Firstly, let’s congratulate each other for making a mark in the Indian history by driving the largest vaccine inoculation program across India with a faith that the coming days will be most wonderful as it was a year before.
Well done “ Made in India” Covaxin and Covishield for your timely turnaround and while several others being in the pipeline.
Also, we will surely not forget the warriors who lost their lives while making us safe, battling with the deadly virus. A real tribute to them by making them the priority vaccine receivers.
If you get into the details during the initial stages of virus transmission most medical scientists and virologists said that the virus is not as serious as its predecessor and can be controlled with self-isolation and improving immunity.
Do you know on what basis they made this statement?
Or do you know how the medical authorities or the news channels use to show the number of cases across several states on a daily basis?
Does anyone know how the vaccine companies were successful not only in preparing the vaccine but also rolling out into different states as planned?
Do you still think the scientist and virologists are successful with the trials they made?
Well, I don’t want the suspense to make you go mad, the best and well-known answer to all the above questions is “ DATA “ YES !!
Data played a major role in showing the number of covid cases across several states on a daily basis.
Data was very crucial in knowing more about the virus based on the studies they made initially and came to a mental state that this is not as deadly as its predecessor.
Data played a very significant role in the process of vaccine preparation based on the data that they have been put together in the past and with that reference they have enhanced the trials for making a new vaccine that suppresses the virus transmission. In short most of the clinical trials are made on the basis of DATA makes any decision and discussion effective.
“Without big data, you are blind and deaf and in the middle of a freeway.” – Geoffrey Moore.
Knowing the types of Data and Its Importance
There are two types of data: quantitative data and qualitative data. Quantitative data is any data that is in numerical form — e.g., statistics and percentages. Qualitative data is descriptive data — e.g., color, smell, appearance, and quality.
In addition to quantitative and qualitative data, some organizations might also make use of secondary data to help drive business decisions. Secondary data is typically quantitative in nature and has already been collected by another party for a different purpose. For example, a company might use U.S. Census data to make decisions about marketing campaigns. In the media, a news team might use government health statistics or health studies to drive content strategy.
“Data really powers everything that we do.” – Jeff Weiner
Now that you all got to know the importance of Data being a crucial element for any business and industry making it a prime asset it makes most obvious that companies face a lot of challenges to streamline the data and maintain it securely. let’s start with
The data management process includes a combination of different functions that collectively aim to make sure that the data in corporate systems is accurate, available, and accessible.
But a lack of proper data management can saddle organizations with incompatible data silos, inconsistent data sets, and data quality problems that limit their ability to run business intelligence (BI) and analytics applications — or, worse, lead to faulty findings.
Let’s look into some of the functions that contribute to the DATA cycle and ensure its importance in all business and operational decision making.
Lets start with Data Collection a process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes.
Surveys, interviews, and focus groups are primary instruments for collecting information. Today, with help from Web and analytics tools, organizations are also able to collect data from mobile devices, website traffic, server activity, and other relevant sources, depending on the project . datacollectionmethods ,#datacollectiontechiniques qualitativedatacollection
. “With data collection, ‘the sooner the better’ is always the best answer.” – Marissa Mayer
Data Analysis – Once the data is collected it’s then analyzed through 2 ways that are data analytics in which the data is viewed from the historical data and on the other side Data science which includes machine learning and Predictive modeling primarily includes algorithms that help to solve the complex business problems. Click here to know more about machine learning in detail.
Predictive modeling, also called predictive analytics, is a mathematical process that seeks to predict future events or outcomes by analyzing patterns that are likely to forecast future results. … Once data has been collected, the analyst selects and trains statistical models, using historical data.
Predictive analytics is the most widely used techniques that suit most of the business to carry their operational decision industries like Retail & wholesale, Healthcare & Pharma, IT, etc.
After analyzing the data then it needs certain data policies that ensure data quality and security.
Data Governance: The policymakers
“Without a systematic way to start and keep data clean, bad data will happen.” — Donato Diorio
- integrity, and security of enterprise data.
- -A sound data governance program includes a governing body or council, a defined set of procedures, and a plan to execute those procedures.
- An organization with a strong data governance practice will have better control over its information
- Check more information # datagovernancestrategy #datagovernancebestpractices #datagovernencetools #datagovernenceframwork
Metadata is required to place the data into proper categories for determining the controls that are needed to assure quality and to determine which regulations apply to the data. For example, SOX applies to financial data, HIPAA applies to healthcare data, and so on.
- Metadata answers the who, what, when, where, why, and how questions for users of the data.
- Good data quality starts with metadata. Metadata characterizes data, providing documentation such that data can be understood and more readily consumed by your organization.
Data Quality: Data quality is an overarching industry problem
–Data quality is: “the planning, implementation, and control of activities that apply quality management techniques to data.
-According to data quality expert Thomas C. Redman, payroll record changes have a 1% error rate; billing records have a 2% to 7% error rate, and; the error rate for credit records: as high as 30%. techniques that help to preserve the Data quality. dataqualitytools ,
–Data Profiling –is the process of examining the existing data in the database and collecting statistics and other information about that data. With data profiling, you can discover the quality, characteristics, and potential problems of information. dataqualityservices
The final part of the Data cycle is STORAGE –
Data Storing in a data science process refers to storing user data that you may use in your data science process to dig the actionable insights out of it. Data Storing in data science itself is an orderly process that needs many things to be kept in consideration before jumping to more advanced or fancy things.
The Data can be stored in 2 ways such as on-premises like holding a physical data center and allocating servers and storage boxes where data can be accessed through proper authentication. but this process needs incurs huge investment. on the other side, you can store the data based on the priority bases on cloud storages where you will only pay as you use and data can be stored based on the severity of the information Ex: AWS, MS – Azure and Google cloud, etc.
Having all the above-mentioned functions and techniques that ensure the importance of data, the process also includes different tools to integrate for a better outcome.
But still, there is a gap in ensuring the importance and company end up losing huge data and making them spend huge investment.
If you are looking for stepwise data management solutions and cutting edge integration tools that provide business, technology consulting solutions then Click here.
This is bit out of regular genere of artcles thought to share the importance of Data in every aspect of our lives .
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