Artificial intelligence has completely dominated every other field of the world today. There is no domain left that has not integrated AI solutions and applications. The corporate sector is just one of the prime examples of the solutions provided by artificial intelligence. Augmented business intelligence (BI) is the new face of technology that is evolving with each passing day.
Augmented analytics is fundamentally an AI approach that uses technology and software in data analysis and preparation and provides an insight into different BI platforms. Large sets of complex data are continuously being generated by companies on daily basis. Data is more like the currency of the modern world now. These huge numbers of data sets cannot be dealt with by the traditional plan of action. The companies are striving now to inculcate artificial intelligence solutions in these enterprises to help them grow. To deliver the promised results, augmented business intelligence seems to be the only option available. To quote Gartner, from their October 2018 research: “Augmented Analytics is the future of data and analytics.
Augmented analytics work on the basic principles of artificial intelligence algorithms that use machine learning (ML) to automate data handling procedures. Specific input signals are fed into the machines, which then analyze and interpret the results. Using augmented business analytics, the procedures are now much more simplified and handier to use. The automatization of repetitive tasks such as data preparation, pattern recognition, and code generation is done through augmented analytics. By automatically analyzing users’ behaviors and intentions, it also provides suggestions and insights for users. In this way, users can assess their goals rather fast, speeding up time to value.
Traditional Business Intelligence (BI):
BI and analytics have been here for quite some time now. An IBM researcher Hans Peter Luhn published “A Business Intelligence System” in which he stated that information is now being generated and utilized at an ever-increasing rate because of the accelerated pace and scope of human activities.
The focus of traditional BI was to connect to single databases and generate basic reports so that better decisions based on the available data could be made. It was a rather unsophisticated and untimely method. A very small class of data analysts and professionals would be previewing the analysis. Hence, there was undoubtedly room for more advancement. This advancement came in the form of advanced business intelligence with augmented analytics
How self-service BI is better than traditional BI?
Self-service BI tools were developed to make augmented analytics in business intelligence more effective and accessible. With the help of tools like graphical user interfaces (GUIs), businesses can now stop relying on data professionals and analysts for handling a large amount of data in relatively easier ways in rather less time. Deeper analyses can be performed with a larger volume of data extracted from multiple data sources like clod storage apps and excel spreadsheets etc. This also makes security checking and data governance much simple for IT teams. Thus, augmented analytics, when incorporated into business intelligence gives better results when compared to the traditional BI.
Artificial intelligence vs Business Intelligence: similarities and differences:
The demerits and advantages have often been compared between artificial intelligence vs business intelligence. Despite their differences, they’ve to act and work together as a unit to solve the problems of the future.
- Artificial Intelligence vs Business Intelligence basic difference: AI uses computer systems to mimic human attributes while BI involves all the technical tools that are required for the analysis of large amounts of business data. This helps understand the superiority that AI has over BI at the moment. It covers more domains as compared to BI and helps augment BI to enhance its efficacy.
- Artificial Intelligence vs Business Intelligence main goals: The main aim of BI is to streamline the whole process of collecting, analyzing, and visualization of data. The collection of data and the consistency with which it is collected is improved with BI. According to the professor of operations management and decision science at the University of Dayton in Ohio, Michael F. Gorman, “BI doesn’t tell you what to do; it tells you what was and what is.”
AI on the other hand uses human attributes to learn and make rational decisions. The use of AI-driven applications such as chatbots can lead to greater efficiency and more profits. Thus, when compared, artificial intelligence vs business intelligence surely weighs more in support of AI in terms of its goals and aims. No doubt, BI makes it easy for the machines to collect and analyze data but the decision-making part is still left to the human attributes of the AI. Thus, it is mandatory to augment BI with AI software for better and more efficient results.
- Artificial intelligence vs Business Intelligence enterprise use cases: Today, more and more companies are relying on BI than they realize. For example, the use of Microsoft Excel or other spreadsheet tools, data visualization, and warehousing tools all come under the category of BI. They allow businesses to collect, organize and visualize data proficiently. Many companies also use BI for a better understanding of their customer priorities mainly through social media platforms and emails. Not only this but BI can also be used to enhance the operational efficiency of the companies.
When in comparison, artificial intelligence vs business intelligence is undoubtedly going to give more strength points in favor of AI since it covers a vast range of domains from business companies to industrial investors, from healthcare and medicine to retail customers, from fraud detection to increasing total gross revenues of the companies. According to a Harvard Business Review article, AI-powered enterprise applications fall into three buckets: process automation (46%), cognitive insight (38%), and cognitive engagement (16%).
- Artificial Intelligence vs Business Intelligence research areas and issues: Research areas for AI include expert systems, Neural networks Natural language processing, and robotics but it faces issues such as a threat to privacy, human dignity, and safety. BI, on the other hand, includes research areas such as data mining in social networks, process analytics, and big data. The issues associated with BI are normally categorized into two domains; Technology and Data and Organization and People.
- Does BI need augmentation with AI? From all of the above points considered while discussing the comparison of artificial intelligence vs business intelligence, it must be noted that no matter how distinct they may seem, they’re complementary to each other’s performance. A BI system that is powered with help of AI helps deal with data on a more granular level. It helps human operators develop an understanding of how the given data can be translated into real business decisions. AI helps the development of more adaptive and smart BI tools. It provides incremental improvements to these tools that help BI reach a more expert level.
Future of BI: It is noteworthy that the future of BI depends on artificial intelligence. To analyze the comparisons between artificial intelligence vs business intelligence on a deeper level of understanding gives us an idea of how and why AI is crucial to the efficient working of BI. Despite their differences, they undoubtedly make a good team. Only in working together would they allow for the full potential of the systems to be seen and fully explored.
Advantages of Augmented Business Intelligence:
Augmented Business Intelligence is noticeable different from the traditional BI. Today, the integration of AI elements into analytics by augmented business analytics and further processing to help users in the preparation of their data and getting new insights are some of the prominent key advancements in business domains. Some of the other, much talked about uses of augmented business intelligence are given below:
How are AI & ML connected?
- Streamlined data ingestion: Augmented business analytics will reduce the human workload for cleaning and clearing data, organizing it into relative component domains and their analyses. This transition from traditional BI to the most advanced augmented business intelligence tools has allowed humans to get to their desired results instantaneously. For more streamlined and modern BI systems, this smooth data ingestion is a huge time-saver.
- Uncovering hidden insights: Traditional BI always needed a hypothesis to uncover the desired possibilities. AI-augmented tools do the quick search, provide contextual suggestions, uncover relationships and help users identify the insights that even they were not aware of. This also helps build the user confidence in the advanced techniques that are being implemented more frequently throughout the business domains.
- Faster data preparation: AI and data science come together to provide valuable advanced algorithms, quick data preparation, visualizations, and accelerated insights are achieved. The productivity of the overall system is undoubtedly enhanced. Based on the chosen data, a quick drag-and-drop auto-generates bar charts, maps, KPI objects, and other visualizations.
- Enhances data literacy: Businesses normally require a large set of data analysts to handle the massive data input because it is not so easy to deal with. Now, it is important that regardless of analytical skills, all users have equal access to the information of value. Augmented BI can enhance data literacy in a way that it can help organize large sets of data with minimum consumption of time and make it available for all sorts of users, from an expert to a mediocre level of understanding. Data can be assessed simply by using natural language and insights can be visualized with minimum effort. In this way, the creation of a data literate workforce becomes far more accessible.
- Increases trust: With each interaction of the user with data, clues are given to machine learning algorithms about their role, skillset, business context, and intent. Based on these clues, more and more relevant suggestions are given over time, causing an increase in the user’s trust in data. Wider adoption of analytics is a direct result of this trust.
- Decreasing human workload: When compared with traditional business analytical tools, the advanced augmented business intelligence tools have a way of collecting and analyzing data in a faster way. Moreover, the insights that are built resultantly are always aware of the context given. This whole process is rather efficient and requires less human work since the algorithms are always predicting the future results from the current data being fed into them. In this way, they decrease the workload on humans.
- Stronger social component: All the augmented analytical platforms have a stronger social platform appearance such as Facebook, Twitter, Amazon, etc. A shared experience is created on these platforms through mutual interactions such as sharing stories, tagging friends, etc. A similar social network effect is used in augmented analytics platforms. After the discovery of insights and creation of visualizations, users share and interact with them much in the same way as they do on social media platforms. Hence, the building of a larger narrative and fitting of the overall data into the business’s goal is done, all within the platform.
Helps boost adoption: An augmented analytics platform acts as an efficient, immersive system that is always helping people cover the stages from questioning to finding answers to giving them deeper insights across various departments and locations. This simplified method helps encourage technical as well as non-technical users to adopt augmented analytics.
Implement Artificial Intelligence at work to meet the changing expectations of teams around the world
How augmented business analytics work?
Three main steps are followed while using augmented analytics in business intelligence:
- Data preparation: Detection of schemes, profiles, and catalogs by the AI algorithms and the resultant recommendations of enrichment, data lineage, and metadata.
- Finding patterns in data: In natural language queries, all relevant patterns in data are found by algorithms, and models are autogenerated.
- Operationalization of the findings: Narration of insights in natural languages, visualizations of users on what is important and actionable. It can also be incorporated into apps or conversational UI. Some of the important and valuable ways in which augmented business analytics have enhanced the overall business processes are given below:
- Conversational analytics: No matter what level of literacy a person is at, conversational analytics will allow him to uncover insights simply by asking questions in natural language that is detected by the AI algorithms. Every time a question is asked, these algorithms provide the user with all the relevant information including graphs, charts, images, and fixtures.
- Automation of tasks: Normal routine tasks such as data preparation, visualization, and analysis are automated through augmented analytics. A user is helped by machine learning in finding all the relevant data that he is in search of by looking for patterns, and features and it creates visualizations through auto-generation of codes.
- Insight suggestions: The insights that are generated by augmented analytics are always aware of the context in which they are given and make sure that they take into account the intents, behaviors, and patterns of the user. It gives the user to look at the data in newer ways which provide him with a broad spectrum of carefully constructed insights.
Limitations and key challenges of augmented business intelligence:
Adopting augmented analytics in BI is not as easy as it may seem. There are some of the key challenges that act as barriers to the successful adoption of augmented business intelligence by the companies:
- Accuracy and precision: Make sure that the data being fed into the predictive analytics tools is accurate, comprehensive, and free of error. Only then can a company make sure that its augmented business tools are generated precise, error-free, and accurate insights from the given data.
- Data Bias: Data bias is normally produced when incomplete data is fed into the system. Companies have to ensure that sufficient context-relevant data is built into algorithms that can then analyze all of your data to give more objective results
- Make the software relevant: For the augmented business intelligence tools to provide valuable results, it is important to filter out all the irrelevant data. Users normally don’t have time for this so augmented analytics tools in use must ensure this. Otherwise, users will ultimately stop using tools.
Make the data worthy: Maintenance and regular updating of analytical models are always necessary to ensure the quality of data and the insights provided from this data. Right data should always be incorporated into analytical tools to correctly train the BI analytical tools.
Augmented analytics as to the future of Business Intelligence (BI):
Augmented analytics is a boon to BI tools. Augmented analytics provides immediate automated analysis, enhanced data literacy, rapid data preparation, rapid data preparation, reduced analytical bias, data democratization, and conversational analytics, all these key benefits help augmented analytics cover the demerits and drawbacks of the traditional BI. All modern business intelligence tools make use of augmented analytics.
Its enforcement has acted crucial in the expansion of various businesses and their evolution in modern terms. According to the Forrester Wave report, “the modern augmented BI platform plays a critical factor that determines the difference between successful and other industries.”
No wonder the future of business intelligence is currently relying on advanced artificial intelligence software and augmented analytical tools. With better insight discovery and a detailed understanding of correlations in data along with a powerful platform, interactions will undoubtedly mark the stupendous era for business intelligence in near future.