Augmented Analytics and the Rise of AI-Driven Insights

Sudhakar Singh, Editor, CIOTechOutlook | Sunday, 21 January 2024, 13:18 IST

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With every passing day where around 2.5 quintillion data bytes are generated, the potential of data analytics is about to rise to a level beyond which enterprises will not be able to comprehend its full worth or benefit. In order to keep up with this rapid evolution, the companies have to learn new ways of pattern recognition and trend prediction using the artificial intelligence. This is where the augmented analytics is emerging as a really useful tool to achieve this goal. According to a Mordor Intelligence report, the Augmented Analytics market is projected to attain USD 80.25 billion by 2029 with a CAGR of over 28 percent. In terms of augmented analytics, machine learning and natural language processing are the most relevant techniques as they allow the enterprises to automate the entire process from data preparation through visualization right up to interpretation in order to speed up their decision-making. This can make the enterprises competitive in the contemporary business setting.

Let us discuss three ways in which businesses can leverage augmented analytics.

Automated Data Preparation

Augmented analytics can be used by businesses to achieve effective and efficient data processing workflows. This technology relies on machine learning algorithms that do the cleansing, structuring, and integration of data from multiple datasets. It not only helps to increase the pace of the data readiness stage, but also guarantees precision and uniformity, thereby reducing the chances of errors caused by manual handling. Organizations can achieve the best possible utilization of resources, enable analysts to focus on insight discovery rather than data complexity management and hence speed up decision making through automated data preparation. This time efficient method enables the organizations to respond rapidly towards changes in market and also to avail opportunities as they arise swiftly, within such a quick changing environment.

Amazon utilized the augmented analytics for automated data to actually revolutionize client Personalization. Machine learning-enabled algorithms enable Amazon to automate data cleansing and merging from multiple touchpoints. However, the precision and timeliness of submission were ensured by an extremely meticulous process. Personalized real-time product recommendations that converted 25% and left a customer satisfied. Amazon improved on data jobs using augmented analytics and automation so that the site could deliver incredible personalization experiences to customers, establishing a strong leadership position for it in the highly competitive market.

Enhanced Predictive Analytics

Enterprises are empowered by the utilization of augmented analytics, securing a competitive edge through the elevation of predictive analytics. Meticulous scrutiny of historical data, unraveling complex patterns, and forecasting future outcomes with heightened precision are enabled by the integration of intricate machine learning algorithms. Positioned as a strategic asset in the retail domain, the facilitation of forecasting consumer demand, optimization of inventory management, and refinement of the efficacy of supply chain operations is achieved by augmented analytics. Businesses are empowered by this proactive approach to foresee market shifts, alleviate risks, and capitalize on emerging opportunities. The adoption of enhanced predictive analytics not only streamlines decision-making processes but also sees organizational adaptability and resilience being bolstered by the infusion of data-driven insights into dynamic market scenarios.

Verizon, a telecommunications powerhouse, harnessed the prowess of augmented analytics to fine-tune network performance and elevate predictive analytics. By unleashing sophisticated machine learning algorithms, they foresaw potential network hiccups and automated the orchestration of network data. Proactively nipping issues in the bud before they could tarnish service quality, the company executed a savvy game plan. Conducting timely maintenance, guided by predictive analytics uncovering patterns indicative of impending outages, yielded a substantial 30% drop in network downtime, robust cost savings, and a spike in customer satisfaction. Verizon strategically paraded the game-changing impact of real-time decision-making in the telecommunications sphere by deftly wielding AI-driven insights, highlighting its distinct role in this unfolding narrative.

NLP for User-Friendly Interaction

Within the business domain, augmented analytics emerges as a conduit for nurturing user-friendly interactions through the application of Natural Language Processing (NLP). By embedding NLP within analytics platforms, companies facilitate unambiguous language queries, allowing users to seamlessly navigate intricate data sets. This broadens access to data, empowering non-technical individuals, including executives and decision-makers, to effortlessly glean insights. This human-centric strategy not only expedites decision-making across organizational tiers but also fosters enhanced collaboration. User interfaces propelled by NLP metamorphose data exploration into an intuitive dialogue, effectively dismantling barriers between technically adept and less-technically-inclined stakeholders. Consequently, businesses engender a more all-encompassing, data-driven culture, where readily available actionable insights engender well-informed and nimble decision-making.

Salesforce employed augmented analytics with Natural Language Processing (NLP) for user-friendly interaction, redefining their customer support paradigm. NLP-driven interfaces empowered service representatives to effortlessly navigate and analyze complex datasets using natural language queries. This seamless interaction facilitated rapid access to customer information, enabling personalized responses and efficient issue resolution. Salesforce's adoption of user-friendly augmented analytics not only minimized training efforts for non-technical staff but also fostered enhanced collaboration across teams. The outcome was a notable 20% surge in customer satisfaction, showcasing the tangible advantages of Salesforce's human-centric approach to data interaction in the realm of customer support.

"What used to require weeks of writing code can now be accomplished with a few mouse clicks and a lot of automation on the back end. The new capabilities of automated ML reduce the need to deeply understand how the variables affect each other, automatically choosing the best combination of algorithms for a given task. It is a much more automated and augmented process, so it is more accessible.” - Carlie Idoine, Research Director for Business Analytics and Data Science for Gartner.

In the field of augmented analytics, enterprises glimpse a future with revolutionary potential, vowing unparalleled insights and operational efficiencies. As machine learning forges ahead, augmented analytics, in turn, readies itself to autonomously unravel intricate patterns, discern trends, and establish correlations within expansive datasets, thus curtailing the need for extensive human involvement. Seamlessly incorporating augmented analytics into decision-making frameworks is the key that corporations can turn to, endowing stakeholders with instantaneous, actionable insights. Through the embracement of these cutting-edge advancements, a distinctive competitive advantage can be unlocked by businesses, innovation spurred, and adept navigation through dynamically evolving landscapes achieved.


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