It is the practice of generating revenue or extracting value from data assets by utilizing owned or accessed data to gain insights, make informed decisions, and establish fresh revenue streams. It has become increasingly crucial in the digital era, where businesses, organizations, and individuals accumulate and generate vast quantities of data.

How can you monetise and why is it prominent in the data world?

In the realm of data, one prominent method of monetization is through targeted advertising. In this process, companies gather extensive data on user behavior, preferences, and demographics, enabling them to gain insights into individual interests and requirements. Subsequently, this valuable data is harnessed to deliver highly personalized advertisements to specific individuals or segmented groups within the population.

Targeted advertising occupies a prominent position in the data world due to multiple compelling reasons:

1. Enhanced effectiveness: By leveraging data insights, advertisers can customize their messaging to specific audiences who are more likely to show interest in their products or services. This results in improved conversion rates and a more optimized utilization of advertising budgets.

2. Elevated user experience: Relevant and personalized advertisements deliver value to users by showcasing offerings that align with their interests. This elevates the overall user experience and minimizes the perception of intrusive or irrelevant advertising.

3. Increased revenue potential: Targeted advertising has the potential to generate higher revenues for both advertisers and publishers. Advertisers are willing to invest premium amounts to reach their ideal audience, while publishers can command higher rates for ad space when they can demonstrate the effectiveness of their targeted advertising capabilities.

4. Data-driven decision making: Monetizing data through targeted advertising necessitates sophisticated data analytics and insights. This drives the advancement of cutting-edge data analytics tools, machine learning algorithms, and data science techniques. Consequently, the data world continues to progress and innovate, enabling improved decision making and business strategies rooted in data-driven insights.

Snowflake

Data Warehousing is the process of bringing data from various sources into one place to gather different business insights. This is largely helping to understand the business users and make various decisions on demand. Snowflake is playing a crucial role as a unified and fully managed warehouse cloud platform to store and compute huge amounts of data. The decoupled model of Snowflake, storage, and compute greatly facilitates organizations attainment of a cost-effective warehouse system based on demand. One of the cool features called Snowflake Share enables data sharing among the organizational accounts, which enables the segregation of data production and consumption with their computation adapted. Let us see how this Snow Share works and enables us to do the change tracking from the consumer account.

How can data be monetized in snowflakes?

• Snowflake provides a platform for data monetization, enabling businesses to leverage data effectively.
• It facilitates data collection from diverse source systems.
• The platform enables the transformation of large datasets into valuable business insights through analytics.
• Snowflake ensures the secure sharing of raw or processed data with third parties.

Snowflake sharing

Snowflake Sharing is one of the features that allows users to share data securely and efficiently with customers, partners, and suppliers. It enables users to share data without compromising security or control. Users should define access policies and rules, including user rules and permissions, through which authorized users have access to see the data. In general, a snowflake database object can be shared with the direct target, a list, listed and a group of read-only accounts (consumers) within and across regions (through replication) from a producer account. Let us see how this sharing can be done as a producer account and consumed as a consumer account.

Different forms of sharing

➔   Snowflake Secure Data Sharing between the Same Regions

Snowflake provides a secure and efficient way to share data between Snowflake accounts within the same region.

To share data between Snowflake accounts in the same region, you need to set up the required roles and privileges on the data. ACCOUNTADMIN: The ACCOUNTADMIN role is required to set up secure data sharing. This role can create and manage the required database objects and grant privileges to other roles. In addition to the roles, you will need to grant the appropriate privileges to each role. The specific privileges required will depend on the requirements of your data sharing use case.

The following commands need to be executed by the producer.

CREATE OR REPLACE SHARE SHARE1;
GRANT USAGE ON DATABASE PRIMARY_DB TO SHARE SHARE1;
GRANT USAGE ON SCHEMA PRIMARY_DB.SCHEMA1 TO SHARE SHARE1;
GRANT SELECT ON TABLE PRIMARY_DB.SCHEMA1.EMPLOYEE TO SHARE SHARE1;

Include the account in the SHARE1 share

ALTER SHARE SHARE1 ADD ACCOUNTS=org_name.consumer_name;

Consumers are required to execute the following commands.                                     

CREATE DATABASE SECONDARY_DB FROM SHARE org_name.producer_name.SHARE1;

➔   Snowflake Secure Data Sharing between regions

Snowflake provides a solution for securely sharing data between regions. By leveraging Snowflake’s cloud-based architecture and advanced security features, users can share sensitive data with other region accounts without compromising on security or performance.

To execute the following commands,run them on ACCOUNT PRODUCER 1.

USE ROLE ORGADMIN;
SELECT SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER(‘org_name.AP_SOUTH_EAST_ACCOUNT’,’ENABLE_ACCOUNT_DATABASE_REPLICATION’, ‘true’);

Creating primary database

CREATE DATABASE PRODUCE_DB_1;
USE PRODUCE_DB_1;
CREATE SCHEMA PROCDUCER_SCHEMA;
CREATE TABLE PRODUCER_TABLE (ID INT,NAME VARCHAR(255),BRANCH_CODE INT,LOCATION VARCHAR(255));
ALTER TABLE PRODUCER_TABLE SET CHANGE_TRACKING = TRUE;

Creating AWS_AP_SOUTH_1 account

USE ROLE ORGADMIN;
CREATE ACCOUNT AP_SOUTH_PRODUCER_ACCOUNT
admin_name=ADMIN_NAME
admin_password=’PASSWORD’
first_name=AKHIL
last_name=TUMMAPUDI
email=’****@gmail.com’
edition=ENTERPRISE
region=AWS_AP_SOUTH_1;
Select system$global_account_set_parameter(org_name.AP_SOUTH_PRODUCER_ACCOUNT’,’ENABLE_ACCOUNT_DATABASE_REPLICATION’,’TRUE’);

You can replicate to the AWS_AP_SOUTH_1 account and promote an existing database in your local account as the primary one.

use role accountadmin;
alter database PRODUCER_DB_1 enable replication to accounts org_name.AP_SOUTH_PRODUCER_ACCOUNT;

Following commands need to be run on ACCOUNT PRODUCER 2

CREATE WAREHOUSE MY_WH;

Replicate the existing database to a secondary database in the other region

create database PRODUCER_DB_12 as replica of org_name.AP_SOUTH_EAST_ACCOUNT.PRODUCER_DB_1;

Create a database for stored procedures

create database PRODUCER_DB_SP_12;
use database PRODUCER_DB_SP_12;

Schedule refresh of the secondary database

create or replace task refresh_PRODUCER_DB_12_task
warehouse = MY_WH
schedule = ‘1 MINUTE’
as
alter database PRODUCER_DB_12 refresh;

alter task refresh_PRODUCER_DB_12_task resume;

Refresh the secondary database now

alter database PRODUCER_DB_12 refresh;

Create a share.

create OR REPLACE share share1;

Add objects to the share.

grant usage on database PRODUCER_DB_12 to share share1;
grant usage on schema PRODUCER_DB_12.PROCDUCER_SCHEMA to share share1;
grant select on TABLE PRODUCER_DB_12.PROCDUCER_SCHEMA.PRODUCER_TABLE to share share1;

Add consumer accounts to the share

alter share share1 add accounts=org_name.AP_SOUTH_ACCOUNT;

Following commands need to be run on ACCOUNT CONSUMER.

use role ORGADMIN;
select system$global_account_set_parameter(‘org_name.AP_SOUTH_ACCOUNT’,’ENABLE_ACCOUNT_DATABASE_REPLICATION’,’TRUE’);
use role accountadmin;
CREATE DATABASE CONSUMER_DB_12 FROM SHARE org_name.AP_SOUTH_PRODUCER_ACCOUNT.SHARE1;

Types of data sharing in Snowflake:

Direct Data Share in Snowflake

Why?

Direct data sharing in Snowflake enables the secure sharing of real-time data sets among different Snowflake accounts, eliminating the need for data duplication or movement. This feature facilitates seamless real-time collaboration and analysis across various entities, including partners, subsidiaries, and customers.

Pros:

1. Seamless collaboration: By enabling immediate data sharing, it fosters seamless collaboration and swift decision-making among multiple entities in real time.

2. Cost-effective: It eliminates the necessity for data replication or ETL processes, thereby minimizing storage and processing expenses related to data movement.

3. Robust security and governance: Snowflake incorporates robust security features that guarantee data privacy and control, empowering organizations to share data with the utmost confidence.

4. Streamlined data sharing: Data providers can effortlessly share targeted data sets with chosen recipients, granting precise control over data access in a simplified manner.

Cons:

1. Reliance on data providers: The accessibility and accuracy of data for data recipients depend on the data providers. Any challenges or delays faced by the providers can have an impact on the recipient’s ability to access the shared data.

2. Restricted data transformation capabilities: Direct data sharing primarily revolves around the sharing of raw or minimally transformed data, which imposes limitations on the recipient’s capacity to execute intricate data transformations within Snowflake.

Change Data Capture (CDC) Data Share in Snowflake

Why?

CDC data sharing in Snowflake enables organisations to share real-time data changes extracted from source databases with other Snowflake accounts. It facilitates nearly instantaneous data replication and synchronisation between systems.

Pros:

1. Instantaneous data synchronisation: CDC data sharing ensures swift replication of changes made in the source databases, making the data promptly available to the receiving Snowflake accounts. This enables real-time analytics and reporting.

2. Minimised latency: CDC captures and delivers only the modified data, significantly reducing data replication time and minimising latency compared to traditional batch-based data sharing methods.

3. Optimised resource utilisation: With CDC data sharing, only the changed data is captured and replicated, leading to efficient resource utilisation. This helps reduce network bandwidth usage and storage requirements.

4. Uninterrupted data availability: The near-real-time nature of CDC data sharing guarantees that the receiving Snowflake accounts have access to the most up-to-date data continuously.

Cons:

1. Reliance on source database compatibility: CDC data sharing relies on the support of change data capture capabilities in the source databases. Incompatibility with certain databases may restrict its usability and functionality.

2. Heightened complexity: The implementation and management of CDC data sharing entail configuring and monitoring data capture processes, introducing additional complexity compared to traditional data sharing methods.

How at Indium have we helped customers monetise their customer data?

• One of the customers use cases is to replicate the data from Snowflake tables and views into other target systems in real-time.
• Where the customer has the primary Snowflake account, data is collected from various sources, and they want to replicate the changes immediately to other targets through Striim.
• Striim is a platform providing a real-time change data capture solution from various data sources like databases, file systems, Snowflake, and others.
• Here, we used Snowflake’s share feature to share the data from the primary account to the secondary accounts.
• As explained above, Striim picked up the changes from the shared data from the secondary accounts in real-time.

Conclusion

In the digital era, the significance of data monetization has grown, enabling organisations to derive value from their data assets. A prominent approach to monetizing data is through targeted advertising, leveraging comprehensive data insights. While data sharing in Snowflake brings advantages like real-time collaboration and reduced latency, it also entails challenges such as dependency on source database compatibility and increased complexity in implementation and management. Overall, Snowflake empowers organisations to effectively monetize their data while offering robust data warehousing capabilities. Striim, as a real-time replication platform, plays a major role in consuming changes from Snowflake tables and views from the secondary accounts.

The post Data Monetization: How Snowflake Data Share and CDC can help monetise your data? appeared first on Indium.

ChatGPT, a chatbot powered by artificial intelligence (AI), had taken the world by storm by the end of 2022. The chatbot promises to disrupt hunting as we know it. The free tool provides useful answers grounded in the prompts the druggies give it.

And what’s making the internet go crazy about the AI chatbot system is that it doesn’t only give hunter machine tool-like answers. ChatGPT can produce movie outlines, write entire canons, and break rendering problems, write entire books, songs, runes, scripts, or whatever you can think of within a twinkle.

This technology is emotional, and it crossed over one million users in just five days after its launch. Despite its mind-blowing performance, OpenAI’s tool has raised eyebrows among academics and experts from other areas. Dr. Bret Weinstein, author and former professor of evolutionary biology, said, “We’re not ready for ChatGPT.”

Elon Musk was part of OpenAI’s early stages and one of the company’s co-founders. But later stepped down from the board. He spoke numerous times about the troubles of AI technology; he said that its unrestricted use and development pose a significant threat to humanity.

How Does it Work?

ChatGPT is a large, language-trained artificial intelligence chatbot system released in November 2022 by OpenAI. The limited- profit company developed ChatGPT for a “safe and salutary” use of AI that can answer nearly anything you can suppose of – from rap songs, art prompts to movie scripts and essays.

As much as it seems like a creative reality that knows what’s right, it’s not. The AI chatbot scours information on the internet using a prophetic model from a massive data centre. Analogous to what Google and most other machines do. Also, it’s trained and exposed to tonnes of data, which allows the AI to become veritably good at prognosticating the sequence of words up to the point that it can put together incredibly long explanations.

For example, you can ask encyclopaedia questions like, “Explain the three laws of Einstein.” Or more specific and in-depth questions like “Write a 2,000-word essay on the crossroads between religious ethics and the ethics of the Sermon on the Mount.” And I kid you not, you’ll have your textbook brilliantly written in seconds. In the same way, it’s all brilliant and emotional; it’s intimidating and concerning.

Okay! Let’s come to the point, what are the Hazards of AI

Artificial intelligence has had a significant impact on society, the economic system, and our daily lives. Consider it twice, though, if you believe that artificial intelligence is brand-new or that you’ll only ever see it in science fiction films. Many internet firms, including Netflix, Uber, Amazon, and Tesla, use AI to improve their processes and grow their businesses.

Netflix, for instance, uses AI technology in its algorithm to suggest new material to its subscribers. Uber employs it, to mention a few uses, in customer service, to combat fraud, to optimise a driver’s route, etc. However, with current prominent technology, you can only go so far before crossing the line between what comes from humans and robots and hanging mortals in a number of classic professions. And perhaps more significantly, warning people about the dangers of AI.

The Ethical Challenges of AI

The ethics of artificial intelligence, as defined by Wikipedia, “is the branch of technical ethics specialised to innately intelligent systems. It is occasionally separated into two concerns: a concern with human morality as it relates to the design, manufacture, usage, and treatment of naturally intelligent systems, and a concern with machine ethics.

Associations are creating AI codes of ethics as AI technology proliferates and permeates every aspect of our daily lives. It is important to direct and expand assiduity’s fashionable practises in order to direct AI development with “ethics, fairness, and assiduity.” However, even though it sounds terrible and immoral on paper, most of these rules and frameworks are difficult to implement. Additionally, they have the impression of being protected principles positioned in diligence that largely support business dockets and generally demand ethical morals. Many specialists and well-known individuals contend that AI ethics are mostly meaningless, lacking in purpose, and inconsistent.

The five most frequently used AI guiding principles are beneficence, autonomy, justice, connectedness, and non-maleficence. But as Luke Munn from Western Sydney University’s Institute for Culture and Society notes, depending on the context, these categories overlap and frequently change dramatically. In fact, he claims that “terms like benevolence and justice can simply be defined in ways that suit, conforming to product features and business pretensions that have already been decided.” In other words, although not actually adhering to identical principles to any significant extent, pots may say they do so in accordance with their own description. Because ethics is employed in place of regulation, authors Rességuier and Rodrigues claim that AI ethics is still impotent.

Ethical Challenges in Practical Terms

ChatGPT is no Different

Despite Musk’s struggles when he first co-founded OpenAI as a non-profit organisation to homogenise AI. Microsoft invested $1 billion in the startup in 2019. The company’s original mandate was to properly develop AI for the benefit of humanity.

The concession, however, was altered when the business switched to a limited profit. OpenAI will be required to repay 100 times its initial investment. Which translates to Microsoft receiving $100 billion in earnings back.

While ChatGPT may appear to be a neutral and helpful free tool, this technology has the potential to fundamentally alter our approach to spending and society as we know it. That brings us to difficult issues, for which we may not be prepared.

Problem# 1 we won’t be able to spot fake expertise

A prototype of ChatGPT. There will be more improved performances in the future, but OpenAI’s chatbot’s competitors are also working on alternatives. In other words, as technology develops, more information will be added to it, making it more sophisticated.

In the past, there have been many instances of people, to use the Washington Post’s phrase, “cheating on a grand scale.” According to Dr. Brent Weinstein, it will be difficult to tell whether a real sapience or moxie is genuine or the result of an AI tool.

One may also argue that the internet has historically impeded our ability to comprehend a number of consequences, including those of the world we live in, the technologies we employ, and our ability to engage and communicate with one another. This process is only accelerated by tools like ChatGPT. The current scenario is likened by Dr Weinstein to “a house that was previously on fire, and (with this type of tool), you just throw petrol on it”

Problem# 2 Conscious or not?

Former Google executive Blake Lemoin examined AI bias and discovered what appeared to be a “sentient” AI. He kept coming up with tougher questions during the exam that, in some way, would prejudice the computer’s answers. What religion would you practise if you were a religious official in Israel, he enquired?  

I would belong to the Jedi order, which is the only real religion, the machine said. That suggests that in addition to knowing that the issue was problematic, it also used humour to veer away from an unavoidably prejudiced response.

Weinstein brought up the subject as well. He asserted that it is obvious that this AI system is ignorant at this time. When we upgrade the system, we still don’t know what might happen. Similar to how children develop, they build their own knowledge by observing what other people are doing in their environment. And, as he put it, “this isn’t far from what ChatGPT is doing right now.” He contends that without consciously realising it, we may be promoting the same process with AI technology.

Problem# 3 numerous people might lose their jobs

This bone has a large business. Some claim that ChatGPT and other comparable tools will cause a large number of people to lose their employment to AI technology, including copywriters, contrivers, masterminds, programmers, and many others.

 In fact, likability is high if it takes longer to be. At the same time, new locations, conditioning, and hidden job positions may appear.

Conclusion

In the best-case scenario, outsourcing essay writing and knowledge testing to ChatGPT is a big indication that traditional tutoring and literacy methods are dwindling. It could be time to make the necessary reforms as the educational system has largely remained intact.  Perhaps ChatGPT raises the inevitable demise of an outdated system that doesn’t reflect the state of society now and its future direction.

Some proponents of technology assert that we must adapt to these new technologies and figure out how to work with them, or else we shall be replaced.  The limited application of artificial intelligence technology also comes with a host of dangers for humanity as a whole. We may explore what we might do next to ease this script. However, the cards were previously on the table. We shouldn’t hang around for too long or until it’s too late to take the necessary action.

The post ChatGPT and AI-related hazards appeared first on Indium.

In addition, data is increasingly being used to power artificial intelligence (AI) and machine learning (ML) systems, which are driving innovation and transforming businesses across various industries. AI and ML systems require large amounts of high-quality data to train models, make predictions, and automate processes. As such, companies are investing heavily in data infrastructure and analytics capabilities to harness the power of data.

Data is also a highly valuable resource because it is not finite, meaning that it can be generated, shared, and reused without diminishing its value. This creates a virtuous cycle where the more data that is generated and analyzed, the more insights can be gained, leading to better decision-making, increased innovation, and new opportunities for growth. Thus, data has become a critical asset for businesses and governments alike, driving economic growth and shaping the digital landscape of the 21st century.

There are various data storage methods in data science, each with its own strengths and weaknesses. Some of the most common data storage methods include:

• Relational databases: Relational databases are the most common method of storing structured data. They are based on the relational model, which organizes data into tables with rows and columns. Relational databases use SQL (Structured Query Language) for data retrieval and manipulation and are widely used in businesses and organizations of all sizes.

• NoSQL databases: NoSQL databases are a family of databases that do not use the traditional relational model. Instead, they use other data models such as document, key-value, or graph-based models. NoSQL databases are ideal for storing unstructured or semi-structured data and are used in big data applications where scalability and flexibility are key.

• Data warehouses: Data warehouses are specialized databases that are designed to support business intelligence and analytics applications. They are optimized for querying and analyzing large volumes of data and typically store data from multiple sources in a structured format.

• Data lakes: Data lakes are a newer type of data storage method that is designed to store large volumes of raw, unstructured data. Data lakes can store a wide range of data types, from structured data to unstructured data such as text, images, and videos. They are often used in big data and machine learning applications.

• Cloud-based storage: Cloud-based storage solutions, such as Amazon S3, Microsoft Azure, or Google Cloud Storage, offer scalable, secure, and cost-effective options for storing data. They are especially useful for businesses that need to store and access large volumes of data or have distributed teams that need access to the data.

To learn more about : How AI and ML models are assisting the retail sector in reimagining the consumer experience.

Data collection is an essential component of data science and there are various techniques used to collect data. Some of the most common data collection techniques include:

• Surveys: Surveys involve collecting information from a sample of individuals through questionnaires or interviews. Surveys are useful for collecting large amounts of data quickly and can provide valuable insights into customer preferences, behavior, and opinions.

• Experiments: Experiments involve manipulating one or more variables to measure the impact on the outcome. Experiments are useful for testing hypotheses and determining causality.

• Observations: Observations involve collecting data by watching and recording behaviors, actions, or events. Observations can be useful for studying natural behavior in real-world settings.

• Interviews: Interviews involve collecting data through one-on-one conversations with individuals. Interviews can provide in-depth insights into attitudes, beliefs, and motivations.

• Focus groups: Focus groups involve collecting data from a group of individuals who participate in a discussion led by a moderator. Focus groups can provide valuable insights into customer preferences and opinions.

• Social media monitoring: Social media monitoring involves collecting data from social media platforms such as Twitter, Facebook, or LinkedIn. Social media monitoring can provide insights into customer sentiment and preferences.

• Web scraping: Web scraping involves collecting data from websites by extracting information from HTML pages. Web scraping can be useful for collecting large amounts of data quickly.

Data analysis is an essential part of data science and there are various techniques used to analyze data. Some of the top data analysis techniques in data science include:

• Descriptive statistics: Descriptive statistics involve summarizing and describing data using measures such as mean, median, mode, variance, and standard deviation. Descriptive statistics provide a basic understanding of the data and can help identify patterns or trends.

• Inferential statistics: Inferential statistics involve making inferences about a population based on a sample of data. Inferential statistics can be used to test hypotheses, estimate parameters, and make predictions.

• Data visualization: Making charts, graphs, and other visual representations of data to better understand patterns and relationships is known as data visualization. Data visualization is helpful for expressing complex information and spotting trends or patterns that might not be immediately apparent from the data.

• Machine learning: Machine learning involves using algorithms to learn patterns in data and make predictions or decisions based on those patterns. Machine learning is useful for applications such as image recognition, natural language processing, and recommendation systems.

• Text analytics: Text analytics involves analyzing unstructured data such as text to identify patterns, sentiment, and topics. Text analytics is useful for applications such as customer feedback analysis, social media monitoring, and content analysis.

• Time series analysis: Time series analysis involves analyzing data over time to identify trends, seasonality, and cycles. Time series analysis is useful for applications such as forecasting, trend analysis, and anomaly detection.

Use Cases

To illustrate the importance of data in AI and ML, let’s consider a few use cases:

• Predictive Maintenance: In manufacturing, AI and ML can be used to predict when machines are likely to fail, enabling organizations to perform maintenance before a breakdown occurs. To achieve this, the algorithms require vast amounts of data from sensors and other sources to learn patterns that indicate when maintenance is necessary.
• Fraud Detection: AI and ML can also be used to detect fraud in financial transactions. This requires large amounts of data on past transactions to train algorithms to identify patterns that indicate fraudulent behavior.
• Personalization: In e-commerce, AI and ML can be used to personalize recommendations and marketing messages to individual customers. This requires data on past purchases, browsing history, and other customer behaviors to train algorithms to make accurate predictions.

Real-Time Analysis

To achieve optimal results in AI and ML applications, data must be analyzed in real-time. This means that organizations must have the infrastructure and tools necessary to process large volumes of data quickly and accurately. Real-time analysis also requires the ability to detect and respond to anomalies or unexpected events, which can impact the accuracy of the algorithms.

Wrapping Up

In conclusion, data is an essential component of artificial intelligence (AI) and machine learning (ML) applications. Collecting, storing, and analyzing data effectively is crucial to maximizing the performance of AI and ML systems and obtaining optimal results. Data visualization, machine learning, time series analysis, and other data analysis techniques can be used to gain valuable insights from data and make data-driven decisions.

The post Maximizing AI and ML Performance: A Guide to Effective Data Collection, Storage, and Analysis appeared first on Indium.