How Does Data Extraction With Machine Learning Build BI?

How Does Data Extraction With Machine Learning Build BI?

Every decision has a unique history. The shortcomings or flaws lay foundation of any invention. This is what we are known as an upgrade.

Evoke the picture of two decades ago. How did the traders do business? How did they derive decisions that yielded millions of dollars?

Answer: Switching to balance sheets of the previous financial year used to enrich them with wise ideas. The accountants scrambled through the pan collected stats. And eventually, it’s the trial-and-error method that used to dominate for drawing the real time analysis. Unlike today, it was a pure hectic and a time consuming method.

The present scenario is quiet jiffy. A lot is happening to make the advanced technology viral. It has the cutting edge that reflects through automation, virtual reality and IoT (Internet of Things).  If you utilize it with the wisdom, it appears as a crucial strategic asset. The pan insight of customers, partners, suppliers, employees and the productivity together create a great learning experience.

Undoubtedly, the information resides in the silos. To fetch it, several sources are hired. Many data extraction tools and techniques are deployed. Finally, data mining process filters the most relevant desirable details.

Why is big data complex?

It’s noteworthy that drilling the voluminous big data is a mind game. It’s so because:

  1. It’s a huge pool of complex data.
  2. However, it’s a strategic asset that illustrates what experience the enterprise has gone through previously.
  3. By extracting the data through software and apps, the business intelligence is built up. That intelligence is churned to derive untraced new patterns. Only the stalwart data analysts can do so.
  4. The information today streams through mobile phones, internet, desktop and IoT devices. All these devices assimilate a big collection which is hard to sift through.
  5. Storing the big data requires Hadoop, NoSQL data warehouses or cloud hosting.
  6. Manual derivation of patterns for business decisions is an uphill battle.

What is machine learning?

The science of unveiling untapped patterns is what we are known as machine learning. This process furthers to forecasting the potential strategies. It helps in configuring a predictive analysis that paves the way to business intelligence.

Its biggest epitome is the fraud investigation via Panama papers. Around 380 journalists heaved a sigh of relief.  It took a team of only 20 people who extracted complicated data from the silos of hard copies. Around 13.4 million files of loan agreements, financial statements and emails are stockpiled into digital landscape. The data conversion technique like OCR & content extraction technology exhibited their value in the data extraction services. The data entry experts installed Apache Solr for indexing the scrambled data. Eventually, the intricate volume of data was transformed and interlinked into a searchable format.

The digital data ready to analyse as the interlinked information was encapsulated as a pop up. Thereby, the visualization became handy to scrutinize the panama case effectively. It’s very much similar to the conversion of the business intelligence into machine learning.

Machine learning enables the systems to automatically learn and provide explicit solutions accordingly. In the panama case, the digitally streamlined data were shrunk to the comprehensive patterns. This is how the inter-connectivity of each fraud was pointed out.

Difference between business intelligence and advanced analytics:

As stated above, the business intelligence is an ability to utilize the intelligence driven from the previous experience of the machine. The machine does so automatically sans prior programming. It is developed as a sensible tool that accesses and uses the stored data automatically.

Advanced analytics are an upgraded version of the predictive analytics. It inserts alterations in the business process so that its efficiency and productivity can upsurge. Data mining, business intelligence, data processing, data extraction and many other techniques are utilized step by step. Several small and big business decisions are routed through this analytics.