[et_pb_section fb_built=”1″ _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on” da_disable_devices=”off|off|off”][et_pb_row _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
In this day and age <\/span>there is a data overload<\/b>, and this makes it difficult to analyze your business\u2019 performance. Fortunately <\/span>data enrichment can turn an overwhelming dataset into actionable insights<\/b>.<\/span><\/p>\n We’ll go over the meaning of data enrichment, some use cases and how you can use AI to enhance your datasets. We’ll even uncover how you can implement Sheetgo to aggregate all your data.<\/span><\/p>\n [\/et_pb_text][et_pb_text module_id=”what” _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Data enrichment is a series of mechanisms to improve raw data<\/b>. These processes add value to the original dataset, making it easier to analyze and understand. Enriched data can help organizations gain deeper insights by offering a more comprehensive view of important information.<\/span><\/p>\n Data enrichment can involve: appending data, cleaning inconsistencies, updating information, and adding context. This process is crucial whether your database has very little information or is completely overwhelming.<\/span><\/p>\n [\/et_pb_text][et_pb_text module_id=”techniques” _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Data enrichment is a very broad concept that encompasses a wide range of techniques<\/b>. Below are some of the most important amongst these procedures.<\/span><\/p>\n By refining data through these methods, organizations can unlock deeper insights, make more informed decisions, and ultimately drive better outcomes. Nowadays information abounds, and this makes data enrichment increasingly important.<\/span><\/p>\n [\/et_pb_text][et_pb_text module_id=”use” _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n There are a lot of tried and tested data enrichment applications. Matter of fact, it’s likely that you already relied on data enrichment without knowing of its existence. Let\u2019s go over examples of data enrichment techniques and their applications.<\/span><\/p>\n A retail company collects their customers\u2019 names and email addresses through online sign-ups.<\/span><\/p>\n To enhance their marketing strategies, the company uses data enrichment to integrate additional data from external sources to include demographic information (age, gender, and income levels), and purchasing behaviors.<\/span><\/p>\n This enriched data allows the company to create more personalized marketing campaigns, segment their audience more effectively, and improve customer engagement by targeting their communications based on specific customer interests and demographics.<\/span><\/p>\n A real estate agency has lots of listings of different properties up for sale. Each one has basic property information like location, size and number of rooms.<\/span><\/p>\n The company enriches property data to provide better estimates and to improve their sale strategy for each listing. They add layers of information regarding neighborhood crime rates, school district ratings, and historical property values.<\/span><\/p>\n This enriched data helps buyers make more informed decisions and enables real estate professionals to adjust their sales strategies.<\/span><\/p>\n A bank needs to know how much credit they can offer each customer.<\/span><\/p>\n The financial institution enriches customer data with external credit scores, employment history, and transaction patterns to assess the risk of lending. This process includes integrating data from lots of sources: credit bureaus, public records, and other financial institutions.<\/span><\/p>\n This enriched data empowers financial organizations to make more accurate risk assessments, tailor their loan offerings, and reduce the likelihood of defaults. In doing so, they protect both their interests and those of their customers.<\/span><\/p>\n [\/et_pb_text][et_pb_text module_id=”how” _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Data enrichment and AI are a great match. Obviously, there is dedicated software for data enrichment, but AI lets you do a lot without breaking the bank.<\/span><\/p>\n Let\u2019s look at a few applications of AI you could start using today. These prompts are all centered around the dataset shown below, and they are merely a reference of potential uses.<\/span><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2024\/03\/harnessing_ai_for_data_enrichment_1.webp” alt=”ai for data enrichment 1″ title_text=”harnessing_ai_for_data_enrichment_1″ align=”center” _builder_version=”4.24.1″ _module_preset=”default” width=”100%” custom_margin=”30px||30px||true|false” border_radii=”on|12px|12px|12px|12px” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n We could feed a list of customer emails into Chat GPT and have it extract useful information. For this example we asked for the three easiest data points to extract: first name, last name and company name.\u00a0<\/span><\/p>\n [\/et_pb_text][et_pb_text module_class=”spreadsheet-function” _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Based on the email column from this spreadsheet, extract the following entities and include them in new columns: First name, Last name, Company the person works for (if no company can be extracted leave blank, if it’s a generic domain leave blank).<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n The results give us a first name to send a personalized email. They also provide a company name, which we could use to see how many purchases a certain business has made. <\/span><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2024\/03\/harnessing_ai_for_data_enrichment_2.webp” alt=”ai for data enrichment 2″ title_text=”harnessing_ai_for_data_enrichment_2″ align=”center” _builder_version=”4.24.1″ _module_preset=”default” width=”100%” custom_margin=”30px||30px||true|false” border_radii=”on|12px|12px|12px|12px” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n On the same dataset, you could have ChatGPT create and apply categories for existing products.<\/span><\/p>\n [\/et_pb_text][et_pb_text module_class=”spreadsheet-function” _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Give the products a broad category (Column “Department”) and a specific category (Column “Category”). For example, the broad category for Echo Speakers is “electronics” and the specific category is “audio”. Export as a downloadable tsv file. If you don’t know how to categorize a certain item ask me one item at a time, ideally give me 2-4 options to choose from.<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Now that we have categorized items we can analyze our customer preferences and spot opportunities for improvement. For example, based on this information we could decide to dedicate more space to wearable electronics.<\/span><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2024\/03\/harnessing_ai_for_data_enrichment_3.webp” alt=”ai for data enrichment 3″ title_text=”harnessing_ai_for_data_enrichment_3″ align=”center” _builder_version=”4.24.1″ _module_preset=”default” width=”100%” custom_margin=”30px||30px||true|false” border_radii=”on|12px|12px|12px|12px” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Finally, this spreadsheet has a really messy date column, all of its values are expressed differently. We can ask ChatGPT to apply a single date format across the board.<\/span><\/p>\n [\/et_pb_text][et_pb_text module_class=”spreadsheet-function” _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Having corrected the date format, we can use that data to analyze buying patterns across days of the week or times of the month. This could be a great indicator of when to put items up for sale and increase purchases.<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Having corrected the date format, we can use that data to analyze buying patterns across days of the week or times of the month. This could be a great indicator of when to put items up for sale and increase purchases.<\/span><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2024\/03\/harnessing_ai_for_data_enrichment_4.webp” alt=”ai for data enrichment 4″ title_text=”harnessing_ai_for_data_enrichment_4″ align=”center” _builder_version=”4.24.1″ _module_preset=”default” width=”100%” custom_margin=”30px||30px||true|false” border_radii=”on|12px|12px|12px|12px” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.24.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Humble AI offers powerful data enrichment tools, they are all accessible through the website.<\/span><\/p>\n These are just some of the tools available on the platform:<\/span><\/p>\nWhat is data enrichment?<\/span><\/h2>\n
Data enrichment techniques<\/span><\/h2>\n
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Data enrichment use cases<\/span><\/h2>\n
Customer Data Enrichment in Marketing<\/span><\/h3>\n
Real Estate Market Analysis<\/span><\/h3>\n
Financial Risk Assessment<\/span><\/h3>\n
How AI fits into all of this<\/span><\/h2>\n
ChatGPT for data enrichment<\/span><\/h3>\n
Extract information from emails<\/span><\/h4>\n
Categorize information<\/span><\/h4>\n
Correct date format<\/span><\/h4>\n
Humble AI<\/span><\/h3>\n
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