Myths are a waste of time; they prevent progression – Barbara Streisand
In addition to making conclusions about the data, the science of evaluating raw data is what we call data analytics. Many techniques of data analytics and procedures have been converted via automation into mechanical operations and algorithms that operate over raw information for use by humans.
It is a booming field and many young and ambitious professionals are opting for data analytics courses. Many universities are offering data analytics courses online.
Due to its complexity and distinctive language, many amateurs don’t understand it and are hence oblivious of its activities in the backend. Its insignificance has led to the emergence of good and bad myths that have forayed into people’s minds. It can discourage any organization from effectively capitalizing on data analytics since they treat the myths as reality.
Here are the 10 data analytics myths debunked.
- It contributes to new findings: Theoretically, data analytics helps in finding significant data, and practically it helps in making some important decisions. Reaching new findings with AI via data analytics is untrue.
An accurate understanding comes from the gathered and modeled data, and evidence is collected that proves to refute the theories. Data analytics should be used as a valuable platform for learning.
- It is time exhaustive: Some market leaders are of the view that using data analytics in a sensible manner is too time-consuming. One should check for answers which will align with the existing networks and then provide a complete view of the revenue-driving activities and provide execution services. In less time, the right software tools will help extract data insights
- It needs an exorbitant amount: The misconception that data analytics is a costly affair prevents many companies from effectively leveraging it. In fact, a solution for data analytics can be very functional and cost-effective, it is all based on the type of solution needed.
- Value can only be derived if an individual is an analyst: Another misconception is the above. All the credit goes to the pathbreaking development in the fields of automation along with AI for enabling the process through which anyone can avail an insight into the data information and quickly transform this knowledge into effective business decisions.
- Data is the force behind every business: Not all companies have data as their driving force. When the business offering makes sense, only then data is important. It is necessary to concentrate on the information, whether it is important to the company, and then join the battle, if not, keep concentrating on important progress.
- Bounce rates – useless to keep track of it: It is the perception of some company heads that keeping a record of bounce rates serves no purpose. The logic behind it is, these figures are usually inaccurate, and the real value is not given by the data.
In reality, the bounce rate is important in increasing the SEO value and gives an indication of the consumer’s understanding of the said business, aiding them in identifying the faults responsible for people making an early exit from their site.
- Decisions made by machines are impartial: It confirms the already existing social biases, transforming into a “black box,” without any means of describing the logic behind choices. When the organizations are asked to explain decisions, they aren’t in charge of the manner in which models are designed, rendering them insecure and accountable.
- The loss of jobs is directly related to data analytics: This is a common misconception that data analytics connects to AI and that further transpires into job loss. Data analytics is akin to a business tool that produces jobs and productivity and reduces waste.
- More data is key: Another prevalent myth is that the more the data, the better it always is. The most important thing is that data has been well-sourced, is reliable, and also meaningful. As they always say, quality is better than quantity.
- Analytics runs your business: An organization cannot expect their business to grow and flourish only with the help of data analytics. It’s also about building a rapport with their customers and understanding their needs. It also depends on their processes and their products. When an organization incorporates better insights into its business processes, it can add more value.