Data-Driven Decision-Making for Companies: Making Reliable Decisions Through Verified Data

Any business wants to make good decisions that fit with the market and help generate revenue. A data-driven approach can facilitate decision-making, especially when enhanced by powerful technology like AI. 

However, finding or creating data suitable for your business is not easy. Creating data-sharing ecosystems could make quality data more accessible for enterprises, allowing them to draw insights, innovate, and compete more efficiently.

Why You Need Data for Decision-Making

Today’s businesses generate vast amounts of data. Whenever we use our cell phones, pay online, take public transport, or go shopping, companies receive our data. This information helps enterprises make optimal decisions: target advertisements, optimize transport availability, identify bottlenecks, and find new revenue streams.

Companies can analyze user behavior on their websites to determine where conversion drops. In healthcare, data analysis helps uncover hidden patterns between diseases and make more accurate diagnoses. 

Businesses that want to remain competitive in today's economy can’t afford to make decisions blindly. A data-driven approach can help identify invisible or hard to detect patterns, while using these insights can provide a competitive advantage.

AI facilitates such analysis as it can quickly run through large volumes of data and propose a solution. But how can we know these solutions are based on quality data?

What Data Should AI Be Trained on? 

Large Language Models are well-trained on large amounts of unstructured data. It excels at assisting with daily operations and providing some general knowledge. Still, when it comes to tasks requiring precision and up-to-date data, LLMs can "hallucinate”, meaning they can make up important details.

Utilizing structured datasets from verified sources can significantly expand the use cases of AI – and reduce hallucinations, as it is trained on accurate, up-to-date, and factual data. It could engage in fact-based conversations, demonstrate advanced reasoning skills, conduct more complex analyses, and provide specialized professional consultations. This would be particularly helpful in industries like legal, healthcare, and finance, where data accuracy plays a crucial role.

However, finding quality datasets for AI training is anything but easy. 

Marketplace for Efficient Data Exchange

There’s no lack of data – companies generate vast amounts of it. The problem is that data often remains unused on local servers and is locked away in legacy systems. It is also often “dirty” or unchecked or represented in various incompatible formats; you'd need multiple tools and custom solutions to access data from different sources and combine it.

Putting all this data in one place would solve the problem. For example, it can be a data-sharing ecosystem where parties can easily share data with each other. Businesses could connect their data sources, while others could use them to draw insights and improve their decision-making.

Such data exchange platforms could open up a whole array of new use cases. Efficient data exchange allows AI to be trained to provide accurate insights with attention to detail and results in precise advanced analytics. By combining data from various sources, AI can identify trends that were previously unseen due to the fragmented nature of the data within or across organizations.

How Do You Prove the Source of Data?

I started this discussion with a simple statement – it’s vital that companies create solutions based on quality data. You need to rest assured that the AI model has been trained on high-quality data from a proven source. Data-sharing ecosystems provide AI with the opportunity to learn from high-quality industry data, but how can you be sure about its origin?

Any company can offer its data on a marketplace and create an additional revenue stream. Businesses are incentivized to provide quality data to attract the maximum number of users to subscribe to their data and pay them commissions. For example, in some data ecosystems, the most successful modern enterprises make their data publicly available. To make it more informative and understandable, companies can enrich it with metadata, providing detailed descriptions for all data points.  

Such marketplaces run on blockchain, meaning that all contributions to the data, data exchange, and usage are trackable and traceable. Blockchain technology adds transparency to data exchange. Trained on this data, AI can generate innovative business solutions – and you always know that they are based on data from companies that successfully operate in the market.

Accessibility of AI-driven Business Solutions

Besides offering transparency of data, data-sharing ecosystems should be easily accessible to teams in different industries. The presence of APIs in such marketplaces would allow businesses to integrate data processing into their own workflows without a lot of additional development costs. All data should be structured into a generalized format to ensure a consistent interface for interacting with any type of data.

Armed with high quality and structured data from a clear source, companies can enhance their competitive edge, unlock new business models, and seize new opportunities for growth.