10 Questions to Ask Before Buying External Data
The value of external data might be apparent, but are you ready to purchase it?
Data is the key to solving analytical problems. Just as organizations seek out new revenue streams and new customers, they must seek out new data. Relevant data enables better data analytics, predictive models, and the ability to make more accurate predictions. It is important for organizations to develop a data strategy, and seek out external data sources to enrich their internal data. They must look beyond their four walls and seek external datasets that will improve their business processes. Proper data acquisition and data management provide a competitive edge.
In order to acquire the external data that is the most relevant to a business problem or use case, organizations need to devise comprehensive data strategies that not only focus on acquisition, but also on integration with their existing internal data.
Our latest eBook, ‘10 Questions to Ask Before Buying External (Alternative) Data’, proposes 10 questions that you should ask before beginning your external data acquisition journey. In this blog post, we will briefly explore each of these 10 questions, outlining what your organization should be looking for and the reference materials that can help you answer these questions.
1) What data do we need?
The first step is to define the specific problems that you are trying to solve. Perhaps you need better, or complementary data to answer a particular question. Maybe you’re struggling to fine-tune an existing model to improve the accuracy of your predictions. The first step is to look for relevant data that will uncover insights. External data such as foot traffic, social media presence, pricing, demographic data, and weather data can help improve predictive models for a variety of use cases.
2) Where to get the data from?
Once you have an idea of the new data you want to acquire, the next step is to choose where to purchase it. Finding the right external data presents a new set of challenges. There are many different data types to choose from a wide variety of data providers and data marketplaces. Selecting the right data providers can be time-consuming – there is a lot of available data out there to purchase.
3) How to validate the quality of data?
Once you find the data providers that you want to purchase new data from, it is essential to validate their data quality, coverage, gaps, recency, frequency of updates, risks, and relevance to your particular use case.
4) How to evaluate the impact of data on analytics and machine learning (ML)?
You must determine the ROI of purchasing a dataset by examining the use case, expected outcomes, and cost of acquisition. In many cases, you cannot determine the value of a data source until you use it in a real-life scenario.
To combat this issue, you must first understand the uplift in machine learning models and feature explainability before deploying your predictive process.
5)Who is going to use the data?
There are many uses cases for external data, and different types of users (marketers, sales teams, operations managers, business analysts, data scientists). Users will have varying skill sets and needs. You need to understand their skill sets and requirements in order to provide them with data, in the right format, that they can effectively evaluate and consume. The data needs to fit and work within the organization’s data ecosystem.
6) How to prepare the data for consumption?
When acquiring data from an external database, it is often not consumption-ready. The data preparation process includes data cleaning, data transformation, and configuring data pipelines for data consumption.
7) How to match and integrate data?
External data must be matched with your internal data, or training data sets, before it can be put to use. Matching data files can be a complex, resource-intensive process.
If you purchase data from a data marketplace, it will likely need to be reformatted to match your internal data. Another option is to use an external data platform, like Explorium’s, which will automatically match and integrate data signals to your own data. This saves your business precious time and resources.
8) How to use this data for analytics and machine learning?
Once you have acquired, prepared, and integrated your external data, the next step is to evaluate how you are going to consume the data within your analytics and machine learning platforms. There are a number of connection options that you should check. Ideally, you need to be able to access the data via Excel, CSV export, or API integration, and have connection capabilities with storage tools such as AWS S3 and Snowflake.
It’s also worth planning for connections to platforms and applications like Google Big Query, Salesforce, and Microsoft Azure.
9) How frequently will this data be refreshed?
Given your use case and its data signal requirements, you need to determine how often to refresh the data. Predictive model performance (or drift) should be continuously monitored to ensure the model is still performing according to your business expectations and requirements.
Having the right data onboarding frequency will keep your data current, accurate, and relevant. If you ever run into signal loss (such as loss of third-party cookies) then immediately seek out alternative data sources to protect your business continuity.
10) What are the privacy, security, and compliance requirements?
External data management is harder than ever thanks to privacy regulations like GDPR and CCPA. When acquiring data that comes from external databases, it must be checked for compliance.
Working with fully compliant data will help you realize your data analytics, business intelligence, and machine learning ambitions faster.
Data acquisition with an external data platform
Having the right tools and technology in place will have a massive impact on the success of your external data acquisition journey. An external data platform not only provides data access, but also enables every step of the process from data discovery, acquisition, and integration to predictive model training and deployment. To read more about the 10 questions outlined above, and to find out how Explorium’s External Data Platform can help, read the eBook in full.
Explorium provides the first External Data Platform to improve Analytics and Machine Learning. Explorium enables organizations to automatically discover and use thousands of relevant data signals to improve predictions and ML model performance. Explorium External Data Platform empowers data scientists and analysts to acquire and integrate third-party data efficiently, cost-effectively, and in compliance with regulations. With faster, better insights from their models, organizations across fintech, insurance, consumer goods, retail, and e-commerce can increase revenue, streamline operations and reduce risks. Learn more at www.explorium.ai