Why a mature analytics framework is the building blocks for tomorrow’s AI
The reigning sentiment is that while companies are racing to integrate artificial intelligence in enterprises, businesses should first build up their layer of data and analytics excellence. Why: because analytics is the foundation for building a successful AI-led enterprise. The proliferation of data is good news for enterprises who are on a quest to find out ways to automate certain processes, thereby delivering a better customer experience. But many organizations in the healthcare, insurance, agriculture and government sector are still grappling with petabytes of data and struggling to embrace it. Bottomline, a mature analytics infrastructure is the building for tomorrow’s AI. If you want to invest in automated capabilities, you need to have analytics modules that can be adapted to interface with ML algorithms. Before one jumps on the AI bandwagon, analytics is still a priority for many enterprises. One of the key areas to emphasize on is analytics maturity, if businesses want to become truly successful in integrating data and dive into AI. A solid analytics foundation is the key to better data models, leading to mature algorithms, thereby creating more effective AI systems over a period of time. A key question to ask is where does your organization figure in the analytics maturity curve. The chain of evolution in analytics looks like this – starting from descriptive, to diagnostic, predictive, prescriptive and cognitive analytics. Industry experts believe most organizations are still in the descriptive stage, leveraging simple BI approaches to find out what’s happening through visualizations. In other words, organizations need to consolidate analytics capabilities to fully build enterprise machine learning systems. AIM lists down top 5 reasons why you need a mature analytics foundation before diving into AI Analytics is part of the evolution that can lead to successful AI system. Case in point, machine learning models are trained on huge datasets. In analytics-aware organization, that deal with data discovery, big data and tasks such as data wrangling, data preparation and integration, AI is a natural progression. Here’s why AI is a straightforward transition for analytics-aware organizations that have a mature model. and a mature analytics system will underpin the success for Artificial Intelligence. Data & more data: AI systems draw on a constantly evolving database of information. Without a mature database to learn from, companies struggle in their AI efforts. A mature analytics framework is the first formative step to set up a successful AI system. AI systems require petabytes of data and a mature enterprise has access to troves of data for crunching solutions. It was the need for data that led to Amazon acquire Whole Foods chain. And earlier, Microsoft’s high profile buyout of LinkedIn was to gain access to millions of data points that Microsoft could take advantage of and turn into successful products. Predictive Analytics: As an organization matures in the analytics journey, predictive analytics is massively leveraged to uncover insights and provide a cutting edge over competitors. Machine learning models can deliver deeper insights and actionable recommendations. In other word, global enterprises that have the biggest databases and the enterprise grade analytics system have the power to harness AI as a major differentiator. Data Quality: Enterprises spend a considerable amount of time in data wrangling and data prep. Here’s why – analytics and AI are both purely data-driven and only the right data can yield the right results. AI systems have to be fed quality, accurate data if businesses want to make informed, data-driven decisions. Experts estimate that misinformed machines lead to a negative business outcomes and can have a bad impact on the organizations. AI systems mature over a period of time as they are fed more data and the right, quality data. According to Darian Shirazi, Co-founder and CEO of Radius predicts, “Quality data is a must for quality AI predictions. Over the years, we will see more companies focusing on solving the challenge of maintaining accurate data, so that AI can live up to the promise of driving change for businesses.” Improved Data Literacy: An enterprise-wide policy on data standards can greatly help in streamlining analytics and machine learning practice. Maintaining a data policy can help in clearly identifying the stakeholders and monitoring the enterprise wide access, thereby reducing employee confusion. Global tech leaders that are most successful at adopting AI techniques incorporate Data Policy into their core business functionality. This is manifested in the form of APIs and interfaces. IoT data: With connected being the buzzword du jour for manufacturing and heavy industries, sensor-laden machines are producing petabytes of data that can only be crunched by an advanced AI system. Case in point – GE Predix positioned as the OS of the manufacturing world is helping drive the digital transformation in industrial settings by providing real-time alerts and embedding intelligence in machines and devices.  One of the biggest areas shaping up in IoT is predictive maintenance where timely analysis of data gives feedbacks on machine requiring immediate repairs. Case in point – Amazon & Facebook have aced the game For Google, Search is a playground for data: A first mover in the space, the search engine is Google’s playground in mining historical data related to searches, cached webpages, web links and browser history. Search is the Mountain View giant’s predominant service and Google bots collect information by crawling the webpages. Based on our clicks and searches, Google  predict the best sites from the first keystroke. Amazon launched custom models and algorithms: Last year, Amazon launched Amazon Machine Learning – a service that allows developers to leverage predictive analytics without requiring any deep machine learning expertise. Primarily aimed at small businesses and developers with limited artificial intelligence experience in training thousand-layer networks or who lack access to huge datasets. Amazon now provide models that are pre-trained using cutting edge deep learning algorithms. Facebook: Facebook is a trailblazer when it comes to leveraging AI. Now with 2 billion people Facebook has access to petabytes of unstructured data. One of the biggest users of Deep Learning algorithms, Facebook uses DL algorithms to understand text.  In a recently published blog, Facebook explained how it uses AI to weed out terrorism related posts from FB. It uses image matching and language understanding to identify terrorists clusters.
The reigning sentiment is that while companies are racing to integrate artificial intelligence in enterprises, businesses should first build up their layer of data and analytics excellence. Why: because analytics is the foundation for building a successful AI-led enterprise. The proliferation of data is good news for enterprises who are on a quest to find […]
Analytics is foundation for building a successful AI-led enterprise. AIM gives 5 reasons why you need a mature analytics foundation before diving into AI
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["machine learning", "artificial intelligence", "AI", "R", "ML", "RAG", "Aim", "deep learning", "analytics", "predictive analytics"]
["AI", "artificial intelligence", "machine learning", "ML", "deep learning", "analytics", "Aim", "RAG", "predictive analytics", "R"]
Richa Bhatia is a seasoned journalist with six-years experience in reportage and news coverage and has had stints at Times of India and The Indian Express. She is an avid reader, mum to a feisty two-year-old and loves writing about the next-gen technology that is shaping our world.