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8 Tips For Successful Data Science Projects

Despite companies being passionate about recruiting data scientists for data science projects, they ought to keep in mind that data-powered initiatives cannot solve all problems.

More than ever, organizations from various industries are currently collecting additional data and seeking the assistance of data analysts and scientists to gather useful insights for improving business.

Considering the immense hype surrounding big data, the expectations of data science projects can easily go out of control.

“Companies are super excited that data will solve every problem that they have,” Andrea Danyluk, a professor of computer science at Williams College and co-chair of the Association for Computing Machinery’s taskforce on data science.

“It very well maybe that data and data science will solve many of their problems and will move their business forward. But with every project you do, you should sit back and think very hard about the specific data you’re collecting and the potential implications about what that’s going to mean.”

According to Danyluk, this means taking into consideration all the possible biases found in the data, and how they could affect your organization moving forward.

Ultimately, “data science is not a silver bullet,” said Dave McCarthy, vice president of Internet of Things (IoT) provider Bsquare.

“Instead it’s the highly advanced and ongoing mathematical analysis of extremely large data sets in search of unique and actionable insights.”

Check out these 8 tips for successful implementation of data science projects.

1. Start with Low-risk Data Science projects

Meta S. Brown, a business analytics consultant and the author behind Data Mining for Dummies, said that you should begin with a low-risk initiative.

This means starting with a project that does not worry you too much presently, but one that has a high potential of being successful in the future.

“One of the most common places to do that most organizations are not really doing is testing something in your email,” Brown said.

For instance, almost all email newsletter vendors provide the ability to try out different email versions.

As such, you can begin by testing all your subject lines to see which ones generate more clicks and opens.

“That’s as low-risk as you possibly can go—you have nothing to lose, and you don’t have to spend any money, because your vendor already provides the technical capabilities,” Brown said.

“And you might find out that, hey, this subject line works better than that subject line. It’s a good example of something that might be right there for you to do, and where you could start to show value.”

READ MORE: What is Machine Learning? All You Need to Know

READ MORE: Top 10 Tips on Building a Data Science Team

2. Develop an Analytical Process and Plan

Companies must have an analytics process, Brown said. “When people complain that analysts are not solving the right problems or giving them the right information, that’s a reflection of a process problem,” she added.

According to Brown, the process can start by attaining an agreement, especially on what in the company is a problem, and selecting a given issue that everyone can describe and agree to address.

Subsequently, you must assess whether you have the appropriate data to solve the problem.

3. Disregard the Trends

According to Brown, you should not begin with a flashy project. “Don’t worry about what’s cool. Worry about what’s cost-effective for you,” she added.

“The cool factor can be a really big problem.”

4.  Data Science Tools should not be a Major Concern

When embarking on a data-powered project, “tools are the last thing you should think about,” Brown said.

Nonetheless, Brown added that organizations have to identify the important projects and spend their money only when the need arises, as opposed to spending a lot of it seeking or waiting for an alternative solution.

5. Understand the Limits

Even though data analysis can boost numerous processes, “there are mathematically provably things that cannot be done unconditionally,” Danyluk said.

“It’s a wonderful thing to think that one field would be able to do everything to solve the world’s problems with data. But there are things that cannot be done through the end—unless we have a completely different framework for how we think about computation, it’s just not going to happen.”

6. Bear in Mind that not all Data can be Used

According to McCarthy, companies have to keep in mind that gathering a large volume of data does not necessarily mean that the data is useable or clean.

“While organizations may have large volumes of data, it is not always the case that the right data is collected, is structured correctly, or is rich enough to be able to garner the insights they are looking for,” he added.

“Often the data needs to be refined, cleansed, restructured and even combined with other data sources before it can truly add value. Failure to understand this is the principle reason expectations often go unmet.”

7. Avoid the Expectation of Hiring a Data Science ‘Guru’

When recruiting a data scientist, most organizations make the mistake of looking for a magical candidate with virtually every qualification possible.

According to Brown, finding such people often proves impossible.

Furthermore, when they hire a data scientist, they often have too much expectation regarding what the particular expert can do, she added.

“Frankly, a lot of people hire a data scientist, and don’t get what they want out of them,” Brown said. “Start with something modest, and establish a good process as your mode of operations from the start.”

8. It’s a Learning Curve

Organizations ought to make their data-driven initiatives “special projects” that are provided with the necessary resources and support.

However, they should not be included in the daily activities of the companies, particularly when starting, claimed Kristen Sosulski, the author of Data Visualization Made Simple, and a clinical associate professor of operations, management, and information sciences at the New York University-based Leonard N. Stern School of Business.

With the buzz surrounding data-driven projects nowadays, it is easy to get carried away by the wave.

The above tips will help your organization to come up with achievable goals, instead of setting unrealistic expectations.

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KC Cheung
KC Cheung has over 18 years experience in the technology industry including media, payments, and software and has a keen interest in artificial intelligence, machine learning, deep learning, neural networks and its applications in business. Over the years he has worked with some of the leading technology companies, building and growing dynamic teams in a fast moving international environment.
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