How can AI accelerate the process of analyzing Big Data efficiently?

Cognitive Business Intelligence & Artificial Intelligence

Cognitive Business Intelligence (BI) is the next stage of Machine Learning to Design and Analyse Unstructured Data, Video, Images and Human Language. Along with Cloud Computing, this has made a remarkable progress in the field of Computing.

The main concern here is: We are generating massive data on a large scale, but is this data being analyzed efficiently so as to create better insights for businesses to run effectively?

This potential can be leveraged using Business Intelligence. Using BI, we can understand what really the data means.

At the moment, less than 0.5 percent of all data is analyzed and used globally.

Cognitive BI will make the entire data analysis and decision making instantaneous. Helical Insight not only provides features such as email scheduling, multi-tenant environment, variety in visualization but also empowers end users to add functionalities on the go using their in-house resources.

There are various BI tools like Helical Insight, Tableau, Microsoft’s Power BI, etc. that allow business users with zero technical knowledge to just type in their questions and get immediate business answers in the form of data.

Today, there is an increased demand for data science experts who help generate reports using BI.

AI can take the place of these data scientists to generate reports and other visualizations. Most importantly, AI-powered BI can transform real-time data into written reports so businesses can make informed decisions with the data which is seconds old.

During a conference in New York regarding Big Data, I heard everything about the importance of data and how by 2020, there would be around 40 zettabytes of data.

The main question I raised during the conference was, how would we avoid sorting and filtering the data but instead directly scan through only relevant data and obtain what we require, so as to fasten the results and response time.

No one had any clue on this topic but all assumed by then a powerful technology would be created to accelerate the process.

Being an impatient nerd, I scanned through Google as to: How can AI be used to support Big Data? And Boom…there it was! That’s gonna be the Future!!

AI and BI

I read through various links and books on how AI and BI could be brought together and why AI is essential for BI in the long run:

  1. Big Data Overload!

    Big Data is growing in volume, variety and at an unprecedented speed. It has the potential to offer substantial insight to businesses. Globally companies are investing in Big Data tools to analyze the data and aggregate it. But how do you explain the results of that analysis in a way that everyone understands?  That’s why we need Artificial Intelligence to transform data into easy-to-understand written insights at scale.

  2. Shortage of Experts

    The United States faces a shortage of 190,000 people with analytical data skills. More staggeringly, there is a shortage of 1.5 million analysts who can make decisions based on data. It is cost prohibitive to assign data experts to every department in a company. Even if a company could afford to do this, data analysts just cannot analyze and explain data fast enough.

  3. Real-Time Insights

    Due to the staggering growth rate of Big Data and the speed at which the market moves, it is impossible to make strategic decisions from old data. Artificial Intelligence and Natural Language Generation, however, allows businesses to perform real-time data analysis with one click of a button. Fresh data has additional value when it can be understood and acted upon in real time.

  4. Dashboards Aren’t Enough

    An analyst can help with one dashboard from one dataset but what if you have thousands of data points coming from multiple data sources in real time? How can dashboards be explained all at once? Artificial Intelligence can apply reasoning to data and explain what it means at scale.

  5. Money Needs to be Spent Wisely!

    AI systems like Yseop Compose can reduce the cost of writing Business Intelligence reports by 77%. But the first step is the figure out that value of Natural Language Generation and where it could fit into your business workflow. Download our recent eBook to get a sense of when you need Natural Language Generation and AI and when you don’t.

AI adoption is imminent, despite marketplace confusion – 38 percent of the survey group are using AI technologies and another 26 percent plan to do so by 2018.

Predictive analytics is dominating the enterprise – 58 percent of respondents confirmed the use of the technology.


62% of Organizations Will Be Using Artificial Intelligence (AI) Technologies by 2018.


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