Data is the new Gold in today's environment, However data collected in isolation has no value.
So like gold, it needs to be manipulated and analysed to get the most out of it. Data is a commodity and it is the insight mined from this valuable resource gives company the means to drive organic growth.
In most of the companies there is lots of data - transactional, analytical, master and historical in nature (lots of Structured or unstructured data) ... we might have heard that, it is a problem sometimes because of performance issues involved running BI reports.
There might be projects to purge data from the systems to improve performance. In today's world, I would say the other way - it is a great opportunity that we should leverage.
Current BI practice at many places has static reports and interactive dashboards where we can drill into information to try and understand why it happened.
I would recommend to transition from self-service BI to more algorithmic approach in BI by finding expertise in AI and ML.
Business Intelligence and artificial intelligence are increasingly crucial yet often misunderstood tools in an enterprise context.
Using Machine Learning algorithms and Advanced predictive analytics, we can calculate trends and future possibilities, predicting potential outcomes and making recommendations.
That goes well beyond the traditional queries and reports in familiar BI tools like Qlik, Essbase to more sophisticated methods like statistics, descriptive and predictive data mining, machine learning, simulation and optimization that look for trends and patterns in the data.
This shifts the traditional role of BI from ”What happened?” to an AI driven model which layers in answers to ”What will happen next?”.
This was never a possibility before because of computing power and tools involved. we can gain an edge over the market to discover hidden patterns in data sets and uncover new information.
With machine learning, computer algorithms can become successively better at optimizing rules, without the need for human intervention or reprogramming. They do this by building upon rules or behaviors in the data they process.
What's required to create good machine learning systems? Highlighted are the ones needed for us :
• Data preparation and connectivity capabilities
• AI and Machine learning Algorithms expertise
• Automation and iterative processes
• Scalability
If AI is new to your organization, Azure, Google Cloud, and AWS provide good general-purpose and specialized machine learning services, you will probably want to choose the platform for specialized machine learning services to add intelligent capabilities without training or deploying your own models.
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