Guide to Managing Third-Party Data

In our previous blog, we explored the importance of third-party for modern businesses, as well as the various types of data. In this blog, we will guide you through the steps necessary to manage such external, third-party data.

Third-party data can be a major challenge for IT professionals when it comes to handling hybrid data environments. The crux of this challenge is the need to trust third-party data in such an environment. It is critical to transform any third-party data to meet data quality standards of in-house data so that it can be accepted and used as a reliable data asset. This applies even to tiny percentages of data that come to you from external sources.

In order for data to be of a suitable quality level that will serve your organization, it needs to be complete, accurate, and highly available. When your organization has access to data that meets these criteria, it can drastically lower operation costs. This is due to improved efficiency, mitigating the risks of bad data and elevating both customer and employee confidence.

Four steps are necessary to manage third-party data in hybrid environments:

1. Collection of External Data

External, third-party data is available from a number of sources and common formats for external data are JSON or data files. While JSON is semi-structured data, data files are structured data. Data files necessitate going to the site and downloading the data. Perhaps your data source is D&B – in this case, you can buy the data and use a REST API to programmatically call the data. APIs are easy to access – all you need is a subscription and a token. An alternative way to get third-party data is batch data from sources where data is published periodically, perhaps once a month.

Another source is collecting real-time streaming data such as live updates on the news and social media regarding a situation such as a hurricane or some other event that impacts business. Web scraping that scrapes websites for data provides data in an unstructured .doc format.

Regardless of its source, external, third-party data can be analyzed to provide unique insights into opportunities and trends for your business. Companies must have a robust data collection strategy in place to reap value from this data.

2. Data Preparation aka Data Harmonization

Once the external data has been collected, it must be prepared or harmonized. This is necessary as the data is in various formats that are unsuitable for the business database and analysis. The first step is AI-based scraping. This requires specifying relevant data to be extracted by AI.

After that data indexing is necessary to format the data appropriately. This is followed by data blending which standardizes the data into a consistent format. When data is in a standardized format, the next step is data enrichment which adds any missing details to the data, then data catalogue which generates a header for the data and lastly data security which protects sensitive information.

3. Maintaining Data – Data Governance & Cataloging

After preparation or harmonization, it is imperative to maintain the data. This typically  involves cataloging the data to provide a clear list of what data is available. It also entails policies and rules for governance of the data catalog and rules for accessing it.

4. Monitoring the Data

Remember that data preparation and maintenance do not serve much purpose without the crucial step of active data monitoring. You must regularly monitor data throughout its lifecycle in order to ensure that it stays both accurate and trustworthy. Your team that oversees this aspect must have capabilities to quickly evaluate data health and resolve any issues that arise with the data. Early action is imperative as it can nip problems in the bud before the spread and cause expensive problems.

This agile stance that can respond quickly to any data issues depends on having the right technology to support your team. You need a solution that provides monitoring abilities with automated workflows, while ensuring pre-built data rules that can be customized. It must be able to handle both real-time streaming and batch requirements.

How to Consume This Data

Once external data has gone through the aforementioned steps, it is ready for consumption by your business users. You have the option of setting up various models for the consumption of data. User could consume data in the manner of a “Data Buffet” that is self-serviceable using an AI bot and conversational natural language. This is like providing an Amazon store of data – all a user has to do is search and find what suits their requirement. Another possibility is to create a Data API studio where the relevant data is requested by API. Users can then use this data for their analytics and reap actionable insights to drive greater business value. The advantage of these modes of consumption is that any user can access data with no dependency on data engineers.

The Importance of the Right Data Solution

Picking the right solution is crucial for data migration, modernization and improving the quality of third-party data. DataSwitch is a trusted partner for cost-effective, accelerated solutions for digital data transformation, migration and modernization through a Modern Database Platform. Our no code and low code solutions along with enhanced automation, cloud data expertise and unique, automated schema generation accelerates time to market. It is faster, highly cost-effective, eliminates error-prone manual effort and completes the project in half the typical time frame. We can help you prepare, maintain and monitor your data to ensure that third-party data is trustworthy.

DataSwitch’s DS Migrate provides Intuitive, Predictive and Self-Serviceable Schema redesign from traditional model to Modern Model with built-in best practices, as well as fully automated data migration & transformation based on redesigned schema and no-touch code conversion from legacy data scripts to a modern equivalent. DataSwitch’s DS Integrate provides self-serviceable, business-user-friendly, metadata based services, providing AI/ML driven data aggregation and integration of Poly Structure data including unstructured data. It consolidates and integrates data for domain specific data applications (PIM, Supply Chain Data Aggregation, etc.). DataSwitch’ s DS Democratize also provides intuitive, no code, self-serviceable, conversational AI Driven “Data as a Service” and is intended for various data and analytics consumption by leveraging next gen technologies like Micro Services, Containers and Kubernetes. DS Integrate handles data preparation and DS Democratize provides Knowledge Services. DS Democratize’s Data Buffet enables easy consumption of data.

An automated data and application modernization platform and toolkit minimize the risks and challenges in your digital transformation. Book a demo to know more.