By Anand Mahurkar, CEO of Findability Sciences
Enterprise artificial intelligence is a game changer in today’s data-driven world, and as such, manufacturing organizations are increasingly adopting AI to help improve business processes, transform products and business models, resulting in revenue growth, reducing costs and improving customer service. In other words, we are living in a time of AI-led digital transformation.
However, to make this all happen, organizations require data—not just a huge volume of data, but rather a variety of data from disparate internal and external sources.
Manufacturing organizations require “wide data.”
The concept of big data has been around for a long time. Manufacturers have long tapped into big-data analytics to gain an edge over their competitors. But with today’s machine learning applications, big data simply isn’t enough. To provide meaningful training data to ML applications—think predictive analytis for optimal decision making—manufacturers must adopt the concept of wide data.
Whereas big data focuses on analytics that can only tell you what happened in your organization (honing in on volume, velocity and variety), wide data narrows its focus on variety.
Let’s explore why…
Wide data needs variety of data
When it comes to AI applications, variety matters the most. That means combining internal, external, structured and unstructured data. Utilizing a variety of data sources is critical in this world of globalization, where there are many parameters and dependencies beyond an organization’s control. Variety enables organizations to harness the power of data to obtain meaningful insights, make smarter predictions, and gain valuable analytics for optimal decision making. Therefore it’s important that organizations tap into the data that is beyond their organization or applications (such as ERP, CRM solution), enabling their ML applications to learn correlations with the factors beyond their organization’s control.
Wide data enables more meaningful data engineering, offering organizations the ability to understand leading indicators for their businesses. This is particularly important in the manufacturing industry, as there are many external factors that can affect the performance of a business. Take the procurement of coils from China, for example; when an organization looks at how this specific act impacts the larger economy, it is going beyond the boundaries of the organization and analyzing external data to have a more holistic view of the situation.
Making use of wide data means preparedness
Large enterprises are directing resources toward modernizing their IT initiatives this year, and this number will continually grow. Creating an information architecture (IA) to tap into wide data is going to be fundamental in this respect. For some organizations, however, this is a struggle. In short, using AI and similar technologies can be daunting.
As such, many solution providers are striving to make AI tools more accessible, so that organizations can drive their digital transformation without having deep technical know-how, and they can invest more of their resources into other areas of the business.
Traditional data products and software often have their own data sets. They are siloed. However, when properly implemented, AI empowers organizations to collect and unify data from different sources, which allows them to produce more valuable insights for users.
As data powers AI applications, assets are optimized. This allows organizations to more efficiently utilize wide data, which can, in turn, generate leading indicators and offer return on investments back to an organization’s AI applications.
