Is Data Reasoning What You Need? Find Out by Asking Yourself These 5 Questions

To help you decide if data reasoning is something your organization might want to consider, here are five questions you should ask yourself. Each question highlights an area where data reasoning could drive significant improvements in efficiency, accuracy, and strategic decision-making within your business.

1. Do Your Business Goals Fall Into One of These Two Buckets?

Data reasoning unlocks new possibilities for enterprise businesses, especially if you are looking for ways to improve your bottom line. Most businesses that leverage data reasoning in their workflows often find success in:

1. Reducing Overall Costs: Regulatory compliance or manual processes that are critical to business operations and require substantial people to run.

2. Creating New Revenue Generating Opportunities: New data or automation features that improve product quality and customer experience.

Consensus: Data reasoning can be used to reduce overhead and help create new revenue opportunities. If these business goals are important to your team, it’s worth a shot to look into how data reasoning could help you support these goals.

2. Are There Manual Tasks That Are Consuming Too Much Time?

Routine, repetitive tasks can consume a large portion of your team’s resources. Data reasoning is ideal for unlocking the data needed to automate complex operational tasks, reducing overhead and allowing your team to focus on more strategic initiatives. While most will assume that Robotic Process Automation (RPA) is the answer, RPA operates on predefined rules and has limited contextual understanding - making it extremely difficult or impossible to automate tasks that are far more sophisticated. According to a recent study from Deloitte, 69% of RPA projects generally fail due to data complexity.

Consensus: If you’re looking for a solution to automate a manual or repetitive task that relies heavily on data, data reasoning is for you.

3. Do Your Tasks Require Higher-Order Reasoning?

Data reasoning is designed for tasks that need go beyond simple calculations and a simple plug-and-play framework. Higher order operational tasks generally go through a multi-step process where logic and context is applied to your data to draw insight. Some examples may include:

• Sentiment Analysis: Interpreting the tone or emotional context of data.

• Forecasting: Ability to take existing or historic data to forecast future trends.

• Insight Generation: Synthesize conclusions and insights from disparate pieces of information.

• Entity Recognition: Identifying key entities (people, organizations, etc.) from unstructured data.

• Inferring Relationships: Understanding how different data points relate to each other, such as cause-and-effect or correlations.

For most, this will either be a manual process or it will require a fully staffed data science and machine learning team to execute.

Consensus: Companies often avoid automating tasks that require higher-order reasoning due to the complexity or lack of technical expertise. However if there was an easy way to make this possible, what are some tasks that you would have in mind?

4. Are You Dealing with High-Context Scenarios That Require Measuring Qualitative Information?

When your data involves complex, context-rich information (such as subjective, qualitative data), data reasoning is a perfect solution for transforming this into measurable insights. Whether you need to analyze customer feedback or compare nuanced data from diverse sources, a data reasoning platform can automate much of the data wrangling, making it easier to gain insights from complex information. This can be particularly useful if you’re trying to integrate subjective metrics into your quantitative analyses to create more well-rounded, actionable output.

Consensus: Not all tasks are black and white and may require subjective metrics. Data reasoning applies logic and reasoning to your data to help navigate these nuances. By using platforms like Gradient, you’ll also be able to provide human-feedback in order to align the model to improve accuracy and deliver the desired outputs.

5. Are You Leaving Data on the Table?

Many organizations possess vast amounts of data that aren’t fully utilized due to their inability to tap into the unstructured nature of their data. According to Gartner, 80% of enterprise data is unstructured - leaving valuable information and institutional knowledge untouched or forgotten. If this sounds familiar, leveraging a Data Reasoning Platform like Gradient can help you unlock the latent potential of this data. By making sense of unstructured data, you can uncover trends, patterns, and correlations that were previously hidden, ensuring that no valuable data goes to waste. Below are just a few examples of unstructured data, regardless of which industry you’re in.

• Documents and PDFs: Contracts, reports, manuals, and scanned documents often lack structured organization, making it difficult to parse information without specialized tools.

• Emails and Messages: Messages, attachments, and metadata in emails lack a standardized format, making it challenging to analyze and extract insights without advanced processing. Messages present similar challenges if not more, since they are often scattered due to the consistent back and forth nature of communication.

• Audio, Video, and Images: Recordings from calls, meetings, or videos contain rich, unstructured data that requires transcription and analysis tools to interpret. Images are also difficult to interpret and require logic to analyze and present a consensus of the message that it’s trying to tell.

• Customer Support Tickets: Text from customer service chats, complaint forms, and service notes are usually unstructured, containing valuable but hard-to-analyze information about customer sentiment and issues.

Consensus: Your ****institutional knowledge is your most valuable asset! ****Don’t let raw and unstructured data stop you from maximizing the ROI from your data and unlocking new possibilities.