How To Power Your BI and ML with External Data, Pain Free
Companies today understand the value of external data and the need to look beyond their four walls to get the data required for accurate predictive models. This is especially true after the events of the past year, where the COVID-19 pandemic rendered internal data inadequate in predicting future trends. McKinsey & Co. noted: “In a few short months, consumer purchasing habits, activities, and digital behavior changed dramatically, making preexisting consumer research, forecasts and predictive models obsolete. Moreover, as organizations scrambled to understand these changing patterns, they discovered little of use in their internal data.” According to Deloitte “Companies are increasingly seeking better insights by tapping into third-party data. Outside data can bring lots of opportunity, but using it effectively can be challenging.”
Using external data to augment decision making helps organizations better meet their customers’ needs by predicting customer preferences and demand trends. A 2018 MIT Sloan Management Review data and analytics report found that the most analytically mature organizations use external data sources from customers, vendors, regulators, and competitors. Using external data improves a company’s predictive analytics, customer analytics, strategy analysis, and forecasting. It can help predict sales, guide product development, and assess customer sentiment.
Acquiring the Right External Data Can Be Painful
As business conditions evolve and new regulations restrict access to crucial sources of information, businesses are looking outside the organization for data to support machine learning and analytics. Despite the benefits of external data, there are several challenges related to acquiring it and integrating it with internal data. We recently commissioned a study, “2021 State of External Data Acquisition,” where we found out that enterprises understand the need for external data but lack a clear strategy for obtaining it.
The challenges with external data can be summarized into three main pain points:
Hard to Access:
- Too many sources (geospatial, satellite, private business, consumer, web/app-harvested, weather, news, IP, legal, public, and industry specific)
- Organizations don’t know what to look for.
- Procurement can take months
Tedious to Use:
- Not easily consumed for machine learning and business intelligence.
- Matching with internal data is painful.
- Organizing external data for any analytical process takes time.
- It is not easy to determine the ROI of data purchases prior to purchasing.
- Data collected from public sources poses compliance risk.
- Data quickly gets stale.
- Signal loss (data sources stop becoming useful due to events such as the pandemic)
Unlocking the Potential of External Data
It is evident that organizations need a data acquisition strategy that makes it easy to access and use third-party data, and a single platform that can simplify the process which ensures results and compliance. They need a platform that can discover the ‘unknowns’ and provide immediate access to external data and relevant insights. This is the solution that Explorium’s External Data Platform provides. The unique all-in-one platform automatically discovers all relevant data signals, and seamlessly integrates them with internal data.
To learn more about the challenges associated with leveraging external data, and how to use Explorium to overcome them, watch our most recent webinar “Power your BI and ML with External Data Pain Free”.