The 5 Biggest Challenges of Sourcing External Data
Organizations today understand the value of data. Most use their internal data to drive important decision-making processes. Many are catching on to the value of external data and understanding how leveraging it can create a competitive advantage. Historical data is not sufficient to drive accurate predictions. By enriching their own data with external data (also known as alternative data or third-party data), companies can build more accurate predictive models to improve decision-making processes.
There are many benefits of leveraging external data including lower costs, increased sales, and operational efficiency.
Sourcing external data can be tough. With the volume of big data available, it is hard for organizations to know what type of external data to look for, and where to find it. Data marketplaces provide the platform to purchase data, but they don’t help users understand what type of data is needed for their use cases or business problems. It can also be difficult to ensure data quality, and understand what impact datasets will have on predictive models prior to purchasing them. Ultimately, it is challenging to glean valuable insights from the amount of data available. Read on to learn more about the challenges of sourcing external data.
Challenge #1 – Managing multiple data providers
It is costly, time-consuming, and an administrative burden to maintain contracts with various data vendors and data coming from multiple external sources. Thousands of data products can be obtained through a multitude of channels—including data brokers, data aggregators, and data analytics platforms—all of which accommodate different types of models and use cases.
Sourcing external data through multiple contracts with separate vendors means each contract has to be managed independently and therefore does not present opportunities for economies of scale. It is difficult to know if a dataset will bring value to an organization or if it will boost a predictive model’s accuracy prior to purchasing it. Not knowing the ROI of a new data purchase can lead to wasted time and money if the dataset doesn’t deliver the intended results. Including administration or subscription fees, these types of purchases can easily cost hundreds of thousands of dollars. On top of that, some vendors demand payment in the form of a share in any revenue derived from the data—a tricky metric especially if the data is being used for AI or analytics.
Challenge #2 – Data Preparation, Matching, and Enrichment
The purchase of a dataset is just the beginning of the external data sourcing process. After a new dataset is acquired, it needs to be prepared, matched, enriched, and integrated with an organization’s internal data which requires an abundance of resources including different tools, platforms, and data science teams.
Data preparation is an important step before analysis, but it’s a lengthy process often involving reformatting and correcting data.
After data preparation, the next step is data matching, which is the task of identifying and assigning two seemingly different records as the same across multiple data sources. This also leverages the data enrichment process where third-party data is merged with existing internal datasets.
This process allows the organization to have clear and accurate data records, along with a more complete view of each entity. However, the financial and time cost of performing these tasks adds up, even when organizations purchase the exact type of data they need.
Challenge #3 – Data Recency
Data can get stale or outdated quickly. When working with multiple sources or vendors, it’s difficult to ensure that datasets are regularly kept up to date. For data-driven decision-making, high-quality, frequently updated data is essential. Invalid or inaccurate data leads to faulty analysis, leading to potential losses.
Once an organization identifies valuable external data sources, those sources must be kept current. Some use cases will require data to be updated in real-time, which not all vendors can guarantee.
Challenge #4 – Governance and Compliance
Data compliance goes hand in hand with data integrity. Purchasing external data often includes demographic data that contains personally identifiable information and other sensitive information, which must be protected.
Purchased datasets from third-party vendors need to be managed carefully to comply with data usage policies such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Or else organizations can face heavy fines or even bans. Organizations must ensure that data vendors are providing compliant data. This is also further complicated when managing multiple external data sources and vendors.
Some questions to consider are:
- Do your vendors have strict validation and verification processes in place?
- Are they protected against malware or cybersecurity threats?
- What kind of data security strategy do they have in place?
Data governance is also important. The goal of data governance is to create an environment to effectively use data to gain insight into business processes. Without proper governance, the data fails to meet regulations and quality standards, and risks being exposed to security threats.
Challenge #5 – Monitoring and Auditing Usage
Consistent monitoring and auditing the usage of external datasets is required, not only for compliance, but also for understanding what is being used and how.
Auditing data essentially involves tracking and understanding how each of your records is used, monitoring each interaction with the data, and logging it to an audit trail.
If one part of the organization has not recognized the value of external data or isn’t using it, this will be revealed in the usage patterns. This can also help organizations collaborate around the best ways to get value from external data.
When it’s time to renew or renegotiate subsequent purchases, usage data will be critical. Then, where the costs of data purchases are shared across departments, usage data can be used to allocate costs among multiple cost centers.
The solution: Adopt a platform to encourage the use of external data
An external data platform addresses the challenges associated with acquiring external data via ad hoc purchases or managing multiple different subscriptions. It enables data access and acts as a centralized repository of external data signals. It not only supplies you with more data but the right data signals to enrich your internal data and boost your analytics and predictive machine learning models. An external data platform handles matching, merging, and integrating external and internal data. Once external data is incorporated into a predictive model, the platform will identify and measure how the new signals have improved the model.
Overall, an external data platform can help encourage the use of external data to improve your bottom line.
Learn more about the requirements of an external data platform by downloading our eBook “5 Key Requirements of an External Data Platform”.
Explorium provides the first External Data Platform to improve data analytics and machine learning. Explorium enables the automation of data discovery to improve predictive ML model performance. Explorium External Data Platform empowers data scientists and analysts to acquire and integrate relevant external data signals 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.