Knowledge graph builder series#
Below, I share in detail a series of steps and successful approaches that will serve as key considerations for turning your information and data into foundational assets for the future of technology. The most pragmatic approaches for developing a tailored strategy and roadmap toward AI begin by looking at existing capabilities and foundational strengths in your data and information management practices, such as metadata, taxonomies, ontologies, and knowledge graphs, as these will serve as foundational pillars for AI. However, given the technological advancements and the increasing values of organizational knowledge and data in our work and the marketplace today, organizational leaders that treat their information and data as an asset and invest strategically to augment and optimize the same have already started reaping the benefits and having their staff focus on more value add tasks and contributing to complex analytical work to build the business. Commonly, these capabilities fall under existing functions or titles within the organization, such as data science or engineering, business analytics, information management, or data operations. Our experience at Enterprise Knowledge demonstrates that most organizations are already either developing or leveraging some form of Artificial Intelligence (AI) capabilities to enhance their knowledge, data, and information management. Lack of the required skill sets and training.Enterprise data and information is disparate, redundant, and not readily available for use.There are multiple initiatives across the organization that are not streamlined or optimized for the enterprise.Not knowing where to start, in terms of selecting the most relevant and cost-effective business use case(s) as well as supportive business or functional teams to support rapid validations.Limited understanding of the business application and use cases to define a clear vision and strategy.The most common challenges we see facing the enterprise in this space today include: Despite developing a business case, a strategy, and a long-term implementation roadmap, many often still fail to effect or embrace the change.
Think about the multiple times organizations have undergone robust technological transformations. As organizations explore the next generation of scalable data management approaches, leveraging advanced capabilities such as automation becomes a competitive advantage. We rely on Google, Amazon, Alexa, and other chatbots because they help us find and act on information in the same way and manner that we typically think about things. The scale and speed at which data and information are being generated today makes it challenging for organizations to effectively capture valuable insights from massive amounts of information and diverse sources.
0 kommentar(er)
