A digital nervous system is an information system that enables an organization to respond to external events by accumulating, managing, and distributing knowledge. The term was coined by Bill Gates, who used it in the late 1990s to describe the ideal functioning of an IT infrastructure. Like the biological nervous system, it is about providing all necessary information at the right time and in the right place. Search and artificial intelligence (AI) play an essential role in delivering data insights in the business analytics context.
Progressive organizations are implementing new tools to help them uncover structured and unstructured data in their networks to make crucial decisions faster. Companies with digital nervous systems have a mix of culture, IT architectures, and tools that deliver the correct information to the right people promptly to make the best decisions. The last point is crucial. Suppose the information does not arrive on time. In that case, it is useless, and there is a lack of a functioning nervous system capable of reacting quickly enough to events or making predictions that lead to profitable decisions.
In the business analytics environment, the two technologies that democratize and accelerate optimal decision-making are search and AI. Search is based on relational search and allows users to enter any data-related question into a search bar, just like in a Google search. The solution then deterministically analyzes each query, searches the company data, interprets the relationships between the data elements, and calculates the answer, ensuring from the start that the user only has access to the data for which he has been authorized. The solutions are then made available to users in the appropriate visualization within seconds.
AI helps those users who don’t know what question to ask of the data in the first place. The AI algorithms can identify trends and outliers in the data and personalize the data analysis for specific users and their roles.
Together, search, and AI-driven analytics enable every company to make well-founded and profitable decisions. The most advanced companies give not only their users but also their customers the ability to conduct analysis. BT, Clarity, Fannie Mae, and Just Eat are examples of companies that do this.
Thanks To Machine Learning, Information Is Provided Faster
Information can be brought to the surface using either “pull” or “push” mechanisms. Pull is a process where a user asks a data question and gets the answer. By combining Search and Natural Language Understanding (NLU – a branch of machine learning and a sub-discipline of Natural Language Processing, NLP for short), systems can be developed that enable any user to call up data without technical expertise by asking questions in the natural, easy-to-understand language without specialist Latin.
However, this requires AI capabilities that make it possible to determine the intention of the search queries made by the user and a likely result. More importantly, the systems must be able to capture the analytical intent to provide the only correct answer to a question asked. For example, if a user searches for “how many McDonalds are there in Berlin,” an NLP engine needs to know whether “McDonalds” means the restaurant chain, a street name, or a family name.
In many companies, employees have to wait weeks for the data they need because their requests to the IT department get stuck in a queue. This delay ultimately discourages them from asking questions so that no digital nervous system can develop here. Thanks to the technologies, employees can use the pull process to access information more quickly and without detours via the IT department.
On the other hand, Push is about getting the correct information to the right person at the right time, even if they don’t know which data-related questions to ask. There are two significant problems here. First, with all the “noise,” how can you search through large amounts of data to find new, insightful information that will improve business results? Second, how do you see in a sea of data the most relevant information to a particular user based on their specific needs? Both are problems that search, and AI-based analytics solutions solve with the help of machine learning, as shown above.
The Next Step In The AI Evolution: Intuitive Interfaces For Handling Large Amounts Of Data
Intuitive interfaces on substantial data sets are already available today. The further development of machine learning should enable the software systems to understand the business context of the data. As they evolve, these systems will make predictions about likely outcomes and even suggest business transactions that will ultimately deliver tremendous business value.
At present, machine learning and AI systems are still far from the capabilities of the innate intelligence of the human mind. However, these systems fill an existing void by absorbing vast amounts of training data and computing power. You have to experience them in video, audio, and speech comprehension to see the incredible success of machine learning.
However, if you look at machine learning and AI in the corporate context, very few tools are designed to process sufficiently large amounts of data. As a result, most machine learning is done with aggregated or sampled data. This fundamentally limits the performance of AI systems. Systems that can gain granular insights by learning from large amounts of data.
Also Read : The Top 10 Enterprise Machine Learning Use Cases
AI And Search In Practice
In summary, it can be said that AI and search-based analytics solutions provide companies with a digital nervous system that enables new insights, process improvements, and better support for employees. Companies like Walmart, Suncorp, and British Telecom are good examples of this. Using search and AI-driven analytics, Walmart made many of its pricing decisions 100 times faster. Suncorp saved millions by finding anomalies in its claims settlement data. For example, in the UK, BT has massively improved the experience of its business customers by providing a search and AI-driven interface that allows them to view their billing data to ensure that the best pricing plans are always followed.
There are many possible uses for AI and search for supporting the digital nervous system. For example, by looking at the anomalies generated by AI in their billing, companies can massively reduce their costs. Manufacturers can save vast amounts of money by making it easier for their employees to find price variances. However, these are only a few examples. In our fast-paced world, the ability to provide the correct information at the right time and place can make a difference in a company’s success, and this is where AI and search-based analytics are unbeatable.
Also Read – Artificial Intelligence In HR: Knowing What The Future Holds
