The Latest Trends in Technology: What’s Shaping the Future in 2025

 

Technology continues to evolve at a breakneck pace, reshaping industries, economies, and daily life. As we move through 2025, several transformative trends are emerging that promise to redefine how we interact with the digital and physical world. Here’s a look at the most significant technological trends that are shaping the future right now.

1. Generative AI Goes Mainstream

Generative AI has moved beyond niche use cases to become a cornerstone of modern productivity and creativity. Tools like OpenAI's Sora and GPT-4.5 have democratized video creation, code generation, and content design. Enterprises are increasingly integrating generative AI into workflows, automating customer support, marketing content, legal document review, and even scientific research.

Trend Insight: AI agents are now capable of managing complex, multi-step tasks autonomously, moving closer to truly intelligent digital assistants.

2. Quantum Computing Breakthroughs

While still in its infancy, 2025 has seen several breakthroughs in quantum hardware and error correction. Companies like IBM, Google, and startups such as PsiQuantum are scaling up qubit counts and reliability, bringing us closer to practical quantum advantage. Financial modeling, materials science, and pharmaceutical research stand to benefit first.

Trend Insight: Governments and corporations are investing billions in quantum research to secure future dominance in this game-changing field.

3. AI + Robotics Integration

Robotics, supercharged by AI, is making significant strides in sectors like healthcare, agriculture, and logistics. Humanoid robots are now assisting in eldercare and hospitality, while autonomous delivery drones and warehouse bots are becoming commonplace.

Trend Insight: With AI-driven perception and decision-making, robots are becoming more adaptable and collaborative in human environments.

4. Spatial Computing & Mixed Reality

Apple Vision Pro and similar devices have brought spatial computing into the consumer space. Mixed reality (MR) is redefining digital interaction, blending physical and virtual environments for gaming, education, design, and remote work.

Trend Insight: The metaverse is maturing from a hype-driven concept into practical MR applications with real utility.

5. Personalized Health Tech

Wearable devices and AI-powered diagnostics are creating a revolution in personalized healthcare. Real-time biometric tracking, genetic analysis, and digital twins allow for predictive and preventive medicine tailored to individuals.

Trend Insight: The fusion of AI, genomics, and wearable tech is empowering consumers to manage their health proactively, while reducing healthcare costs.

6. Sustainable Tech Innovations

Environmental sustainability is driving innovation in green technologies. From carbon capture and battery recycling to AI-optimized energy grids and climate modeling, technology is playing a key role in tackling climate change.

Trend Insight: Startups and governments are embracing “climate tech” as both a necessity and an economic opportunity.

7. Cybersecurity in the AI Era

With the rise of AI-generated content and deepfakes, cybersecurity challenges are escalating. In 2025, there’s a major focus on AI-driven defense systems, zero-trust architectures, and quantum-safe encryption.

Trend Insight: The arms race between attackers and defenders is being reshaped by AI, making cybersecurity a priority across sectors.

8. Decentralized and Edge Computing

The limitations of centralized cloud models are giving way to decentralized infrastructure and edge computing. Data is increasingly processed at the source — from factories to self-driving cars — enabling faster, more private, and more reliable applications.

Trend Insight: Decentralized AI (like federated learning) is improving privacy while maintaining performance in sensitive environments.

---

Final Thoughts

As 2025 progresses, it’s clear that the convergence of AI, quantum computing, spatial computing, and sustainability efforts will be the defining force of the next decade. The key for individuals, businesses, and governments is to adapt swiftly, embrace responsible innovation, and anticipate how these technological advances will reshape society and the global economy.

Configuring Survivorship in Informatica IDMC MDM SaaS

 This article provides a comprehensive guide to configuring survivorship rules within Informatica IDMC MDM SaaS, using the example of a person business entity. Survivorship is a critical process for establishing the "best version of truth" for data records by defining how conflicting information from various source systems should be resolved.

Understanding the Basics

The process begins by accessing the "person" business entity, which encompasses various components such as the data model, data flow, data quality rules, match survivorship, and event configurations. Before diving into the configuration, it's essential to identify several key prerequisites:

• Source Systems: Determine all the source systems that contribute data and will be part of the survivorship rules.

• Target Fields: Pinpoint the specific fields that require survivorship application. For instance, fields like first name, middle name, last name, and date of birth are typically subject to survivorship, while others like place of birth might not be.

• Survivorship Rules: Define the specific rules, including any maximum and minimum percentage scores, that will govern how data conflicts are resolved.

Navigating Survivorship Settings

The survivorship configuration options are conveniently located within the left-hand side panel of the business entity interface. Here, you'll find a clear display of fields like first name, middle name, and last name, along with their associated rules. It's important to note that some fields, such as "prefix" and "suffix," may inherit their rules from the parent level (in this case, the person business entity). Conversely, fields like "first name," "middle name," and "last name," often have specific ranks applied to them, indicating their priority in the survivorship process.

Step-by-Step Configuration

Configuring survivorship involves several key steps:

1. Source Ranking: Select a field (e.g., "prefix name") and configure its source ranking. All source systems previously set up in the Business 360 console will be available for selection and ranking.

2. Creating New Ranks: You have the flexibility to create new ranks by choosing specific source systems and adding them to the rank. For example, you might create "rank five" that includes "Informatica customer 360" and "default" source systems.

3. Applying Ranks: Once a new rank is created, it must be explicitly applied to the relevant field to take effect.

4. Rule Configuration (Decay Rules): A powerful feature is the ability to apply "Decay minimum and maximum trust score" rules. This involves selecting a rule type (Decay, minimum, or maximum), choosing a source system, and setting parameters such as the maximum trust score (e.g., 80%), minimum trust score (e.g., 40%), Decay unit (e.g., years), and Decay period (e.g., 10 years). After applying Decay rules, a visual graph is displayed, allowing you to further refine the Decay pattern (linear, rapid initial/slow later, or slow initial/rapid later).

5. Dependent Fields: Survivorship for one field can be made dependent on another. For instance, the "prefix name" might be evaluated based on the "full name" field.

6. Applying Rules to Multiple Fields: For efficiency, you can select multiple fields simultaneously (e.g., suffix name, title, description) and apply the same survivorship rules and source ranking to all of them in one go.

7. Saving Configuration: After making any changes to the survivorship configuration, it is crucial to save your work to ensure the new rules are applied.

By following these steps, users can effectively configure survivorship in Informatica IDMC MDM SaaS, leading to more accurate and reliable master data.

Learn more about Informatica IDMC here

Understanding the Consolidation Indicator in Informatica IDMC Customer 360 MDM SaaS

 The Consolidation Indicator (CI) field is a crucial element in Informatica IDMC Customer 360 MDM SaaS, playing a vital role in managing and consolidating data. This indicator helps track the state of data records as they move through the matching and merging process, ultimately leading to the creation of a "best version of truth" for each unique entity.

The CI field can take on four distinct values, each signifying a different stage in the data consolidation lifecycle:

• Match Dirty: This is the initial state for data records when they are first loaded or updated within MDM SaaS. It indicates that the record is new or has been modified and needs to be processed for potential matches.

• Match Index: After the indexing job runs, the CI value transitions from "Match Dirty" to "Match Index." In this state, records are prepared to participate in the matching process. If re-indexing is required for any reason, the CI value can be reset back to "Match Dirty."

• Matched: A record receives the "Matched" CI value once it has gone through the match and merge process. This applies whether a matching candidate was found or not.

• Consolidated: This is the final and most desirable state for a merged record. "Consolidated" signifies that a unique and accurate "best version of truth" record has been successfully created.

---

Beyond these core values, there are several important aspects related to the CI field:

• Accept as Unique: If the "accept as unique" option is enabled for a record that undergoes the match and merge process but doesn't find any matching rules, its state will change from "Match Index" to "Matched." This allows the record to be treated as unique even without a direct match.

• XREF Record Updates: When an XREF (cross-reference) record is updated, its CI value automatically reverts to "Match Index." This ensures that the updated XREF record is re-evaluated through the match and merge process.

• CI Field Location: A key distinction in MDM SaaS compared to on-premise MDM is the placement of the CI field. In MDM SaaS, the CI field is located at the XREF level, not the business entity level. This granular placement provides more precise control over data consolidation.

• Extracting Consolidated Records: To extract only the consolidated records, a two-step extraction process is necessary. This involves creating one extract for business entity records and another for XREF records. These two extracts can then be joined to filter and retrieve only the consolidated values.

---

In summary, the Consolidation Indicator is a fundamental component of Informatica IDMC Customer 360 MDM SaaS, providing clear visibility into the data consolidation journey and enabling robust data management practices.

Introduction to Reltio Master Data Management

 In today’s digital economy, data is the foundation of successful business operations. However, with data pouring in from countless sources — sales platforms, marketing systems, customer service channels, and more — many organizations struggle with fragmented, inconsistent, or outdated information. This is where Master Data Management (MDM) comes in, and Reltio is a leading player in this space.

Reltio Master Data Management is a modern, cloud-native MDM platform designed to help enterprises consolidate, cleanse, and unify their critical data assets. By creating a single, trusted source of truth, Reltio enables businesses to drive better decisions, improve customer experiences, and enhance compliance.

---

A Brief History of Reltio

Reltio was founded in 2011 by Manish Sood, a data industry veteran who saw the limitations of legacy MDM systems firsthand. With its headquarters in Redwood City, California, Reltio set out to build a next-generation MDM platform, designed for the cloud and for the demands of modern data-driven enterprises.

Since then, Reltio has grown rapidly, attracting investment from major venture capital firms and building a customer base across Fortune 500 organizations in healthcare, life sciences, financial services, retail, and other sectors. Its platform is recognized by industry analysts (such as Gartner and Forrester) for its innovation, scalability, and business value.

---

Why Reltio?

Unlike traditional on-premise MDM solutions, Reltio offers a cloud-first, API-driven architecture that supports real-time data processing and integration. Here are some standout features that make Reltio a compelling choice:

• Multi-Domain MDM: Manage customer, product, supplier, and location data in one place
• Cloud-native & scalable: Handles high-volume, high-velocity data seamlessly
• Data quality & governance: Cleansing, validation, survivorship, lineage tracking
• Graph technology: Discover and leverage entity relationships with connected graph models
• API-first & real-time: Modern integration to power digital ecosystems

---

Detailed Business Use Cases with Attributes

Let’s look at some practical business use cases where Reltio is especially valuable, with examples of typical attributes managed in each:

1️⃣ Customer 360 for Financial Services

Use Case: A bank needs to create a unified customer profile to improve onboarding, risk assessment, and personalized product offerings.

Typical attributes managed:

• Name

• Address

• Social Security Number / National ID

• Date of Birth

• Contact numbers

• Email addresses

• Account numbers

• KYC documents

• Risk rating

• Credit score

• Relationships to other customers or accounts (beneficiaries, joint account holders)

Business value:

• Improved compliance (KYC/AML)

• Better fraud detection

• Personalized cross-selling opportunities

---

2️⃣ Product 360 for Retail & E-commerce

Use Case: A global retailer needs a single view of its products across all sales channels, to drive consistency in pricing, promotions, and supply chain.

Typical attributes managed:

• SKU

• Product name

• Description

• Brand

• Price

• Categories

• Product images

• Inventory levels

• Supplier details

• Related products / bundles

Business value:

• Faster time-to-market for new products

• Accurate inventory planning

• Seamless omnichannel experience

---

3️⃣ Healthcare Provider 360

Use Case: A healthcare network needs to manage consistent information about its providers (doctors, specialists, clinics) to streamline referrals and claims processing.

Typical attributes managed:

• Provider name

• NPI (National Provider Identifier)

• Specialty

• License details

• Affiliated hospitals

• Availability

• Contact information

• Insurance acceptance

• Certifications

Business value:

• Reduced claim rejections

• Improved care coordination

• Enhanced provider search tools for patients

---

4️⃣ Supplier 360 for Manufacturing

Use Case: A manufacturer wants to manage supplier information globally to optimize procurement, quality, and compliance.

Typical attributes managed:

• Supplier name

• Tax ID

• Supplier location

• Product categories supplied

• Pricing agreements

• Contracts

• Quality certifications

• Risk assessments

• Relationship hierarchy (parent/subsidiary)

Business value:

• Reduced supplier risk

• Consolidated spend

• Better contract compliance

---

Typical Industries Benefiting from Reltio

• Retail & E-commerce — better product and customer data for omnichannel
• Financial Services — single customer view for compliance and fraud
• Healthcare — provider and patient data management
• Life Sciences — compliance and product data governance
• Manufacturing — supplier and product data optimization

---

Conclusion

Master Data Management is no longer a “nice-to-have” — it’s a business imperative. Reltio’s modern, flexible, and scalable approach helps enterprises build a trustworthy data foundation to thrive in the digital era.

With its rich history of innovation, strong multi-domain capabilities, and focus on real-time, API-driven architecture, Reltio is well positioned to support modern businesses as they navigate increasingly complex data challenges.

If you’re exploring a future-ready MDM solution to unify and unleash the power of your data, Reltio is absolutely worth a closer look.

Understanding NULL Handling in Informatica MDM: Allow NULL Update vs. Apply NULL Values

 NULL handling in Informatica MDM plays a crucial role in data consolidation and survivorship. Two key properties that determine how NULL values are managed are Allow NULL Update and Apply NULL Values. Let’s break them down:

1. Allow NULL Update on the Staging Table

This property controls whether a NULL value can overwrite a non-NULL value during a load job.

• Enabled: A non-NULL value in a column can be updated to NULL.

• Disabled: Prevents NULL updates, retaining existing non-NULL values.

• Behavior in Cross-Referenced (XREF) Records:

  • If a Base Object has a single XREF, a NULL can overwrite a non-NULL value.

  • For multiple XREFs, NULL updates are managed based on the Allow NULL Update setting.

  • To maintain consistency across single and multi-XREF records, a user exit can be implemented.

---

2. Apply NULL Values on the Base Object

This property determines how NULL values are treated during the consolidation process.

• By Default (Disabled):

  • NULL values are automatically downgraded, ensuring non-NULL values survive.

• When Enabled:

  • NULL values are treated normally with trust scores.

  • NULLs may overwrite non-NULL values during put-operations or consolidations.

  • Higher trust scores allow NULL values to survive in the Base Object.

---

3. Comparison: Allow NULL Update vs. Apply NULL Values

---

4. How MDM Determines NULL Survivorship?

For each XREF column, MDM follows these steps:

1. Identify the source stage table:

   • If the XREF record has a non-null STG_ROWID_TABLE, use it.

   • If not, use ROWID_SYSTEM to find the source stage table.

2. If only one source stage table exists:

   • Use the Allow NULL Update setting of that table.

3. If multiple source stage tables exist:

   • If all have the same setting, use it.

   • If inconsistent, refer to Apply NULL Value setting in the Base Object.

4. If no stage table is found, use Apply NULL Value setting in the Base Object.

5. If Allow NULL Update is false, the trust score of NULL values is significantly downgraded, reducing the likelihood of NULLs surviving.

---

5. Operations Affected by NULL Handling

All operations involving Best Version of Truth (BVT) calculation follow these rules, including:

• Load/Put/CleansePut

• Merge/Unmerge

• Recalculate BVT

• Revalidate

By understanding these settings, you can better manage data integrity and ensure accurate MDM processing!

Understanding Survivorship in Informatica IDMC - Customer 360 SaaS

 In Informatica IDMC - Customer 360 SaaS, survivorship is a critical concept that determines which data from multiple sources should be retained when records are merged or updated. It's a set of rules and strategies designed to ensure data accuracy, consistency, and reliability.

   

Key Concepts

1. Source Ranking:

   • Assigning Trust: Each source system is assigned a rank based on its reliability and data quality.   
   • Prioritizing Data: Higher-ranked sources are considered more trustworthy and their data takes precedence.
   • Example: If you have two sources, "HR" and "Sales," with HR being more reliable, you might assign it a rank of 1 and Sales a rank of 2. When a conflict arises, data from HR would be prioritized.

2. Survivorship Rules:

   • Defining the Rules: These rules dictate how conflicts between field values from different sources are resolved.
   • Common Rule Types:
     • Maximum: Selects the maximum value.
     • Minimum: Selects the minimum value.
     • Decay: Considers the trust level and decay rate of a source over time.   
     • Custom: Allows for more complex rules based on specific business requirements.
   • Example: For a "Customer Address" field, a decay rule might be applied, giving more weight to recent updates from a trusted source.







3. Source Last Updated Date:

   • Resolving Ties: When multiple sources have the same trust level and ranking, the source with the most recent update is prioritized.
   • Example: If two sources, both ranked equally, provide different values for a "Phone Number" field, the value from the source with the latest update would be chosen.

4. Block Survivorship:

   • Grouping Fields: Allows you to treat a group of related fields as a single unit.
   • Preserving Consistency: When a block survives, all fields within the block are retained together.
   • Example: A "Customer Address" block might include "Street," "City," "State," and "ZIP Code." If the block survives from one source, all these fields are retained.

5. Deduplication Criteria:

   • Identifying Duplicates: Defines the conditions for identifying duplicate records.
   • Resolving Duplicates: Determines how to merge duplicate records, often based on survivorship rules.   
   • Example: You might deduplicate customers based on a combination of "First Name," "Last Name," and "Email Address."

Practical Example: Customer Data Merge

Imagine you have two source systems: "HR" and "Sales." Both systems have customer data, but there are inconsistencies and missing information.

1. Source Ranking: HR is ranked higher than Sales.
2. Survivorship Rules:
   • For "Name," the maximum value is chosen.
   • For "Address," the most recent update from the higher-ranked source is selected.
   • For "Phone Number," a decay rule is applied, giving more weight to recent updates.
3. Block Survivorship: The "Address" block is treated as a unit.

If a customer record exists in both systems with conflicting data, the merge process would:

• Prioritize the "Name" from HR if it's different.
• Use the most recent "Address" from HR.
• Select the "Phone Number" with the highest trust score, considering recency.

Effective Survivorship Configuration

• Clear Understanding of Data Sources: Assess the reliability and quality of each source.
• Prioritize Critical Fields: Focus on configuring survivorship rules for fields that are essential to business operations.
• Consider Data Quality and Consistency: Analyze data quality issues and inconsistencies to optimize survivorship rules.
• Regular Review and Refinement: Continuously monitor and adjust survivorship configurations as data sources and business requirements evolve.
• Test Thoroughly: Implement a robust testing strategy to validate survivorship behavior and identify potential issues.

By carefully configuring survivorship rules, you can ensure that your master data is accurate, consistent, and reliable, enabling better decision-making and improved business processes.

Learn more about Informatica MDM SaaS - Customer 360 in Informatica IDMC

What is Glue Job in AWS?

An AWS Glue job is a managed ETL (Extract, Transform, Load) job used to process data in AWS. AWS Glue makes it easy to discover, prepare, and integrate data from various sources for analytics, machine learning, and application development.

How AWS Glue Jobs Work

AWS Glue jobs let you process large datasets using Apache Spark or small tasks with Python Shell scripts. The main workflow includes:

1. Data Extraction: Reading data from sources like Amazon S3, RDS, Redshift, etc.
2. Data Transformation: Applying transformations to clean, enrich, or format the data.
3. Data Loading: Writing the transformed data back to storage or analytical services.

Sample Glue Job Code

Below is an example of a Glue job script written in Python that reads data from an Amazon S3 bucket, applies a simple transformation, and writes the result back to another S3 bucket. This script uses the glueContext object, which is part of Glue’s Python API for Spark.

import sys

from awsglue.transforms import *

from awsglue.utils import getResolvedOptions

from pyspark.context import SparkContext

from awsglue.context import GlueContext

from awsglue.dynamicframe import DynamicFrame

# Initialize Glue context

args = getResolvedOptions(sys.argv, ['JOB_NAME'])

sc = SparkContext()

glueContext = GlueContext(sc)

spark = glueContext.spark_session

job = Job(glueContext)

job.init(args['JOB_NAME'], args)

# Step 1: Read data from S3

source_data = glueContext.create_dynamic_frame.from_options(

    's3',

    {'paths': ['s3://source-bucket/path/to/data']},

    'json'

)

# Step 2: Apply transformation (Filter rows where 'age' > 30)

filtered_data = Filter.apply(frame=source_data, f=lambda row: row['age'] > 30)

# Step 3: Write transformed data back to S3

output = glueContext.write_dynamic_frame.from_options(

    frame=filtered_data,

    connection_type='s3',

    connection_options={'path': 's3://target-bucket/path/to/output'},

    format='parquet'

)

# Commit the job

job.commit()

Explanation of the Code

1. Initialization: Sets up the Glue job context, which provides the Spark session and AWS Glue API.
2. Data Extraction: Reads JSON data from the source S3 bucket into a DynamicFrame, which is a Glue-specific data structure for Spark.
3. Transformation: Filters records to include only those where the age field is greater than 30.
4. Data Loading: Writes the transformed data back to an S3 bucket in Parquet format, which is optimized for analytics.
5. Commit: Completes the job.

Features of AWS Glue Jobs

• Job Scheduling and Triggers: AWS Glue jobs can run on a schedule, on-demand, or based on events.
• Serverless and Scalable: Glue jobs scale automatically with the volume of data and remove the need to manage infrastructure.
• Data Catalog Integration: Glue jobs can leverage the Glue Data Catalog, a central repository for storing metadata about data sources.

AWS Glue jobs streamline data engineering tasks and are widely used in AWS-based data pipelines for data analytics and machine learning projects.

Learn more about Python here