Use AI to Harvest More Data and Identify Insights Faster

"We need to buy more data," has been a presumptive, knee-jerk reaction to data management challenges over the past 30 years. Organizations spend millions of dollars gathering as much data as possible to fill their data-source gaps. The hope is to have enough data to fuel the insight needed to improve the customer experience. Yet, the cost and complexity of having the required number of data sources typically outweighs any organization's budget. Many times, the data quality is inaccurate and or becomes stale.

While there may always be a place for purchasing data from data vendors, we believe that there is a better way. Within organizations and throughout the Internet, massive amounts of data is untapped and inaccessible to the business. The challenge is that this data tends to be unstructured and/or in documents. Collecting more data adds to the problem and slows down your ability to effectively make decisions based off that data, which undermines the business potential of your data. According to HBR.org, cross-industry studies show that on average, less than half of an organization's structured data is actively used in making decisions - and less than 1% of its unstructured data is analyzed or used at all. If this is true for you, imagine using your data to:

Optimize how you interact with your current customers
Integrate the voice of the customer into risk and compensation systems
Grow revenues by providing more to your current customers and successfully pursuing other customers
Comply with regulations at a lower cost
Provide real time alerts to your staff based on what your organization and customers are doing

These are impossible to do with traditional approaches such as buying more data.

Harvest More: A Different Approach to Big Data

As an example, let's examine a typical, large bank. Millions of customers, PDF agreements, tens of millions of transactions, mentions and posts on the Internet create a small fraction of the data. At the same time, a regulator asks, "How do you know your customer?" The traditional answer is to look at a KYC platform, but that is only a small portion of the answer. Instead, what if you are able to take that structured data and marry it with countless unstructured sources within and outside the bank - PDFs of contracts, legal entity actions, scanned images of credit review documentation, and pipeline information. The result is true knowledge of a customer.

This is one of several use cases to "Harvest More" data - using an artificial intelligence engine to uncover actionable insight.

Organizational inertia can hold back the change to "Harvest More" because entire teams of data relationship managers have been built. ETL centers of excellence have been built to on-board a number of data sources. Technologies have been acquired to track market data terms and conditions. Leading organizations have found that they are always behind when attempting to drive client growth and regulatory compliance.

To be clear, this approach is a journey, and the journey requires the right expertise and tooling. I encourage you to read related blogs on this topic to understand more of what you will need to consider. The promise is substantial: