Together, generic Keppra without prescription Big Data and Artificial Intelligence create a synergy that can advance the digital transformation goals of an enterprise.
Big data as a standalone technology has a might of its own to process large volumes of structured and unstructured data. Artificial Intelligence has touched base with all modern-day industries where scalable data analysis is required for decision making. Big Data can be an excellent catalyst for the Artificial Intelligence and Machine Learning initiatives of data-driven enterprises.
According to NewVantage Partners’ annual Big Data Executive Survey 2017, 88.5% of top-level executives agreed that Artificial Intelligence and Machine Learning to have disruptive capabilities that can impact their firm over the next decade.
At least 37% of the same population of executives have invested more than $1 Million in Big Data initiatives with more than 6.5% investing over $1 Billion.
The trend is clear. Big Data and Artificial Intelligence convergence will help enterprises gain the maximum benefit out of their data.
Reasons that drive Big Data-Artificial Intelligence Adoption
There are 4 broad trends that have catapulted Big Data into an empowering technology.
- An exponential increase in global data creation. Data can now be sourced from social media, mobile phones, Internet of Things, wearables, etc.
- A massive increase in computing power for data processing. Today’s smartphones are as powerful as the computers that powered the Apollo mission
- Cheap commodity hardware for data storage. Drastic reduction in computer memory prices, increase in hard drive capabilities, cloud storage, data center facilities and so on.
- Advancements in quantitative analysis and mathematical algorithms used in Artificial Intelligence and Machine Learning.
Artificial Intelligence and Machine Learning have been a fantasy for technologists since the 1990s. But, it is only now with technological advancements and market maturity that they have become to be mainstream technologies. This excites enterprises as they can run real-time data analysis on large datasets using sophisticated algorithms to unearth new business insights.
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AI-adopting enterprises need to build data pipelines to unify all their diverse datasets into a single database. The unified database would be large in size making it difficult to pick the right dataset which can be fed to the AI and ML system for arriving at predictions. Big Data with its analytical prowess can help serve the right data at the right time in a uniform manner thus augmenting the abilities of the AI and ML system.
Take, for instance, the need for customer segmentation in business planning. Big Data can help spot groups of data that share similar patterns. It can also help sort out customer data that exhibit specific buying behavior. An AI system can build upon this segmented data and help the business plan accurate procurement, production and distribution plans for the future.
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Until Big Data arrived, data scientists relied on sample datasets to make conclusions. With Big Data their scope of analysis expanded exponentially enabling them to have data agility at any scale. Big Data removed all barriers of data volume, velocity, variety, and veracity. The access to real data helps data scientists to narrow down to the last fine detail with ease.
Based on their analysis, data scientists can group and regroup data clusters which can be loaded onto “analytical sandboxes” or “Big Data Analytics Center Of Excellence” from where the AI system can pick up data patterns. Ultimately, it would advance an enterprise’s transformation into a data-first organization.
Open-source Big data tools can help quickly scale data workloads to meet the current and future requirements of the organization. Big data also offers unlimited data scalability which helps process large data volumes in a single application.
Success Story: MetLife advances business results by empowering AI with Big Data
MetLife – the American insurance giant is deploying Big Data-enabled AI in a number of ways. Peter Johnson, MetLife’s VP of Enterprise data services and application services says, “We have 90 million customers and a lot of deep data on them, but it is unstructured stuff like web data, phone dialogs, and applications. We need to have very long-term relationships with our customers and data is a key.”
To overcome the challenges of unstructured data in MetLife has adopted the following practices:
- Using speech recognition to track incidents and analyze doctor reports that were previously in the written form
- Improving back-office claim processing cost-effectiveness and productivity by analyzing unstructured data using Machine Learning
- Deploying automated underwriting to quicken claim processing based on patient insurance telematics
With data creation increasing to insane levels, Big Data has attained a new level of maturity that is hard to ignore. Artificial Intelligence relies on data lakes to analyze and arrive at predictions. It is quite natural that enterprises who want to make the most of their data would bring these two technologies together.
To sum it up, Big Data and Artificial Intelligence need each other in to reap benefits of a bigger scale.