Synthetic Intelligence Business As Well As Software Application Advancement – Chances are you’re already using artificial intelligence (AI) in some way. Tools and services like Google Maps, email spam filters, and online banking are all examples of how AI-powered technology has entered our daily lives. Artificial intelligence-powered technology makes our everyday lives easier as consumers, but what does it mean for the business world? The opportunities to automate and speed up the world’s tasks and the ability to eliminate human error certainly have significant implications for every business, enterprises included. As described in Forrester’s “Pedications 2023: AI Will Become an Indispensable, Trusted Enterprise Worker,” AI will become a core part of what makes a successful business. Machine learning (ML) describes how computers “learn” without the help of a programmer. ML is probably one of the most important ways that artificial intelligence is used, providing the technology behind chatbots, language translation apps, or generated content (like the “recommended shows” that pop up on Netflix). With ML, computers can learn and program themselves, allowing organizations to rapidly innovate and customize based on user demand. Empowering Enterprise Architects for Better Business Decisions Businesses across all sectors generate large amounts of information every day. This includes anything from financial transactions to logging HR information to application usage. For data to be valuable, it must be digestible and produce actionable insights. While data management is typically the responsibility of the data analyst or architect, shaping the data into business-relevant insights falls into the role of the enterprise architect (EA). With today’s new EA tools, it is possible to deliver role-specific and data-driven insights into an organization. Below, let’s look at four ways organizations can use the power of artificial intelligence and machine learning to make better decisions, including in enterprise architecture. 1. Digesting patterns and anomalies along with opportunities and threats and identifying patterns or spotting anomalies has traditionally been human-led. It requires time, effort and expense. However, machine learning and AI have the ability to analyze these insights and immediately spot trends and opportunities. Traditionally, it might have taken the enterprise architect weeks or months to find out. Artificial intelligence can be highly susceptible to “guessing,” but with quick tuning and training, you can shape AI tools to be proficient and effective at spotting threats and anomalies. 2. Predicting Strategy Execution and Beating Competition Artificial intelligence can be another win for businesses by helping them make the right decisions to stay ahead of the curve. Forecasting is an important tool for any organization, helping to decide where to focus specific resources, determining which project should come next, or identifying a new product line. This can include anything from simple trend analysis to complex statistical models. EAs play an integral role in forecasting, thanks to their unique understanding of the business and IT ecosystem. Their expertise can help a business determine which plan to change, for example, to help meet broader company goals. AI is already proving to be a predictive game changer. It can now help businesses more accurately predict their future, for example, by analyzing consumer habits and finding opportunities to improve their performance. These forecasts can help companies decide which product to promote or whether a particular service is lacking in the market. In addition, the ability to compare data from competitors and analyze aspects like customer demand and product costs means that EAs can leverage AI to help their companies ensure that their products and services are priced correctly. All of these components allow companies to use their data to innovate and stay competitive. Thanks to their advanced understanding of linking this information to the business, EAs are also cementing their role as an integral part of ensuring the future success of the business. 3. Better, cleaner data Data collected by EAs is already a critical part of a business’s decision-making process. But what if this data was the best version of itself? For example, data from EAs capture information about relationships and identify connections between systems and people to manage organizational processes. Experian’s 2022 Global Data Management Report found that better data management and better quality data helped 75 percent of businesses worldwide exceed their annual goals in some way. The quality of the data, however, has declined in the past two years. Experian’s report found that 77 percent of respondents interviewed for their report said inaccurate data hindered their efforts to respond to market changes following the COVID-19 pandemic. While new EA tools can help EAs collect data and turn it into insights, artificial intelligence in enterprise architecture has the potential to ensure that information is of high quality. Enterprise architects can use artificial intelligence to help them identify anomalies in their data. This may help identify system errors or spot patterns of “bad” data. AI also has the power to spot duplicates, which can skew reporting and compromise data quality, calling into question a single source of truth. It’s hard to spot with the human eye, but ML techniques can instantly remove duplicated details. AI can help standardize information. For example, an EA may not want to consider data collected before a certain date, so the AI can ensure that no data is stored before that date. It is also possible to standardize “clean” data according to specific metrics or standards, ensuring consistency. Finally, AI can also automate and streamline data entry using AI, a technology that can create different types of content. This approach removes human error and automates a very time- and resource-intensive task, thereby improving data quality and freeing up time for EAs to focus on other, more critical projects. 4. Powering and connecting data Knowledge graphs can be likened to a large digital map; They are extremely powerful in helping to understand how information is connected. First conceived in the 1980s, knowledge graphs gained momentum in 2012 with the introduction of Google’s Knowledge Graph. In 2020, Gartner defined the knowledge graph as the peak of its artificial intelligence hype cycle. In short, the hype around them became more than what was initially possible. Despite the slow start, the technology is now actively used in many large digital companies, helping to provide a better user experience – think AirBnb and Uber’s UI. Structured representations of knowledge and relationships between entities and related information are a large part of how conversational AI, such as Chat GPT, works. The ability to combine knowledge graphs with conversational AI platforms can revolutionize an organization’s contextual understanding. For example, AI integrates with knowledge graphs to create accurate, context-aware, and meaningful interactions between AI systems. This has major implications for areas such as AI-enabled chatbots, for example, by providing a route and context for responses, ensuring the most relevant, up-to-date information is provided. Unlike information stored in traditional tabular form, knowledge graphs are perfect for revealing connections and patterns between data from multiple sources, including structured and semi-structured, that might otherwise be difficult to detect. Such ability means that these graphs help in even deeper understanding of the data. They are beneficial to enterprise architects because they can: integrate and make sense of vast amounts of complex information; These insights provide an EA with a holistic view of their organization, helping them find relevant data sources and their relationships. An EA can also implement the concept of governance and data quality by creating a unified view of their data assets, including all properties and lineages. This view can then define data standards and regulations, helping to ensure data consistency and accuracy. Capture the meaning behind the data (i.e. semantic understanding), or the next-level insights from the data that support better, more accurate decision-making. Deep insights also help EAs to discover relationships and interdependencies that exist in their company’s data. This helps identify any bottlenecks, as well as finding opportunities for improvement, such as informing resource allocation. Search for data in different contexts. EAs can scale and expand data by building data sets that capture and maintain vast data sets. Extending the data allows adding additional information by adding new nodes and edges without the need for reconstruction. EAs can use this feature to select related concepts, discover connections, or better understand how different entities relate to each other. These factors collectively help to increase innovation and enhance the discovery of strong knowledge. Share knowledge and collaborate. Knowledge graphs are a great way for EAs to capture and synthesize organizational information, for example, to help establish best practices or implement guidelines and provide a mechanism for sharing and applying lessons learned. Knowledge graphs are undoubtedly already impressive. However, AI offers an opportunity to improve this technology. Thanks to the ability to automatically recognize entities and relationships, using AI to automatically extract knowledge from unknown data (such as documents or long texts) will help an EA to speed up the population and improve the knowledge graph. In addition, powering knowledge graph analytics with artificial intelligence can help an enterprise architect identify differences, patterns, or trends while supporting decision making.
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