FROM RAW TO READY: HOW SCRIPTING UNLEASH DATA POTENTIAL

• UNDERSTAND RAW DATA  

• SCRIPTING IN DATA PROCESSING  

• DATA PROCESSING STAGE  

• DATA EXPLORATION AND VISUALIZATION 

• STATICAL SURVEY AND MACHINE LEARNING 

• AUTOMATION AND INTEGRATION 
• CONCLUSION

Data as a Strategic Asset :- 

Data has become a powerful business this is why it is called a strategic asset. An asset is one of several holdings that appreciate and generate a  return. Strategic Asset is the most important thing a company owns. When organizations approach data as a strategic asset, it can open the  door to new possibilities, insights, and capabilities while also opening  path for upcoming technologies. 

RAW DATA :-  

Raw Data is sometimes called source data, primary data, atomic data is  data which is not prepared for use. A difference is sometimes made  between data and information to the effect that information is the end  product of data processing. Raw data that has gone through processing is  sometimes called as cooked data.  

Raw data has a lot of possible resources, as it comes in a variety of forms  from a wide range of sources. Collecting raw data is the first step for the process of gaining a more thorough understanding of a demographic,  system, concept, or environment. Unstructured data is very important because it allows organizations to open valuable data, make informed  decisions and get a competitive edge. 

SCRIPTING IN DATA PROCESSING:- 

In computer language script is a program of instructions that is to be explained or  carried out by another program rather than by the computer processor.  Some languages are mostly called script languages. The most popular are; - Perl, Rexx, JavaScript, and Tcl/Tk . In general, script language is faster and  easier than C+ and C++ . A script takes longer time to run than C+ and  C++ because each instruction is being handled first by another program  rather than the basic instruction processor. 

• DATA COLLECTION AND EXTRACTION:- 

This uses a special computer program (script) which automatically collect  information from all different places like database and website, and it saves  time give us that data which we needed.  

• DATA SCRUBBING :- 

This process uses a script to clean all the unimportant data by making it in a  proper way, but it just need to be accurate and analysis it.  

• DATA TRANSITION :- 

Script arranges the data nicely and in order so that we can understand  easily, and this make data easier which help to get the important patterns. 

• DATA EXPLORATION :- 

These scripts summarize data which help us to understand and explore it ,  which helps us find interesting data . 

• MATHEMATICAL ANALYSIS AND MACHINE LEARNING :- 

For complicated data they use script which make predictions from data, and  helps us understand new trends and make decision.  

• MECHANICAL REPETITIVE :- 

Automation makes scripts do tasks again and again without needing manual  help , and because of this it makes sure that all time work is done in the  same way. 

STAGES OF DATA PROCESSING: 

Data processing means transforming raw data into useful data so that it  can be used for business purposes. Data processing can be done by using three methods i.e., manual, mechanical, and electronic. The aim is to  increase the value of information, and this allows businesses to improve their decision making. Six stages for data processing;-

• DATA COLLECTION:- 

Data is collected from various sources including databases like data lakes  and data warehouses, and it helps to collect data because accurate data is very important and crucial. It is an essential step in all types of research,  analysis, and decision-making, including that done in the social sciences, business, and healthcare. Accurate data is very important for all work.  Data collection tools are; - work association, sentence completion, role playing, in person survey, web, mobile, phone surveys, observation.  

• TO PREPARE DATA:- 

The data collected in that first stage is then taken to another step that is to  be prepared. In this stage data is organized for further stages. Data is  cleaned by removing error, removing noise, and removing bad data. Talent  data preparation tools work for data preparation.  

• DATA INPUT :- 

Now the clean data has come to a point where it get translated into that  language where it can be understood. Data input is the first step or  beginning of raw data into usable information.  

• PROCESSING :- 

The processing method is done by using a machine learning algorithm,  depending upon different data being processed the method can slightly  change. 

• DATA OUTPUT :- 

Now in this stage data is finally usable to non-data scientists. It is now  translated, readable, and now companies or institutions can use it  according to their need. Data output is very important step which should be  done on a regular basis for efficient result. 

• DATA STORAGE :- 

Now this is the final stage in data processing that is storage, after all that  data is processed it is stored for future use. Some information is used  immediately, much is used for future use. When data is stored it is used  by the organization when needed.  

After these steps the raw data got converted to useful data which is  used now and can store for future use.  

DATA EXPLORATION AND VISUALIZATION:- 

Data exploration is the first step in data analysis involving data visualization  tools and statistical techniques to open data set characteristics and initial  patterns. During exploration, raw data is reviewed by man power and  automation both, this is also sometimes called exploratory data analysis.  

Humans are visual learners and always love to learn from seeing, visual data  processes more than numerical data. Data visualization tool and elements  like colors, shape, line, graphs, and angle aid in effective data exploration.  

STATISTICAL SURVEY AND MACHINE LEARNING: 

Statistical survey:-

A statistical survey is like asking people some questions to find out  information. In surveys we collect data from a large or small group and then  use different patterns and numbers to learn more about the group or their  behavior.  

Machine Learning:- 

Machine learning is like computers learning to make decisions by  themselves. In ML developers have added new data because of which it will  grow independently. ML algorithms use computation method to learn directly  from this data rather than depending on any equations.  

Today, machine learning is one of the most common forms of artificial  intelligence and often powers many of the digital goods and services we use every day. Machine learning nowadays is used in different commercial uses.  

AUTOMATION AND INTEGRATION:

Automation means use of technology to perform different tasks where human  work is less used. This includes applications such as business process automation (BPA), IT automation, network automation, automating  integration, and robotics. The main use of automation is to do less human work and less error. Automation is used in manufacturing, utilities, transportation and security. In the technology domain, the impact of  automation is increasing rapidly, both in the software/hardware and  machine layer. Automation is your personal helper in the digital world, it follows  our instructions and works according to it, and works repetitive tasks for more  error free tasks.  

Integration:- 

Integration allows different tools to work together , data integration is the  process of combining data from multiple source systems to create unified  sets of information for both operational and analytical uses. Integration is  technology helping teams collaborate very easily. It is like completing a  whole puzzle with broken pieces, which is fun and works nicely. 

CONCLUSION:- 

By learning about the language of data through scripting  with python, for learning more about this you can do a  course of it as well which is in high demand. There are  some of the common language programs for scripting  that are; - Python, R, JavaScript. To come up with the final  useful step from raw data, data has to go through a  lot of steps that are mentioned earlier. Through  exploratory data analysis, statistical analysis, and  machine learning scripting helps to give hidden  patterns and trends within data because of which  complex data will also be understandable. There  are different institutes which provide Data Analytics course in Indore, Noida, Pune,  Delhi and other cities also. Scripting makes a bridge  between raw data and valuable data. Through this  process raw data transforms into valuable data,  because of which data scientists, decision makers and  developers get information efficiently. Ultimately, through scripting raw data transformation is very  important and can be done easily which helps to get  valuable information.