Data is a necessary component of modern organizations, revealing how consumers behave, how they think, and what they want. It can also help organizations mitigate risk and improve operational efficiency. Data management is essential, yet many companies suffer from ‘Not Invented Here Syndrome’, focusing solely on data generated internally rather than looking to leverage external data sources.
Internal data is valuable, but the insights it provides are limited. It might provide an understanding of how a company's existing customers behave—but this doesn’t necessarily reflect how their entire target audience behaves. Integrating internal data with external datasets provides richer insights, increases machine learning model accuracy, and boosts advanced analytics. According to McKinsey, “A well-structured plan for using external data can provide a competitive edge.”
This article will outline why relevant external data is so important, dig into the benefits that it can offer your organization, and explore the ways in which it is beneficial to use an external data platform.
Data points that are collected from outside of an organization are considered external data (also known as alternative or third-party data). Internal data, on the other hand. is collected by organizations in their operations and transactions.
Some examples of internal data:
Some examples of external data:
External data platforms provide data access and act as a centralized repository of external data signals. The right data signals will have a tangible impact on data analytics and machine learning model performance. An external data platform, unlike a data marketplace, should not only supply you with more data, but also offer up the right data types, automated data discovery, and the data science platform to integrate the new data and train predictive models. Using machine learning for the automation of the data discovery process can save data analysts, data scientists, and organizations a lot of time.
Using an external data platform to incorporate third-party data in predictive models can lead to an accelerated competitive advantage, increased operational efficiency, and reduced risk. This blog post will go into more detail about how marketers, business analysts, and data scientists, across a wide variety of industries and use cases, can benefit from using an external data platform. An external data platform is an essential tool to leverage big data.
By enriching internal data with external data signals, marketers can provide prospects and customers with more personalized experiences. Customizing the user experience and targeting cross-sell promotions based on insights about which customers are more likely to respond to which offers and discounts can boost conversions. Understanding which customers are worth focusing marketing efforts such as ad retargeting campaigns on boosts revenue.
External data helps organizations create better customer experiences. External data integration with internal data helps organizations create more nuanced customer profiles and more accurate customer segmentation. This allows organizations to personalize customer interactions.
Most customers now expect personalized experiences. Companies that get this right are more likely to get customers to purchase their products or services. Personalization helps organizations attract and retain a steady stream of happy, repeat customers. It can also lead to word-of-mouth advocacy—with existing customers turning into a brand’s biggest promoters.
Using the right data is essential in building more accurate lead scoring models. Better lead scoring makes the sales process more efficient by correctly predicting each lead’s propensity to buy, and focusing efforts on targeting customers who are most likely to convert.
B2B lending can be risky when dealing with newer small to medium size businesses (SMBs) that aren't able to provide enough information to make informed lending decisions. External data augments the ability to build more accurate fraud and risk models, which can protect online lenders from unnecessary risk. More accurate loan default risk models can help lenders gauge which potential borrowers are more likely to default on loans. Lending application fraud risk models can help lenders validate potential borrowers' identities and flag fraudulent loan applications.
Obtaining external data is only one step in the entire data acquisition process. An external data platform should enable every step of the process from data discovery, data access, data integration, and predictive model training and deployment. When choosing an external data platform, there are a few key considerations to keep in mind.
First, you need to ensure that it provides you access to a wide variety of relevant external data. The value of external data lies in the fact that it helps close the knowledge gaps of limited internal datasets. An external data platform should offer a comprehensive array of data types and sources (such as public, premium, and proprietary).
The external data also needs to be in a matching format to your internal data, or you will have to spend a long time on data preparation. An external data platform should have built-in capabilities to recommend, search, automatically match, and integrate relevant external signals with your internal data. It should also help build data pipelines that can be used in business intelligence and analytical processes.
Security is also a major factor in considering an external data platform. It is important that new external data is compliant with all necessary data privacy regulations. Not all external data providers are GDPR and CCPA compliant.
In summary, an external data platform should offer the following capabilities:
The right external data platform will possess these key features, allowing you to centralize access to external data. Streamlining your data discovery and utilization will save time, money, and resources.
Companies are scrambling to use data as effectively as possible. They are aiming to leverage machine learning models to gain deeper insights into their customers, and to carve out an unsurpassable advantage over their competitors. But, they cannot do this with internal data alone. Organizations must look beyond their four walls and seek out the external data that will help them build more accurate predictive models. Doing this without the help of an external data platform can be a long and expensive process. Companies must purposefully and systematically integrate up-to-date external data sources with their internal data to get the richest insights. Using an external data platform will expedite this process and help improve customer experiences, boost conversion rates, and build agile data-driven strategies.
To find out more about Explorium’s innovative external data platform and the value it can provide for your business, download our white paper, ‘The Business Value and Benefits of External Data Platforms’.
Learn more about why Explorium included in the list of Cool Vendors in AI Core Technologies by Gartner.