Enterprises extract, transform, and load (ETL) data from multiple sources and applications to create a single data repository, a.k.a. data warehouse. ETL allows enterprises to effectively design and create an environment to mine and analyse data for making informed decisions. It isolates data from transactional systems, which ensures business-as-usual while data is analysed in an optimized environment.

However, traditional ETL tools have many limitations. They are time consuming, expensive and error-prone to use, and lack the scalability, agility, and integration capabilities needed to succeed in today’s fast-paced business landscape.

To address these challenges, data-driven enterprises are increasingly shifting to next-generation ETL tools, which can run workloads on-premise and in the cloud. Unlike traditional ETL tools, these modern tools can extract value from extensive datasets. They also leverage the cloud without compromising security and provide better value for money.

​Driving speed and agility with smart ETL tools

While next-generation ETL tools offer many attractive business benefits, the journey of modernization is not easy. A successful migration involves seamlessly porting existing ETL workflows to a new environment within the stipulated budget and time, without impacting business processes. However, ready-to-use, low code tools can help you effortlessly migrate ETL workloads to the cloud, without having to rebuild, thereby saving time, effort, and money. Let’s take a closer look at one of the industry’s most powerful tools for ETL migration.

Impetus’ self-service analytics, ETL, and ML platform, StreamAnalytix enables you to assess your existing ETL workloads and swiftly transform them to visual Spark. With StreamAnalytix’s automatic migration agent, you can preserve your structure, logic, and execution rules, and migrate ETL workflows to a new environment in 3 easy steps:

· Assessment: Existing workloads are assessed for complexity, size, and compatibility operators

· Migration: Traditional ETL workloads are transformed into Spark-based distributed workflows

· Validation: Ensuring successful migration of existing workloads without data loss

The StreamAnalytix advantage

StreamAnalytix’s migration agent drastically reduces the amount of time ETL practitioners spend on performing repetitive tasks across key areas like data cleansing, coding, versioning, workflow orchestration, and others. It provides a far more comprehensive environment for your migration compared to traditional platforms. Users can easily onboard new sources/ targets and seamlessly manage, monitor, and optimize converted workloads. The platform’s power-packed features provide unparalleled scalability and extensibility to drive strategic business benefits:

1. Massively reduce migration efforts: Ensure automated translation with an innovative engine

2. Business continuity: Business-as-usual on existing systems while validating new workflows

3. In-built assessment: Prioritize transformation candidates with an in-built assessment

4. Cost-effective: Reduced costs with significantly higher ROI

5. Visually equivalent modern platform: Get a near-identical visual experience of all your legacy ETL workloads on Spark

6. Future-ready: Build a future-ready competitive enterprise by on-boarding to a cutting-edge platform

StreamAnalytix enables Fortune 500 companies to build and operationalize big data applications faster across industries, data formats, and use cases. As a unified platform for stream and batch processing with an intuitive drag-and-drop visual interface and advanced operators to enrich pipelines, StreamAnalytix is one of the most advanced ETL migration tools available today. To learn more, read this white paper.

“StreamAnalytix provides a beautiful and thoughtfully designed visual user interface for application development and management.” — The Forrester Wave™ 2019

Impetus Technologies’ StreamAnalytix is a powerful, enterprise-grade visual analytics tool for businesses looking to derive real-time intelligence from their data. StreamAnalytix enables users to build and operationalize big data applications five to ten times faster using a visual drag-and-drop interface, an exhaustive set of pre-built operators, and one-click options for on-premise and cloud deployments.

To help enterprises tap the unmatched elasticity and scalability of the cloud, a new version of StreamAnalytix has been launched on AWS Marketplace. This will soon be available on Azure and Google Cloud as well. With StreamAnalytix Cloud, users can move, cleanse and transform data in the cloud from any source within minutes, and build ETL flows on the cloud, effortlessly. They can ingest data from multiple on-premise and cloud sources, enrich this data, and swiftly build applications for a wide range of analytics use cases.

Here is a glimpse of the tool’s power-packed features:

· A visual canvas to create ETL flows with minimal coding

· 150+ drag-and-drop Apache Spark connectors and operators

· Cloud-native execution engines for optimal execution of workloads

· Auto-scaling to manage costs and execution efficiency for changing loads

· Integration with machine learning and analytics to add advanced enrichment and decision pointers to data

· Support for leading cloud providers, Spark platforms and cloud data warehouses

StreamAnalytix Cloud acts as a multi-tenant, unified platform for end-to-end Spark-based ingestion, data processing, quality, blending, enrichment, analytics, machine learning, and visualization. Most significantly, the tool enables users to analyse and respond to events on a large scale, in real-time, for meeting business needs. With support for Apache Storm and Apache Spark in both batch and streaming modes, it is currently the industry’s only multi-engine product that provides a visual developer interface for end-end data processing support.

Several Fortune 500 companies have leveraged StreamAnalytix to realize major business benefits across a host of strategic use cases. Recently, it empowered a US-based cable TV and telecom provider with a real-time 360-degree view of its customers, enabling micro-segmentation and targeting, dynamic marketing campaigns, and contextualized customer service for enhancing the customer experience across touch points.

StreamAnalytix Cloud is available in three options based on the number of users. Each option supports all stages of the application delivery lifecycle-including design, build, test, debug, deploy, monitor, and manage.

To learn more about how you can leverage StreamAnalytix’s ETL capabilities for your enterprise, visit StreamAnalytix.

Limitations of traditional data processing systems

1. Siloed data

Due to the use of siloed systems across the enterprise, critical customer information often remains spread across multiple systems. This prevents organizations from getting a unified view of all their customer data, and makes it difficult to personalize customer experiences.

2. Batch workloads vs. real-time processing

Unlike real-time data processing, batch processing does not allow decision-makers to take immediate action in response to real-time customer activities. This often results in missed opportunities, especially in mission-critical scenarios where response is needed in seconds or minutes. By adopting an advanced real-time data processing framework, organizations can obtain the insights needed to act promptly in order to drive business outcomes.

3. Lack of scalability

Storing and processing large volumes of data requires a high degree of efficiency that traditional data processing systems may not provide. Advanced data processing frameworks based on distributed programming techniques are faster and more efficient. They have their own machine learning library for big data processing and offer greater scalability.

4. Difficulty in processing unstructured data

Unstructured data needs to be processed before it can serve as an input to any machine learning algorithm. While traditional data processing systems do not allow pre-processing of data, an advanced framework like Apache Spark enables processing and structuring of data to make it consumable by machine learning algorithms.

5. Minimal support for machine learning

Traditional systems do not effectively support identification of new data sources and application of machine learning algorithms. With advanced frameworks, businesses can leverage their growing volumes of data and apply necessary algorithms to predict customer behavior, proactively serve their needs, and deliver a delightful experience.

To overcome the challenges mentioned above, organizations need a solution that provides an ‘always on’, unified view of the customer’s journey, including past and present actions. The solution must also offer predictive modeling to analyze patterns and predict business outcomes with accuracy. Such solutions go far beyond data integration and empower decision-makers with a customer view that is most recent, comprehensive, accurate and relevant, while simultaneously addressing privacy concerns.

Business benefits of a customer 360 view

A customer 360 view gives you the critical insights you need to enhance the overall customer experience and boost customer retention. On the marketing front, a unified view enables micro-segmentation of customers, personalization of content, and multi-channel messaging. It also opens up new avenues of automation, provides better campaign analytics and helps maximize conversion and ROI.

While most organizations are taking rapid strides towards enabling a 360-degree view of their customers, they still have a long way to go. Industry analysts believe that issues like costs, employee buy-ins, and siloed data are primary factors holding enterprises back. Those that can overcome these barriers and embrace new, cutting edge technologies will eventually reap the benefits of higher customer satisfaction levels and increased revenues.

To learn more about how you can leverage customer 360 for your enterprise, visit www.streamanalytix.com.

Real-time streaming analytics is transforming business intelligence by empowering leaders, marketers, service teams, and other business units to address events as they occur. Not only can this provide actionable insights more quickly, but you also can act on intelligence that generally isn’t available or useful after-the-fact.

With intelligence and analytics capabilities following the cloud trajectory, it’s become easy for businesses of all sizes to perform real-time data analysis, staying relevant in the moment. However, the promise of these capabilities still comes with concern for enterprises.

The best way to understand the potential of this technology is to look at proven cases where it is already making a significant business impact. Let’s explore some of those capabilities and how they’ve benefited Impetus customers.

Easily create a deeper customer understanding

Perhaps most important for real-time stream analytics is the ability to contextualize experiences. Impacts are most profound in the customer realm, where companies can understand interactions and questions or build personalized communications at any moment.

Micro-segmentation of customers

The more personalized your customer experience, the more likely they are to leave satisfied. It also helps with company efforts to increase customer lifetime value (CLV) and increase the effectiveness of promotions and marketing campaigns.

A real-time streaming analytics solution like StreamAnalytix provides context via real-time analysis of all customer interactions. For a pay-TV provider, for example, this can include purchases, channel usage, error reports, complaints, and outside efforts such as ads. Leveraging this data can allow a company to specifically target individual user preferences and present the most compelling offer. Here, it might be sports packages based (think NBA vs. NFL or MLS vs. Premier League) on existing habits or new service offers for the early-adopting elite.

Multi-lingual sentiment analysis

Companies looking to enhance offers and services need to know their customers and understand preferences to get the best data.

It would take considerable time to train every single marketer or service agent on your entire portfolio, customer segments, and user plans. However, real-time sentiment analysis can operate as that brain, with no delay in how a user is profiled.

By enabling this capability across customer locations and languages, a company can mine feature-specific opinions and even dynamically change the questions a customer is asked to get a statistically relevant sample size for as many questions as possible. A real-time streaming analytics solution like StreamAnalytix can enable your survey platform to adapt and adjust based on real-time metrics of customer segmentation or concerns.

Improving service and security capabilities

Real-time streaming data typically rely on direct customer input. For many companies, the most abundant source of that direct interaction is in their support and call centers. So, call center analytics has become an early source of use cases and gains for these advances in analytics.

Real-time agent support

Call centers can handle millions of minutes’ worth of calls each day. Each call provides dozens of unique data points from feature issues and software versions referenced to wait times and complaint resolution speeds.

Companies not only want to understand what this information means but are also looking for call center analytics that gives this data proper context. With a robust, real-time analytics solution like StreamAnalytix, call centers can adjust the volume a specific agent receives, route calls to agents with the best track record for addressing a problem, and identify who may need additional training.

By optimizing agent-caller pairings, companies can generate higher customer retention rates and improve their customer satisfaction index (CSI) scores. It’s a wealth of data to improve customer service as well as systems and processes that are proven to improve agent productivity and reduce after-call work.

Risk-profiling in real-time

Sometimes it isn’t what the customer says but what they’re doing that businesses need to address. The major areas here are risk assessment and anomaly and fraud detection.

Real-time data shows how people behave, which is vital for industries like auto insurance, where company risk and liability depends on these actions at a particular moment. Driver behavior, vehicle sensor data, overall usage data, etc. can be collected and processed through machine learning to create the most accurate risk profile of a customer. It would help the insurer protect their business and also potentially provide greater savings for customers who are safe or infrequent drivers.

StreamAnalytix also helps companies understand threats from fraudulent customers in real-time. By enabling banks to process the data in 5x more applications, at a fraction of the cost of previous systems, financial institutions can look for malicious attempts to use stolen personal information, see and potentially match patterns of fraud, and improve risk scoring models to understand historical data accurately. Platform management not only speeds up the processing of applications but can significantly reduce false positives while also highlighting behavior that is proven malicious.

Conclusion

Today’s cloud infrastructure makes real-time data available to companies. The next step is implementing stream analytics, designed to turn that data into relevant, useful business intelligence.

Now, affordable platforms exist to empower enterprises of all sizes to unlock this potential. It often starts with call center analytics, but the possibility for in-stream understanding exists anywhere real-time data has a use.

However, as data complexity increases, modern data science is simplifying and streamlining traditional approaches to anomaly detection.

How can today’s enterprises ride the modern data science wave to effectively address the evolving challenges of real-time anomaly detection? And what are the key differentiators businesses must look for, to identify a platform that meets their needs?

Let’s explore how modern data science is transforming anomaly detection as we know it.

Originally published at https://www.streamanalytix.com on May 21, 2020.