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<br>Big data analytics projects often fail because of the following seven mistakes, which can be easily avoided if you know how to spot them and take the necessary steps to correct them. These mistakes are all too common in the big data analytics industry.
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HOW TO DO BIG DATA ANALYTICS FOR YOUR BUSINESS
KEY STEPS STEP 1: CATEGORIZE YOUR BUSINESS DATA. ·STRUCTURED DATA. THIS IS THE MOST COMMON TYPE OF DATA, AND IT'S USUALLY STORED IN A SPECIFIC FORMAT. FOR EXAMPLE, YOU MAY HAVE AN EXCEL SPREADSHEET WITH COLUMNS FOR EACH CUSTOMER AND COLUMN FOR EACH PRODUCT SOLD TO THAT CUSTOMER.
STEP 2: ANALYZE EFFICACY AND DATA RELIABILITY. THE NEXT STEP IS TO ANALYZE THE EFFICACY AND DATA RELIABILITY. THIS CAN BE DONE THROUGH THE USE OF BIG DATA ANALYTICS TOOLS, WHICH WILL HELP YOU TO DETERMINE WHETHER YOUR BUSINESS HAS ENOUGH DATA FOR YOU TO MAKE GOOD DECISIONS
STEP 3: IDENTIFY THE APPROPRIATE ANALYTICS TOOL. YOU HAVE A NUMBER OF TOOLS TO CHOOSE FROM WHEN IT COMES TO ANALYZING YOUR DATA. THERE ARE MANY ANALYTICS TOOLS AVAILABLE, BUT THEY ALL HAVE THEIR STRENGTHS AND WEAKNESSES. BEFORE SELECTING A TOOL,
STEP 4: COLLECTING, PREPARING AND PRE-PROCESSING OF DATA DATA COLLECTION IS THE PROCESS OF COLLECTING DATA FROM MULTIPLE SOURCES. IT INVOLVES BRINGING IN INFORMATION THAT MIGHT NOT BE EASILY ACCESSIBLE TO YOUR BUSINESS WHEN YOU NEED IT. THIS CAN INCLUDE THINGS LIKE WEATHER REPORTS, CUSTOMER FEEDBACK, SALES FIGURES AND MORE.
STEP 5: SELECTING MODELS, TRAINING AND TESTING DATA. ONCE YOU’VE CHOSEN THE RIGHT MODEL, IT’S TIME TO SELECT TRAINING AND TESTING DATA. TRAINING DATA IS THE INPUT THAT WILL BE USED IN THE MACHINE LEARNING ALGORITHM. THE TESTING DATA IS WHAT WILL BE USED FOR VALIDATING WHICH MODELS ARE MOST ACCURATE FOR YOUR BUSINESS PROBLEM AT HAND.
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