Advanced Analytics – what lies today and what tomorrow will bring

Advanced Analytics – what lies today and what tomorrow will bring

Nov 26, 2019

Background Check

A few decades ago, one video game featuring a plumber named Mario became quite famous. It was a simple game where collecting the points and moving forward level by level was the main target. For children, it was a source of recreation, but for analytics engineers, it was a source of stiff competition. The challenge was to come up with the best gaming algorithm. Gaming is not only about fun & frolic; it also helps uncover multiple modeling techniques. One of the students took the challenge and amended the existing algorithm with a Genetic Algorithm. Just like Mario, Nintendo was also popular with video games in its era. Tom Murphy, one of the computer graduates, automated different versions of the game using Machine Learning techniques.

Though the core concept of advanced analytics remains the same, the contour of its application has seen tremendous growth. From the meager first gaming consoles to real-time simulated games, we have seen the technologies upgrading successfully. Today, we see almost all industries are banking upon the need to go for analytics. Undoubtedly, technology is experiencing an exponential growth due to enormous demand.

Analytics-driven automation will diminish the manual tasks by 40% in the days to come.

In today’s context

The globalized logo that has become mainstream is that The saying “change is the new constant” goes well with Analytics. Though the term has become ubiquitous, one can’t deny the fact that the world is witnessing an analytical makeover in many different fields every now and then. However, in this intelligence governed era, the main core lies in the data. With every passing year, we see positive escalation in almost all technical spheres. The concepts of artificial intelligence & machine learning, which was once a knowledge portfolio, has now gained a real-time practical touch. Today, we might forget to boil the milk, but our refrigerator might remind us of the same by sending a notification over our smartwatch. Such is the beauty of connected devices, which is nothing but the extended application of AI.

Below are some of the trends that are going to revolutionize and disrupt the data-driven technologies by paving its way into the business field through the digitized path:

Gleaning the quality-intensive data

The countless sources of information available to supplement data are often redundant and laden with errors. Such mammoth size data does nothing but add to the complexity of operations. To counter this anomaly, the concept of Data Quality Management is trending in the market. This management technique will clarify the context of data usage, thereby ruling out the data not relevant to the business in question.

Intelligent deployment of Artificial Intelligence

Trending as one of the most sought-after strategic technologies, Artificial Intelligence has paced up data management & business intelligence operation. The best application where AI has found its place is the streaming of Live Dashboards. This facilitates the option to keep a real-time eye on every function. Another technology named Generative Adversarial Networks is exhaustively using the AI application. This technique deploys AI in a dual manner, whereby one AI application creates a realistic image and the other one checks whether it’s artificial or not.

Connect the dots by connecting clouds.

The proliferation of cloud-based tools has accelerated the movement over to the cloud much more smoothly. Seizing the remarkable zeal for cloud adoption, the multi-cloud strategy is gaining momentum. This technology works on multiple platforms and ensures enhanced flexibility. Major giants like Apple, Amazon, Google, etc. are trying to gain more insights out of this AI-enabled solution.

Advanced Predictive Mechanisms

Basically, predictive tools look over past data to bring forth predictions for the future. This extended model of data mining helps in troubleshooting the potential risks that are in forecasting radar. With many tools actively involved in doing futuristic studies, Artificial Neural Networks have gained tremendous popularity in their domain. Being a replica of Biological Neural Networks, the neural networks offer the flow or framework, where multiple machine learnings can work together on complex input. Composed of artificial neurons, this mechanism works in the same fashion as the brain does. One artificial neuron sends signals to another. The output of one neuron serves as the input for another neuron. Upon receiving the signal, the neuron processes it and sends it further. This process continues over a period. Similarly, Autoregressive Integrated Moving Average (ARIMA) works on the existing model or data by taking inputs from the past and guiding future footprints. This model functions by inspecting the autocorrelations, which compares current data values based on past values. It also ascertains the number of steps from the past that should be optimum and advisable to conclude strategic predictions.

When computing makes a leap

Though the term computing may seem quite a generalization, once the term like Quantum Edge gets attached to it, it brings forward a revolutionizing shift. The advanced & dynamic application of Quantum Computing offers data encryption seamlessly. The multiple features of quantum techniques offer better data modeling and real-time actionable insights into domains that generate large amounts of data every second. The smart algorithms of this computing mechanism facilitate the real-time conversations between the involved parties. Likewise, Edge Computing, in accordance with the data analytics tool, attenuates the performance of the network management. This computing ensures the availability of data sets to be closer to the users by parting it from the silo setup.

Technology Culmination

To add smartness to technology, analytical approaches should become more advanced. Since we are living in an era of vast information, countering redundant and pointless data, and acknowledging the necessary kinds becomes one of the unavoidable challenges. A renaissance in the era of data analysis is the prerequisite for the ever-expanding bits and bytes. The journey of inhibiting intelligence has already started, but the destination seems boundless, as it can neither be defined nor framed within a periphery.

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