Big Data & Data Scientists: #3 Myths Busted
The term big data and data analytics has taken off in a big way. More and more businesses have started making the most of the emerging tech to draw meaningful insights and come up with an effective decision. The following article intends to evade all the myths associated with these technologies. Let’s take a look!
With the increasing advent of the digital economy, unprecedented growth of information is being generated across the globe. This is what big data is all about- it refers to the large collection of heterogeneous data from different sources. Right from structured to unstructured data every information is used in big data. Let me elaborate on the types of data for you:
- Unstructured data – social networks, emails, blogs, tweets, digital images, digital audio/video feeds, online data sources, mobile data, sensor data, web pages, and so on.
- Semi-structured – XML files, system log files, text files, etc.
- Structured data – RDBMS (databases), OLTP, transaction data, and other structured data formats.
About Big Data & Data Science
I am pretty sure you people have must come across terms like big data, data science, and data analytics but do you exactly know what these technical jargons mean?
Big data- Big volumes of various types of data are generated through multiple channels like mobile, internet, social media, e-commerce websites, etc. Over these years, big data has proven to be of great use compelling everyone to jump into the bandwagon.
Data science- The concept deals with slicing and dicing big chunks of data to a great extent. Other than this, professionals here strive hard to find insightful patterns and trends using the tech, mathematics and statistical technique. Heuristics algorithms and models are developed by data scientists for significant purposes.
Overall, data has become an important baseline of the economy. Every activity performed today whether in regards to education, entertainment, media, oil & gas, research, software development, healthcare, technology, retail or any other industry- data is the core element. It is a safe bet to say that the orientation of businesses has changed from being product-focused to data-focused. Even small possible information has become valuable for companies these days to bring meaningful insights and take decisions accordingly.
Further below I would like to mention a few myths that require to be busted regarding data science
Myth 1- Applied on humongous data
We all know the 4Vs of data science but what we don’t know is these elements don’t necessarily require having terabytes of data to perform data science. Let me elaborate it for you:
- Velocity-based data: Do you really believe that the size of data is the only thing that concerns data science? Probably not! The velocity of the data also generated equally matters. Although being produced in small amounts, data is more likely to aid you in making the right decision at the right time
- Variety-based data: When it comes to varied data, data science can be applied to a great extent. Right from CRM systems to social media, call logs, you can also get better insights on your customer profiles, desires and so on, that ultimately leads to better-informed decision making.
- Veracity-based data: Data is valuable especially when it is consolidated, conformed, and current.
Myth 2- Data is all about a higher accuracy
Have you wondered gathering more data might lead to bigger potential oversights? Many companies don’t have a precise understanding of which datasets you need to analyze, or they have no idea where to start. This small yet crucial information can be better used to examine the issue on a smaller scale. This surely helps data science model to understand a pattern and accordingly work on other data sets as well. Quantity does not always equal quality.
It is very important to know that data doesn’t always imply good in terms of quality. Instead, it implies on higher cost, more burdensome data collection and time-opportunity costs and so more. So what you can do is opt for smart data. Think of analyzing internal datasets, before integrating them with public or external sources.
Myth 3- Replaced by artificial intelligence
Although, there is a fair chance of machine learning, artificial intelligence carrying some data science activities in an autonomous manner such as data gathering and cleansing. However, as a ‘data scientist,’ you will always have to carry further operations ordering machines on what needs to be done. Today, people are building more sophisticated algorithms with a tendency to eliminate the need for a dedicated data scientist. However, that is highly unlikely to happen.
Conclusion
Big data, data science have aided businesses in several ways. As soon as the organization sorts out things in the context of data requirement, the next logical step is to close the gap between data and decision-making. So that’s all for now! Keep watching the space to get a better perspective regarding the same.