercharging customer insights with advanced data enrichment automation techniques, best practices and methodologies play a crucial role in ensuring the success of such initiatives. Building on the tools discussed earlier, such as Salesforce and HubSpot, organizations can leverage data enrichment automation to gain a deeper understanding of their customers. According to a report by MarketsandMarkets, the global market for AI in data enrichment is projected to reach $5 billion by 2025, up from $2.5 billion in 2020.
This significant growth highlights the increasing need for accurate and comprehensive data insights. To capitalize on this trend, organizations must adopt best practices and methodologies that enable them to effectively leverage data enrichment automation. One such methodology is to focus on data quality, which is critical for ensuring that the insights generated are accurate and reliable. A study by Gartner found that poor data quality costs organizations an average of $12.9 million per year.
Key Principles of Data Enrichment Automation
To implement data enrichment automation effectively, organizations must adhere to several key principles. These include:
- Defining clear goals and objectives for the data enrichment automation initiative
- Developing a comprehensive data governance framework to ensure data quality and security
- Implementing a robust data integration strategy to combine data from multiple sources
- Utilizing advanced analytics and machine learning algorithms to generate insights
- Continuously monitoring and evaluating the performance of the data enrichment automation system
By following these principles, organizations can create a robust data enrichment automation framework that provides accurate and actionable insights. For example, LinkedIn uses data enrichment automation to enhance its customer data, resulting in a 25% increase in sales productivity.
Comparison of Data Enrichment Automation Tools
Several data enrichment automation tools are available in the market, each with its strengths and weaknesses. The following table provides a comparison of some popular tools:
| Tool | Key Features | Pricing | Best For | Rating |
|---|---|---|---|---|
| InsideView | Data enrichment, company profiling, contact data | $1,000 – $5,000 per month | Medium to large-sized businesses | 4.5/5 |
| Datanyze | Technographic data, company profiling, predictive analytics | $500 – $2,000 per month | Small to medium-sized businesses | 4.2/5 |
Detailed Listings of Data Enrichment Automation Tools
The following are detailed listings of some popular data enrichment automation tools:
1. InsideView
InsideView is a data enrichment automation tool that provides company profiling, contact data, and sales intelligence. It offers a range of features, including:
- Company profiles with detailed firmographic data
- Contact data with email addresses, phone numbers, and job titles
- Sales intelligence with predictive analytics and lead scoring
The pros of using InsideView include its comprehensive company profiles, accurate contact data, and robust sales intelligence. However, some users have reported that the tool can be expensive, especially for small businesses.
2. Datanyze
Datanyze is a data enrichment automation tool that provides technographic data, company profiling, and predictive analytics. It offers a range of features, including:
- Technographic data with information on technologies used by companies
- Company profiles with detailed firmographic data
- Predictive analytics with lead scoring and sales forecasting
The pros of using Datanyze include its comprehensive technographic data, accurate company profiles, and robust predictive analytics. However, some users have reported that the tool can be complex to use, especially for those without technical expertise.
In conclusion, data enrichment automation is a rapidly growing field that offers significant benefits for organizations seeking to gain a deeper understanding of their customers. By following best practices and methodologies, such as focusing on data quality and implementing a robust data integration strategy, organizations can create a robust data enrichment automation framework that provides accurate and actionable insights. By leveraging tools like InsideView and Datanyze, organizations can enhance their customer data, resulting in improved sales productivity and revenue growth.
Challenges and Future Developments
As we’ve explored the various aspects of data enrichment automation, it’s essential to acknowledge the challenges that come with implementing and utilizing these advanced techniques. Building on the tools discussed earlier, such as Salesforce and HubSpot, we’ll delve into the obstacles that businesses may face and the future developments that will shape the industry.
The global market for AI in data enrichment, a key component of data enrichment automation, is projected to reach $5 billion by 2025, up from $2.5 billion in 2020. This significant growth highlights the increasing need for accurate and comprehensive data insights. However, with this growth comes a set of challenges that businesses must address to maximize the potential of data enrichment automation.
Challenges in Data Enrichment Automation
One of the primary challenges in data enrichment automation is the issue of data quality. According to a study by Gartner, poor data quality costs businesses an average of $12.9 million per year. To overcome this challenge, businesses must invest in data validation and cleansing tools, such as Talend and Trifacta.
Another challenge is the integration of multiple data sources. A survey by Syncsort found that 70% of businesses struggle with integrating data from different sources. To address this challenge, businesses can utilize data integration tools, such as MuleSoft and Apache.
In addition to these challenges, businesses must also consider the issue of data security. A report by IBM found that the average cost of a data breach is $3.92 million. To mitigate this risk, businesses must invest in robust security measures, such as encryption and access controls, to protect their data.
Future Developments in Data Enrichment Automation
As the industry continues to evolve, we can expect to see significant advancements in data enrichment automation. One area of development is the use of machine learning and artificial intelligence to improve data quality and accuracy. For example, Google Cloud AI Platform provides a range of machine learning tools that can be used to automate data enrichment tasks.
Another area of development is the integration of data enrichment automation with other technologies, such as the Internet of Things (IoT) and blockchain. A study by IDC found that 60% of businesses plan to invest in IoT and data analytics in the next two years. By integrating data enrichment automation with these technologies, businesses can gain a more comprehensive understanding of their customers and operations.
The following are some of the key trends that will shape the future of data enrichment automation:
- Increased use of cloud-based data enrichment tools, such as Amazon Web Services and Microsoft Azure
- Greater emphasis on data security and compliance, with tools such as Varonis and SailPoint
- More widespread adoption of machine learning and artificial intelligence, with tools such as TensorFlow and PyTorch
To illustrate the benefits of data enrichment automation, let’s consider the example of Starbucks, which has implemented a data enrichment automation system to improve customer insights. The system uses machine learning and artificial intelligence to analyze customer data and provide personalized recommendations. As a result, Starbucks has seen a significant increase in customer engagement and sales.
The following table highlights the benefits of data enrichment automation:
| Benefit | Description |
|---|---|
| Improved Data Quality | Data enrichment automation helps to ensure that data is accurate and up-to-date, reducing errors and inconsistencies. |
| Increased Efficiency | Data enrichment automation automates many manual tasks, freeing up staff to focus on higher-value activities. |
| Enhanced Customer Insights | Data enrichment automation provides a more comprehensive understanding of customers, enabling businesses to make more informed decisions. |
In conclusion, data enrichment automation is a rapidly evolving field, driven by the increasing need for accurate and comprehensive data insights. While there are challenges to be addressed, the benefits of data enrichment automation are clear. By investing in the right tools and technologies, businesses can improve data quality, increase efficiency, and gain a more comprehensive understanding of their customers.
As Forrester notes, “Data enrichment automation is a critical component of any data strategy, enabling businesses to make more informed decisions and drive business growth.” By staying ahead of the curve and embracing the latest trends and technologies, businesses can unlock the full potential of data enrichment automation and achieve their goals.
Conclusion
In conclusion, supercharging your customer insights with advanced data enrichment automation techniques is no longer a luxury, but a necessity in today’s fast-paced business landscape. As we’ve seen throughout this blog post, the benefits of leveraging data enrichment automation are numerous, from improved data accuracy and completeness to enhanced customer experiences and increased revenue. With the global market for AI in data enrichment projected to reach $5 billion by 2025, it’s clear that this technology is revolutionizing the way businesses approach data insights.
Key Takeaways and Insights
The key to unlocking the full potential of data enrichment automation lies in understanding the current trends and insights from research data. As noted earlier, the significant impact of AI on data enrichment processes is driving growth in this field, with a projected twofold increase in market size from 2020 to 2025. By embracing this technology, businesses can stay ahead of the curve and reap the rewards of more accurate and comprehensive data insights.
To get started with data enrichment automation, consider the following actionable next steps:
- Assess your current data enrichment processes and identify areas for improvement
- Explore the various tools and software available for data enrichment, such as those mentioned on our website at www.superagi.com
- Develop a comprehensive strategy for implementing data enrichment automation, taking into account best practices and methodologies
As expert insights and industry quotes have shown, the future of data enrichment automation is bright, with immense potential for growth and innovation. By staying informed about the latest trends and developments, businesses can position themselves for success and stay ahead of the competition. To learn more about how to supercharge your customer insights with advanced data enrichment automation techniques, visit our website at www.superagi.com and discover the power of data-driven decision making.
In the words of industry experts, data enrichment automation is the key to unlocking deeper customer insights and driving business growth. Don’t miss out on this opportunity to revolutionize your approach to data insights – take the first step today and discover the transformative power of data enrichment automation for yourself.
