Do you know why it is important for enterprises to adapt to the concept of Wide Data?
These days, “big data” simply isn’t enough. To provide meaningful insights and valuable analytics for optimal decision making, companies must adopt the concept of “wide data.”
Whereas big data focuses on the so-called “three V’s” — volume, velocity, and value — wide data homes in on value, according to Anand Mahurkar, founder and CEO of leading enterprise AI company Findability Sciences. That is, it’s not just a mass of data for data’s sake, or data derived from a few sources. It’s tying together data from a wide range of sometimes seemingly disparate sources to allow for deeper, more purposeful analysis.
“Wide data means not only the data in my organization; it’s going beyond the boundaries of my organization and combining external data, internal data, structured and unstructured data,” Mahurkar explained to VentureBeat. “If you want to know what will happen and what to do, you will need ‘wide data’ and not just ‘big data.’”
The evolution of enterprise AI
This is a foundational concept in enterprise AI, which involves embedding artificial intelligence methodologies into an organization’s data strategy. The software category is undergoing rapid growth as more companies across all sectors undergo digital transformation. Global Industry Analysts Inc. projects the global market for enterprise AI to reach $15.9 billion by 2026. That’s up from $1.8 billion in 2020.
Findability Sciences is working to set itself apart in a market whose dominant players include the likes of C3 AI, Abacus.ai, Microsoft, and Snowflake. Specifically, the Boston-headquartered company is lasering its focus on what Mahurkar called traditional companies — such as those in the manufacturing and retail spheres — that are still making use of legacy software products. This remains a sizable market: there are more than 60,000 companies worldwide with revenues of $200 million or more.
Particularly post-pandemic, these enterprises are beginning to understand the necessity of digital transformation and AI, but they struggle with adoption and deployment, Mahurkar said. Undertaking a custom build to embed AI into existing infrastructure can be a paralyzing proposition, and outsourcing can be costly while taking an undue amount of time.
Embedding AI technology
To help companies tackle — and ideally master — the transition, Findability today launched its new white-label suite Findability.Inside. Quickly deployable and repeatable, it allows companies to embed AI technology into their already existing hardware and software, in turn enhancing features and functionalities and driving new insights and efficiencies. The suite makes use of advanced capabilities including computer vision, machine learning, and natural language processing to aid with predictions and forecasting, price optimization, market targeting and segmentation, sales prospecting, online advertising, and customer service.