Data is like money nowadays, and in the coming days, we might be able to use data as currencies to purchase goods and services. Speculations apart, we have a lot of data that we give to numerous services to make them even better. Even though some data is used to show us ads, it doesn’t mean all the data that we give our used for advertisement purposes. As we have an immense amount of data, proper treatment of that data can lead to outstanding new outcomes to make our lives even better.
At this point in time, we have a lot of data for the purpose of analytics, and the amount of data for analytics will keep increasing, which will eventually make the process of data analytics more streamlined. Data Analytics is all about the correct treatment of bulk data using specialized statistical and analytical tools to convert that data into meaningful information and accomplish one or multiple tasks. Data Analytics might not be as easy as you might think. But, if it is accomplished in a proper way, it can lead to exciting new things and today I will talk about the scopes of Data Analytics, or how Data Analytics can help us build a better future for ourselves and the next generation that is coming.
But before understanding the scopes of data analytics, let’s have a look at the fundamental steps of data analytics before the information is used for something useful. The steps of data analytics can be broken down into 5 tasks, and here is the explanation of each.
Understanding the objectives or requirements
Just like everything that we do in our lives has some objectives, Data Analytics should also have some clear objectives. This is essentially the primary and most important step. As there is a lot of data available, a little change in the objectives will lead it to the requirement of extra data and if your objective is reduced for some reason, it will not be worth doing the extra tasks to collect unnecessary data for the objective that you do not have. So, the objectives and the goals of Data Analytics should be made clear in every possible way.
Collection of data
Data is a raw material when it comes to Data Analytics. Depending on your objectives, data should be collected from the appropriate sources to accomplish your task. If you want to start your own business you can collect data about what the majority is looking for. If you want to offer some health services you can collect data from hospitals and other places and so on. So Data Collection is the next most important step and you should always choose a relevant source to collect your data before you start the task of Analytics.
Depending upon the type of data you have collected as per your objective, most of the data that you have collected might not be of any use and if you keep them in the system that will slow down a process of analytics using the specialized tools that you are using. When you are collecting text data, there will be a lot of unnecessary data and no matter what type of data you are dealing with, there will be some unnecessary data, which you should filter out using some specialized tools available. You might need to take the help of Data Analytics professionals for this task, and needless to say, it is an important step to filter unnecessary data and keep only those that are useful.
Now comes the most awaited Data Analytics, which includes everything from finding patterns within the data to drawing conclusions and everything in between. This is the step when you get answers to all your questions, and the data is converted into some useful information, which can later be used for even better and advanced forms of Analytics. The cherry on the pie is, in this step, you are at the threshold of getting your results. You can now get your final information in different ways depending upon the type of data and the way you want it.
After the data has been collected, it is converted to information, and it is ready for presentation, the data should be available to us in meaningful ways to draw some conclusions to use the available information and accomplish some set of tasks. Depending upon the type of information, it can be represented graphically or in the form of tables and it all depends upon the requirement that we have and the volume of the data that was processed. It is only after this step, the available information can be converted to some other format that can be fed to a different system for machine learning, business intelligence and other uses that I will discuss later on.
So, those adjust apps associated with Data Analytics. Depending upon how exactly you want to use the available information that you obtain, it can involve a few more steps and it also depends upon the type of data that you are dealing with.
Now let’s have a look at a few ways, Data Analytics can be useful in the following century and for the coming generation in different ways.
Data mining is not a new term in the world of Technology and it is one of the most productive applications of Data Analytics. Data mining basically refers to finding out patterns in the data available which can be useful to take useful decisions, pertaining to business, governance, and every other step of life.
For example, data mining can be used to study some Complex patterns and make some unique decisions like, what products the customers of a particular region can be interested in at a certain time of the year and so on. Data mining is a useful implication of Data Analytics, which can take into consideration a lot of data and use some specialized algorithms and statistical tools to make decisions.
I already talked about how data visualization is an important aspect when it comes to Data Analytics. Data visualization is an implication of data analytics, which can be useful to represent huge sets of data in a visually friendly way with the help of charts, graphs, and other graphical elements. Depending upon the goals of Data Analytics in a company or organization, the data visualization task can be carried out in multiple ways and data visualization can also be used as a tool to take important business decisions.
Some programs can also be intelligent enough to emphasize the most useful parts of the derived information while visually presenting data, which can also be equally useful in different organizations and enterprises to make decisions.
Data doesn’t only refer to numbers. Useful information can also be derived from texts and this can be accomplished with the help of some specialized tools. Data from a text can be used for a variety of tasks and a lot of useful information can also be derived from the text, which can later be used for AI-based language learning, and a lot more.
Furthermore text analysis, aka. Text mining that is used for AI-based language learning, can also enable the machines to respond in human languages as well. Additionally, text mining can also be useful to understand the emotional state of a person by finding patterns within a written text in different states of emotion, by one or a group of persons. So, keeping the complexity of text mining apart, it is undoubtedly a powerful tool in data analytics.
Data Analytics can also be very useful in making business decisions, which can include everything from product pricing to product launch, by understanding the current trends and many more. With the help of Data Analytics in business, the market condition can easily be understood which can eventually help the business persons to make tactical decisions to maintain the profit margins and push up sales.
Business intelligence in Data Analytics takes the help of data mining, which can help our company understand the patterns about what kind of products the customers can be interested in at different times of the year and in different Geographic locations. Business Intelligence can also be accomplished with the help of data visualization which can help an organization to get better insights about, the market, demands of the customers, and other necessary information. Business intelligence is one of the driving forces behind the advent and more practical usage of Data Analytics.
Almost 2.1 million people died of a cardiac attack in 2015 in India. That is a big number, and that’s when Data Analytics can be useful in making health predictions to help the patients take precautionary measures to prevent cardiac attacks or other deadly attacks and diseases. Even though the health of a human being cannot be predicted with cent per cent accuracy, using the historical data to relate the symptoms to the actual diseases can come in handy and can help doctors to find out what a patient is heading towards, and this can be achieved with the help of Data Analytics.
Data from all the hospitals around the world can be accumulated to do a study that can help build smart computer systems, that can be useful to treat patients better. The doctors can also find out historical data about what steps he should take in order to bring a patient to normalcy when the symptoms are not satisfactory. With smart wearables along with Data Analytics, the doctor can know about when the physiological parameters are breaking the normalcy and what it can lead to by looking at the historical data. This can help the doctor take immediate steps to bring the patient back to normalcy.
Making more accurate weather predictions
It hardly matters if you are Google Assistant is giving you wrong information about whether it will rain tomorrow or not. But weather catastrophes can bring uncontrollable disasters and human beings are very weak and feeble when it comes to dealing with weather apocalypse. The best way to fight the weather is to make arrangements to minimize disasters as much as possible.
That’s when Data Analytics comes into play. Even though there are smart sensors, which can automatically track weather calamities, even if the slightest things go wrong, the data from the sensors along with smart Data Analytics can also be useful for more precise and accurate weather forecasting and can eventually save human civilization someday. With the help of Data Analytics, a lot of parameters can be taken into consideration, which can help the weather forecasting stations do weather forecasting well in advance, which can help the government plan rescue operations or take precautions well before the meltdown starts.
Besides all the different ways, which I have mentioned here, where Data Analytics can come useful, it can also be useful in case of governance, as well. When it comes to governance, it includes everything from better traffic management to understanding the grievances of the citizens to offer even better services.
In that aspect, Data Analytics can create new breakthroughs. With the help of data mining and text Analytics, and with smart language understanding by computers, the government can find out the problems most citizens are facing, and that’s when Data Analytics can be so the problems can be addressed as soon as possible, and even enable the government to roll out more citizen-centric services.
Future of data analytics
The future will see the use of quantum computers, which are way more powerful than traditional computers, and they can accomplish a lot of tasks. When it comes to Data Analytics a few hundred times faster than our traditional computers.
So, Quantum computing can make the world a better place, and Data Analytics will be the heart of such a system that will potentially drive quantum computers. Alongside all the different ways, which I am mentioned here, where Data Analytics can be useful, we can even find the number of different fields, where Data Analytics can create wonders, especially when machine learning and artificial intelligence will gain pace and will become mainstream.
I didn’t go into many technical details about how Data Analytics can be useful, or I didn’t mention the name of any tool that can be used for the purpose of Data Analytics. This story has given you a basic idea about what Data Analytics is all about, and how it can be useful.
So, those are the places, where Data Analytics will change the world in a better way and will make the earth a better place to live in. Do you have any questions about Data Analytics? Feel free to comment on the same below.