Click to learn more about author Yuvrajsinh Vaghela.
The amount of data that we collect and share every second in our daily lives may well be more than the number of times we breathe; the world has now become so data-driven that data is a central part of our entire existence.
With so much data around, it becomes difficult for us to live and balance everything. For that, there are Data Heroes out there who can streamline data and make the job easier for us. But, who are these Data Heroes? Data Scientists are such data excavators, who by using their formidable skills in math, statistics, and programming, dig out an enormous mass of messy data then clean, manage, and organize it. They use their analytic powers that include industry knowledge, contextual understanding, and skepticism of existing assumptions to help businesses unearth hidden solutions to complex challenges.
Though it sounds cool, becoming a Data Scientist is not an easy nut to crack. One needs intelligent and defined technical skills, and an eye for detail to successfully adorn the hat of Big Data Analyst or Data Scientist. And with businesses generating gigabytes and petabytes of data, the world needs highly intellectual and efficient Data Heroes to save businesses from complex situations.
But nobody is perfect, mistakes happen, though that can in fact be a good thing. It is rightly quoted by the famous Irish novelist, James Joyce that, “Mistakes are the portals of discovery.” For Data Scientists, mistakes help them become sharper and discover new data trends, but that doesn’t mean mistakes in Big Data Analytics are not sometimes quite problematic. Although Data Scientists rarely commit egregious mistakes, since they are hired with precision, some newbies in this field can make them, which we are going to discuss here. So let’s see what common mistakes Data Analysts/Scientists make and how to avoid them.
Mistake 1: Going Overboard on Tools
When encountered with a business problem, some Data Scientists jump into a pool of analytics tools directly without giving a thought to what exactly the issue is. Sometimes all the problem needs is a different angle of observation, but in the promptness to solve it, they waste time by experimenting with all their expertise and knowledge on the myriad tools available, instead of the one needed. Hence, the first step to avoid this mistake is to define the goal first and then choose the right kind of tool to render results in a compelling way.
Also, avoid learning multiple tools at once as it is better than ending up mastering none of them. Pick one and stick to it, master and then move to next. Try not falling for fancy tools. Remember “all that glitters is not gold,” so advanced and expensive tools will not necessarily make things easier. Choose tools according to the goals and needs of the business and not by how advanced they are. “There’s no need to invest a lot of money on big analytics tools and a team of experts for an analytics program when some advanced features of free tools like Google Analytics could give you the answers you actually need,” says Mike Le, the co-founder and COO of CB/I Digital, a New York-based digital agency.
Mistake 2: Not Being an Explorer and Visualizer
Data Visualization is the core of Data Science and taking it too lightly is a mistake. Some Data Scientists skip this process and head over directly to the model building stage and this is where they go wrong. Nothing can go right until and unless the understanding of data is done right.
Spend some time in exploring and visualizing to get hold of datasets in the right manner. Things will get easier afterwards. Don’t be afraid of discovering. Be curious, ask, research, practice, learn, this will clear the understanding of data furthermore.
Mistake 3: Ignoring Possibilities
There can be more than one probability for solving the problem, and holding on to one that may even have a very less chance of success is not helpful. Always remember, there are more than one possibility for every problem which one needs to explore, it will help make decisions better and faster.
Mistake 4: Solving Problems Randomly
Data Scientists need to think in an organized and structured manner always, to get things to fall in the right place. Not having a structured approach to problem-solving can cost them a lot. By implementing a structured method, Data Scientists will get to segregate the problems in a more logical way. It will help them plan their approach and prepare for it.
Focused training can help structure the mindset and make Data Scientists ready to deal with problems more maturely.
Mistake 5: Fearing to Communicate and Compete
Communication and competition; participation in both are must in Big Data Analytics or else growth is not possible. Shed shyness and fear, discuss and share the findings with others. Welcome feedback and criticisms and learn from it. As a Data Scientist, working with the community is important. Participate in discussions and brainstorming sessions, collaborate and understand other data expert’s ideas and perspectives. Contribute, compete, winning is just a bonus, the real reward is knowledge and experience.
Communication can level you up. What use are the findings and analysis when it can’t be communicated well to others? Not just clients, communicate with the team and other colleagues to upgrade into an efficient Data Scientist. Use simple and layman languages so that a non-technical person can also get what’s being told. Polish your personality, presentation, and communication skills.
There is a long list of mistakes that newer Data Scientist make and those listed above are some common ones. Certainly, it’s something they already know, but they skip or forget practicing and later find themselves trapped in complex situations figuring out what went wrong. These are not huge mistakes that can’t be undone or avoided. If taken a bit of care before starting with analysis, the date with data will go faultless and flow smoothly like a stream.