The enhancement of unstructured data is the new buzz in the industry. When it aligns with the company’s vision, mission, and objectives, it amplifies the business growth. By leveraging data enhancement for strategic planning and decisions making, companies achieve their goals more precisely. To increase accuracy and accountability in making decisions, data enhancement has become an integral part of business.
In this blog, we will understand the impact of data enhancement on businesses by gleaning valuable insights and delving into every nook and corner of data enhancement and its importance in improving operations and meeting business objectives.
Table of Contents
Definition of Data Enhancement:
Data enhancement is a process of elevating the existing data by providing context to it, which helps in creating more precise and valid contact databases. The filtration of unstructured information improves the quality of data to make it more understandable; this is known as data enrichment. Data enrichment eliminates redundant or unnecessary data, resulting in a more robust marketing campaign and improved data quality.
On the other hand, data append is adding additional relevant information or other variables to existing data records, increasing the volume of data assets. Parameters can be entirely consumer-centric, like demographics, geographics, or firmographic, such as industry type, number of employees, turnover, business age, job titles, etc.
Performing both data enrichment and data append is crucial for a successful data enhancement process, guaranteeing optimal data performance and effectiveness.
The Process of Data Enhancement:
Data enhancement is a continuous revolving cycle of upgrading and updating your organization existing database. A company’s data is found chiefly as an unstructured pattern, and to turn that into useful information and maximize its value, data enhancement is the key. The process generally involves both data enrichment and appending to elevate existing data structures to support ultimate business goals. Here, we will be discussing the data enrichment process in two ways:
Process one:
Simply put, when your data enhancement is catering your business goals, it becomes highly necessary to maintain data quality. Data needs to be approachable in any organization, and creating structured data can be time-consuming and involves complex methods. The process involves a few simple steps:
- Accumulate unstructured and uncategorized internal data.
- Proceed with data cleansing by removing unnecessary or repeated records to increase ROI.
- Categorize the data by following different attributes aligned with the business objectives.
- Search for missing information in the database.
- Get the missing records filled with additional data.
- Make a deal with a third-party data provider and enhance the data with relevant information.
Process two:
Sometimes, data enhancement requires customized attention. When marketers need information to meet their campaign goals, existing data may not be sufficient to cater to large-scale marketing operations. When a situation arises, you need to follow another set of processes to enhance the data that attain your need.
- Define the campaign goal & segment the market for the campaign.
- Identify the attributes that will help achieve campaign goals.
- Start with enriching your internal data source.
- Collaborate with external agencies to acquire relevant data.
- Appended data will provide volume and quality in your existing data.
- Extract valuable insights from the enhanced data for the campaign.
Role of Data Enhancement:
The cluster of gigantic unstructured data is making information more complex than ever to understand. Creating a simpler form of data set to produce reports and utilizing it for campaigns has become a priority. By enhancing your in-house data, you will be able to eliminate the chaos of invalid attributes from your assets. This, in turn, will aid the development of a comprehensive business strategy that will be effective and efficient, getting you the results you want.
Now, let’s explore the domains where role of data enhancement has become more essential.
Direct Marketing:
In the business spectrum, B2B companies always seek enriched and accurate information to make strategic decisions. Data enhancement helps businesses to customize data to target niche markets for campaigns by enabling them to access necessary information in one click. Enhancing the database also saves advertising funds by avoiding unnecessary marketing attempts targeting the wrong demographic. So, saving money and increase return on investment (ROI) – it’s a win-win situation for everyone. Based on Marketing Charts survey 47% marketers agreed that data-driven personalized marketing helps in meeting the marketing objectives.
Product Data Classification:
Data enhancement shaped the product marketing area as well. Demographic, geographic, and firmographic segmentation of data provides more clarity on how to market a product or service to influence a buyer’s journey.
Effective product data management is crucial in online shopping portals to improve the user experience. For example, the filtration of products allows users to land on products they are searching for. A perfect filter requires a structure with perfectly categorized data sets to enhance the user experience on the portal. With a proper data enhancement process, businesses can ensure that all the suitable information related to products is inserted in the portal so that it helps users to filter according to their needs. It enhances the user satisfaction parameter and conversion rate.
Machine Learning:
Artificial intelligence is primarily an automated tool that functions based on what data has been inserted into it. Feeding AI with the right kind of data boost functionality, providing humans with more constructive and precise information. With the augmentation of AI-powered tools, a machine needs to learn more, and for that reason, computers need a large scale of data to be inserted for its advancement. And data enrichment and appending processes are required to improve the raw and unstructured data sets, guaranteeing progress in AI learning. One of reputed market analysis report stated that the global AI training dataset market is forecasted to expand at a CAGR of 22.1% from 2023 to 2030.
Importance & Benefits of Data Enhancement:
Data enhancement is an effective tool to empower your business in multiple ways. As it helps in decluttering clustered data sets for businesses, it also provides a wider and clear vision to understand information. Enhancing data helps in acquiring insights on potential clients that you otherwise wouldn’t be able to get. Your business can effectively target the ideal customer segment with the latest marketing techniques through data-driven insights. Likewise, there are plenty of benefits that make data enhancement a powerful method to help firms in business growth.
Saves Time:
By enhancing data, businesses can save a lot of hours of operations. Whether it is survey or marketing data, capturing data about your prospects organically from all platforms is quite an exhaustive process to be carried out in less time. Spending hours searching, extracting, validating, and segmenting internal and external records tailored to the campaign requirement will not be needed anymore. When enhanced data is in place, it becomes convenient to pull out information adhering to business goals immediately and precisely, saving a lot of business hours.
Saves Money:
Sending communication through any channels to an unfiltered record costs you more as it is not targeted towards your potential clients. It decreases the overall engagement rate of campaigns, resulting ineffectiveness of your marketing strategy. However, data appending and enrichment help verify the information and exclude unnecessary data from the list providing the business with accurate and responsive data sets. Now with the newly customized and targeted data, you’re less likely to send communications to prospects via any means that won’t yield a response. This saves valuable dollars that can then be directed toward other business needs.
Record Maximization:
Data enhancement process is not only about refreshing your current record by replacing them. But also provides context to the existing data by enhancing and adding new parameters or filling out empty fields. Which, in turn, helps the business strategy to meet objectives and beyond.
Potential Growth:
Every business in the world is expecting growth in the market, whether it be through unveiling a new product line or expanding the business into a new region – the reason can be anything. To support the new business initiatives, every panel of the leadership team needs to analyze data to reach informed decisions. And to back business growth, data enhancement plays a significant role. Enhancing first party data allows you to target new markets with demographics and geographic information without building a new contact list. With the power of insider data, you may get a better insight into the target segment and start right away rather than gradually expanding your network in that sector.
Know Your Audience:
Connecting your brand with your customer has a superficial effect on business growth. Often, a business fails to understand the influential factors that trigger the customers buying decisions. That happened mainly because of not understanding what really drives them to create a connection with your brand.
Also, by providing your audience with personalized communications, you may enhance the customer experience throughout their entire journey. By understanding the customers’ preferences, you’ll be able to produce effective marketing communications that are pertinent and worthwhile. Data enhancement will be beneficial to understand the parameters to personalize your content which will make the business reach the heights of success.
Challenges Associated with Data Enhancement:
The introduction of big data in the industry multifold the importance of data enhancement. As the large scale of data has become challenging to manage, enhancement of data helps solve the problem by enhancing the unstructured data tailored to the business needs. However, data enhancement has its own setbacks to address, even though it solves many challenges in other data fields.
One of the biggest obstacles to data enhancement is TIME. It’s true that a company can undoubtedly saves a considerable amount of time after enhancing the data. But the extensive process of data segregation and adding new data sets consumes a massive chunk of time. But the integration of automated tools is making data enrichment less challenging. As per Statista report, the AI processor for cloud and data centers is expected to touch the $38 billion industry by 2026, which reflects the potential adoption of AI processors in the data processing industry.
Also, knowing what information to add to data sets can be strenuous, which is another problem with data improvement. Using data mining techniques to find patterns and trends in the record that can be used to direct the enrichment process is one way to overcome this problem.
Conclusion:
The importance of data enhancement has significantly increased in recent years with the implementation of big data in the decision-making process. 57% of businesses claimed that they use data-driven insights to make informed decisions. As we could see, data enrichment and data appending are two pillars of data enhancement and these processes can help any organization in streamlining the database usage. So with an improved and valuable data structure, any business can compete in the marketplace, enabling companies to grow and increase profitability.
