The worlds of AI and BI occupy distinct places in the analytics continuum, which is most often understood with concepts like descriptive analytics, predictive analytics, and prescriptive analytics. Users can leverage descriptive analytics and BI tools to explore what happened in the past, while predictive analytics makes use of ML models trained on real-world data to generate an educated guess about what will happen next. However, the lines separating these two camps are getting more blurry by the month. For years, Gartner has talked about how BI tool vendors are adding more ML and AI capabilities to their wares. In its latest Magic Quadrant for Analytics and BI Platforms, the firm talked about how the next generation of "augmented analytic" products will bring ML and AI to bear on things like data prep, query generation, and insight generation.

The ways of businesses are evolving. Influence of artificial intelligence (AI), machine learning and business intelligence is constantly making sure that users become more and more comfortable online. As organizations are constantly choosing to take their businesses online, they are being increasingly flooded data, rather say Big Data. They are so much complex that traditional Business Intelligence (BI) has become incompetent to handle them. This is where augmented analytics are required.

If 2020 has proven anything, it is that organisations need to be more efficient, further leverage data, and more accurately plan ahead. For companies looking to invest in the future the solution may lie in adopting the machine learning concepts in SAP Analytics Cloud (SAC). Machine Learning sounds like a daunting and complicated concept especially for end users with minimal technical experience, SAC delivers a selection of simple but powerful machine learning features categorised within the tool as "augmented analytics" which provide the opportunity for any business to empower their employees and increase the effectiveness of their Business Intelligence (BI) solution. This next step in the evolution of business analytics will ensure that organisations have access to key information at the right time to best influence a positive future. Augmented Analytics:When we talk about augmented analytics what do we actually mean?

Augmented analytics: the combination of AI and analytics is the latest innovation in data analytics. For organizations, data analysis has evolved from hiring "unicorn" data scientists – to having smart applications that provide actionable insights for decision-making in just a few clicks, thanks to AI. Augmenting by definition means making something greater in strength or value. Augmented analytics, also known as AI-driven analytics, helps in identifying hidden patterns in large data sets and uncovers trends and actionable insights. It leverages technologies such as Analytics, Machine Learning, and Natural Language Generation to automate data management processes and assist with the hard parts of analytics. The capabilities of AI are poised to augment analytics activities and enable companies to internalize data-driven decision-making while enabling everyone in the organization to easily deal with data.

Business intelligence and analytics platform vendors are now providing augmented analytics capabilities that empower citizen data scientists. Specifically, they use natural language understanding capabilities to enable natural language searches and deliver the results using natural language generation. The result is a "conversation" between the user and the system. Augmented analytics tools also come with pre-built machine learning models to empower any user to do single click forecasts, identify trends and trend reversals, anomalies, outliers -- tasks that in the past required involvement from professional data scientists. In short, the opportunities are many, but many enterprises have a way to go before their businesses are truly "insight-driven."

Adverity, a leading force in M, launched Augmented Analytics, an automated tool for marketers. Using artificial intelligence (AI) and machine learning, it can access hidden insight and provide critical intelligence on data to improve marketing measurement effectiveness and maximize performance. Providing an insight-led approach to marketing, Augmented Analytics enables marketers to stay agile in the new, rapidly accelerating marketing landscape, and empowers brands and agencies to realize the full value of their data. It optimizes performance by uncovering the deep insights that conventional analytics approaches can miss and significantly decreases the time to discover them. This allows marketers to identify patterns and detect anomalies as they happen so opportunities are not missed, and hidden issues are revealed before becoming a problem.

Big data has been called a strategic asset, a competitive advantage, and for a short while, was often referred to as "the new oil." Today, the idea that data is valuable isn't exactly news. However, many organizations don't have a reliable way to transform raw data into action. Today's businesses have access to massive amounts of data, yet it's becoming harder to manage all of those data points, identify what's important, and chart a path toward making meaningful improvements. In this article, we'll provide an augmented analytics definition, explore it's business intelligence (BI) origins, and discuss the game-changing potential just on the horizon.

If data is the gas in a car, then, analytics is the car itself. Currently, there are a few trends and topics in tech without which the talk around technology and innovation is incomplete -- analytics, artificial intelligence, blockchain to name a few. Augmented analytics is an extension of analytics that focuses on three main areas -- Machine Learning, Natural language generation (NLP) and, Insight automation. The basic premise of augmented analytics is the elimination of painstaking tasks in the process of data analysis and, replacing them by automation thus, refocusing human attention on modern analytics, business process, and business value generation. As per predictions made by Gartner, over 40% of tasks involved in data science will be automated thus, increasing productivity, quickening the process, and initiating broader usage of data and analytics.

Data science is the discipline of making data useful. There is absolutely no doubt that this decade has bought loads of innovation in Artificial Intelligence. Besides Artificial Intelligence, we are witnessing a massive boost in the data generated from thousands of sources. The fact that millions of devices are responsible for this enormous spike in data brings us to the topic of its smart utilization. The domain of Data Science brings with itself a variety of scientific tools, processes, algorithms, and knowledge extraction systems from structured and unstructured data alike, for identifying meaningful patterns in it.

Data heavily drives today's marketplace. Hence, it is imperative to have solutions in place that will help to analyze and will provide better insights for informed decision making. Today, enterprises have massive amounts of data at their disposal. Though having such a huge chunk of information is advantageous, it also has the potential to cripple an enterprise's decision-making ability. Conventional BI solutions aren't built to handle such large datasets, which are complex, ever-evolving.