The dawn of the digital age was marked by the monumental shift in the way information is processed and analyzed. The widespread use of the internet further resulted in the increased production of data in the form of text sharing, videos, photos and internet log records, which is where big data (large data sets) emanated […]
The dawn of the digital age was marked by the monumental shift in the way information is processed and analyzed. The widespread use of the internet further resulted in the increased production of data in the form of text sharing, videos, photos and internet log records, which is where big data (large data sets) emanated from. Data is now deeply embedded in the fabric of society. Over the last couple of years, the world has been introduced to Artificial Intelligence in the form of mobile assistants (Siri, Alexa, Google assistant), smart devices, self-driving cars, robotic manufacturing and even selfie drones. The ubiquitous availability of open source Machine learning (ML) and AI frameworks and their cognate automation simplicity is redefining how digital information is being processed. AI has already begun impacting how we live, work and play in profound ways.
With the realization that big data on its own is not enough to provide valuable insights. Businesses are now turning to Machine learning to uncover the hidden potentials of big data by supercharging performance and implementing innovative solutions to complex business problems. Judging by the massive rise in popularity for venture investment in recent years, It’s no secret that AI and ML are conceivably the most instrumental technologies to have gained momentum in recent times. Here are some ways by which coupling A.I with big data has helped improve business intelligence.
Content classification is fundamentally used to predict the grouping or category that a new or incoming data object belongs to. Data streams are continually becoming more and more complex and varied. Simply structuring, organizing, and preparing the data for analysis can take up a lot of time and resources. Data classification challenges at this scale are relatively new. The more data a business has, the more strenuous it is to analyze; however, on the other side of the spectrum, the more data the business has, the more precise its predictions will be. Whether this data is in the form of technical documents, emails, user reviews, customer support tickets or even news articles. Finding that balance is crucial. Doing it manually is implausible because it will not scale and in some cases may lead to privacy violations.
Machine learning and big data analytics are a match made in heaven given the needs for operating on anonymized datasets and data analysis. With an artificially intelligent tool, data classification can be used to predict new data elements on the basis of groupings found via a data clustering process. Multi-label classification captures virtually everything and is handy for image and audio categorization, customer segmentation and text analysis. In an instant, the content is classified, analyzed, profiled and the appropriate policies required to keep data safe is applied.
The analytics maturity model is used to represent the stages of data analysis within a company. Analytics maturity traditionally starts with an intent to transform raw data into operational reporting insight to lessen intuition-based decision making. With mounds of data at your disposal, the assumption is that more decisions will be rooted in data analysis than on instinct. But, that is often not the case. Countless Excel models, PhDs, and MBAs have taken number crunching to an entirely new level and yet data analysis is becoming increasingly more complex. Data-driven decision tools often require manual development processes to aggregate sums, averages, and counts. In many instances, the findings lack a holistic reflection and don’t generally put statistical significance into consideration.
An AI and ML driven model facilitates automatic learning without any prior explicit programming. This basically means that they have the ability to efficiently analyze enormous volumes of data that may contain too many variables for traditional statistical analysis techniques or manual business intelligence. All the answers are in the data; you just have to apply AI to get them out. A machine learning algorithm automatically discovers the signal in the noise. Hidden patterns and trends in the data that a human mind would be unable to detect are easily identified. Additionally, the AI acquires skill as it finds regularities and structure in the data; becoming a predictor or classifier. The same way an algorithm can teach itself to play Go, it can teach itself what product to push next. And the best part about it is that the model adapts each time new data is introduced.
Naturally, businesses are more interested in outcomes and action as opposed to data visualization, interpreting reports, and dashboards. The good news is that ‘forecasting’ doesn’t require crystal balls and tea leaves. After gaining insight into historical data, machine learning answers the question ‘what next?’. ML can be utilized to develop generalizations and go beyond knowing what has happened, to offering the best evaluation of what will occur in the future. Classification algorithms typically form the foundation for such predictions. They are trained by running specific sets of historical data through a classifier. The machine learning model learns behavior patterns from the data and determines how likely it is for an individual or a group of people to perform specific actions. This facilitates the anticipation of events to make futuristic decisions.
By powering high-performance behavioral analytics, machine learning has taken anomaly detection to greater heights; making it possible to examine multiple actions on a network in real-time. The self-learning abilities of AI algorithms allow them to offer an investigative context to risky behaviors, advanced persistent threats, and zero-day vulnerabilities. A good use case is in fraud detection – AI algorithms can adapt to varying claim patterns, learn from new unseen cases, and evaluate the legitimacy of a claim. Additionally, ML and AI algorithms can aid enterprises to conform to strict regulatory oversight by ensuring all regulations, policies, and security measures are being followed. By pinpointing the outliers in real time, AI gives businesses an opportunity to take immediate action and mitigate risks.
In a data-driven world, machine learning will be a key differentiator. As business processes become reliant on digital information, organizations have to adopt next-gen automation technologies to not only survive but thrive. The beauty of combining Business Intelligence (BI) and Artificial Intelligence (AI) lies in the fact that business insights can be discovered at incredible speed. From detecting fraud attempts and cyber breaches to monitoring user behavior to establish patterns and predict customer actions, the purview to boost performance and streamline processes is prodigious. Nonetheless, machine learning tools are only as good as the data used to train them.
Author: Gabriel Lando