The pandemic has slowed us down- that’s the bottom line. The physiological and psychological toll on the people is immense. In such an atmosphere, the productivity of the businesses is at an all-time low. As a result, the economy of the country is deeply affected by this pandemic. Many businesses were forced to cease their operations as the ends didn’t meet. The only businesses that survived were those which adopted an aggressive digital strategy and stick with data-driven decision making.
Data analytics are helping businesses across various domains to evolve into a “precision machine”. Data engineering is helping businesses to develop a scientific approach towards their operations and help them gain a better understanding of their business operations. Strictly talking in terms of numbers, facts, and figures have helped businesses to identify their key performance areas.
Digital platforms help businesses to incorporate data analytics into their business model. To accumulate and organize bazillion organized and unorganized data is not an easy task. Leveraging technologies like artificial intelligence and machine learning can simplify this process and help you understand the ground reality of the market.
Consumerism- whether it is bad or good, the market is rapidly moving towards it. On top of it, customer-centric businesses have shown greater results than their counterparts. for the first time in history, the customers are aware of their powers. They understand that they are the centre of the universe and the business world is revolving around them.
With such stakes in hand, the information on your target audience, the market, the competitors, the global trend, etc. will give a significant edge for your operations. Painting the picture of your average customer can help you place the product in the market. It is not a bad thing to understand the flow of your market and create loyal brand advocates, right?
So, what is big data?
To understand big data, you have to first understand what is a data set. Don’t be afraid of the word as it is exactly what it sounds like. A dataset is nothing but a collection of data. The nature of data doesn’t matter, but most of the time, a single data set contains data under the same context.
Big data is nothing but a collection of high volume data sets that contains complex data collected from various reliable data sources called data lakes. If the data set is a pond, then big data is the ocean.
To analyze such complex data that keeps pouring from the data lakes you need to have capable technologies and a system that can comprehend the complexities of the greater force. This is where a variety of analytics tools and techniques come into the picture.
There are many tools that leverage state-of-the-art Artificial Intelligence, Machine Learning, Visualization tools, etc. but the process requires a strong foundation of technology and mathematics to utilize the full potential of the system.
There are several new trends in big data analytics. These change the way we perceive data. There are a lot of Data analytics companies that help you through the process.
What are the 4 main types of analytics?
Though this is a wider discipline, it is easier for businesses to get a clear picture of big data analytics by breaking the whole process into different segments.
The 4 main types of data analytics are as follows.
- Descriptive data analytics
- Diagnostic data analytics
- Predictive data analytics
- Prescriptive data analytics
There has been a common misconception about big data analytics. Many businesses believe that through big data alone they can move towards their goal but this cannot be any farther away from the truth.
Combining the big data along with the analytics to form an airtight plan is only the first step. Executing this plan and optimizing the plan along the way, tweaking any adjustments on this plan according to the demand- all these plays an important role to achieve greater outputs and efficiency.
DESCRIPTIVE DATA ANALYTICS
This answers the question, “what is happening/happened?”.
It analyzes the data from the past events that occurred in the business. Descriptive data analytics help you gain a thorough understanding of the business operations that brings the clash flow into the business. The day-to-day operations of the business are the major contributor of revenue for the businesses.
Descriptive analytics dives deep into the various performance areas to better understand the efficiency of the operations. This data can be used to form decisions that can shape the business metrics according to the demand in the market.
One of the most important uses of descriptive data analytics is in identifying the key performance areas. This can help create short term goals towards which the company has to move. Achieving daily goals is one of the sure-shot ways to scale your business, and descriptive data analytics can help you frame this chain of short term goals with relative ease.
DIAGNOSTIC DATA ANALYTICS
This answers the question, “Why is this happening?”
It analyzes the particular event to find the root cause of the occurrence. Diagnostic data analytics can help you understand the reason a particular event takes place.
This could be your day to day events or events that are outside the daily operations or a market trend that is impacting the business operations- the bottom line is, through diagnostic data analytics, you understand the data to establish the cause and effect phenomenon of your business.
Diagnostic descriptive analytics emphasizes the present scenario and dives deep into the cause of that event. From the present event, it goes back to the point of origin by following the chain of events. this data is very much used in the next step of data analytics as the process requires identifying the data patterns.
PREDICTIVE DATA ANALYTICS
This answers the question, “will it or when will it happen?”.
It analyzes the whole set of why’s and what’s to draw a realistic time period of when a particular event might happen? This is one of the places where a model that leverages AI can make a huge impact. The predictive analysis depends on the numbers. It is nothing but a high tier statistics combined with a data mining and predictive analysis model that encompasses the different aspects of the business.
For efficient predictive analysis, complete dominance of the mathematics along with the data collected from the descriptive analytics and diagnostic analytics is needed. The goal is to identify recurring patterns and establishing a correlation between that pattern and business metrics.
Identifying a pattern can help you prepare for any surprises. Shedding light on the existing patterns can help you gain a better understanding of the work process. Predictive analysis is strictly number oriented, which makes this more reliable when the push comes to the shove.
PRESCRIPTIVE DATA ANALYTICS
This answers the question, “how to make it happen?”.
It makes use of the raw data to help devise a process that leads to a greater goal. Every move of the business right from the outlook to the process to the brand image- prescriptive data analytics are used to make strategic decisions.
Prescriptive data analytics can also help draw a map to align the work process towards the greater vision of the organization. Every decision of the business is monitored by the data provided by prescriptive analytics.
Prescriptive data analytics make use of the latest technologies like machine learning and artificial intelligence to gain a better understanding of the raw data at hand. Simply put, every other data collected is to aid the prescriptive analytics process.
WRAPPING THIS UP,
Data engineering, particularly big data solutions have opened up limitless possibilities in the business world. Optimizing the internal operations is a sure shot way to increase the overall output of the business. Channeling the energy to create a better workflow to minimize friction is all that matters.
The pandemic has certainly imposed various restrictions on the consumers, but this didn’t stop people from engaging with the market. This is only the beginning of greater power. Data is the determining factor in many situations and businesses should focus on effective data harvesting to make the most out of it.
There are many big data solutions providers in the market who can set you up with the process.