Efficient Data Entry in the Age of Big Data
Vast sets of information determine important and strategic decisions for corporate and governmental organizations. Businesses run on data as do governments. That data entry is one of the most outsourced services really shouldn’t come as a surprise in this data-driven age.
Data Entry in the Big Data Age Data entry processing does provide the power of information, which can make a world of difference to the organization. It helps make the best use of big data. Here are a few applications and examples:
Retail Industry Supermarkets engage in customer surveys to know:
What areas of service they need to improve, and
What products and services they need to focus on
Retailers can actually benefit enormously from market research when they combine data gathered from surveys along with:
User web browsing and social media patterns
Industry forecasts, and
Records of existing customers
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This gives them the power to predict trends as well as prepare for demand. It also helps them identify prospective customers and focus on some while optimizing pricing and monitoring real-time analytics. This is exactly what Walmart, Sears, Kmart and Amazon have done.
Automotive Technology Automobile manufacturers utilize big data extensively to:
Improve their products
Gain insight on launching new models, and
Improve technology
Back in 2013, Ford was reported to have its hybrid Fusion model generate data of around 25 GB every hour to better understand driving behaviors of users, wear and tear, and collision causes so as to improve further models.
Entertainment Industry Entertainment industry giants such as Time Warner and Comcast have been known to use big data for tracking:
User engagement, and
Media consumption
Video game manufacturers analyze over 4TB worth of operational logs of users each day as well as structured data worth 500GB.
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This is exactly what “big data” is about. In fact, experts believe that companies missing out on big data opportunities will be at a significant disadvantage and lag behind the competition in terms of productivity, innovation and, ultimately, profitability.
Understanding Big Data and Data Sets Big data is a collection of large and complex data sets that cannot be processed through traditional data processing applications. Advanced methods such as predictive analytics are used to extract value from it.
A data set normally contains information corresponding to data in a single database table or data matrix, with each column representing a variable and each row corresponding to a member of the set. Values are listed for each variable. The data set may also include information regarding multiple members or an event or experiment.
Data Entry Errors Affect Analyses Accuracy is important to truly benefit from this highly empirical research that largely consists of figures, percentages and comments. Errors can be costly, time consuming and wasteful. Typical data entry errors include:
Missing entries
Incorrect value
Merging of data, and Multiple entries
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Manual examination and validation are time consuming and are therefore usually restricted to small data sets. Large scale data sets require computerized algorithms, offered by dedicated services, to identify errors.
Understanding Why Errors Occur Data errors can creep in due to various factors, many of which are unintentional. Mistakes occur when people fill in web forms.
Zip codes could be wrongly entered, and fields could be filled with other wrong information such as the name of city and the state in the field where only the city name is required.
House numbers could be incorrectly entered in.
Such inadvertent errors can be identified by data cleansing software. It can check if the zip codes entered match with the respective addresses. Some software can also correct the errors.
Mistakes also occur when software produces table entries that are based on more complex data:
This normally happens when the software needs to condense complex data for the purpose of quantitative analysis.
The condensed form could be just a few words that describe that complex data, such as adjectives that indicate whether a review is positive or negative through certain parameters such as “service”, “value for money”, etc.
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Errors could occur when the condensed description in incorrect or inadequate.
Improving Data Collection to Reduce Intentional Errors All aforementioned inaccuracies are inadvertent errors. Intentional errors - such as users entering incorrect information about themselves in a survey to protect their privacy – cannot be rectified by software though, unless more details about the survey participants are available to it. A lot hinges on the way the information is collected. Some websites implement measures to confirm the email IDs by sending verification links to the entered IDs and asking users to click them.
With reliable websites and organizations, though, people would not be hesitant to fill forms or surveys with true details. Organizations need to build a reputation for trust and reliability to prevent users from providing false information.
Efficient Data Processing – the Key to Value All said and done, the true value of big data depends on its accuracy. This is why most entities outsource their data capture, data entry, and data conversion tasks. Professional service providers are well-equipped to provide quality services at costeffective rates.
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