#dataops software

[public-cloud-computing]

Unlock organizational success by harmonizing DataOps & DevOps—propel towards efficiency & innovation in a data-driven landscape.

[enterprise-cloud-services]

Unlock organizational success by harmonizing DataOps & DevOps—propel towards efficiency & innovation in a data-driven landscape.

[rubylogan15]

DataOps vs. DevOps: A Comparative Strategy Analysis - Powershow

Discover the power of merging DataOps & DevOps! Unlock efficiency and drive success through seamless collaboration. Dive into synergy-driven triumph today.

[strategictech]

The Dawn of Unified DataOps-From Fragmentation to Transformation

DataOps, an adaptation of what's traditionally known as DevOps, has evolved into an essential component of modern business operations. DataOps applies the concepts that have fostered more agility and value creation in software development to the data ecosystem. This adaptation enables organizations to bring the same efficiency and responsiveness to their data operations that DevOps brought to software delivery.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[thoughtfullyraggedpsion]

The Data Value Chain: Integrating DataOps, MLOps, and AI for Enterprise Growth

Unlocking Enterprise Value: Maximizing Data Potential with DataOps, MLOps, and AI

In today’s digital-first economy, data has emerged as the most valuable asset for enterprises striving to gain competitive advantage, improve operational efficiency, and foster innovation. However, the sheer volume, velocity, and variety of data generated by modern organizations create complex challenges around management, integration, and actionable insights. To truly harness the potential of enterprise data, businesses are increasingly turning to integrated frameworks such as DataOps, MLOps, and Artificial Intelligence (AI). These methodologies enable streamlined data workflows, robust machine learning lifecycle management, and intelligent automation — together transforming raw data into powerful business outcomes.

The Data Challenge in Modern Enterprises

The explosion of data from sources like IoT devices, customer interactions, social media, and internal systems has overwhelmed traditional data management practices. Enterprises struggle with:

Data silos causing fragmented information and poor collaboration.

Inconsistent data quality leading to unreliable insights.

Slow, manual data pipeline processes delaying analytics.

Difficulty deploying, monitoring, and scaling machine learning models.

Limited ability to automate decision-making in real-time.

To overcome these barriers and unlock data-driven innovation, enterprises must adopt holistic frameworks that combine process automation, governance, and advanced analytics at scale. This is where DataOps, MLOps, and AI converge as complementary approaches to maximize data potential.

DataOps: Accelerating Reliable Data Delivery

DataOps, short for Data Operations, is an emerging discipline inspired by DevOps principles in software engineering. It emphasizes collaboration, automation, and continuous improvement to manage data pipelines efficiently and reliably.

Key aspects of DataOps include:

Automation: Automating data ingestion, cleansing, transformation, and delivery pipelines to reduce manual effort and errors.

Collaboration: Bridging gaps between data engineers, analysts, scientists, and business teams for seamless workflows.

Monitoring & Quality: Implementing real-time monitoring and testing of data pipelines to ensure quality and detect anomalies early.

Agility: Enabling rapid iterations and continuous deployment of data workflows to adapt to evolving business needs.

By adopting DataOps, enterprises can shorten the time-to-insight and create trust in the data that powers analytics and machine learning. This foundation is critical for building advanced AI capabilities that depend on high-quality, timely data.

MLOps: Operationalizing Machine Learning at Scale

Machine learning (ML) has become a vital tool for enterprises to extract predictive insights and automate decision-making. However, managing the entire ML lifecycle — from model development and training to deployment, monitoring, and retraining — is highly complex.

MLOps (Machine Learning Operations) extends DevOps principles to ML systems, offering a standardized approach to operationalize ML models effectively.

Core components of MLOps include:

Model Versioning and Reproducibility: Tracking different model versions, datasets, and training parameters to ensure reproducibility.

Continuous Integration and Delivery (CI/CD): Automating model testing and deployment pipelines for faster, reliable updates.

Monitoring and Governance: Continuously monitoring model performance and detecting data drift or bias for compliance and accuracy.

Collaboration: Facilitating cooperation between data scientists, engineers, and IT teams to streamline model lifecycle management.

Enterprises employing MLOps frameworks can accelerate model deployment from weeks to days or hours, improving responsiveness to market changes. MLOps also helps maintain trust in AI-powered decisions by ensuring models perform reliably in production environments.

AI: The Catalyst for Intelligent Enterprise Transformation

Artificial Intelligence acts as the strategic layer that extracts actionable insights and automates complex tasks using data and ML models. AI capabilities range from natural language processing and computer vision to predictive analytics and recommendation systems.

When powered by DataOps and MLOps, AI solutions become more scalable, trustworthy, and business-aligned.

Examples of AI-driven enterprise benefits include:

Enhanced Customer Experiences: AI chatbots, personalized marketing, and sentiment analysis deliver tailored, responsive interactions.

Operational Efficiency: Predictive maintenance, process automation, and intelligent workflows reduce costs and downtime.

Innovation Enablement: AI uncovers new business opportunities, optimizes supply chains, and supports data-driven product development.

By integrating AI into enterprise processes with the support of disciplined DataOps and MLOps practices, businesses unlock transformative potential from their data assets.

Synergizing DataOps, MLOps, and AI for Maximum Impact

While each discipline delivers unique value, the real power lies in combining DataOps, MLOps, and AI into a cohesive strategy.

Reliable Data Pipelines with DataOps: Provide high-quality, timely data needed for model training and real-time inference.

Scalable ML Model Management via MLOps: Ensure AI models are robust, continuously improved, and safely deployed.

Intelligent Automation with AI: Drive business outcomes by embedding AI insights into workflows, products, and customer experiences.

Together, these frameworks enable enterprises to build a continuous intelligence loop — where data fuels AI models that automate decisions, generating new data and insights in turn. This virtuous cycle accelerates innovation, operational agility, and competitive differentiation.

Practical Steps for Enterprises to Maximize Data Potential

To implement an effective strategy around DataOps, MLOps, and AI, enterprises should consider the following:

Assess Current Data Maturity: Understand existing data infrastructure, pipeline bottlenecks, and analytics capabilities.

Define Business Objectives: Align data and AI initiatives with measurable goals like reducing churn, increasing revenue, or improving operational metrics.

Invest in Automation Tools: Adopt data pipeline orchestration platforms, ML lifecycle management tools, and AI frameworks that support automation and collaboration.

Build Cross-functional Teams: Foster collaboration between data engineers, scientists, IT, and business stakeholders.

Implement Governance and Compliance: Establish data quality standards, security controls, and model audit trails to maintain trust.

Focus on Continuous Improvement: Use metrics and feedback loops to iterate on data pipelines, model performance, and AI outcomes.

The Future Outlook

As enterprises continue their digital transformation journeys, the convergence of DataOps, MLOps, and AI will be essential for unlocking the full value of data. Organizations that successfully adopt these integrated frameworks will benefit from faster insights, higher quality models, and more impactful AI applications. This foundation will enable them to adapt rapidly in a dynamic market landscape and pioneer new data-driven innovations.

Read Full Article : https://businessinfopro.com/maximize-enterprise-data-potential-with-dataops-mlops-and-ai/

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[nareshitofficial]

The Future of Data Science: Trends to Watch in 2025

In today's fast-paced digital world, data science continues to be one of the most transformative fields. As we step into 2025, the role of data scientists is evolving rapidly with new technologies, tools, and business demands. Whether you're a budding analyst, a seasoned data professional, or someone curious about the future, these trends will shape the data science landscape in the coming year and beyond.

1. AI and Machine Learning Get Smarter

In 2025, AI and ML models are not just getting more accurate — they’re getting more context-aware. We’ll see a rise in explainable AI (XAI), helping businesses understand why an algorithm made a specific decision. This will be crucial for industries like healthcare, finance, and law where transparency is vital.

2. The Rise of AutoML

Automated Machine Learning (AutoML) will continue to democratize data science by enabling non-experts to build models without deep coding knowledge. This trend will accelerate productivity, reduce human error, and allow data scientists to focus on strategy and interpretation.

3. Data Privacy and Ethics Take Center Stage

With stricter regulations like GDPR and India’s Digital Personal Data Protection Act, data scientists must prioritize ethical data use and privacy compliance. 2025 will see more organizations embedding responsible AI practices in their workflows.

4. Edge Computing + Data Science = Real-Time Intelligence

Expect to see data science moving to the edge — quite literally. With IoT devices generating massive amounts of real-time data, processing this data locally (at the edge) will allow faster decision-making, especially in industries like manufacturing, logistics, and autonomous vehicles.

5. Natural Language Processing (NLP) Reaches New Heights

Thanks to advancements in large language models, NLP will power smarter chatbots, voice assistants, and search systems. Data scientists will increasingly work with unstructured data — text, audio, and video — to uncover deeper insights.

6. Low-Code and No-Code Platforms

Low-code tools will continue to empower business users to perform data analysis and visualization without needing deep technical skills. These platforms bridge the gap between data science and business intelligence, fostering greater collaboration.

7. DataOps and MLOps Maturity

In 2025, organizations are treating data like software. With DataOps and MLOps, companies are streamlining the lifecycle of data pipelines and machine learning models, ensuring version control, monitoring, and scalability across teams.

8. Data Literacy Becomes Essential

As data becomes central to decision-making, data literacy is becoming a key skill across all job roles. Companies are investing in training programs to ensure employees can interpret and use data effectively, not just collect it.

Final Thoughts

Data science in 2025 is more than just crunching numbers — it's about building responsible, scalable, and intelligent systems that can make a real-world impact. Whether you're an aspiring data scientist or an experienced professional, staying updated with these trends is essential.

At Naresh i Technologies, we’re committed to preparing the next generation of data professionals through our industry-focused Data Science and Analytics training programs. Join us and become future-ready!

[infernovm]

What we know now about generative AI for software development

Last year, I wrote about the 10 ways generative AI would transform software development, including early use cases in code generation, code validation, and other improvements in the software development process. Over the past year, I’ve also covered how genAI impacts low-code development, using genAI for quality assurance in continuous testing, and using AI and machine learning for dataops. Now…

[insurgentepress]

HighByte amplía su colaboración con Novotek Group

La empresa de software industrial renueva su asociación con Novotek Group y expande su distribución a Alemania, Austria y Francia.

HighByte, empresa de software industrial, ha anunciado hoy la ampliación de su acuerdo de distribución con Novotek Group lo que le otorga los derechos de comercialización, venta y distribución de HighByte Intelligence Hub en Alemania, Austria y Francia. El acuerdo se produce tras el reciente anuncio de Novotek de su propia expansión a la región DACH y Francia a principios de este año.

«Novotek cuenta con casi 40 años de experiencia ofreciendo soluciones y productos innovadores para empresas manufactureras con las que pueden conectar, digitalizar y optimizar sus procesos de producción», apuntó Tobias Antius, consejero delegado de Novotek. «Nuestra expansión a Alemania y Francia es un paso natural en nuestro modelo de negocio y nuestro propósito de impulsar la transformación digital para nuestros clientes. Nos congratula ofrecer HighByte Intelligence Hub, un producto estratégico de nuestra cartera, a estos mercados con una demanda de soluciones de gestión de datos industriales grande y en crecimiento.»

Según el análisis de la consultora independiente Verdantix, el mercado de software de gestión de datos industriales crecerá a una tasa compuesta anual del 19,9 % hasta alcanzar un valor de 6 100 millones de dólares en 2029. Norteamérica y Europa son las regiones predominantes en este mercado, y representan en conjunto más de las tres cuartas partes del gasto global en este sector, según el informe de la empresa Market Size and Forecast: Industrial Data Management Software 2023-2029 (Global). Se prevé que el crecimiento esté impulsado por la necesidad de mejorar la calidad de la gestión de los datos y la creciente concienciación sobre el retorno de la inversión que ofrecen los análisis basados en la inteligencia artificial a partir de datos industriales.

«Nos sentimos privilegiados de poder de ampliar nuestra relación con Novotek. En los últimos cinco años, han demostrado en todo momento ser una extensión de nuestra marca en los mercados a los que representan», explicó Tony Paine, consejero delegado de HighByte. «Comparten nuestra visión y poseen la experiencia y el liderazgo para llevarla a cabo. HighByte reafirma su compromiso con Novotek y con el sector industrial alemán, austriaco y francés».

Con una sólida trayectoria en Suiza y Austria, la reciente apertura de oficinas de Novotek en Alemania refuerzan su presencia en toda la región DACH, llevando la experiencia de la empresa y su innovadora cartera de soluciones a los fabricantes de este mercado estratégico. Novotek está autorizada para distribuir y dar soporte a HighByte Intelligence Hub en el norte y el oeste de Europa, incluidos Suecia, Dinamarca, Finlandia, Noruega, Reino Unido, Irlanda, Países Bajos, Bélgica, Francia, Alemania, Austria y Suiza. HighByte Intelligence Hub es una solución de DataOps industrial que contextualiza y estandariza datos industriales de diversas fuentes en el borde para ayudar a salvar la brecha entre los sistemas, redes y equipos de OT y TI.

Los representantes de HighByte y Novotek estarán presentes en Hannover Messe, que se celebra del 31 de marzo al 4 de abril, en el stand de AWS situado en el pabellón 15 - Stand D76.

[trescong]

Hewlett Packard Enterprise and Team Computers join Trescon’s Big CIO Show as Co-Powered Sponsors 

The 10th edition of the globally acclaimed show virtually connected over 300 online participants from across India, who discussed the emerging tech solutions and strategies for 2021 and beyond. Ranganath Sadasiva, CTO, Hybrid IT, Hewlett Packard Enterprise shared his insights on fueling edge-to-cloud digital transformation and more. 

Friday, 06 August 2021: As the leaders in the edge-to-cloud platform, Hewlett Packard Enterprise and Team Computers Co-Powered the 10th edition of Big CIO Show – India. The show virtually connected over 300 online participants from across India, including major stakeholders of India’s technology ecosystem such as government think-tanks, technology experts and leading technology solution providers. 

The virtual conference explored sectors of critical infrastructure where Digital Transformation can help the nation boost economic competitiveness with the region’s top technology leaders and the global technology fraternity. 

One of the top technology leaders and speakers who joined the conversation was Ranganath Sadasiva, Chief Technology Officer, Hybrid IT, HPE. Ranganath is responsible for bringing in thought leadership across HPE’s Hybrid IT business and delivering the best-in-class technology experience for customers. 

He enlightened the audience through the technical session on ‘Fuelling edge to cloud Digital Transformation with Hewlett Packard Enterprise.’ 

His presentation covered the macro trends leading to accelerated Digital Transformation and Digital Technologies enabling this transition. He spoke of ‘HPE Ezmeral software’ portfolio that enables the transformation of apps, data, and operations by running modern containerized applications and optimally manage their environments from infrastructure up the stack, allowing customers to harness data and turn it into insights with enterprise-grade security, and cost and compliance visibility. 

While talking about HPE’s latest announcements, he spoke of how it was time to reimagine Data Management and how this will be a game-changer. HPE’s vision of Unified DataOps empowers customers to break down the silos and complexity to accelerate data-driven transformation. He introduced the audience to their new offerings called Data Storage Cloud Central – a Unified cloud data services and HPE Alletra - cloud-native data infrastructure. 

While talking about HPE’s new Compute Launch for the data-driven transformation, he stated, "HPE essentially focuses on workloads that prevail in the marketplace today and ensure that it delivers the right kind of compute resources for everything as a service." He concluded the keynote by stating that, “It is your data, it is your agility, it is your innovation, and we will ensure that you unleash it till the last". 

He was also a part of an interesting panel discussion about 'How SDX enables Digital Transformation' where the panellists discussed how the rising customer expectations and global trends are forcing shifts in computing, storage, security, and networking; and how the evolution of the cloud is fuelling the software-defined revolution and driving the need for next-gen, “cloud-first” infrastructures and much more. He remarked “Software-Defined help’s deliver SCALE + AGILE = SCAGILE enterprises” 

The panellists who joined him in the discussion include Golok Kumar Simli - CTO, Ministry of External Affairs, Govt of India; Kaushik Majumder - Head of IT, Digital Services & Information Protection Officer, South Asia BASF India Ltd; Milind Khamkar - Group CIO, SUPER-MAX; and Prasanna Lohar - Chief Innovation Officer, DCB Bank. 

The show was hosted on the virtual events platform Vmeets to help participants network and conduct business in an interactive and immersive virtual environment. Participants could also engage with speakers in Q&A sessions and network with solution providers in virtual exhibition booths, private consultation rooms and private networking rooms. 

Here are a couple of upcoming events you might be interested in attending: Event calendar. About Big CIO Show – India 

Big CIO Show is a thought-leadership-driven, business-focused initiative that provides a platform for CIOs who are looking to explore new-age technologies and implementing them in their organisations. 

[public-cloud-computing]

Unlocking success: Merge DataOps & DevOps for unparalleled efficiency & innovation in a data-driven world. Optimize processes now!

[enterprise-cloud-services]

Unlocking success: Merge DataOps & DevOps for unparalleled efficiency & innovation in a data-driven world. Optimize processes now!

[rubylogan15]

DataOps vs. DevOps: A Comparative Strategy Analysis - EDocr

Discover the power of merging DataOps & DevOps! Unlock efficiency and drive success through seamless collaboration. Dive into synergy-driven triumph today.

[govindhtech]

IBM Watsonx.data Offers VSCode, DBT & Airflow Dataops Tools

We are happy to inform that VSCode, Apache Airflow, and data-build-tool a potent set of tools for the contemporary dataops stack are now supported by IBM watsonx.data. IBM Watsonx.data delivers a new set of rich capabilities, including data build tool (dbt) compatibility for both Spark and Presto engines, automated orchestration with Apache Airflow, and an integrated development environment via VSCode. These functionalities enable teams to effectively construct, oversee, and coordinate data pipelines.

The difficulty with intricate data pipelines

Building and maintaining complicated data pipelines that depend on several engines and environments is a challenge that organizations must now overcome. Teams must continuously move between different languages and tools, which slows down development and adds complexity.

It can be challenging to coordinate workflows across many platforms, which can result in inefficiencies and bottlenecks. Data delivery slows down in the absence of a smooth orchestration tool, which postpones important decision-making.

A coordinated strategy

Organizations want a unified, efficient solution that manages process orchestration and data transformations in order to meet these issues. Through the implementation of an automated orchestration tool and a single, standardized language for transformations, teams can streamline their workflows, facilitating communication and lowering the difficulty of pipeline maintenance. Here’s where Apache Airflow and DBT come into play.

Teams no longer need to learn more complicated languages like PySpark or Scala because dbt makes it possible to develop modular structured query language (SQL) code for data transformations. The majority of data teams are already familiar with SQL, thus database technology makes it easier to create, manage, and update transformations over time.

Throughout the pipeline, Apache Airflow automates and schedules jobs to minimize manual labor and lower mistake rates. When combined, dbt and Airflow offer a strong framework for easier and more effective management of complicated data pipelines.

Utilizing IBM watsonx.data to tie everything together

Although strong solutions like Apache Airflow and DBT are available, managing a developing data ecosystem calls for more than just a single tool. IBM Watsonx.data adds the scalability, security, and dependability of an enterprise-grade platform to the advantages of these tools. Through the integration of VSCode, Airflow, and DBT within watsonx.data, it has developed a comprehensive solution that makes complex data pipeline management easier:

By making data transformations with SQL simpler, dbt assists teams in avoiding the intricacy of less used languages.

By automating orchestration, Airflow streamlines processes and gets rid of bottlenecks.

VSCode offers developers a comfortable environment that improves teamwork and efficiency.

This combination makes pipeline management easier, freeing your teams to concentrate on what matters most: achieving tangible business results. IBM Watsonx.data‘s integrated solutions enable teams to maintain agility while optimizing data procedures.

Data Build Tool’s Spark adaptor

The data build tool (dbt) adapter dbt-watsonx-spark is intended to link Apache Spark with dbt Core. This adaptor facilitates Spark data model development, testing, and documentation.

FAQs

What is data build tool?

A transformation workflow called dbt enables you to complete more tasks with greater quality. Dbt can help you centralize and modularize your analytics code while giving your data team the kind of checks and balances that are usually seen in software engineering workflows. Before securely delivering data models to production with monitoring and visibility, work together on them, version them, test them, and record your queries.

DBT allows you and your team to work together on a single source of truth for metrics, insights, and business definitions by compiling and running your analytics code against your data platform. Having a single source of truth and the ability to create tests for your data helps to minimize errors when logic shifts and notify you when problems occur.

Read more on govindhtech.com

[jcmarchi]

Basil Faruqui, BMC Software: How to nail your data and AI strategy - AI News

New Post has been published on https://thedigitalinsider.com/basil-faruqui-bmc-software-how-to-nail-your-data-and-ai-strategy-ai-news/

Basil Faruqui, BMC Software: How to nail your data and AI strategy - AI News

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BMC Software’s director of solutions marketing, Basil Faruqui, discusses the importance of DataOps, data orchestration, and the role of AI in optimising complex workflow automation for business success.

What have been the latest developments at BMC?

It’s exciting times at BMC and particularly our Control-M product line, as we are continuing to help some of the largest companies around the world in automating and orchestrating business outcomes that are dependent on complex workflows. A big focus of our strategy has been on DataOps specifically on orchestration within the DataOps practice. During the last twelve months we have delivered over seventy integrations to serverless and PaaS offerings across AWS, Azure and GCP enabling our customers to rapidly bring modern cloud services into their Control-M orchestration patterns. Plus, we are prototyping GenAI based use cases to accelerate workflow development and run-time optimisation.

What are the latest trends you’ve noticed developing in DataOps?

What we are seeing in the Data world in general is continued investment in data and analytics software. Analysts estimate that the spend on Data and Analytics software last year was in the $100 billion plus range. If we look at the Machine Learning, Artificial Intelligence & Data Landscape that Matt Turck at Firstmark publishes every year, its more crowded than ever before. It has 2,011 logos and over five hundred were added since 2023. Given this rapid growth of tools and investment, DataOps is now taking center stage as companies are realising that to successfully operationalise data initiatives, they can no longer just add more engineers. DataOps practices are now becoming the blueprint for scaling these initiatives in production. The recent boom of GenAI is going make this operational model even more important.

What should companies be mindful of when trying to create a data strategy?

As I mentioned earlier that the investment in data initiatives from business executives, CEOs, CMOs, CFOs etc. continues to be strong. This investment is not just for creating incremental efficiencies but for game changing, transformational business outcomes as well. This means that three things become very important. First is clear alignment of the data strategy with the business goals, making sure the technology teams are working on what matters the most to the business. Second, is data quality and accessibility, the quality of the data is critical. Poor data quality will lead to inaccurate insights. Equally important is ensuring data accessibility – making the right data available to the right people at the right time. Democratising data access, while maintaining appropriate controls, empowers teams across the organisation to make data-driven decisions. Third is achieving scale in production. The strategy must ensure that Ops readiness is baked into the data engineering practices so its not something that gets considered after piloting only.

How important is data orchestration as part of a company’s overall strategy?

Data Orchestration is arguably the most important pillar of DataOps. Most organisations have data spread across multiple systems – cloud, on-premises, legacy databases, and third-party applications. The ability to integrate and orchestrate these disparate data sources into a unified system is critical. Proper data orchestration ensures seamless data flow between systems, minimising duplication, latency, and bottlenecks, while supporting timely decision-making.

What do your customers tell you are their biggest difficulties when it comes to data orchestration?

Organisations continue to face the challenge of delivering data products fast and then scaling quickly in production. GenAI is a good example of this. CEOs and boards around the world are asking for quick results as they sense that this could majorly disrupt those who cannot harness its power. GenAI is mainstreaming practices such as prompt engineering, prompt chaining etc. The challenge is how do we take LLMs and vector databases, bots etc and fit them into the larger data pipeline which traverses a very hybrid architecture from multiple-clouds to on-prem including mainframes for many. This just reiterates the need for a strategic approach to orchestration which would allow folding new technologies and practices for scalable automation of data pipelines. One customer described Control-M as a power strip of orchestration where they can plug in new technologies and patterns as they emerge without having to rewire every time they swap older technologies for newer ones.

What are your top tips for ensuring optimum data orchestration?

There can be a number of top tips but I will focus on one, interoperability between application and data workflows which I believe is critical for achieving scale and speed in production.  Orchestrating data pipelines is important, but it is vital to keep in mind that these pipelines are part of a larger ecosystem in the enterprise. Let’s consider an ML pipeline is deployed to predict the customers that are likely to switch to a competitor. The data that comes into such a pipeline is a result of workflows that ran in the ERP/CRM and combination of other applications. Successful completion of the application workflows is often a pre-requisite to triggering the data workflows. Once the model identifies customers that are likely to switch, the next step perhaps is to send them a promotional offer which means that  we will need to go back to the application layer in the ERP and CRM. Control-M is uniquely positioned to solve this challenge as our customers use it to orchestrate and manage intricate dependencies between the application and the data layer.

What do you see as being the main opportunities and challenges when deploying AI?

AI and specifically GenAI is rapidly increasing the technologies involved in the data ecosystem. Lots of new models, vector databases and new automation patterns around prompt chaining etc. This challenge is not new to the data world, but the pace of change is picking up. From an orchestration perspective we see tremendous opportunities with our customers because we offer a highly adaptable platform for orchestration where they can fold these tools and patterns into their existing workflows versus going back to drawing board.

Do you have any case studies you could share with us of companies successfully utilising AI?

Domino’s Pizza leverages Control-M for orchestrating its vast and complex data pipelines. With over 20,000 stores globally, Domino’s manages more than 3,000 data pipelines that funnel data from diverse sources such as internal supply chain systems, sales data, and third-party integrations. This data from applications needs to go through complex transformation patterns and models before its available for driving decisions related to food quality, customer satisfaction, and operational efficiency across its franchise network.

Control-M plays a crucial role in orchestrating these data workflows, ensuring seamless integration across a wide range of technologies like MicroStrategy, AMQ, Apache Kafka, Confluent, GreenPlum, Couchbase, Talend, SQL Server, and Power BI, to name a few.

Beyond just connecting complex orchestration patterns together Control-M provides them with end-to-end visibility of pipelines, ensuring that they meet strict service-level agreements (SLAs) while handling increasing data volumes. Control-M is helping them generate critical reports faster, deliver insights to franchisees, and scale the roll out new business services.

What can we expect from BMC in the year ahead?

Our strategy for Control-M at BMC will stay focused on a couple of basic principles:

Continue to allow our customers to use Control-M as a single point of control for orchestration as they onboard modern technologies, particularly on the public cloud. This means we will continue to provide new integrations to all major public cloud providers to ensure they can use Control-M to orchestrate workflows across three major cloud infrastructure models of IaaS, Containers and PaaS (Serverless Cloud Services). We plan to continue our strong focus on serverless, and you will see more out-of-the-box integrations from Control-M to support the PaaS model. 

We recognise that enterprise orchestration is a team sport, which involves coordination across engineering, operations and business users. And, with this in mind, we plan to bring a user experience and interface that is persona based so that collaboration is frictionless. 

Specifically, within DataOps we are looking at the intersection of orchestration and data quality with a specific focus on making data quality a first-class citizen within application and data workflows. Stay tuned for more on this front!

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: automation, BMC, data orchestration, DataOps

[datasciencewithgenerativeai]

Data Science with Generative AI Training Hyderabad | Data Science Training

The Future of Data Science? Key Trends to Watch

Introduction Data Science with Generative AI Course continues to transform industries, driving decision-making, innovation, and efficiency. With the rapid advancement of technology, the field is evolving at a breakneck pace. From automation to ethical AI, data science is entering an exciting new era. This article highlights the key trends shaping the future of data science and what to expect as the discipline continues to grow. Data Science Course in Hyderabad

AI and Machine Learning Integration AI and machine learning (ML) are at the heart of data science advancements. The ability to automate complex tasks and generate insights is driving innovation in various sectors.

Automated Data Processing: AI can streamline data cleaning and preparation, reducing the manual labor required by data scientists.

Predictive Analytics: ML models will become even more sophisticated, leading to better forecasting and real-time decision-making.

AI-Powered Applications: Expect more integration of AI into everyday software and business processes, improving productivity.

Augmented Analytics Augmented analytics leverages AI to enhance data analysis. This trend democratizes data science by making analytics accessible to a broader range of users.

Self-Service Tools: Businesses will see an increase in user-friendly platforms that allow non-technical users to generate insights without needing a data scientist.

AI-Driven Insights: Automation will help uncover hidden patterns in data, speeding up the decision-making process.

Ethical Ai and Responsible Data Usage As AI grows in prominence, ethical concerns around data privacy, bias, and transparency are gaining attention.

Bias Mitigation: Efforts to reduce algorithmic bias will intensify, ensuring AI models are fair and inclusive.

Privacy Protection: Stricter regulations will push companies to prioritize data privacy and security, promoting responsible use of data.

The Rise of DataOps DataOps, the data-focused counterpart to DevOps, will become central to managing data pipelines efficiently.

Automation: Expect greater automation in data workflows, from data integration to deployment.

Collaboration: DataOps encourages better collaboration between data scientists, engineers, and operations teams, improving the speed and quality of data-driven projects.

Real-Time Analytics As businesses demand faster insights, real-time analytics is set to become a significant focus in data science.

Streaming Data: The rise of IoT devices and social media increases the demand for systems that can process and analyze data in real time. Data Science Training Institute in Hyderabad

Faster Decision-Making: Real-time analytics will enable organizations to make more immediate and informed decisions, improving responsiveness to market changes.

Conclusion The future of data science is promising, with trends like AI integration, ethical practices, and real-time analytics reshaping the field. These innovations will empower businesses to harness data's full potential while navigating the challenges that come with responsible and effective data management.

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Simplifying Complex Data Operations with Smart Tools: Match Data Pro LLC Leading the Charge

In the data-driven economy, businesses are increasingly relying on accurate, actionable, and streamlined data to make informed decisions. But as the volume of data grows, so do the challenges: mismatched entries, inconsistent formats, manual data handling, and disconnected systems. That’s where Match Data Pro LLC steps in with robust, user-friendly solutions built to simplify the most complex data tasks.

From intuitive point-and-click data tools to enterprise-ready on-premise data software, Match Data Pro LLC offers a full suite of data ops software that helps businesses regain control of their data environments. Let’s explore how our tools are transforming data workflows and improving business intelligence across industries.

The Challenge of Mismatched Data

Modern businesses often operate across multiple platforms — CRMs, ERPs, marketing suites, accounting software, and more. With so many systems exchanging data, inconsistencies and mismatches are inevitable.

Common mismatched data issues include:

Duplicated records with minor variations

Inconsistent formatting across platforms

Incomplete or outdated entries

Data schema conflicts

These mismatches don’t just clutter your systems — they lead to flawed analytics, poor customer experiences, and inefficient operations. Match Data Pro LLC offers specialized mismatched data solutions that identify, resolve, and prevent these inconsistencies before they impact your business.

Mismatched Data Solutions That Actually Work

Our intelligent matching algorithms use fuzzy logic, pattern recognition, and customizable rules to identify mismatches across large datasets — whether it's customer records, product inventories, or financial transactions.

With our solutions, you can:

Detect and correct field-level mismatches

Merge records with varying structures

Align data formats across multiple systems

Automate reconciliation and cleanup processes

Whether your data is siloed in spreadsheets or flowing through APIs, Match Data Pro LLC helps you achieve consistency and reliability.

Empowering Users with Point-and-Click Data Tools

Not every business has a dedicated IT or data science team. That’s why Match Data Pro LLC designed point-and-click data tools — intuitive interfaces that empower non-technical users to manage, match, and clean data without writing a single line of code.

With our user-friendly dashboard, you can:

Drag and drop datasets for instant processing

Match records with customizable logic

Filter, group, and sort data visually

Schedule automated data operations

Generate real-time reports with one click

These tools are perfect for marketing teams, sales professionals, analysts, and operations managers who need quick results without technical overhead.

Optimize Workflow with Data Ops Software

DataOps, or data operations, is the practice of automating, monitoring, and improving the data pipeline across your organization. Match Data Pro LLC offers scalable data ops software that bridges the gap between IT and business, ensuring that clean, accurate data flows freely across systems.

Our DataOps platform supports:

Data ingestion and transformation

Real-time validation and matching

Workflow automation

Custom pipelines with REST API integration

End-to-end visibility into data flow

By implementing a robust DataOps framework, organizations can break down silos, accelerate decision-making, and reduce the time from data collection to business action.

On-Premise Data Software for Total Control

While cloud-based solutions offer flexibility, some businesses — especially in finance, healthcare, and government — require strict control over their data infrastructure. For these clients, Match Data Pro LLC provides secure, customizable on-premise data software.

With our on-premise solution, you get:

Full ownership of your data and environment

Greater compliance with regulatory standards (GDPR, HIPAA, etc.)

No dependency on third-party cloud providers

Seamless integration with legacy systems

Offline capabilities for remote or secure locations

Whether you're managing sensitive customer data or maintaining a private data warehouse, our on-premise offerings ensure peace of mind and operational integrity.

How Match Data Pro LLC Delivers Value

We understand that every organization’s data landscape is unique. That’s why we offer flexible configurations, expert support, and scalable features that grow with your business.

Key benefits of our platform:

Accurate data matching and cleaning

Automation that saves hours of manual effort

Tools accessible to both technical and non-technical users

API integration for seamless system connectivity

On-premise and cloud deployment options

Whether you’re a startup seeking better customer segmentation or a multinational enterprise trying to unify datasets across geographies, Match Data Pro LLC has a solution that fits.

Real-World Use Cases

Marketing: Clean up and deduplicate lead lists from multiple sources using point-and-click tools

Finance: Reconcile transactions across multiple ledgers with automated workflows

Retail: Align product data across warehouses, stores, and e-commerce platforms

Healthcare: Match patient records across systems while complying with data privacy regulations

Government: Maintain accurate citizen records with secure on-premise deployment

Our flexible software supports use cases across every industry — because clean, reliable data is a universal need.

Match Data Pro LLC: Your Data, Unified and Simplified

In a world driven by data, the ability to unify and streamline your information is what sets top-performing companies apart. Match Data Pro LLC provides the tools, support, and infrastructure to turn messy, mismatched datasets into clean, actionable intelligence.

With point-and-click data tools, mismatched data solutions, data ops software, and on-premise data software, we give you the power to take control of your data — and your business future.