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Who Cleans your Data for Machine Learning Models?

Who Cleans your Data for Machine Learning Models?

A few years ago organizations who operate in the mass markets and sell their services and products to millions of consumers decided to leverage one of their most neglected assets - data.

The larger the organization and wider their market, the more data there was to play with.

You see, most human transactions are complex and inefficient.

If analyzing data could offer hitherto unknown insights on buyer behavior, service delivery, product distribution, etc., then wouldn't you hire the right resources to look at your data and identify opportunities to sell more and better?

On paper, artificial intelligence and the power of machine learning can help organizations leverage data and make sense of it.

However, the endeavour of the promised land of data-driven-profits is a double-edged sword.

One one hand, organizations need visionary leaders to guide highly qualified data scientists and machine learning engineers to make sense of the data and turn insights into business opportunities in the short, medium, and long-term.

On the other hand, data scientists and machine learning engineers need high quality, structured data in large enough quantities and variety to build unbiased, realistic machine learning models.

Vision + data = great machine learning models!

Business leaders with over 10, 15, 20 plus years of experience in an industry will bring all the know-how and vision to execute a great data-driven, machine learning projects, but what about the data?

Who will structure the data?

Who will label, annotate, and classify data?

One way is to hire resources to clean data in-house. But that's expensive.

While the job of data scientists is to extrapolate and share insights and help businesses solve vexing problems, instead they end up spending 80% of their time in preparing data. Per Forbes.

A new survey of data scientists found that they spend most of their time massaging rather than mining or modeling data. (Forbes)

Data scientists spend 60% of their time on cleaning and organizing data.

Collecting data sets comes second at 19% of their time, meaning data scientists spend around 80% of their time on preparing and managing data for analysis.

57% of data scientists regard cleaning and organizing data as the least enjoyable part of their work and 19% say this about collecting data sets.

Mike Driscoll popularized the term “data munging,” describing the “painful process of cleaning, parsing, and proofing one’s data” as one of the three sexy skills of data geeks.

In 2013, Josh Wills (then director of Data Science at Cloudera, now Director of Data Engineering at Slack ) told Technology Review “I’m a data janitor. That’s the sexiest job of the 21st century. It’s very flattering, but it’s also a little baffling.”

And Big Data Borat tweeted that “Data Science is 99% preparation, 1% misinterpretation.”

While many organizations outsource data preparation tasks to a vendor, there are challenges in keeping the data secure and getting the job done at a higher quality and at a decent cost.

That's why we built Traindata Inc., a service started by 3 Ex-Yahoo!s with 15+ years in managing data for large projects. We label, annotate, classify and prepare data sets for machine learning models.

We deliver high quality data preparation at affordable costs.

To know more about our data preparation services, visit www.traindata.us