Data Science helps businesses gain actionable insights from various sources of structured and unstructured data by applying scientific methods, processes, and systems. It requires a proper understanding of the different techniques used for preparing the data and knowledge about various data models that may be used to finally measure the outcome from the full process.
In this entire cycle, there may be numerous factors that may be overlooked even by the most seasoned data scientists. Through this article, we share some of our insights on some of the most common mistakes made by data scientists.
Growing demand for data scientists and their role in the information age
According to a recent survey made by KMPG on C-Level executives, 99% of them affirmed that big data would be a core part of their company’s strategy in the coming year. It’s believed that enterprise data will exceed by around 240 exabytes per day in 2020, which will create a greater demand for data scientists with key skills for extracting actionable insights from data.
A striking example is that of the popular social networking site LinkedIn where data scientists have played a vital role in boosting business intelligence for the company. LinkedIn relies mainly on the data that is transferred by its 3,80,000 users who have built connections with each other. LinkedIn is utilizing the skills of such professionals to explore the world of Big data.
Apart from LinkedIn, other big names such as Google and Facebook are utilizing the role of data scientists to give a better structure to large quantities of formless data to help them establish the significance of its value and bring a standard relationship between the variables.
Most of the data architects extract information through large volumes of data and use SQL queries and data analytics for slicing these datasets. On the other hand, data scientists have a larger role to play as they need to have advanced knowledge of machine learning and software engineering to manipulate the data on their own to provide deeper insights. They mainly use advanced statistics along with complex data modeling techniques to come up with their future predictions.
What are the common mistakes made by data scientists?
Failure to address the real questions
The entire process of data science revolves around addressing the business questions and in most cases, it is the most neglected issue due to lack of communication between the sponsors, end users, and the data science team. To get the most benefits from the data science initiatives, it’s important for all the stakeholders to stay connected and share information and knowledge which can help in defining the real business issues.
Beginning with excessive data
In many companies, the team members are involved in working on a huge chunk of data which is a waste of valuable time and efforts. Instead, it can be more worthwhile for them to choose a subset of a specific data to make the process much easier. For example – Is it possible to focus on just a single region or look for a data from the last three months? To start with the prototype, random sampling may be taken into consideration. When the initial exploration, cleaning, and preparation is done, the bigger data set may be included in the process.
Trying to complicate things
Sometimes, even if the current project requires a simpler solution, data scientists often make the mistake of complicating the matters by introducing more complicated models into the process. This can jeopardize the chances of completing the project on schedule and make it more difficult to achieve the main purpose.
Not validating results
The models that are created by the team of data scientists should enable the business to take a suitable action. And once an action has been taken, it’s necessary to measure its effectiveness for which the team needs to have a validation plan ready, even before the actual implementation. Only this can help in making the process more efficient and give more meaningful results.
More focus on tools than on business problems
The major function of any data-driven role is to focus on solving problems through data extraction, but sometimes, the data scientists get overwhelmed and obsessed with using new tools than solving the real issues at hand. They need to understand the problem first and find out the requirements for finding the solution and finally decide on the best tools that may be used to solve the problem.
Lack of proper communication
There is a plenty of communication involved in assessing the business problem and providing constant feedback to the stakeholders. The greatest risk comes when the data scientists do not ask enough questions and make their own assumptions, which actually can result in providing a different solution than what is required.
The Key Ingredients of Data Science
Data science requires knowledge of Statistics and Applied Maths
Data science requires actual application of Statistics along with Applied Maths, which can provide guidance regarding uncertainty in data and allow companies to gather valuable insights from it
Data Science involves solid communication
A data scientist needs to be an effective team player who helps to initiate, iterate and drive some core decisions in the company. The role of data scientist involves working along with product managers and the other team members to influence them to take vital business and product related decisions.
Data Science is about using creativity and dealing with people
Data scientists need to have a creative approach as they need to understand the needs of the users in the system and convey their findings to the other core members of the team. At the same time, they need to be creative enough to derive insights from the system that generated the data in the first place.
In this age of Big data, the biggest challenge will be on collecting data and extracting value from it which will get more demanding in the coming years. Data Scientists will have a key role to play in shaping the economy of the future by extracting insights from large volumes of data and predict future patterns based on the past and current data.
With the growing demand for Data Scientists, there is also a severe shortage of talent. According to a research by MGI, the United States will experience a shortage of 190,000 skilled data scientists by 2018. To address the growing demand for Data Scientists, Inteliment has innovated iDS, a cloud platform, which helps organizations get the benefits of Data Science without a Data Scientist. Know more about iDS at http://www.inteliment.com/ids/.