However, it alone cannot lead to success. Structuring and analyzing data are essential for drawing actionable insights that lead to revenue. But how can manual effort lead to the analysis of this plethora of data? Won’t there be more data waiting by the time we draw insights from the data already available to us?
The key is AI (Artificial Intelligence) driven decisions and database solutions like Kobai’s Semantic Graph. Your organization can now process and store vast amounts of information while generating truly actionable insights. You can train AI to infer intricate patterns, trends and results from data to drive innovation, efficiency, and informed choices.
Let Your Data Help Your Business
There is a huge increase in generating and computing data, making it difficult to organize and analyze. This impacts organizations because they are unable to convert their enterprise and operational data into actionable insights. The absence of analytical data leads to poor margins, one-dimensional customer experience and security concerns.
A study from Gartner reported that poor data quality costs organizations an average of $12.9 million every year.
This brings us to helping you make the most out of your data, and that is where Kobai’s semantic graph comes in. Let’s see how:
Semantic graphs are an impactful tool for AI, and they can help organize your data so that it can present crucial, actionable insights that can help your business. Their power lies in structuring data in a way that it reflects real-world connections. Think of a web of entities (like people, places, things) linked by meaningful relationships. This means you have informed ideas about where to allocate more resources, slash down costs, optimize operations, and re-strategize.
This structured and contextually powerful data allows AI to not only process information but understand the reasons behind it. By organizing data in this way, semantic graphs facilitate data integration from various sources, leading to a more comprehensive and interconnected knowledge base for AI systems to learn from.
Best Practices for Data Preparation and Success in AI
Without high-quality data, machine learning algorithms are essentially guessing. Raw data frequently contains errors, such as missing data or outliers. That’s why, data preparation is an important part of any successful machine learning workflow since it ensures that the correct data is used during the learning process. It also speeds up model training by eliminating extraneous information.
When preparing data for machine learning, there are five critical processes to follow:
- Collect appropriate data, compensate for bias
- Use the best machine learning techniques
- Clean your data
- Structure your data
- Consider sampling
With so many steps involved, data transformation turns into a time-consuming, resource-intensive, and expert-driven operation. However, with Kobai’s Semantic graph, organizations can now easily prepare and analyze their data and draw out the most useful insights to formulate their future strategy.
The Role of Semantic Graphs in Data Preparation
Semantic graphs provide a powerful way to structure data for AI, capturing not just facts but also the relationships between them. Breakthroughs in AI, like GenAI create models that leverage these graphs for better decision-making. As an addition, Kobai’s Genie Spaces Accelerator Kit helps prepare your data for success by automatically generating contextualized semantic models within your Databricks environment. This ensures consistent data interpretation and unlocks powerful features like codeless query authoring and API publishing. With GenAI, organizations can empower their data teams to build robust AI models and reports from a well-organized knowledge base.
Building Your AI Focused Semantic Data Fabric and Increasing Success Rates with Kobai
Kobai sits at the intersection of future digital factories and semantic graph technologies. Saturn, the organization's latest offering, is a semantic graph designed to leverage the size, performance, and cost effectiveness of data lakehouse architectures. Kobai's semantic graph expands the possibilities of its basic platform by combining every use case and function into a single semantic layer.
"Kobai’s semantic graph allows Databricks’ customers to accelerate decision-making and time to insights by enabling intuitive, rapid, and reusable connections between data sources.” said Parag Goradia, CEO at Kobai.
Hyperlinked, semantic graphs are the future of structuring and reasoning datasets. Kobai's platform exemplifies helping organisations make the most of their data easily. We bring to you a reliable way to integrate your operational, enterprise and engineering data to enhance discovery, reuse and governance.
Book a demo with Kobai to know more.
