Unless we’ve been living under a rock, we likely are aware that AI (Artificial Intelligence) is one of the fastest growing technology segments. How to keep pace with this technology advancement is a major question being discussed in corporate boardrooms. This article discusses why AI and big data analytics are an important pairing.
For businesses that have been doing great work with data analytics, Artificial Intelligence is the next logical step – AI algorithms enable organizations to add further intelligence to data analytics systems, helping them uncover new possibilities, identify future developments and make intelligent predictions that could transform everything from business processes to customer experiences.
But what about the companies that don’t have a handle on their data, and want to leapfrog quickly into AI? The results could be disastrous. The point here is simple – AI can’t work without data. Data is the oil that powers AI algorithms, and the success and magnitude of AI applications rely on organizations’ ability to engage with their data.
It will help to learn from the numerous examples of organizations that are making breakthroughs in the AI space by doing progressive things with their data. The common strengths are a strong data analytics backbone and a fair degree of automated processes that make them ready to take on AI. Let’s use an example in the retail sector. Pricing is a crucial factor in the online retail space, with retailers facing constant pressures to offer all-time discounts, better than their competitors. This requires them to have access to real-time competitor pricing data. Now, imagine a scenario where a retailer has to collect this data manually. Even the most sophisticated AI algorithms will not help them draw the right conclusions, because the data itself would be obsolete. On the other hand, if the retailer has a strong data manipulation backbone, it would be well-positioned to use real-time data to make daily price adjustments and future decisions on pricing strategies using AI.
Large volumes of data powers AI
AI and its variants – machine learning and Deep Learning thrive on data feeds to derive intelligent insights. Massive volumes of data need to be fed into systems to test various AI algorithms to derive the right conclusions and patterns.
Across sectors, organizations are amassing the data ‘oil’ to power their AI dreams. Take the case of the automaker Tesla — the company is said to be having access to 780 million miles of driving data and is adding another million miles every 10 hours. It has gathered unprecedented data sets from its customers asking them to share driving video clips that it hopes to use to design its autopilot self-driving feature.
Google is also known to be making constant improvements to its self-driving cars continually testing new features using data on simulated driving history of its 55-car fleet. These developments underscore the increasing value being attached to data in organizations in attempts towards making breakthroughs in AI.
Clean data is essential for AI
IDC estimates that the digital universe will create 44 Zettabytes of data by 2020, but only a small percentage of this data will be of value to businesses. Very often it’s seen that organizations create and accumulate huge amounts of data – but these data sets would be scattered across and held in silos, hence making them difficult to use.
For AI to function effectively, data must be integrated and well-structured. This requires employing big data techniques such as data collection, data storage, data cleansing etc. Structured analytics and centralized data processes ensure that data is standardized and offer a uniform view for decision making. For instance, retailers can use structured data analytics to get a uniform view of their customers (i.e. past purchase history, usual preferences, and products least favored etc.). Having this uniform view of customer data can help these companies draw deeper insights from AI to predict further trends — like how certain products would perform in the future, and make highly personalized recommendations based on these insights.
Going ahead, we will see companies that successfully combine big data analytics and AI to be at the forefront of technology led transformations.
Most importantly, AI, or any other technology for that matter, succeeds when it is used with a strategic vision. This requires careful assessment of an organization’s objectives, identifying the critical problem areas/challenges where AI can make an impact, and an understanding of its own data and how this data can be used to support advanced technologies like AI to solve real business problems.
In conclusion, Big Data and AI need each other and together can make a very powerful combination for businesses in their quest for intelligent decision making.
Building intelligent AI products and platforms requires a clear roadmap to leverage value out of data trapped in large siloed pools to answer the most critical business issues and provide greater stakeholder value. Ness enables leading organizations to realize their AI potential helping them uncover real opportunities and value of out of their data.
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