Consider an example of shopping mall. It is segregated into dresses, stationery, crockery, toys, kitchen utensils, beauty products and confectionery sections. All segments are well stocked. Several customers make purchases worth thousands individually. They search commodities and match their requirements. Finally, they pick up what is relevant to them. Now, replace consumers with outsourcing market research companies and the mall with big-data. The former put big data into the funnel of their relevancy criteria. They scrap the most relevant out of it for meeting business requirements. Thereby, business intelligence is crafted through transformative ideas.
Foretold example illustrated how data is explored and extracted. Below given functions will present its deep illustration which helps in business processing.
How data processing functions?
Data refining passes through several processes to release an outstanding analytical report at the end. The commonest of them are below:
Validation: Validations means verifications. Exploring big-data can trap the data miner in puzzles. Most of the time dilemma catches them over which data should be extracted. Thus, they match their requirements with the data on internet and other resources. For example, a financial data researcher extracted transactions details of its client’s company. His aim was to track the banking transactions during a particular financial year. His client did banking transactions with many banks. The researcher collected details from receipts, invoices and cheque-book. Eventually, he cross examined the available information accessing his online banking details. This way, he ensured the correctness of the data.
Likewise, data validation removes flaws and garnishes specific information to develop business intelligence.
Sorting: It stands for arranging items. Extracting data is just the beginning. It should be arranged in proper order. Could it be possible to withdraw prospective business strategies from the messy data? Of course, it’s a herculean act! Classifying the extracted data into patterns wins half the battle of comprehension. Thereafter, analyst analyzes and anticipates future strategies.
Summarization: Synopsis mirrors what the researcher digs out. Like minutes of the meeting, summary highlights the main points. Thus, deriving bottom line becomes a piece of cake. It catches eyeballs and builds intelligence instantly because it’s short and crispy. One can catch all the points there if lacks time to go through the long report.
Aggregation: Aggregation combines multiple chunks of data together. Multiple tables, charts, images and statistics can be a report’s elements. So, aggregation brings them together to prepare an influential report at the end.
Analysis: Interpreting the dug information presents analysis. It’s important as it is served as a report card carrying pros and cons of operational activities. Data processing aims at analyzing to configure future strategies. Expansion, growth and profit ratio stand on this data processing pillar.
Reporting: The entire data processing wraps up in reporting. It determines the loopholes in performance, suggestions, recommendations, betterment strategies, prospective operational parameters etc..
Thus, outsourcing data processing determines operational and prospective operations of the company. And unbelievably, it costs dirt cheap. The entrepreneur gets enough time to maintain core competencies. So, a business can evolve way to success through the power of data.