During one of the conferences that I attended earlier in regards to Big Data Analysis using AI and BI, I perceived that by the end of 2021 there would be nearly 40 Zettabytes(ZB’s) of data.
Moreover, a question struck my mind and I couldn’t hold on to it, so I raised it at that very conference:
How can we avoid sorting & filtering the entire data, and instead directly scan through only relevant pieces of data to obtain what we require, to fasten the overall response time?
Everyone seemed clueless, but all that could be drawn out from the discussions was that by that time there would be a powerful technology created to accelerate the process. Being an impatient nerd, I Googled on the topic:
How Big Data can be analyzed using AI & BI?
And Boom 💥 That’s the Future!!
Navigating across various links as well as reading numerous books on Big Data Analysis using AI & BI, I wondered:
- How can we bring AI and BI together?
- What are the pros and cons for them to go hand-in-hand in the long run?
Some of the immediate after-thoughts were:
- Big Data overload
- Shortage of Experts
- Real-Time wholistic insights
- Dashboards don’t suffice
- Spending Money wisely
Cognitive Business Intelligence and Artificial Intelligence
Cognitive Business Intelligence (BI) is the next stage of Machine Learning to design and analyze unstructured data, video, images, and human languages.
Along with Cloud Computing, this has made remarkable progress, in the field of Computing.
The major concern here is:
We are generating massive amounts of data on a large scale. However, is this data being analyzed efficiently, to obtain better insights & run businesses more effectively?
Using Business Intelligence can help in leveraging this better. Moreover, one can analyze various aspects of data with BI.
What does it mean? What kind of data? Data Usage? and so on.
As per Forbes, less than 0.5% of the overall data is analyzed and used globally. With the advent of Cognitive BI, data analysis and decision-making have become more instantaneous.
“The BI tool”, Helical Insight not only provides features such as Email Scheduling, Multi-Tenant Environment, and variety in Visualization but also empowers end-users to add functionalities on the go, using their in-house resources.
Business users with zero technical knowledge can just type their questions & get immediate answers in the form of visually represented data, using such tools.
Today, there is an increased demand for Data Science experts who help generate reports using BI.
AI can easily replace Data Scientists, generating better and more insightful reports and visualizations.
Above all, AI-powered BI can transform Real-Time Data into written reports. Likewise, businesses can make informed decisions with the data, which is seconds old.
- AI adoption is imminent, despite marketplace confusion – Over 50% of the survey groups are using AI technologies. On the contrary, another 20% plan to do so by the end of 2020.
- Predictive analytics is dominating the enterprise – Over 60% of respondents confirmed the use of the technology.
- Enterprise use of AI grew around 300% over the past 5.5 years – Gartner
- Estimated around 75% of organizations will be using Artificial Intelligence (AI) technologies by the end of 2020.
In my opinion, I’d say that data will keep growing day after day, and AI is only going to make life simpler. Furthermore, the integration of Big Data with such advanced, powerful BI tools will help us analyze structured data better, providing visual representation capabilities as well.
In case you have a difference of opinion or have any additional points that can help contribute to this topic, feel free to connect with me, and we can discuss further!