#Data analytics in business

[ananyamehtablog]

The Evolution of PGDM Curriculums: Adapting to Industry Trends and Demands

[hmfaysal99]

New Business Marketing Tips And Tricks for Success

Starting a new business can be an exciting endeavor, but it also comes with its fair share of challenges, especially in the competitive landscape of today's market. Effective marketing is crucial for the success of any new venture. Here are four essential marketing tips and tricks to help your new business thrive.

Define Your Target Audience: Before diving into marketing efforts, it's essential to identify and understand your target audience. Define your ideal customer persona by considering demographics, interests, pain points, and buying behaviors. Conduct market research to gather valuable insights that will guide your marketing strategies. Tailoring your messages and campaigns to resonate with your target audience will significantly increase your chances of success.

Once you have a clear picture of your audience, choose the most suitable marketing channels to reach them effectively. Social media, email marketing, content marketing, and pay-per-click advertising are just a few options to consider. Your choice of channels should align with where your audience spends their time online.

Create Compelling Content: Content marketing is a powerful tool for new businesses to establish their brand and build credibility. Develop high-quality, informative, and engaging content that addresses the needs and interests of your target audience. This content can take various forms, including blog posts, videos, infographics, and podcasts.

Consistency is key when it comes to content creation. Develop a content calendar to plan and schedule regular updates. Providing valuable content not only helps you connect with your audience but also boosts your search engine rankings, making it easier for potential customers to find you.

Leverage Social Media: Social media platforms have become indispensable for marketing in today's digital age. Create profiles on relevant social media platforms and engage with your audience regularly. Share your content, interact with followers, and participate in industry-related discussions.

Paid advertising on social media can also be a cost-effective way to reach a broader audience. Platforms like Facebook, Instagram, and LinkedIn offer targeting options that allow you to reach users who match your ideal customer profile.

Monitor and Adapt: Marketing is an ever-evolving field, and what works today may not work tomorrow. To stay ahead of the curve, regularly monitor the performance of your marketing efforts. Analyze key metrics such as website traffic, conversion rates, and return on investment (ROI). Use tools like Google Analytics and social media insights to gather data and insights.

Based on your findings, be prepared to adapt your strategies and tactics. If a particular marketing channel isn't delivering the expected results, reallocate your resources to more promising avenues. Stay up-to-date with industry trends and keep an eye on your competitors to ensure your marketing efforts remain relevant and competitive.

In conclusion, effective marketing is essential for the success of any new business. By defining your target audience, creating compelling content, leveraging social media, and continuously monitoring and adapting your strategies, you can position your new business for growth and long-term success in a competitive market. Remember that success may not come overnight, but with persistence and the right marketing approach, your new business can thrive.

[abathurofficial]

Abathur

At Abathur, we believe technology should empower, not complicate.

Our mission is to provide seamless, scalable, and secure solutions for businesses of all sizes. With a team of experts specializing in various tech domains, we ensure our clients stay ahead in an ever-evolving digital landscape.

Why Choose Us? Expert-Led Innovation – Our team is built on experience and expertise. Security First Approach – Cybersecurity is embedded in all our solutions. Scalable & Future-Proof – We design solutions that grow with you. Client-Centric Focus – Your success is our priority.

[nordstarvisions]

Top 5 data management mistakes costing UAE businesses millions

Introduction

In an increasingly digital economy, poor data practices have become more than just an IT issue—they are an enterprise-level risk. 

Across the UAE, businesses are losing millions annually due to fragmented systems, inconsistent governance, and reactive strategies. 

In this article, we’ll unpack five critical data managementmistakes, their tangible costs, and what forward-thinking firms are doing to stay compliant, competitive, and data-resilient in 2025.

1. Fragmented Data Silos Across Departments

While decentralization may speed up local decision-making, it often comes at the cost of data cohesion. Sales, marketing, finance, and operations frequently maintain isolated datasets that never sync—each with its own metrics, definitions, and reporting cycles. The cost? Missed opportunities, duplicated efforts, inconsistent KPIs, and customer insights that are either delayed or distorted due to incompatible sources. Fix: Implement centralized data lakes or unified ERP/CRM systems to bridge these silos. Introduce cross-departmental governance protocols, and enforce scheduled data synchronization to maintain consistency across all business functions.

2. Weak Data Governance and Access Controls

Many UAE businesses still lack formal governance policies. There’s little clarity on who owns the data, who can access it, and how data quality is maintained across systems and touchpoints. The cost? Increased risk of data breaches, GDPR/DIFC non-compliance, unauthorized exposure of sensitive information, and eroded stakeholder trust—especially in sectors like healthcare, finance, and public services. Fix: Deploy a robust data governance framework with clearly defined roles, role-based access controls, automated audit trails, and regular compliance reviews. Embed accountability at every stage of data creation and usage.

3. Overreliance on Legacy Infrastructure

Outdated database architectures, manual Excel trackers, and siloed on-prem systems continue to dominate back-end processes—despite widespread digital front-ends. The cost? Performance bottlenecks during scale, limited real-time data visibility, high IT maintenance overheads, and an inability to integrate with modern analytics or automation tools. Fix: Migrate to cloud-native platforms that support elastic scaling, system redundancy, and embedded analytics. Incorporate APIs for seamless integration with existing digital tools while phasing out legacy dependencies.

4. Lack of Data Quality Assurance

Inconsistent formats, missing fields, outdated records, and duplicated entries remain common issues across enterprise datasets—especially when multiple input sources aren’t standardized.

The cost?

Flawed business reports, poor AI/ML model performance, customer experience setbacks, and incorrect decision-making based on unreliable data.

Fix:

Introduce end-to-end data quality frameworks that include automated validation checks, enrichment protocols, and AI-driven anomaly detection. 

Regular audits and cleansing routines should be part of standard operations.

5. Treating Data Strategy as a One-Off Project

Many businesses initiate data initiatives as one-time efforts—an implementation followed by months (or years) of stagnation. 

Without ongoing refinement, systems become outdated, and processes lose alignment with evolving business needs.

The cost?

Strategic misalignment, increasing technical debt, and declining ROI on digital investments that fail to evolve with the organization’s goals.

Fix:

Create a living data strategy—an adaptive roadmap reviewed quarterly, driven by key stakeholders across departments. 

Tie progress to measurable KPIs like operational efficiency, customer satisfaction, or revenue growth from data-led initiatives.

Turn Costly Data Chaos into Smart Business Decisions: Nordstar Vision

At Nordstar Vision, we help businesses move from fragmented systems to future-ready data ecosystems. 

Whether you’re struggling with outdated infrastructure, data silos, or lack of governance, our team brings tailored solutions to help you scale confidently in a data-first economy.

Let’s turn your data into a growth engine.

Reach out to us today at +(971) 50 1108756 or visit nordstartvision.

[jcmarchi]

Unlock the other 99% of your data - now ready for AI

New Post has been published on https://thedigitalinsider.com/unlock-the-other-99-of-your-data-now-ready-for-ai/

Unlock the other 99% of your data - now ready for AI

For decades, companies of all sizes have recognized that the data available to them holds significant value, for improving user and customer experiences and for developing strategic plans based on empirical evidence.

As AI becomes increasingly accessible and practical for real-world business applications, the potential value of available data has grown exponentially. Successfully adopting AI requires significant effort in data collection, curation, and preprocessing. Moreover, important aspects such as data governance, privacy, anonymization, regulatory compliance, and security must be addressed carefully from the outset.

In a conversation with Henrique Lemes, Americas Data Platform Leader at IBM, we explored the challenges enterprises face in implementing practical AI in a range of use cases. We began by examining the nature of data itself, its various types, and its role in enabling effective AI-powered applications.

Henrique highlighted that referring to all enterprise information simply as ‘data’ understates its complexity. The modern enterprise navigates a fragmented landscape of diverse data types and inconsistent quality, particularly between structured and unstructured sources.

In simple terms, structured data refers to information that is organized in a standardized and easily searchable format, one that enables efficient processing and analysis by software systems.

Unstructured data is information that does not follow a predefined format nor organizational model, making it more complex to process and analyze. Unlike structured data, it includes diverse formats like emails, social media posts, videos, images, documents, and audio files. While it lacks the clear organization of structured data, unstructured data holds valuable insights that, when effectively managed through advanced analytics and AI, can drive innovation and inform strategic business decisions.

Henrique stated, “Currently, less than 1% of enterprise data is utilized by generative AI, and over 90% of that data is unstructured, which directly affects trust and quality”.

The element of trust in terms of data is an important one. Decision-makers in an organization need firm belief (trust) that the information at their fingertips is complete, reliable, and properly obtained. But there is evidence that states less than half of data available to businesses is used for AI, with unstructured data often going ignored or sidelined due to the complexity of processing it and examining it for compliance – especially at scale.

To open the way to better decisions that are based on a fuller set of empirical data, the trickle of easily consumed information needs to be turned into a firehose. Automated ingestion is the answer in this respect, Henrique said, but the governance rules and data policies still must be applied – to unstructured and structured data alike.

Henrique set out the three processes that let enterprises leverage the inherent value of their data. “Firstly, ingestion at scale. It’s important to automate this process. Second, curation and data governance. And the third [is when] you make this available for generative AI. We achieve over 40% of ROI over any conventional RAG use-case.”

IBM provides a unified strategy, rooted in a deep understanding of the enterprise’s AI journey, combined with advanced software solutions and domain expertise. This enables organizations to efficiently and securely transform both structured and unstructured data into AI-ready assets, all within the boundaries of existing governance and compliance frameworks.

“We bring together the people, processes, and tools. It’s not inherently simple, but we simplify it by aligning all the essential resources,” he said.

As businesses scale and transform, the diversity and volume of their data increase. To keep up, AI data ingestion process must be both scalable and flexible.

“[Companies] encounter difficulties when scaling because their AI solutions were initially built for specific tasks. When they attempt to broaden their scope, they often aren’t ready, the data pipelines grow more complex, and managing unstructured data becomes essential. This drives an increased demand for effective data governance,” he said.

IBM’s approach is to thoroughly understand each client’s AI journey, creating a clear roadmap to achieve ROI through effective AI implementation. “We prioritize data accuracy, whether structured or unstructured, along with data ingestion, lineage, governance, compliance with industry-specific regulations, and the necessary observability. These capabilities enable our clients to scale across multiple use cases and fully capitalize on the value of their data,” Henrique said.

Like anything worthwhile in technology implementation, it takes time to put the right processes in place, gravitate to the right tools, and have the necessary vision of how any data solution might need to evolve.

IBM offers enterprises a range of options and tooling to enable AI workloads in even the most regulated industries, at any scale. With international banks, finance houses, and global multinationals among its client roster, there are few substitutes for Big Blue in this context.

To find out more about enabling data pipelines for AI that drive business and offer fast, significant ROI, head over to this page.

[lumenore-datalytics]

Smarter Textile Manufacturing with Data You Can Actually Use

Ever stared at a spreadsheet and thought, "What am I even looking at?"

Yeah, me too.

If you work in textile manufacturing, you already know how chaotic things can get—orders, machines, defects, and deadlines all moving at once. A solid dashboard can seriously make life easier.

I came across this article, and honestly, it’s one of the most practical guides I’ve seen for building a textile manufacturing dashboard that actually helps you run things better.

It’s not just charts for the sake of charts. They talk about tracking the stuff that actually matters:

Order completion rates

Machine downtime

Defect tracking

Predictive maintenance

What I liked most? The idea of role-based views, so your QC team, your production leads, and your plant managers all get dashboards based on what they need. No more clutter. No more info overload.

And yeah it’s not just about monitoring the present. The guide covers how to use predictive analytics to flag potential equipment failures before they happen. That’s the kind of foresight every operations team dreams of.

If you’re managing a textile unit or just love good process optimization, this one’s definitely worth the read.

Check it out here: Create a Textile Manufacturing Dashboard with Lumenore

[datapeakbyfactr]

AI’s Role in Business Process Automation

Automation has come a long way from simply replacing manual tasks with machines. With AI stepping into the scene, business process automation is no longer just about cutting costs or speeding up workflows—it’s about making smarter, more adaptive decisions that continuously evolve. AI isn't just doing what we tell it; it’s learning, predicting, and innovating in ways that redefine how businesses operate. 

From hyperautomation to AI-powered chatbots and intelligent document processing, the world of automation is rapidly expanding. But what does the future hold?

What is Business Process Automation? 

Business Process Automation (BPA) refers to the use of technology to streamline and automate repetitive, rule-based tasks within an organization. The goal is to improve efficiency, reduce errors, cut costs, and free up human workers for higher-value activities. BPA covers a wide range of functions, from automating simple data entry tasks to orchestrating complex workflows across multiple departments. 

Traditional BPA solutions rely on predefined rules and scripts to automate tasks such as invoicing, payroll processing, customer service inquiries, and supply chain management. However, as businesses deal with increasing amounts of data and more complex decision-making requirements, AI is playing an increasingly critical role in enhancing BPA capabilities. 

AI’s Role in Business Process Automation 

AI is revolutionizing business process automation by introducing cognitive capabilities that allow systems to learn, adapt, and make intelligent decisions. Unlike traditional automation, which follows a strict set of rules, AI-driven BPA leverages machine learning, natural language processing (NLP), and computer vision to understand patterns, process unstructured data, and provide predictive insights. 

Here are some of the key ways AI is enhancing BPA: 

Self-Learning Systems: AI-powered BPA can analyze past workflows and optimize them dynamically without human intervention. 

Advanced Data Processing: AI-driven tools can extract information from documents, emails, and customer interactions, enabling businesses to process data faster and more accurately. 

Predictive Analytics: AI helps businesses forecast trends, detect anomalies, and make proactive decisions based on real-time insights. 

Enhanced Customer Interactions: AI-powered chatbots and virtual assistants provide 24/7 support, improving customer service efficiency and satisfaction. 

Automation of Complex Workflows: AI enables the automation of multi-step, decision-heavy processes, such as fraud detection, regulatory compliance, and personalized marketing campaigns. 

As organizations seek more efficient ways to handle increasing data volumes and complex processes, AI-driven BPA is becoming a strategic priority. The ability of AI to analyze patterns, predict outcomes, and make intelligent decisions is transforming industries such as finance, healthcare, retail, and manufacturing. 

“At the leading edge of automation, AI transforms routine workflows into smart, adaptive systems that think ahead. It’s not about merely accelerating tasks—it’s about creating an evolving framework that continuously optimizes operations for future challenges.”

— Emma Reynolds, CTO of QuantumOps

Trends in AI-Driven Business Process Automation 

1. Hyperautomation 

Hyperautomation, a term coined by Gartner, refers to the combination of AI, robotic process automation (RPA), and other advanced technologies to automate as many business processes as possible. By leveraging AI-powered bots and predictive analytics, companies can automate end-to-end processes, reducing operational costs and improving decision-making. 

Hyperautomation enables organizations to move beyond simple task automation to more complex workflows, incorporating AI-driven insights to optimize efficiency continuously. This trend is expected to accelerate as businesses adopt AI-first strategies to stay competitive. 

2. AI-Powered Chatbots and Virtual Assistants 

Chatbots and virtual assistants are becoming increasingly sophisticated, enabling seamless interactions with customers and employees. AI-driven conversational interfaces are revolutionizing customer service, HR operations, and IT support by providing real-time assistance, answering queries, and resolving issues without human intervention. 

The integration of AI with natural language processing (NLP) and sentiment analysis allows chatbots to understand context, emotions, and intent, providing more personalized responses. Future advancements in AI will enhance their capabilities, making them more intuitive and capable of handling complex tasks. 

3. Process Mining and AI-Driven Insights 

Process mining leverages AI to analyze business workflows, identify bottlenecks, and suggest improvements. By collecting data from enterprise systems, AI can provide actionable insights into process inefficiencies, allowing companies to optimize operations dynamically. 

AI-powered process mining tools help businesses understand workflow deviations, uncover hidden inefficiencies, and implement data-driven solutions. This trend is expected to grow as organizations seek more visibility and control over their automated processes. 

4. AI and Predictive Analytics for Decision-Making 

AI-driven predictive analytics plays a crucial role in business process automation by forecasting trends, detecting anomalies, and making data-backed decisions. Companies are increasingly using AI to analyze customer behaviour, market trends, and operational risks, enabling them to make proactive decisions. 

For example, in supply chain management, AI can predict demand fluctuations, optimize inventory levels, and prevent disruptions. In finance, AI-powered fraud detection systems analyze transaction patterns in real-time to prevent fraudulent activities. The future of BPA will heavily rely on AI-driven predictive capabilities to drive smarter business decisions. 

5. AI-Enabled Document Processing and Intelligent OCR 

Document-heavy industries such as legal, healthcare, and banking are benefiting from AI-powered Optical Character Recognition (OCR) and document processing solutions. AI can extract, classify, and process unstructured data from invoices, contracts, and forms, reducing manual effort and improving accuracy. 

Intelligent document processing (IDP) combines AI, machine learning, and NLP to understand the context of documents, automate data entry, and integrate with existing enterprise systems. As AI models continue to improve, document processing automation will become more accurate and efficient. 

Going Beyond Automation

The future of AI-driven BPA will go beyond automation—it will redefine how businesses function at their core. Here are some key predictions for the next decade: 

Autonomous Decision-Making: AI systems will move beyond assisting human decisions to making autonomous decisions in areas such as finance, supply chain logistics, and healthcare management. 

AI-Driven Creativity: AI will not just automate processes but also assist in creative and strategic business decisions, helping companies design products, create marketing strategies, and personalize customer experiences. 

Human-AI Collaboration: AI will become an integral part of the workforce, working alongside employees as an intelligent assistant, boosting productivity and innovation. 

Decentralized AI Systems: AI will become more distributed, with businesses using edge AI and blockchain-based automation to improve security, efficiency, and transparency in operations. 

Industry-Specific AI Solutions: We will see more tailored AI automation solutions designed for specific industries, such as AI-driven legal research tools, medical diagnostics automation, and AI-powered financial advisory services. 

AI is no longer a futuristic concept—it’s here, and it’s already transforming the way businesses operate. What’s exciting is that we’re still just scratching the surface. As AI continues to evolve, businesses will find new ways to automate, innovate, and create efficiencies that we can’t yet fully imagine. 

But while AI is streamlining processes and making work more efficient, it’s also reshaping what it means to be human in the workplace. As automation takes over repetitive tasks, employees will have more opportunities to focus on creativity, strategy, and problem-solving. The future of AI in business process automation isn’t just about doing things faster—it’s about rethinking how we work all together.

Learn more about DataPeak:

[furryreviewdreamland]

Data-Driven Decision Making improves strategies, boosts efficiency and drives business success with accurate insights and informed choices.

[digital-specialist]

Acadecraft Partners with Wadhwani Foundation's Government Digital Transformation Initiative to Develop eLearning Courses

[zentarablog]

10 Ways Technology is Boosting Workplace Productivity

In the contemporary professional landscape, productivity is the currency of success. Businesses and individuals alike are constantly seeking innovative strategies to optimize output, streamline workflows, and maximize efficiency. At the forefront of this revolution is technology, which has fundamentally transformed how we work, collaborate, and manage our time. Far from being a mere convenience,…

[truetechreview]

Top 5 DeepSeek AI Features Powering Industry Innovation

Table of Contents1. The Problem: Why Legacy Tools Can’t Keep Up2. What Makes DeepSeek AI Unique?3. 5 Game-Changing DeepSeek AI Features (with Real Stories)3.1 Adaptive Learning Engine3.2 Real-Time Anomaly Detection3.3 Natural Language Reports3.4 Multi-Cloud Sync3.5 Ethical AI Auditor4. How These Features Solve Everyday Challenges5. Step-by-Step: Getting Started with DeepSeek AI6. FAQs: Your…

[excelhelps]

How to use Microsoft Forms

[samvavlabs]

Data Analyst Roadmap for 2024!

Cracking the Data Analyst Roadmap for 2024! Kick off your journey by mastering and delving into Python for data manipulation magic, and dazzle stakeholders with insights using PowerBi or Tableau. Don't forget, that SQL proficiency and hands-on projects refine your skillset, but never overlook the importance of effective communication and problem-solving. Are you checking off these milestones on your path to success? 📌 For more details, visit our website: https://www.samvavlabs.com  . . . #DataAnalyst2024 #CareerGrowth #roadmap #DataAnalyst #samvavlabs #roadmap2024 #dataanalystroadmap #datavisualization

[jcmarchi]

French initiative for responsible AI leaders - AI News

New Post has been published on https://thedigitalinsider.com/french-initiative-for-responsible-ai-leaders-ai-news/

French initiative for responsible AI leaders - AI News

ESSEC Business School and Accenture have announced the launch of a new initiative, ‘AI for Responsible Leadership,’ which marks the 10th anniversary of the establishment of the role of Chair at ESSEC, titled the ESSEC Accenture Strategic Business Analytics Chair.

The initiative aims to encourage the use of artificial intelligence by leaders in ways that are responsible and ethical, and that lead to high levels of professional performance. It aims to provide current and future leaders with the skills they require when faced with challenges in the future; economic, environmental, or social.

Several organisations support the initiative, including institutions, businesses, and specialised groups, including ESSEC Metalab for Data, Technology & Society, and Accenture Research.

Executive Director of the ESSEC Metalab, Abdelmounaim Derraz, spoke of the collaboration, saying, “Technical subjects are continuing to shake up business schools, and AI has opened up opportunities for collaboration between partner companies, researchers, and other members of the ecosystem (students, think tanks, associations, [and] public service).”

ESSEC and Accenture aim to integrate perspectives from multiple fields of expertise, an approach that is a result of experimentation in the decade the Chair has existed.

The elements of the initiative include workshops and talks designed to promote the exchange of knowledge and methods. It will also include a ‘barometer’ to help track AI’s implementation and overall impact on responsible leadership.

The initiative will engage with a network of institutions and academic publications, and an annual Grand Prix will recognise projects that focus on and explore the subject of AI and leadership.

Fabrice Marque, founder of the initiative and the current ESSEC Accenture Strategics Business Analytics Chair, said, “For years, we have explored the potential of using data and artificial intelligence in organisations. The synergies we have developed with our partners (Accenture, Accor, Dataiku, Engie, Eurofins, MSD, Orange) allowed us to evaluate and test innovative solutions before deploying them.

“With this initiative, we’re taking a major step: bringing together an engaged ecosystem to sustainably transform how leaders think, decide, and act in the face of tomorrow’s challenges. Our ambition is clear: to make AI a lever for performance, innovation and responsibility for […] leaders.”

Managing Director at Accenture and sponsor of the ESSEC/Accenture Chair and initiative, Aurélien Bouriot, said, “The ecosystem will benefit from the resources that Accenture puts at its disposal, and will also benefit our employees who participate.”

Laetitia Cailleteau, Managing Director at Accenture and leader of Responsible AI & Generative AI for Europe, highlighted the importance of future leaders understanding all aspects of AI.

“AI is a pillar of the ongoing industrial transformation. Tomorrow’s leaders must understand the technical, ethical, and human aspects and risks – and know how to manage them. In this way, they will be able to maximise value creation and generate a positive impact for the organisation, its stakeholders and society as a whole.”

Image credit: Wikimedia Commons

See also: Microsoft and OpenAI probe alleged data theft by DeepSeek

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.

[lifescienceintellipedia]

Data Analytics in Marketing Research : Transforming Insights into Strategy

In today’s digital economy, data is the backbone of every successful marketing strategy. With the explosion of data sources and analytics tools, marketing research has evolved beyond traditional surveys and focus groups. Data analytics in marketing research is now essential for gaining accurate insights, predicting customer behavior, and staying competitive in the global market research industry.

[xlsdesignt]

what u think, to much colour, or less?

https://sdesignt.threadless.com/