Despite massive data being generated, access to and availability remains a problem. Although public databases partially address this issue, certain dangers are still involved. One of them is bias caused by improper usage of data sets. The second difficulty is requiring different data to train the algorithms and satisfy real-world requirements properly. The quality of the algorithm will also be impacted by data accuracy. It is regulated to preserve privacy and might be expensive to obtain.

These problems can be resolved by using synthetic data, which enables businesses to quickly produce the data sets required to satisfy the demands of their clients. Gartner predicts that by 2030, synthetic data will likely surpass actual data in AI models, even though accurate data is still regarded as superior.

Decoding the Exceptional Synthetic Data

So, what do you get when we say synthetic data? At the forefront of modern data-driven research, institutions like the Massachusetts Institute of Technology (MIT) are pioneering the utilization of synthetic data. Synthetic data refers to artificially generated datasets that mimic real-world data distributions, maintaining statistical properties while safeguarding privacy. This innovative approach ensures that sensitive information remains confidential, as exemplified by MIT’s creation of synthetic healthcare records that retain essential patterns for analysis without compromising patient privacy. This technique’s relevance extends to various domains, from machine learning advancements to societal insights, offering a powerful tool to unlock valuable knowledge while upholding data security and ethical considerations.

Using synthetic data, new systems can be tested without live data or if the data is biased. Small datasets not being used can be supplemented, and the accuracy of learning models can be improved. Synthetic data can also be used when real data cannot be used, shared, or moved. It can create prototypes, conduct product demos, capture market trends, and prevent fraud. It can even be used to generate novel, futuristic conditions.

Most importantly, it can help businesses comply with privacy laws, mainly health-related and personal data. It can reduce the bias in data sets by providing diverse data that reflects the real world better.

Use Cases of Synthetic Data

Synthetic data can be used in different industries for different use cases. For instance, computer graphics and image processing algorithms can generate synthetic images, audio, and video that can be used for training purposes.

Synthetic text data can be used for sentiment analysis or for building chatbots and machine translation algorithms. Synthetically generated tabular data sets are used in data analytics and training models. Unstructured data, including images, audio, and video, are being leveraged for speech recognition, computer vision, and autonomous vehicle technology. Financial institutions can use synthetic data to detect fraud, manage risks, and assess credit risk. In the manufacturing industry, it can be used for quality control testing and predictive maintenance.

Also read: The Transformative Impact Of Generative AI On The Future Of Work.

Generating Synthetic Data
How synthetic data is generated will depend on the tools and algorithms used and the use case for which it is created. Three of the popular techniques used include:

Technique #1 Random Selection of Numbers: One standard method is randomly selecting numbers from a distribution. Though this may not provide insights like real-world data, the data distribution matches it closely.

Technique #2 Generating Agent-based Models: Unique agents are created using simulation techniques to enable them to communicate with each other. This is especially useful in complex systems where multiple agents, such as mobile phones, apps, and people, are required to interact with each other. Pre-built core components and Python packages such as Mesa are used to develop the models quickly, and a browser-based interface is used to view them.

Technique #3 Generative Models: Synthetic data replicating real-world data’s statistical properties or features is generated using algorithms. Training data learns the statistical patterns and relationships in the data and generates new synthetic data similar to the original. Generative adversarial networks and variational autoencoders are examples of generative models.

The model quality should be reliable to ensure the quality of synthetic data. Additional verification is required and involves comparing the model results with the real-world data that has been annotated manually. Users must be sure that the synthetic data is not misleading, reliable, and 100% fail-safe for privacy.

Synthetic Data with Databricks

Databricks offers dbldatagen, a Python library, to generate synthetic data for testing, creating POCs, and other uses such as Delta Live Tables pipelines in Databricks environments. It helps to:

● Create unique values for a column.
● Allow templated text generation based on specifications.
● Generate data from a specific set of values.
● Generate weighted data in case of repeating values.
● The data generated in a data frame can be written to storage in any format.
● Billions of rows of data can be generated quickly.
● A random seed can be used to generate data based on the value of other fields.

The post Collaboration of Synthetics: ML’s Evolutionary Edge appeared first on Indium.

The Internet of Things (IoT) is a network of linked devices that interact with one another to carry out specific functions. Everything from smart home appliances to industrial machinery may be part of this network. The IoT has the potential to revolutionize industries and open up new business opportunities by utilizing the power of big data. As with any new technology, there are substantial obstacles that need to be overcome.

One of the biggest opportunities that big data and the IoT present is the ability to make data-driven decisions in real-time. For example, in the manufacturing industry, sensors on machinery can provide real-time data on performance, allowing for predictive maintenance and reducing downtime. Similarly, in healthcare, IoT devices can monitor patients and provide data to healthcare professionals, allowing for more personalized care.

However, with the amount of data generated by the IoT, there are also significant challenges in terms of managing, processing, and analyzing this data. Traditional data management tools and techniques are often not sufficient to handle the sheer volume of data generated by the IoT. Additionally, there are concerns around data privacy and security, as the IoT often involves sensitive data being transmitted over networks.

Here are few insights from Gartner or Forrester

According to a Gartner report, the combination of big data and the IoT presents significant opportunities for businesses, particularly in areas such as supply chain management, predictive maintenance, and customer engagement. However, the report also highlights the challenges associated with managing and analyzing the large volume of data generated by the IoT, as well as the need for businesses to ensure data security and privacy.

Similarly, a Forrester report emphasizes the potential of the IoT and big data to drive digital transformation in various industries. The report notes that businesses that effectively leverage these technologies can gain a competitive advantage by improving operational efficiency, reducing costs, and delivering better customer experiences. However, the report also warns that businesses must address challenges such as data management and security to realize the full potential of the IoT and big data.

Here are a few challenges and opportunities we should be aware of.

Opportunities:

Real-time data-driven decisions: The ability to collect and analyze real-time data from IoT devices can enable businesses to make data-driven decisions quickly and efficiently.

Increased efficiency and productivity: By using IoT devices to monitor and optimize processes, businesses can increase efficiency and productivity, leading to cost savings and increased revenue.

Improved customer experience: The IoT can be used to collect data on customer behavior and preferences, allowing businesses to offer personalized experiences and improve customer satisfaction.

New revenue streams: The IoT can open up new revenue streams for businesses by enabling them to offer new products and services, such as subscription-based models or pay-per-use models.

Challenges:

Data management: The sheer volume of data generated by IoT devices can be overwhelming for businesses, and traditional data management techniques may not be sufficient to handle it.

Data security and privacy: The IoT involves the transmission of sensitive data over networks, raising concerns around data security and privacy.

Interoperability: As the IoT involves devices from different manufacturers, there can be challenges in ensuring that these devices can communicate and work together seamlessly.

Skill gaps: As the IoT is a relatively new technology, there may be skill gaps in the workforce, making it challenging for businesses to effectively leverage it.

Use Cases:

One use case for big data and the IoT is in the transportation industry. By using IoT devices to collect data on traffic patterns and road conditions, transportation companies can optimize routes and reduce congestion. In agriculture, IoT devices can monitor soil conditions and weather patterns to optimize crop yields. In the energy industry, IoT devices can monitor power usage and detect inefficiencies, leading to cost savings and reduced carbon emissions.

How Indium Software can address

Indium Software has extensive experience in developing and implementing solutions for big data and IoT use cases. For example, our team can develop customized algorithms and machine learning models to analyze IoT data and provide real-time insights. We can also help ensure data privacy and security by implementing robust encryption and access control measures. In addition, our team can develop and deploy custom dashboards and visualizations to make it easy for businesses to understand and act on IoT data.

Here are a few real-time scenarios that illustrate how the combination of big data and the IoT is being used to drive innovation and growth across various industries:

Smart Manufacturing: A manufacturing company has implemented an IoT system to monitor and optimize its production processes in real-time. The system collects data from sensors embedded in manufacturing equipment and uses big data analytics to identify patterns and optimize production. By leveraging this technology, the company has been able to reduce downtime, increase productivity, and improve product quality.

Predictive Maintenance: A transportation company has deployed IoT sensors on its fleet of vehicles to monitor their performance and detect potential maintenance issues before they become major problems. The system collects data on factors such as engine performance, fuel consumption, and tire pressure, and uses big data analytics to identify patterns and predict maintenance needs. By leveraging this technology, the company has been able to reduce maintenance costs, increase vehicle uptime, and improve customer satisfaction.

Smart Agriculture: A farming company has implemented an IoT system to monitor and optimize its crop production processes. The system collects data from sensors embedded in soil and crop fields, as well as weather data and other environmental factors, and uses big data analytics to identify patterns and optimize crop production. By leveraging this technology, the company has been able to increase crop yields, reduce water and fertilizer usage, and improve overall farm productivity.

Wrapping Up

The potential of big data and the IoT is enormous, and businesses that can effectively leverage these technologies will have a significant advantage in the marketplace. However, it is crucial to address the challenges associated with managing and analyzing the data generated by the IoT. Indium Software has the expertise and experience to help businesses overcome these challenges and unlock the full potential of big data and the IoT.

The post Big Data’s Impact on IoT: Opportunities and Challenges in Analytics 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.

What is Data Management?

Data management is the process of collecting, storing, organizing, maintaining, using, and disposing of data in an efficient and secure manner. It involves establishing policies and procedures for data acquisition, validation, storage, backup and recovery, access, and destruction. Effective data management is crucial to improve operations, and support growth.

Why Should You Adopt a Modern Approach to Data Management?

One of the main forces driving contemporary data management is the growth of big data. Businesses must come up with new methods to store, handle, and analyse data given its exponential increase. With its scalable and adaptable features, cloud computing offers the perfect option.

Increased Business Agility

The processing and analysis of data by organisations has been revolutionised by artificial intelligence (AI) and machine learning (ML). Businesses may use AI and ML to automate complicated data management activities, find patterns and insights, and make decisions more quickly and accurately. Businesses may confidently make data-driven decisions since ML systems can learn from past data to increase their accuracy over time enabling organizations to respond to changing business conditions more effectively..

Improved Data Analytics

Modern data management has increasingly reliant on data analytics and visualisation tools. These tools make it simpler for companies to recognise patterns, trends, and anomalies by transforming raw data into insights that can be used to gain valuable insights from their data.

Data Integration and Interoperability

A modern approach to data management supports the integration of data from multiple sources and systems. The increasing amount of data generated by various sources; data integration has become more challenging. Modern data management solutions must support data integration across multiple platforms and systems, providing a comprehensive view thus enabling organizations to more effectively leverage their data.

Governance with Regulations

Data governance refers to the policies, procedures, and controls used to manage data quality, security, and privacy. With the increasing importance of data in business decision-making, data governance has become critical to ensure data accuracy, security, and compliance with regulatory requirements. A modern approach to data management helps organizations comply with data privacy regulations, such as GDPR and HIPAA, and reduces the risk of penalties for non-compliance.

Also read: Crucial Role that Data Fabric Plays in Data Management Today

What should be considered for a successful approach to modern data management?

Having a successful approach to modern data management requires several key elements, including:

Effective data governance aids for data management to be successful, it is essential to implement clear policies and procedures for data gathering, validation, storage, backup, recovery, access, and deletion. Making sure that data is always correct, comprehensive, and consistent is essential for making well-informed decisions and running a successful organisation, so it is important to have a data quality management system in place.

The key to ensuring that data management operations are successful, efficient, and complement the overall business plan is having devoted, well-trained data management staff. To meet the organization’s changing demands, data management should be a process that is continually evaluated and improved.

Data management can present several challenges, including:

Data Volume: The exponential growth of data, also known as big data, can present a challenge for organizations that need to store, process, and analyze large amounts of data.

Data Variety: The increasing variety of data types and formats can make it difficult for organizations to manage and integrate data from multiple sources.

Data Quality: Ensuring the accuracy, completeness, and consistency of data can be challenging, especially as data is sourced from multiple systems and platforms.

Data Security: Protecting sensitive data from unauthorized access and ensuring compliance with data privacy regulations, such as GDPR and HIPAA, can be challenging.

Data Integration: Integrating data from multiple sources and systems can be difficult, especially when data formats are incompatible or data is stored in silos.

Data Governance: Implementing effective data governance policies and procedures that ensure consistent and compliant data management practices can be challenging.

Data Management Teams: Finding and retaining skilled data management professionals can be difficult, especially as the demand for these skills continues to grow.

Budget and Resources: Securing sufficient budget and resources to implement and maintain a robust data management program can be a challenge.

These challenges highlight the importance of adopting a comprehensive, well-planned, and well-executed data management strategy that takes into account the unique needs and requirements of each organization.

How Do You Create a Modern Data Architecture?

To create modern data architecture, you can follow these steps:

Step 1:  Start by defining the business requirements and determining what information the organisation needs to gather, store, and analyse in order to achieve its objectives. List the numerous data sources, including social media, transactional systems, logs, and third-party APIs. decide what modifications are required to accommodate the new design, and evaluate the existing data infrastructure.

Step 2:  Choose the best technology for data storage, processing, and analysis based on your needs and the sources of your data. Data lakes, data warehouses, and cloud services may all fall under this category. Then Design the data architecture in accordance with the needs, data sources, and technologies you have chosen. Creating data models, data pipelines, and data access patterns may be part of this. finally Implement the data architecture, then test it to see if it functions as expected and satisfies the criteria.

Step 3: Maintain regular monitoring of the data architecture and make changes as necessary to make sure it continues to fulfil the demands of the enterprise. Keep in mind that a modern data architecture needs to be scalable, versatile, and secure to suit the business’s continuously changing needs.

Conclusion

By leveraging the latest technologies and tools, and by having dedicated and well-trained data management teams in place, organizations can ensure that they have the right data at the right time to support their business needs. By adopting a modern approach to data management, organizations can increase operational efficiency, improve customer understanding, and gain a competitive advantage in their respective markets.

In conclusion, modern data management is essential for business success as it enables organizations to effectively collect, store, and analyze data to support informed decision making and drive business growth. The volume, variety, and velocity of data continues to increase, making it more important than ever for organizations to adopt modern data management practices that support effective data governance, security, and privacy.

The post Why Modern Data Management is Essential for Business Success appeared first on Indium.

With the use of interactive dashboards, personalised graphics, and intuitive data visualisations, Domo lets you create your own stories. Furthermore, it provides data governance and role-based access controls to guarantee that users can only access the information that they are authorised to see. To further safeguard your data visualisations, you can quickly and easily set up single sign-on and multi-factor authentication.

This guide is intended to assist beginners in learning the fundamentals of Domo and creating powerful data visualizations that drive business results.

Getting Started with Domo

To get started with Domo, you must first create an account. To do so, visit the Domo Website and register for a free trial. After you’ve created your account, log in to access the Domo dashboard. Domo’s dashboard is the primary interface for interacting with data in the software. It displays a summary of all your data connections, reports, and dashboards. To make a new report or dashboard, go to the dashboard and click the “Create” button.

Given the volume of the client’s data, the current procedure made it necessary for the SAS Data Admin to manually intervene and made it necessary for key decision makers to wait three days before viewing useful processed data.

Workflow for Domo Data Visualization

The workflow for data visualisation in Domo typically includes the following steps:

1. Data Connection

Domo offers numerous data connection options, including databases, spreadsheets, cloud-based applications, and APIs. To connect to a data source, go to the dashboard’s “Data” tab and then click on “Connections.”

Once you’ve selected your data source, follow the on-screen instructions to connect to it. You can also use Domo’s data connectors to bring in data from popular sources such as Salesforce, Google Analytics, and more.

2. Data Transformation

After connecting to your data source, you can begin transforming the data. To do so, go to the “Data” tab and then click “Transform.”

Domo offers a simple drag-and-drop interface for data transformation. Data transformation options include filtering, merging, and pivoting. You can also use the “Calculated Fields” feature to create custom calculations based on your data.

3. Data Visualization

Once your data has been transformed, you can start visualizing it. To do so, go to the dashboard’s “Visualize” tab and then click “Create.”

Domo provides a wide range of visualisation options, such as bar charts, line charts, pie charts, and more. You can also create custom visualisations based on your data by using the “Custom Visualizations” feature. Simply drag and drop the data elements you want to visualise into the visualisation builder to create a visualisation. To further customise your visualisation, you can add filters, calculated fields, and other data elements.

4. Creating Dashboards

Dashboards are an effective way to communicate insights and share data with others. To create a dashboard, go to the dashboard’s “Visualize” tab and click on “Dashboards.”

After you’ve created your dashboard, you can add visualizations, text, and images to create a comprehensive and interactive presentation of your data. You can also use the “Widgets” feature to add interactive elements to your dashboard such as charts, graphs, and maps.

5. Sharing and Collaborating

One of Domo’s most important features is its ability to share and collaborate on data. To share a report or dashboard, simply click the “Share” button.

You can share your report or dashboard with others by emailing them a link or embedding it in a web page. You can also limit who has access to the report or dashboard and what actions they can perform on it, such as viewing, editing, or commenting on it. This facilitates your teams to deliver data-driven collaboration and decision-making.

Business intelligence and data analytics depend heavily on data visualisation because this is how customers will see the outputs and outcomes they need. Check out this blog to learn more.

Domo vs. Tableau vs. Power BI

When comparing top BI tools, many factors must be considered. Keeping track of all business affairs is becoming increasingly difficult for any company, as large volumes of data pose a challenge to companies of all sizes. However, each of these three tools is capable of solving them in its own way.

Domo: How Can It Benefit Your Business?

1. Better Decision Making: By providing a centralized platform for data analysis, visualization, and collaboration, Domo enables businesses to make data-driven decisions, leading to improved outcomes and increased efficiency.

2. Improved Insights: With its powerful visualization options and ability to integrate with a wide range of data sources, Domo helps businesses gain new insights into their data, enabling them to identify trends, patterns, and opportunities.

3. Increased Productivity: By automating manual data collection and cleaning processes, Domo saves businesses time and resources, freeing up employees to focus on more strategic tasks.

4. Better Collaboration: With its sharing and collaboration features, Domo makes it easy for teams to work together on data, improving communication and collaboration among team members.

5. Improved Data Management: With its centralized platform for data management and analysis, Domo makes it easier for businesses to keep track of their data, reducing the risk of data loss or corruption.

6. Increased Flexibility: Domo’s cloud-based platform and ability to integrate with a wide range of data sources makes it easy for businesses to adapt to changing needs, increasing their ability to respond quickly to new opportunities.

7. Better Data Security: With its robust security measures, Domo helps businesses protect their data and ensure the privacy of their information.

Overall, Domo helps businesses turn data into insights and drive results, enabling them to make informed decisions, improve outcomes, and stay ahead of the competition.

Conclusion

When comparing Domo vs. Tableau vs. Microsoft Power BI, consider who will be using these tools.

Power BI is designed for the joint stakeholder, not the data analyst. As a result, the interface is more reliant on drag and drop and automatic features.

Tableau is equally powerful, but the interface isn’t entirely intuitive, making it more difficult to use and learn. Domo, on the other hand, is ideal for businesses looking for an all-in-one cloud-based data and analytics solution. It is used by many customers to supplement existing solutions. Given its flexibility and breadth, domo is a good choice for any organization looking to get more value from its data.

Domo is a powerful data management, analysis, and visualisation tool. Domo’s user-friendly interface, extensive data connections, and powerful visualisation tools make it simple for businesses to turn data into insights and drive results. Domo is a valuable tool for any organisation looking to harness the power of data, whether you are a beginner or an experienced data analyst.

We’ll get to the next interesting topic soon.

I hope this was useful.

Happy Reading…!!!!!

The post Domo for Dummies: A Guide to Creating Powerful Data Visualizations with Domo appeared first on Indium.

“Kubeflow is an open-source project created to help deployment of ML pipelines. It uses components as python functions for each step of pipeline. Each component runs on the isolated container with all the required libraries. It runs all the components in the series one by one.”

In this article we are going to train a custom machine learning model on Vertex AI using Kubeflow Pipeline.

About Dataset

Credit Card Customers dataset from Kaggle will be used. The 10,000 customer records in this dataset include columns for age, salary, marital status, credit card limit, credit card category, and other information. In order to predict the customers who are most likely to leave, we must analyse the data to determine the causes of customer churn.

Interesting Read: In the world of hacking, we’ve reached the point where we’re wondering who is a better hacker: humans or machines.

Let’s Start

Custom Model Training

Step 1: Getting Data

We will download the dataset from GitHub. There are two csv files in the downloaded dataset called churner_p1 and churner_p2, I have created a Big Query dataset credit_card_churn with the tables as churner_p1 and churner_p2 with this csv files. I have also created the bucket called credit-card-churn on Cloud Storage. This bucket will be used to store the artifacts of the pipeline

Step 2: Employing Workbench

Enable the Notebook API by going to Vertex AI and then to the Workbench section. Then select Python 3 by clicking on New Notebook. Make sure to choose the us-central1 region.

It will take a few minutes to create the Notebook instance. Once the notebook is created click on the Open JupyterLab to launch the JupyterLab.

We will also have to enable the following APIs from API and services section of Vertex AI.

1. Artifact Registry API
2. Container Registry API
3. AI Platform API
4. ML API
5. Cloud Functions API
6. Cloud Build API

Now click on the Python 3 to open a jupyter notebook in the JupyterLab Notebook section and run the below code cells.

USER_FLAG = “–user”

!pip3 install {USER_FLAG} google-cloud-aiplatform==1.7.0

!pip3 install {USER_FLAG} kfp==1.8.9

This will install google cloud AI platform and Kubeflow packages. Make sure to restart the kernel after the packages are installed.

import os

PROJECT_ID = “”

# Get your Google Cloud project ID from gcloud

if not os.getenv(“IS_TESTING”):

    shell_output=!gcloud config list –format ‘value(core.project)’ 2>/dev/null

    PROJECT_ID = shell_output[0]

    print(“Project ID: “, PROJECT_ID)

Create the variable PROJECT_ID with the name of project.

BUCKET_NAME=”gs://” + PROJECT_ID

BUCKET_NAME

Create the variable BUCKET_NAME, this will return the same bucket name we have created earlier.

import matplotlib.pyplot as plt

import pandas as pd

from kfp.v2 import compiler, dsl

from kfp.v2.dsl import pipeline, component, Artifact, Dataset, Input, Metrics, Model, Output, InputPath, OutputPath

from google.cloud import aiplatform

# We’ll use this namespace for metadata querying

from google.cloud import aiplatform_v1

PATH=%env PATH

%env PATH={PATH}:/home/jupyter/.local/bin

REGION=”us-central1″

PIPELINE_ROOT = f”{BUCKET_NAME}/pipeline_root/”

PIPELINE_ROOT

This will import required packages and create the pipeline folder in the credit-card-churn bucket.

#First Component in the pipeline to fetch data from big query.

#Table1 data is fetched

@component(

    packages_to_install=[“google-cloud-bigquery==2.34.2”, “pandas”, “pyarrow”],

    base_image=”python:3.9″,

    output_component_file=”dataset_creating_1.yaml”

)

def get_data_1(

   bq_table: str,

   output_data_path: OutputPath(“Dataset”)

):

    from google.cloud import bigquery

    import pandas as pd

    bqclient = bigquery.Client()

   table = bigquery.TableReference.from_string(

      bq_table

    )

    rows = bqclient.list_rows(

        table

    )

   dataframe = rows.to_dataframe(

        create_bqstorage_client=True,

    )

   dataframe.to_csv(output_data_path)

The first component of the pipeline will fit the data from the table churner_p1 from big query and pass the csv file as the output for the next component. The structure is the same for every component. We have used the @component decorator to install the required packages and specify the base image and output file, then we create the get_data_1 function to get the data from big query.

#Second Component in the pipeline to fetch data from big query.

#Table2 data is fetched

#First component and second component doesnt need inputs from any components

@component(

    packages_to_install=[“google-cloud-bigquery==2.34.2”, “pandas”, “pyarrow”],

    base_image=”python:3.9″,

    output_component_file=”dataset_creating_2.yaml”

)

def get_data_2(

    bq_table: str,

    output_data_path: OutputPath(“Dataset”)

):

   from google.cloud import bigquery

   import pandas as pd

    bqclient = bigquery.Client()

   table = bigquery.TableReference.from_string(

       bq_table

    )

   rows = bqclient.list_rows(

        table

    )

    dataframe = rows.to_dataframe(

        create_bqstorage_client=True,

    )

    dataframe.to_csv(output_data_path)

The second component of the pipeline will fit the data from the table churner_2 from big query and pass the csv file as the output for the next component. The first component and second component do not need inputs from any components.

#Third component in the pipeline to to combine data from 2 sources and for some data transformation

@component(

    packages_to_install=[“sklearn”, “pandas”, “joblib”],

   base_image=”python:3.9″,

  output_component_file=”model_training.yaml”,

)

def data_transformation(

    dataset1: Input[Dataset],

    dataset2: Input[Dataset],

    output_data_path: OutputPath(“Dataset”),

):

    from sklearn.metrics import roc_curve

    from sklearn.model_selection import train_test_split

    from joblib import dump

    from sklearn.metrics import confusion_matrix

    from sklearn.tree import DecisionTreeClassifier

    from sklearn.ensemble import RandomForestClassifier

   import pandas as pd

    data1 = pd.read_csv(dataset1.path)

    data2 = pd.read_csv(dataset2.path)

    data=pd.merge(data1, data2, on=’CLIENTNUM’, how=’outer’)

    data.drop([“CLIENTNUM”],axis=1,inplace=True)

   data = data.dropna()

   cols_categorical = [‘Gender’,’Dependent_count’, ‘Education_Level’, ‘Marital_Status’,’Income_Category’,’Card_Category’]

    data[‘Attrition_Flag’] = [1 if cust == “Existing Customer” else 0 for cust in data[‘Attrition_Flag’]]

    data_encoded = pd.get_dummies(data, columns = cols_categorical)

    data_encoded.to_csv(output_data_path)

The third component is where we have combined the data from the first and second component and did the data transformation such as dropping the “CLIENTNUM” column, dropping the null values and converting the categorical columns into numerical. we will pass this transformed data as csv to the next component.

#Fourth component in the pipeline to train the classification model using decision Trees or Randomforest

@component(

    packages_to_install=[“sklearn”, “pandas”, “joblib”],

    base_image=”python:3.9″,

    output_component_file=”model_training.yaml”,

)

def training_classmod(

    data1: Input[Dataset],

   metrics: Output[Metrics],

    model: Output[Model]

):

    from sklearn.metrics import roc_curve

    from sklearn.model_selection import train_test_split

    from joblib import dump

    from sklearn.metrics import confusion_matrix

    from sklearn.ensemble import RandomForestClassifier

    import pandas as pd

    data_encoded=pd.read_csv(data1.path)

    X = data_encoded.drop(columns=[‘Attrition_Flag’])

    y = data_encoded[‘Attrition_Flag’]

   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100,stratify=y)

   model_classifier = RandomForestClassifier()

    model_classifier.fit(X_train,y_train)

    y_pred=model_classifier.predict(X_test)

    score = model_classifier.score(X_test,y_test)

    print(‘accuracy is:’,score)

    metrics.log_metric(“accuracy”,(score * 100.0))

    metrics.log_metric(“model”, “RandomForest”)

    dump(model_classifier, model.path + “.joblib”)

In the fourth component we will train the model with the Random Classifier and we have used the “accuracy” as the evaluation metric.

@component(

   packages_to_install=[“google-cloud-aiplatform”],

    base_image=”python:3.9″,

    output_component_file=”model_deployment.yaml”,

)

def model_deployment(

    model: Input[Model],

    project: str,

    region: str,

    vertex_endpoint: Output[Artifact],

   vertex_model: Output[Model]

):

    from google.cloud import aiplatform

   aiplatform.init(project=project, location=region)

    deployed_model = aiplatform.Model.upload(

        display_name=”custom-model-pipeline”,

      artifact_uri = model.uri.replace(“model”, “”),

        serving_container_image_uri=”us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-24:latest”

    )

    endpoint = deployed_model.deploy(machine_type=”n1-standard-4″)

    # Save data to the output params

    vertex_endpoint.uri = endpoint.resource_name

    vertex_model.uri = deployed_model.resource_name

Fifth component is the last component, in this we will create the endpoint on the Vertex AI and deploy the model. We have used Docker as base IMAGE and have deployed the model on “n1-standard-4” machine.

@pipeline(

    # Default pipeline root. You can override it when submitting the pipeline.

    pipeline_root=PIPELINE_ROOT,

    # A name for the pipeline.

    name=”custom-pipeline”,

)

def pipeline(

   bq_table_1: str = “”,

    bq_table_2: str = “”,

    output_data_path: str = “data.csv”,

    project: str = PROJECT_ID,

    region: str = REGION

):

    dataset_task_1 = get_data_1(bq_table_1)

   dataset_task_2 = get_data_2(bq_table_2)

   data_transform=data_transformation(dataset_task_1.output,dataset_task_2.output)

    model_task = training_classmod(data_transform.output)

    deploy_task = model_deployment(model=model_task.outputs[“model”],project=project,region=region)

In the last we have pipeline function which will call all the components in the sequential manner: dataset_tast_1 and dataset_tast_2 will get the data from the big query, data_transform will transform the data, model_task will train the Random Classifier model and deploy_task will deploy the model on Vertex AI.

compiler.Compiler().compile(pipeline_func=pipeline, package_path=”custom-pipeline-classifier.json”)

Compiling the pipeline.

run1 = aiplatform.PipelineJob(

    display_name=”custom-training-vertex-ai-pipeline”,

    template_path=”custom-pipeline-classifier.json”,

    job_id=”custom-pipeline-rf8″,

   parameter_values={“bq_table_1”: “credit-card-churn.credit_card_churn.churner_p1″,”bq_table_2”: “credit-card-churn.credit_card_churn.churner_p2”},

   enable_caching=False,)

Creating the pipeline job.

run1.submit()

Running the pipeline job.

With this we have completed creating the Kubeflow pipeline and we can see it on the Pipelines section of Vertex AI.

Our Pipeline has run successfully and we have managed to get 100% accuracy for the classification.

We can use this model to get the online prediction using Rest API or Python. We can also create the different pipelines and compare their metrics on Vertex AI.

With this we have completed the project and learned how to create the Pipeline on Vertex AI for custom train models.

I hope you will find it useful.

See you again.

The post Kubeflow Pipeline on Vertex AI for Custom ML Models appeared first on Indium.

L.L. Bean turned to Google Cloud to fulfill its cloud requirements. By modernizing data on, it experienced faster page loads and it was able to access transaction histories more easily. It also focused on value addition instead of infrastructure management. And, it reduced release cycles and rapidly delivered cross-channel services. These collectively improved its overall delivery of agile, cutting-edge customer experience.

Data Modernization with Google Cloud for Success

Many businesses that rely on siloed data find it challenging to make fully informed business decisions, and in turn accelerate growth. They need a unified view of data to be able to draw actionable, meaningful insights that can help them make fact-based decisions that improve operational efficiency, deliver improved services, and identify growth opportunities. In fact, businesses don’t just need unified data. They need quality data that can be stored, managed, scaled and accessed easily.

Google Cloud Platform empowers businesses with flexible and scalable data storage solutions. Some of its tools and features that enable this include:

BigQuery

This is a cost-effective, serverless, and highly scalable multi-cloud data warehouse that provides businesses with agility.

Vertex AI

This enables businesses to build, deploy, and scale ML models on a unified AI platform using pre-trained and custom tooling.

Why should businesses modernize with Google Cloud?

It provides faster time to value with serverless analytics, it lowers TCO (Total Cost of Ownership) by up to 52%, and it ensures data is secure and compliant.

Read this informative post on Cloud Cost Optimization for Better ROI.

Google Cloud Features

Improved Data Management

BigQuery, the serverless data warehouse from Google Cloud Platform (GCP), makes managing, provisioning, and dimensioning infrastructure easier. This frees up resources to focus on the quality of decision-making, operations, products, and services.

Improved Scalability

Storage and computing are decoupled in BigQuery, which improves availability and scalability, and makes it cost-efficient.

Analytics and BI

GCP also improves website analytics by integrating with other GCP and Google products. This helps businesses get a better understanding of the customer’s behavior and journey. The BI Engine packaged with BigQuery provides users with several data visualization tools, speeds up responses to queries, simplifies architecture, and enables smart tuning.

Data Lakes and Data Marts

GCP’s enables ingestion of batch and stream/real-time data, change data capture, landing zone, and raw data to meet other data needs of businesses.

Data Pipelines

GCP tools such as Dataflow, Dataform, BigQuery Engine, Dataproc, DataFusion, and Dataprep help create and manage even complex data pipelines.

Discover how Indium assisted a manufacturing company with data migration and ERP data pipeline automation using Pyspark.

Data Orchestration

For data orchestration too, GCP’s managed or serverless tools minimize infrastructure, configuration, and operational overheads. Workflows is a popular tool for simple workloads while Cloud Composer can be used for more complex workloads.

Data Governance

Google enables data governance, security, and compliance with tools such as Data Catalog, that facilitates data discoverability, metadata management, and data class-level controls. This helps separate sensitive and other data within containers. Data Loss Prevention and Identity Access Management are some of the other trusted tools.

Data Visualization

Google Cloud Platform provides two fully managed tools for data visualization, Data Studio and Looker. Data Studio is free and transforms data into easy-to-read and share, informative, and customizable dashboards and reports. Looker is flexible and scalable and can handle large data and query volumes.

ML/AI

Google Cloud Platform leverages Google’s expertise in ML/AI and provides Managed APIs, BigQuery ML, and Vertex AI. Managed APIs enable solving common ML problems without having to train a new model or even having technical skills. Using BigQuery, models can be built and deployed based on SQL language. Vertex AI, as already seen, enables the management of the ML product lifecycle.

Indium to Modernize Your Data Platform With GCP

Indium Software is a recognized data and cloud solution provider with cross domain expertise and experience. Our range of services includes data and app modernization, data analytics, and digital transformation across the various cloud platforms such as Amazon Web Server, Azure, Google Cloud. We work closely with our customers to understand their modernization needs and align them with business goals to improve the outcomes for faster growth, better insights, and enhanced operational efficiency.

FAQs

The post Data Modernization with Google Cloud appeared first on Indium.

Small data consumes less data but is just as insightful because it leverages techniques such as;

• Time-series analysis techniques
• Few-shot learning
• Synthetic data
• Self-supervised learning

Wide refers to the use of unstructured and structured data sources to draw insights. Together, small and wide data can be used across industries for predicting consumer behavior, improving customer service, and extracting behavioral and emotional intelligence in real-time. This facilitates hyper-personalization and provides customers with an improved customer experience. It can also be used to improve security, detect fraud, and develop adaptive autonomous systems such as robots that use machine learning algorithms to continuously improve performance.

Why is big data not relevant anymore?

First being the large volumes of data being produced everyday from nearly 4.9 billion people browsing the internet for an average of seven hours a day. Further, embedded sensors are also continuously generating stream data throughout the day, making big data even bigger.

Secondly, big data processing tools are unable to keep pace and pull data on demand. Big data can be complex and difficult to manage due to the various intricacies involved, right from ingesting the raw data to making it ready for analytics. Despite storing millions or even billions of records, it may still not be big data unless it is usable and of good quality. Moreover, for data to be truly meaningful in providing a holistic view, it will have to be aggregated from different sources, and be in structured and unstructured formats. Proper organization of data is essential to keep it stable and access it when needed. This can be difficult in the case of big data.

Thirdly, there is a dearth of skilled big data technology experts. Analyzing big data requires data scientists to clean and organize the data stored in data lakes and warehouses before integrating and running analytics pipelines. The quality of insights is determined by the size of the IT infrastructure, which, in turn, is restricted by the investment capabilities of the enterprises.

What is small data?

Small data can be understood as structured or unstructured data collected over a period of time in key functional areas. Small data is less than a terabyte in size. It includes;

• Sales information
• Operational performance data
• Purchasing data

It is decentralized and can fit data packets securely and with interoperable wrappers. It can facilitate the development of effective AI models, provide meaningful insights, and help capture trends. Prior to adding larger and more semi-or unstructured data, the integrity, accessibility, and usefulness of the core data should be ascertained.

Benefits of Small Data

Having a separate small data initiative can prove beneficial for the enterprise in many ways. It can address core strategic problems about the business and improve the application of big data and advanced analytics. Business leaders can gain insights even in the absence of substantial big data. Managing small data efficiently can improve overall data management.

Some of the advantages of small data are:

• It is present everywhere: Anybody with a smartphone or a computer can generate small data every time they use social media or an app. Social media is a mine of information on buyer preferences and decisions.
• Gain quick insights:  Small data is easy to understand and can provide quick actionable insights for making strategic decisions to remain competitive and innovative.
• It is end-user focused: When choosing the cheapest ticket or the best deals, customers are actually using small data. So, small data can help businesses understand what their customers are looking for and customize their solutions accordingly.
• Enable self-service: Small data can be used by business users and other stakeholders without needing expert interpretation. This can accelerate the speed of decision making for timely response to events in real-time.

For small data to be useful, it has to be verifiable and have integrity. It must be self-describing and interoperable.

Indium can help small data work for you

Indium Software, a cutting-edge software development firm, has a team of dedicated data scientists who can help with data management, both small and big. Recognized by ISG as a strong contender for data science, data engineering, and data lifecycle management services, the company works closely with customers to identify their business needs and organize data for optimum results.

Indium can design the data architecture to meet customers’ small and large data needs. They also work with a variety of tools and technologies based on the cost and needs of customers. Their vast experience and deep expertise in open source and commercial tools enable them to help customers meet their unique data engineering and analytics goals.

FAQs

The post Big data: What Seemed Like Big Data a Couple of Years Back is Now Small Data! appeared first on Indium.

That statement has entirely changed today. Data can no longer be metaphorically compared to oil.

Here is why

Data is essentially infinitely durable and reusable, whereas oil is a finite resource.

Data being compared to oil, implies that data has no value after use and decreases in utility like in the case of oil.

To be transported to where it is required, oil needs huge quantities of resources. On the other hand, data can be repeated endlessly and transported across the world via fibreoptic networks at the speed of light, at the lowest possible costs.

The more it is used, rather than its energy being lost as heat or light in case of oil, data often becomes more usable. Data also shows more applications once processed. It would definitely be a mistake to treat data like oil: using it once then believing its value has been exhausted and disposing it.

In this digital era, Data is no longer the new oil. It is the world`s most valuable resource!

Data is not recent, but it is growing at an incredible rate. The increasing interactions between data, algorithms, and analytics of big data, connected data and individuals are opening enormous new prospects.

Enterprises have now started developing products and services based on data-driven analogies. For many communities and economies, the formulation of national data strategies may help in realising the potential of data. Several countries are in the process of implementing such strategies.

The ability to provide an agile environment to serve the data workload is critical with data powering so many innovative approaches, whether it be artificial intelligence, machine learning or deep learning. Data undoubtedly offers them the chance to enhance or redesign almost every part of their business model.

Let us consider the BFSI domain

Tonnes of unstructured, semi-structured and structured data that has been stored had no use two decades ago. Thanks to data analytics solutions and big data technologies, we can use all the data now!

Data that has been stored can be used in numerous ways. Let`s take the banking and financial service institutions domain as an example.

1. Improved levels of customer insights and engagement : Customers are now gradually engaging on digital channels with BFSI organisations. The BFSI institutions are enhancing their product/ service quality by utilising big data technologies to dissect digital channel data as it would help gain a better understanding of customer pain points, needs, and wants. It is important not only to boost the customer experience, but to also keep ahead in the competitive market to obtain customer insights.
2. Improved fraud detection and prevention : Over time, hacking, breaching techniques & strategies have developed and strengthened to become more complex and sophisticated. This is where it comes to data. To detect trends and predict the frauds, big data analytics and instruments can process and analyse large datasets. This helps to a great degree to mitigate financial losses.
3. Improved market trending analysis: The rapidly growing demand for speedy execution of financial sector market trading has led to the adoption of big data for the same reason. Big data`s primary benefactor is trading methods that rapidly exploit advanced algorithms to trade financial markets. The BFSI is a huge ocean of data. The data collected from different markets (based on geographical location, asset classes, type of market, etc.) can be compiled with other structured and unstructured data to generate rich, hybrid datasets using big data tools. This can be used to analyse real-time 360-degree view of business scenarios.
4. Enhanced risk management: Data sees use in fields such as business risks, automated risk control, fraud management, credit management and retail & industrial loans. Data tools can aid in exponentially boosting the predictive power of the risk models, they can also enhance the system`s response time and performance thereby reducing costs and offering extensive risk coverage.
5. Enhance Employee Engagement: Data has another advantage that is to provide employee engagement. Companies can track, control, and evaluate their employee`s performances by implementing data analytics techniques. Motivating and recognising employees through the conduction of employee engement programs and award giving events, a firm can retain many employees thereby increasing employee performance. This has now turned into the new culture of work. and techniques. 

Using data for data`s sake

Some companies are struggling to understand the role that data strategy can play in transforming and delivering productivity in their business operations. Without having a good reason, there is a propensity to try to use data. The first step in incorporating knowledge into strategy should concentrate on finding business issues.

The approach or technique that can illustrate solutions is data analysis, but it is rarely the ‘silver bullet’ for success without a specific target. As several aspects of digital transformation, data is not something that can be supplied as a separate business entity in a silo. It must be a cross-functional service incorporated within an enterprise.

The post Data Is No Longer The New Oil- It Is The World`s Most Valuable Resource appeared first on Indium.