AI enables data to transform the enterprise into the future.
As we all know, AI is transforming industries, even the pandemic-hit tourism industry. With massive amounts of data and the right technology platform, AI will enable real-time digital travel experiences. All he needs is trusted data that can be seamlessly streamed across his IoT network of intelligence sensors, digital portals and wearable devices. AI will be ubiquitous soon, but we need data to scale it to such levels. Data is a very important consideration and will determine the success of any AI initiative. This is why companies have developed a data-first approach, recognizing data as a dynamic, trusted, distributed and democratized multidimensional process (also known as 4D data).
4D data is the result of a combination of skills, technology and approach. Organizations are looking for ways to take advantage of this technology by developing business strategies and models based on knowledge, visible, actionable and connected data. 4D data enables companies to identify and derive insights from different types of data, including structured, semi-structured and unstructured data. A recent survey of 200 global companies by Accenture and Everest Group found that 72% expect double-digit growth in spending on data and analytics. Four data dimensions must be adequately invested to justify this effort. Dynamic data is the information that comes from building endless data value chains. The IT stack, including databases, applications and infrastructure, is no longer a separate entity.
Advanced systems cross the boundaries between data, infrastructure and applications, and between humans and machines. Dynamic data helps businesses harness the speed of insight, realign supply chains, and enable responsive customer interactions. Trustworthy data is key to building trust. As data is both an asset and a liability, companies must ensure an objectivity-focused approach to drive trust as well as growth and profitability. This means that corporate data must be of high quality, provenance and secure. Organizations should also pay attention to data security aspects. Distributed data is information that is not confined to organizational boundaries. Sharing data with customers, vendors, and competitors is critical to data monetization, leading to increased efficiency, increased revenue, and reduced costs.
Data exchange enables rapid business development. As a result, businesses need to securely exchange data between internal and external ecosystems. Democratized data ensures business users are data literate. To become a data-driven enterprise, business users need to access and explore datasets, identify new opportunities, generate relevant and trusted insights, and have the skills and knowledge they need to make sense of their data. We need to build systems that have access to technology. This is a journey of different kinds of data and digital transformation. The right way to start this approach is for the CEO to take ownership of his enterprise data strategy and appoint a C-level manager to build and work on that strategy. Next, businesses should invest in building an enterprise data platform that keeps his chain of data supply agile, secure, and up-to-date. We also aim to create a culture of data literacy where organizations can explore data, find new opportunities, and uncover business insights to move their business forward.