How bad data is weakening the study of big data
By: Shaik Sameeruddin
Tags: Technology, Big Data, Data Science, Machine Learning, Artificial Intelligence

Gify

In a 2013 New York Times post, 2012 is the Big Data breakout year. The big data promise was appealing, digital web data too large for conventional data processing was collected, new software innovations were applied for mining, and infinite problems could be solved. Big data has a record of progress since 2012. Since then. By 2017 Gartner Research reported a failure rate of nearly 85% in large data projects. Another 2018 Gartner study found that 91 percent were not transformational market intelligence levels from the 196 firms interviewed for big data and analysis.

Big Data

A major problem facing many Big Data is that it is unstructured, often incomplete, and of poor quality. It is difficult to find evidence that demonstrates what it is meant by the proliferation of choices. Success can be deriving from fresh clean, privacy-friendliness, accuracy and direct customer responses sources of specific quality data.

While many were grappling with the Big Data Revolution, an ancient assertion was ignored: GIGO, waste in waste. It seemed that quality was not so important as quantity with all new technological resources for collecting, processing and analyzing the ever rising volume of digital data.

Today, the volume of data is high. In the last 2 years, it is estimated that 90% of the worldwide data has been produced. Data science and machine learning disciplines in worldwide business analysis are driven by exponential data development. Although mathematics is the only basis for each aspect and discipline that corresponds with data science, statistics and probability are both applicable sciences. In particular Big Data Applications, each of these “hard-science” will find digital data of highest value.

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For example, a number of popular Big Data AI applications prefer applications relying on device data to predict processes governed by specified standards. Self-correcting, face recognition, chat bots, optical character recognition, medical diagnosis assistance and robotic applications, such as e-mail spam filtering. Each of these applications analyzes objects or images inanimately.

More possibly, the use of an anonymous survey method in the direct questioning of people would allow people to better understand and interpret human behavior. If the data is representative, it becomes more factual and not more likely to be invalid in the context of observed/monitor clicks because it comes “straight from the mouth of the horse.”

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We have completed one of the most painful years in history. A year in which Covid 19 and the consumer’s reaction disrupted all markets. There are also numerous predictive models that have been created by analyzing and predicting history data.

However, performance has fallen behind in respect of big data IA for humans and their actions, behaviors and ambitions, many guided by unconscious pulses that cannot be calculated by random digital clicks. The majority of businesses often strive to find broad data value by mining and analysing consumer and customer data transactions.

For starters, transaction details cannot identify why a consumer has purchased or whether it is a gift to someone. Customer data files contain incorrect details from individuals who travel, die, are married, separated or changed e-mail addresses and are replaced by incomplete, duplicate or mispelled data files.

The sectors of marketing/advertising and investment/hedge funds are two examples of how big data is not analogous to real-world customer behaviours.

Data Science Project’s

Digital ad spending currently constitutes the largest advertising sector in the United States for marketers/advertisers. Despite this haste for marketers to concentrate on digitally, advertisers’ highly profile lawsuits have covered the precision and effectiveness of automated targeting models. Also, numerous research studies have established the shortcomings of the ad targeting model Big Data. A 2018 research paper in Marketing Science Frontiers detailed how online browsing targeting models created and sold by Data Brokers and DMP’s are not only inaccurate but also unprecise.

When additional parameters are applied, reliability gets worse. Dr. Augustine Fou’s latest Forbes article points out that these models are poor because they are all derived from data obtained from behind-the-scene data without users’ awareness or approval. Moreover, there are plenty of these bot data sets.

Big Data

Also under the AKA of Alternative Dates, the investment community in particular hedge funds, was keen to embrace big data. Alternative data is distinct from conventional Wall Street data such as business financial statements or SEC notices. Credit card, social networking, satellite imagery, web browse etc can be used to provide alternative data.

The most successful and first firm to employ Alternative Data is a hedge Fund named Renaissance Technologies founded by Jim Simons. The Man Who Solved the Market: How Jim Simons Launched a Quant Revolution is a book that describes how for the past 30 years Renaissance has delivered a 39% return to investors.

Privacy laws such as GDPR and MFID II have affected alternative data sources and consumers of Hedge Fund have many of the same issues that advertisers currently face with their data collection. Hedge funds risk: Threats for:

Risks of data origin. Can the data be collected from the originator of the information according to the relevant conditions? Scraping data-related websites can violate e-commerce data terms. Risks in terms of accuracy/validity. It is difficult to verify the accuracy of the data set and can lead to more unverifiable conclusions about the data. Risks to privacy. Users must be careful how the data is produced. Can the privacy regulation be breached by individual web purchases or by user browsing behaviour?

Bloomberg recently reported that models and returns to Renaissance technologies for their three main funds by October 2020 decreased from –20% to –27%. Some experts suggest that “sources” depend on models that do not represent the present climate. Historical data trains the models. Poor data again, bad data. Bad data again.

What to do. In order for data sets to be used as an input, data scientists first must consider the reliable, true representative and privacy enforcement. Bad data can hardly be accurately analyzed. Also agree that not all data is the same. Human beings are more than just a single click, and consumer data sources need to expand the distance between digital and consumers’ realities beyond surveillance techniques.

Unplash

If data consistency and validity are a task, the results should improve. It follows. Data scientists can move beyond the 85 percent failure rate for large data projects by paying more attention to accuracy, consistency and validity of the data at the beginning of ML projects.

Note: I am not a data scientist, but a data contractor who built several hundred customer data sets, helping to allow predictive analytics over the last three years. These data contributions have contributed over $1 billion in sales to some of the biggest and most recognized brands worldwide. My aim is to focus on the use of accurate and real-world quality data inputs, particularly in human behaviours. When a Big Data Project begins.

Prosper worked together with AWS to make their data accessible through AWS DataExchange to see how a quality and reliable data set can be applied to target marketing models and time series prediction. A number of US indications, leading indicators, predictive analytics and advanced models of marketing compliant with privacy for the US and China are included in the data:

My advice to you is to be open-minded and think outside of the box while you are looking for a career in data science. It will give you a competitive edge in your career in data science.

Bio: Shaik Sameeruddin I help businesses drive growth using Analytics & Data Science | Public speaker | Uplifting students in the field of tech and personal growth | Pursuing b-tech 3rd year in Computer Science and Engineering(Specialisation in Data Analytics) from “VELLORE INSTITUTE OF TECHNOLOGY(V.I.T)”

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