Data ScienceThere are a lot of obstacles that hinder a data engineer. Let us walk through some of the major challenges faced by data science professionals

The dynamic data engineering technology space of today is propelled ahead by the decisive shift from on-premise databases and BI tools to modern and advanced cloud-based data platforms built on lake house architecture. Today’s challenging data environment stipulates reliance on multiple technologies to keep up with scale, speed, and use cases. With data engineers no longer responsible for managing computation and storage, their role is changing from infrastructure development to more performance-based elements of the data stack or even specialized roles. There are a lot of obstacles that hinder a data engineer. Let us walk through some of the major challenges faced by data science professionals. 

Data Quantity

For a data engineer as well as a scientist, a development of a powerful model is of top priority. A complicated problem requires an intense model with more crucial model parameters. However, the more the model parameters the more the data requirement. Also, it is quite challenging to find quality data to train such models. Even unsupervised learning or algorithms demand a huge amount of data to form a meaningful output.

Multiple Data Sources

Big data allows data engineers to reach a vast range of data from various platforms and software. But handling such a huge amount of data poses a challenge to the data engineer. This data will be most useful when it is utilized properly. To an extent, this problem could be solved with the help of virtual data warehouses which can effectively connect data from innumerable locations using cloud-based integrated data platforms. The deeper the reach of data the more useful insights and conclusions.

Data Preparation

Data engineers and data scientists spend nearly 80% of their time cleaning and preparing data to improve its quality – i.e., make it accurate and consistent, before utilizing it for analysis. However, 57% of them consider it as the worst part of their jobs, labeling it as time-consuming and highly mundane. They are required to go through terabytes of data, across multiple formats, sources, functions, and platforms, on a day-to-day basis, whilst keeping a log of their activities to prevent duplication.

Data Security 

Data Security is a major challenge in today’s world. The plethora of data sources that are interconnected has made it susceptible to attacks from hackers. Thus the data engineers are struggling to get consent to use the data because of the lack of certainty and the vulnerability that clouds it. Following global data protection is one way to ensure data security. The use of cloud platforms or additional security checks could also be implemented. Additionally, machine learning could be also used to protect against cyber-crimes or fraudulent behaviors.

Identifying the Issue

The hardest challenge faced by data scientists while examining a real-time problem is to identify the issue. They have to not only understand the data but also make it readable for the common man. The insights from the analysis should remove the major glitches and hiccups in the business. Data scientists can use dashboard software which offers an array of visualization widgets for making the data meaningful.

Data Quality

Machine learning and deep learning algorithms can beat human intelligence. Algorithms are exemplary at learning to do exactly what they are taught to do but the problem occurs when data given is poorly curated. For example, Microsoft’s Tay chatbot learned about tweets on the internet and ultimately ended up chaotic. Machine language is a boon and a bane, they have the immense power to learn things so rapidly but they will be able to reproduce only what they have been told. Henceforth data quality is of prime importance and data engineers will have the herculean task to curate data.

Understanding The Business Problem

Before performing data analysis and building solutions, data engineers must first thoroughly understand the business problem. Most data engineers follow a mechanical approach to do this and get started with analyzing data sets without clearly defining the business problem and objective.

Setting up the Data Pipeline 

In the modern world, megabytes of data are not dealt with anymore, instead terabytes of unstructured data generated from a multitude of sources are dealt with by the data professionals. This data is voluminous and traditional systems are incapable of handling such quantities. Hence the concept of Hadoop or Spark came into the picture which stores data in parallel clusters and processes it.

Prediction

Sometimes in data science, unexpected results may be obtained which may or may not be the end with the rightful conclusions. In such a challenging situation, data science professionals should press on supervised learning for future exploration, model selection, and appropriate selection of algorithms. With sufficient time and power, a data science professional can generate models of predictive strength having little interpretation.

Communication of the Results 

Managers or Stakeholders of a company are often ignorant of the tools and the working structure of the models. They are required to make key business decisions based on what they see in front of charts or graphs or the results communicated by a data science professional. Communicating the results in technical terms would not help much as people at the helm would struggle to decide what’s being said.