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‘Data is the new science. Big Data holds the key answers’
– Pat Gelsinger
The biggest advantage that the enhancement of modern technology has brought to the businesses is that it has given access to business of all sizes to get rich. Big Data has played a major role in defining the expansion of businesses of all kinds as it helps the companies to understand their audience and devise their business techniques in accordance to the requirement.
But with the increasing amount of data, there is increase in complexity around big data which can cause the business to commit mistakes which can bring their downfall. The mistakes may often look silly, but can have a negative long lasting effect on the performance and the reputation of the company.
Companies can have competent and skilled Data scientists, but mistakes can happen to anyone. Although, committing mistakes is a chance to learn something new but it doesn’t dilute the fact that mistakes in Big Data Analytics can be lethal for the health of your company.
Thus, let’s understand the Pit-falls in Big Data Analytics in order to save your company from a Big Fall.
Using excessive number of tools
Some data scientists just jump in the pool of analytic tools to find out the answers related to the problems they face on the business front. Not every problem is solved when the thinking process is done from a stagnant angle. But the eagerness to solve the problems compels the data scientist to use a plethora of data analytic tools instead of focusing on just one.
Thus, to avoid this mistake, the companies first need to understand the mistake, define a goal and then choose the big data analytic tool accordingly. Moreover, the data scientists should look to master one tool instead of trying a myriad of tools simultaneously. Mastering tools one by one will give you the sense of choosing the best according to the requirement and not according to how elite or advanced the tool is.
Not implementing Data Insight
‘Data is the new Oil’ is a phrase that we have heard quite a few times. Considering the importance of data, the companies are churning and collecting as much data as possible. But the problem arises when the companies don’t take full advantage of the data available and implement the insights.
Make sure that you don’t let the data become stagnant and sit at a stationary position. Big data can play a crucial role in enhancing business operations; solve the prolonged and stubborn troubles and implement road maps for product expansion.
Assessing the data of the wrong audience
An organization cannot depend on the data of just the highly influential people from their targeted audience in order to decide behavioral patterns of the customers. It is advisable to include both the highly influential people and also the people who can be influenced in the future. The overall data will help in calculating the likes and dislikes of the targeted audience and thus help your business to be on course to woo your targeted audience.
Moreover, neglecting the predictive power of the influential group or people who are likely to get influenced can prove fatal for the results of your company. In a skewed up data analytic model, the variables will not fall right giving nightmares to the data scientists. Big Data analytics if done on an overall data will be fruitful and give desired results to the company.
Absence of a Business Intelligence Team
A dedicated Business Intelligence (BI) team can do wonders with Big Data Analytics for a company as they know the art of making the most out of the collected data. The process of data collection is easy, but if it is not used wisely, the motto behind collecting the data is not met. Various companies struggle to get value out of their collected data and thus remain stagnant and behind their peers in this highly competitive business environment.
A BI team will implement the prowess of big data in order to check and maintain the required changes according to the likes and dislikes of the targeted audience.
Failing to automate Big Data energy resources
If your company is working on a national or a global stage, it is advisable to automate your big data energy resources. With automation, the data can be collected, analyzed, reconciled and can be managed better to achieve the sustainability objectives for the organization. Automation will not only boost the efficiency of the data but is a very cost effective method.
Automated Big Data Analytics will enhance the predictive analysis process that will help the companies to properly assess the future risk and solve them. It will play an important role in defining how effective will big data is in risk assessment. Insufficient data or data which is not complete can be disastrous for the company.
Doing analysis without a plan
To the core, data analysis is a structured process that begins with a well defined objective. But if the data scientists tend to jump to a conclusion and start doing analysis without a plan, the advantages given by Big Data is bound to fall flat. It is imperative for the companies to define a model goal and a project goal before the data scientist take even a step forward and use Big Data Analysis.
Companies must understand that the core objective of big data analytics is to answer the ’Why’ kind of questions using the information from Big Data. If the data scientists understand this, it will help them to have a clear understanding of the analysis and ease down the process of answering the questions.
Last ‘Dot’ in the process
The importance of ‘Data’ has been spoken very highly in the modern day business. Thus, while using big data analysis, the companies must keep away from these minor mistakes otherwise it could have a major impact on their performances. Big Data analysis can be the silver bullet that can answer your questions and help your business to scale newer heights.
