Data - The Monarch of the Business Republic
A republic is a system where sovereignty rests with the people. A global business is an organization doing business across the world. Integrating the two results in Business republic, an ideology of global business whose profitability and sustainability rests with its citizens i.e., the global customers. Surprisingly, It’s a republic with a Monarch. Unsurprisingly, Data is King – Who masters the Data will rule the business republic i.e., the World. Its currency being the information and the system of Corporate Governance, the art of Data manipulation and Analytics.
So, let's define the monarch. Data is facts and statistics collected together for reference or analysis. Good to repeat, “It’s the monarch of Business republic. And whoever controls the data rules the business territory. Apparently, consumers own their own financial data. But that data typically resides on the business systems and financial institutions infrastructures where it is stored, managed and protected.
As inhabitants of a society that is becoming more and more digital, data surrounds us and connects us to data and to people, whether they are close friends, co-workers, or strangers. Data in the Business Republic has moved from the desks of accountants and IT to wider stakeholders, including both users inside the Business Republic and clients and suppliers outside the Business Republic. Therefore, businesses are currently looking for new strategies to grow. This could involve looking beyond their own walls and utilizing social media and public data sets to improve the diversity of data they integrate and process, allowing the discovery of fresh insights from their own data. It could also involve looking at their own customer data.
Data is the lifeblood of Google, one of the largest businesses in the world. Google's search algorithm learns to provide us with more helpful results by storing and analysing records of user searches. In the same way that data accessibility has fuelled search advancement in the past, our search logs' data will undoubtedly play a crucial role in future discoveries. Other e-commerce websites like Amazon, eBay, and others estimate consumer demand and preferences in a similar way.
Today, we continue to actively generate data from every item we purchase, interactions with social media sites like Facebook and Twitter, to Internet searches and the items we add to our online shopping carts. How many of us are aware that the item we look for on sites like Amazon, Flipkart, etc. is available on all of the sites we visit? With our implicit or explicit understanding, all of the information we enter on these e-commerce sites and other internet platforms is gathered and recorded. In our increasingly computerized lives, certain data is generated automatically, sometimes even without our knowledge.
Location data is generated by mobile devices, and our vehicles also provide diagnostic data and location data. Data is generated by sensors like GPS in cars and phones, cellular towers, and smart devices without any active action on our part other than going about our daily lives. These machine and sensor data frequently power automated operations that generate more data. Smart meters, for instance, allow utilities to better understand usage patterns and predict demand to prevent both unnecessary power generation and power shortages, while location data aids cellular carriers in managing your phone call. Smart meters are electronic devices that record consumption of electricity in small intervals and communicate that information back to the utility at least daily for monitoring and billing.
Today, data moves and grows at a lightning speed, producing the phenomenon known as big data. Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. As is common knowledge, the outcome will be more dependable the larger the population. In order to deliver genuine business benefits, big data necessitates creative processing solutions for a variety of new and current data. Processing enormous amounts of data or a variety of data types, however, is purely technological unless it is connected to business goals and objectives. For businesses, big data creates both new opportunities and challenges. Fortunately, new models for managing and processing data have emerged as a result of changes in the economics of data, enabling businesses to pursue fresh approaches to improve their current data models and processes.
These days, time is recognized as a crucial aspect of data. Data used to move slowly in the past. But when data speeds up and the space between updates gets smaller, it turns into a flood of constant updates. Businesses used to manage data whenever the last critical operation was finished. However, efficient organizations must manage data at all times, in real-time (streams), near-real-time (operational analytics), and at rest. This is because today's "always on, always connected" world of data is driven by the internet of things and its demanding consumers.
Data is absolutely necessary in the era of data-driven metrics, which are utilized to outsmart rivals by analysing statistics and trends. For us to design the best-fit solutions, having precise and trustworthy data is critically crucial. Regardless of the variety of topics we choose, such as climate change (for example, weather prediction), digitization, regulation, education, global health, stock market fluctuations (share prices), compliance, IT security, or FinTech, we won't be able to drive our business in the fierce competition without clean data. The method that policymakers determine solutions to contemporary challenges has been revolutionized by data.
Data Risks -
While data is so much necessary for a business organization, it's not a wise idea to go for data breach to obtain the data. Our personal information, including residential address, phone number, email address, information about online purchases, email ids, age, marital status, age, income, and occupation, is all up for sale in nations like India where data broking is still in its infancy. The majority of this personal information is sold for less than one rupee per individual. Data from PoS (point-of-sale) systems, third-party data from sources like social media and mobile service providers, agents from hospitals and banks, loan agents, auto dealers, etc. are some of the sources data brokers use to get their hands on our data.
According to a study by Economic Times, depending on the type of data, companies referred to as "data brokers" sell our personal information online for anywhere between Rs 10,000 and Rs 15,000 for data involving 1 lakh or more persons. Lists of high net worth persons, salaried individuals and their income profiles, credit card holders, automobile owners, retired ladies in a particular location, and personal data are all examples of this data type. A sample database of around 3,000 persons with Axis and HDFC credit cards was reportedly accessible for as little as Rs. 1,000. Financial data misuse is the most evident type. Up till December 2016, the Reserve Bank of India recorded 8,689 instances of fraud involving credit cards, ATM/debit cards, and internet banking. Many of these frauds are committed by con artists who gain the trust of their victims by exploiting openly accessible personal information to trick them into sharing sensitive information like CVVs or OTPs. Data broking is technically lawful but operates in a murky area. Data brokerage and privacy are not officially covered by India's IT Act.
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How to get Data?
First and foremost, we should always give priority to data generated within our business. Then we shall look for big data within the industry and if required, it’s good to hunt for other private and public sources. We may seek government data sources which provide information about individuals under limited circumstances related to census, government activity, such as licensing, property transactions etc. Some of the notable sites in India are
https://data.gov.in/
https://data.oecd.org/india.htm
https://www.india.gov.in/data-portal-india
https://www.censusindia.gov.in/ etc.
https://data.worldbank.org/country/india
https://www.youthinfoindia.org/data-bank/ etc
Some of the data sources in Nepal are –
https://cbs.gov.np/
The information's main source, however, is not public but rather private. Purchasing is quicker, simpler, and less expensive than using government resources directly. Data breach, however, is immoral. Membership in private activities like household purchasing organizations is typically not public information. For instance, we'll need information from some private data vendors if we're seeking for customers who are good credit risks. Any data source has a serious problem with quality, and this problem persists even if you pay for the data. Start with a tiny sample when employing a new source, then examine the data and put it to the test. If at all possible, check at the vendor's information about you to obtain a feel of the quality.
The greatest organizations will increasingly focus on collaboration when it comes to maintaining, enhancing, understanding, governing, and transforming data into reliable information. In actuality, data is significant because it both tracks and spurs progress. As a result, it might serve as a meter to gauge success. In a commercial republic, data will therefore rule supreme, in terms of its power and usefulness.
Sustainibility Enthusiast at BDS Services Pvt Ltd
7 个月Insightful piece. Thanks for sharing !