This will help to effect continuous improvement in performance and create differentiated experiences for customers and employees. It will also enable accelerated development of innovative new solutions.

McKinsey also identifies the current challenges to optimizing data sources as

• Limited capabilities of legacy technologies
• Challenges in modernizing the architecture.
• Demand for high computational resources for real-time processing jobs

This results in only a small part of the data from connected devices being leveraged. As companies balance speed and computational intensity, they are unable to perform complex analyses or implement real-time use cases.

Getting the right data technologies to ingest, process, analyze, and visualize in real-time is going to be a game-changer in improving decision-making, enhancing customer experience, and accelerating growth.

Improved Decision Making

Real-time data is critical for conducting real-time analytics, which helps with faster decision-making. Data is collected from a variety of sources, including sensors, databases, operational systems, cameras, and social media feeds with minimal delay and processed and analyzed quickly. They could be alerts and notifications or inputs from user behavior.

Real-time data can be of two types:

• Event Data: The generation of a collection of data points based on well-defined conditions within a system.
• Stream Data: The continuous generation of a large volume of data without any identifiable beginning or end.

Easy access to data in real-time data enables a quick drawing of insights to make informed decisions and be responsive as events unfold. It helps with capturing trends, both past, and present, and can be analyzed in real-time to decide on the next course of action.

Some of the benefits of real-time data include

Being Proactive

In the absence of real-time data, there is a lag between insights and responses. This reactive approach can prove costly, resulting in losing customers or production-related issues escalating. Real-time data analytics allows enterprises to proactively approach developments and respond appropriately.

Enhance Customer Experience

Visibility and transparency have become key in several client-business relationships. It helps improve decision-making based on project status and enhances customer experience and retention. Responding to customer requirements and empowering them with information in real-time further strengthens the relationship between the two.

To know more about how Indium can help you, please check out more.

Unify Data

Different teams end up creating data silos to suit their requirements. This can distort the view when making strategic decisions at the enterprise level and delay the process. A cloud-based data streaming solution helps to provide a unified view in real-time while allowing different teams access to secure and permission-based data they need to make decisions for their department.

Improve Operational Excellence

Real-time data allows you to manage your organization’s assets proactively. It lets you plan downtimes for maintenance and repair, improves the life of the assets, and take timely steps to replace, where needed, with minimum disruption to operations. This naturally leads to a better quality of products and services and improved profit margins as it lowers overheads.

Striim Power For Real-time Data Analytics

The Striim unified real-time data integration and streaming platform unifies data across multiple sources and targets. It offers built-in adapters and supports more than 125 sources and targets, enabling the management of multiple data pipelines in a Striim cluster. Striim 4.1 offers features such as OJet to let customer applications read multi-terabytes of data per day and a high-performance Oracle Change Data Capture (CDC) reader. It also sends real-time alerts and notifications to identify emerging workload patterns and facilitates collaboration between developers and database administrators.

Striim users can build smart real-time data pipelines quickly for streaming large volumes of events daily. It is scalable and secure, and the features are highly available. It is easy to maintain and allows the rapid adoption of new cloud models, infrastructure modernization, and digitalizing legacy systems.

Striim enables data integration using a streaming-first approach, supporting incremental, real-time views in the cloud database and the streaming layer. It includes Streaming SQL to facilitate real-time analytics of data, as also train machine learning models in real-time.

Business analysts, data scientists, and data engineers can use Streaming SQL to build data pipelines quickly and without the need for custom coding. Striim also allows data movement in real-time, because of which stream processing applications need to operate continuously for years. These further speeds up decision-making as insights can be drawn quickly, without latency between receiving the data and running analytics on it.

Check out our case study on real-time data analytics

Case and Point: Simplifying Healthcare Predictions in 3 Expert Steps

Understanding Symptom Patterns: Our first step involves data acquisition and thorough analysis of historical patient data. We tap into the treasure trove of symptoms, medical records, and outcomes to discern intricate patterns that might remain hidden from traditional analysis.

Feature Engineering with Domain Knowledge: With a team of domain experts, we transform raw symptom data into meaningful features. These features are carefully curated to capture the nuances of various symptoms, their interplay, and potential implications. Our domain knowledge empowers us to create a robust feature set that forms the foundation of accurate predictions.

Advanced Machine Learning Models: Equipped with a rich feature set, we employ advanced machine learning models. From ensemble methods to deep learning architectures, we evaluate and fine-tune models that can effectively map symptoms to probable outcomes. This step requires rigorous experimentation to ensure optimal model performance. 

The utilization of Symptom Pattern Analysis, Feature Engineering, and Advanced Machine Learning Models in the healthcare domain, along with Indium’s implementation of Striim for real-time data migration and processing, brings substantial and quantifiable business value to the table.

Healthcare Providers: Reduced diagnosis time through rapid predictions – from days to hours, thereby accelerating patient care. Enhanced efficiency with streamlined operations leads to quicker decisions and resource allocation. Improved patient care is achieved through early intervention based on predictions, resulting in improved treatment outcomes. Informed resource allocation provides predictive insights that optimize staff schedules, room usage, and equipment availability. Optimized treatment plans driven by personalized treatments yield better outcomes and patient satisfaction. The Cost savings achieved through fewer hospital stays, reduced redundant tests, and efficient resource use contribute to lowering costs. This not only benefits the patients but also benefits the providers by optimizing their resources.

Healthcare Payers and Insurance Companies: The implementation offers a competitive edge for healthcare providers, attracting patients and enhancing the providers’ reputation due to quick and accurate diagnoses. This, in turn, leads to efficient resource utilization, potentially reducing the overall cost of treatments. Cost savings arising from reduced hospital stays and redundant tests contribute to lower healthcare expenditures, benefiting healthcare payers and insurance companies. Healthcare payers such as insurance companies can also reduce fraudulent claims as they will have access to patient diagnosis history in real-time.

Medical Researchers and Innovators: The curated data fosters research opportunities, facilitating medical insights and potential innovation generation. The advanced analytical capabilities of Symptom Pattern Analysis and Machine Learning Models open avenues for new discoveries and improvements in medical practices, benefiting the broader healthcare research community.

Overall, the integration of advanced technologies, real-time data processing, and predictive analytics in the healthcare domain offers benefits that extend to healthcare providers, payers, patients, and the research community. This synergy drives efficiency, quality of care, and cost-effectiveness, ultimately transforming healthcare delivery and outcomes.

Indium for Instant Decisions with Striim

Indium Software, a cutting-edge solution provider, has deep expertise in Striim implementation and can help businesses create exciting digital experiences for their customers.

A private sector bank offering specialized services to 9 million customers across various business verticals and with a presence global presence required data to be updated in real-time from its core banking systems to a reliable destination database for downstream analytics. By migrating the data from legacy systems to Striim in real time, Indium helped the customer improve its responsiveness and operational efficiency apart from other benefits.

Indium’s team of Striim experts have cross-domain experience and can provide custom-built solutions to meet the unique needs of our customers.

FAQs

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It allows querying data as per the bank’s requirements and brings together enterprise data warehousing and Big Data analytics. Based on these insights, banks can devise strategies for improved efficiencies in operations and development of products for better customer service.

Striim for CDC

A platform such as Striim enables the transfer of data from heterogeneous, on-premise data warehouses, databases, and AWS into Azure Synapse Analytics with in-flight transformations and built-in delivery validation. This helps with operational decision making on a continuous basis.

For the exercise to be really fruitful in today’s world of instant response, it is necessary for the data being transferred to be as current and close to the source database on the core banking system. A platform like Striim enables this data integration from the source table to the target using Change Data Capture (CDC).

CDC allows for data from on-prem sources, regardless of whether it is an RDBMS, No-SQL, or any other type, to a Synapse table or ADLS Gen-2 (Azure Data Lake Store Generation – 2) to be created and updated in near real-time. It doesn’t hit the source database directly.

Instead, it captures all the transactions, be it an update, insert, or delete, from the source database on-prem on a daily basis from the log and gets it generated and duplicated on the target database

This way, the performance of the source database is not affected while there is access to data on the cloud in near real-time for analysis and response.

Advantage Striim

One of the factors that make Striim the most desired CDC tool is its price point while being feature-rich. An evolving tool, it also allows for features such as UDF (User Defined Function) that can be plugged in on the fly. It allows for data manipulation and querying based on the unique needs of the bank. The icing on the cake is the reporting feature with live dashboards and a diverse set of metrics for effective data monitoring.

Its built-in monitoring and validation features include:

• Ensure consistency through continuous verification of the source and target databases
• Enable streaming data pipelines with interactive, live dashboards
• Trigger real-time alerts via web, text, email

By powering the data integration of the on-prem database of the core banking system with Azure Synapse using Striim, banks can ensure continuous movement of data from diverse sources with sub-second latency.

It is a non-intrusive way of collecting data in real-time from production systems without impacting their performance. It also allows for denormalization and other transformations on data-in-motion.

The data warehouses Striim supports include:

• Oracle Exadata
• Teradata
• Amazon Redshift

Databases:

• Oracle
• SQL Server
• HPE NonStop
• MySQL
• PostgreSQL
• MongoDB
• Amazon RDS for Oracle
• Amazon RDS for MySQL

Striim can integrate data in real-time data from logs, sensors, Hadoop, and message queues to real-time analytics.

Indium – A Striim Enabler

Indium Software is a two-decade-old next-generation digital and data solutions provider working with cutting edge technologies to help banks and traditional industries leverage them for improving their business process, prospects, and efficiencies.

We can help identify the tables in the core banking system that need to be replicated on the target Synapse and set up the Striim platform for smooth integration. Leaders in implementing Striim, we have successfully lead several such integrations across sectors. Our team has cross-domain experience and technology expertise which helps us become partners in the truest sense.

If you would like to leverage cloud and analytics through Striim, contact us here:

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But, that’s not all. There is also an explosion of real-time data coming in, and unless you adopt “Streaming Analytics” to instantly draw insights, you may miss out on spotting opportunities or tackling threats ahead of time. The point is, running batch-mode analytics alone is no longer sufficient.

“Streaming Analytics” can also help mitigate risks from fraud or security breaches. Moreover, one of the key advantages of gathering real-time actionable insights revolves around the ability to capture data that changes.

The Striim Platform automates the process of capturing time-sensitive insights to provide immediate intelligence that can impact the following:

1. Spot critical fraud or security breaches
2. Spot major changes or trends than can in-turn help you spot opportunities and act instantly with modified marketing campaigns or strategies
3. Identify risks and critical events that can impact both short-term and long-term strategy

Needless to add, Striim is designed as an enterprise-grade platform, one that is highly secure, reliable and scalable.

At Indium Software, we’re an authorized implementation partner of Striim.

We’ve worked with a range of clients including banks, financial institutions and retail & e-commerce companies, helping them with Striim implementation. Recently, we worked with one of the world’s leading banks, helping its digital banking division with Real Time data integration using Striim. We had to move a massive database from Oracle to GCP, with a Striim agent handling Change Data Capture (CDC) and real-time integration that was highly secure.

Potential Use Cases for Striim Implementation

The potential of use for this analytical application is unlimited. From the energy sector to banking and financial, ecommerce, airlines and healthcare, real-time analytics can help in improving service levels, strategy formulation as well as prevent potential threats to almost any business with massive real-time data.

• It can be used in the energy sector to capture power outages – and then help with preventing them or restore services on priority – by using real-time intelligence
• In the banking, insurance & financial sector, it can help enable risk-based, real-time policy pricing to reduce exposure; detect and prevent fraud, AML compliance; improve regulatory compliance; provide bespoke solutions to customers based on their real-time search data; and streamline ATM operations through remote monitoring and predictive maintenance
• In the transport and logistics sector, it can provide greater visibility into operations in real-time; ensure timely delivery and reduce fuel costs by optimizing fleet routes and planning staff utilization better; implement predictive maintenance and thereby extend the lifespan of the assets;  enable real-time tracking of vehicles; improve warehouse capacity utilization through real-time inventory data analytics
• For the aviation sector, use cases revolve around getting real-time updates on weather, flight delays and other such events to optimize crew and staff planning and flight schedules; track aircraft parts and rapidly submit work orders; improve real-time staffing decisions depending on actual passenger load; reduce immigration and customs lines; provide relevant and meaningful rewards for customer loyalty
• As you can see from above, the use cases are endless. The key aspect is to run streaming analytics on a proven, secure, scalable platform like Striim.

The ‘Striim – Indium Software’ Value Proposition

Striim uses a combination of filtering, multi-source correlation, advanced pattern matching, predictive analytics, statistical analysis and time-window-based outlier detection to aggregate all relevant data.

By querying the streaming data continuously, it quickly and accurately identifies events of interest and provides a deep perspective into operations by performing in-flight enrichment.

It sends automated alerts, triggers workflows, publishes results to real-time, interactive dashboards and distributes data to the entire enterprise.

Striim continuously ingests data from a variety of sources such as IoT and geolocation. It uses advanced pattern matching, predictive analytics and outlier detection for comprehensive streaming analytics. The analytical applications can be easily built and modified using SQL-like language and wizards-based development.

Indium Software, an authorized implementation partner of Striim, has deep expertise and experience in leveraging the Striim platform for the following processes:

• Real Time Data Integration
• Hybrid Cloud Integration
• Streaming Analytics
• GDPR Compliance
• Hadoop and NoSQL Integration

A cross-domain expert with over 20 years of experience in several industries such as retail, BFSI, e-commerce, healthcare, manufacturing, gaming among others, Indium Software is well-positioned to handle a wide range of Big Data services across Data Engineering and Data Analytics.

Recently, we completed a Data Integration project for a leading bank, helping move their data from Oracle database to Postgres in Google Cloud Platform.

The architecture implemented included effective data monitoring and customised visualization of the streaming data. Additionally, alerts were created when multiple data pipelines were being accessed simultaneously.

Additionally, Indium helped a client with the implementation of Striim in their messaging queue platform. With this setup, the client could stream data in their Kafka queue and write data into the Kafka logs using Kafka writer, which could then be consumed by multiple downstream systems and applications

The Indium team aided in supporting another customer through an open processor (custom scripts) in Striim to provide a data audit feature for every transaction hitting the database. The changes were then updated in a log database, enabling tracking of the data change, for example insert, delete and update. Additionally, our team created another open processor in moving the current system time forward by 7 hours, before replicating the timestamp column in the database.

Give us a shout, if your business generates real-time data. We’ll seamlessly create an automated process to draw insights from this real-time feed. We can also certainly help with any of the other aspects of Striim implementation.

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