Data analytics can immensely impact and improve a business’s decision-making processes. From better strategies to profits, explore the full scope of analytics.
Mohanbir Sawhney, Robert R. McCormick Tribune Foundation Clinical Professor of Technology Kellogg School of Management, Northwestern University presents at the 2012 Big Analytics Roadshow.
Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage.... read more: http://www.biganalytics2012.com/sessions.html#mohan_sawhney
how to successfully implement a data analytics solution.pdfbasilmph
The adoption of data analytics in business has demonstrated a transformative power in modern entrepreneurship. By analyzing vast reservoirs of data, businesses can make informed decisions, optimize operations and predict trends, thus fueling growth.
Big Data is Here for Financial Services White PaperExperian
Conquering Big Data Challenges
Financial institutions have invested in Big Data for many years, and new advances in technology infrastructure have opened the door for leveraging data in ways that can make an even greater impact on your business.
Learn how Big Data challenges are easier to overcome and how to find opportunities in your existing data and scale for the future.
While the interests in analytics and resulting benefits are increasing by the day, some businesses are challenged by the complexity and confusion that analytics can generate.
Companies can get stuck trying to analyze all that’s possible and all that they could do through analytics, when they should be taking that next step of recognizing what’s important and what they should be doing — for their customers, stakeholders, and employees.
Discovering real business opportunities and achieving desired outcomes can be elusive.
Data Analytics has become a powerful tool to drive corporates and businesses. check out this 6 Reasons to Use Data Analytics. Visit: https://www.raybiztech.com/blog/data-analytics/6-reasons-to-use-data-analytics
Mohanbir Sawhney, Robert R. McCormick Tribune Foundation Clinical Professor of Technology Kellogg School of Management, Northwestern University presents at the 2012 Big Analytics Roadshow.
Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage.... read more: http://www.biganalytics2012.com/sessions.html#mohan_sawhney
how to successfully implement a data analytics solution.pdfbasilmph
The adoption of data analytics in business has demonstrated a transformative power in modern entrepreneurship. By analyzing vast reservoirs of data, businesses can make informed decisions, optimize operations and predict trends, thus fueling growth.
Big Data is Here for Financial Services White PaperExperian
Conquering Big Data Challenges
Financial institutions have invested in Big Data for many years, and new advances in technology infrastructure have opened the door for leveraging data in ways that can make an even greater impact on your business.
Learn how Big Data challenges are easier to overcome and how to find opportunities in your existing data and scale for the future.
While the interests in analytics and resulting benefits are increasing by the day, some businesses are challenged by the complexity and confusion that analytics can generate.
Companies can get stuck trying to analyze all that’s possible and all that they could do through analytics, when they should be taking that next step of recognizing what’s important and what they should be doing — for their customers, stakeholders, and employees.
Discovering real business opportunities and achieving desired outcomes can be elusive.
Data Analytics has become a powerful tool to drive corporates and businesses. check out this 6 Reasons to Use Data Analytics. Visit: https://www.raybiztech.com/blog/data-analytics/6-reasons-to-use-data-analytics
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATAijseajournal
Data analytics and Business Intelligence (BI) are essential components of decision support technologies that gather and analyze data for faster and better strategic and operational decision making in an organization. Data analytics emphasizes on algorithms to control the relationship between data offering insights. The major difference between BI and analytics is that analytics has predictive competence which helps in making future predictions whereas Business Intelligence helps in informed decision-making built on the analysis of past data. Business Intelligence solutions are among the most valued data management tools whose main objective is to enable interactive access to real-time data, manipulation of data and provide business organizations with appropriate analysis. Business Intelligence solutions leverage software and services to collect and transform raw data into useful information that enable more informed and quality business decisions regarding customers, market competitors, internal operations and so on. Data needs to be integrated from disparate sources in order to derive valuable insights. Extract-Transform-Load (ETL), which are traditionally employed by organizations help in extracting data from different sources, transforming and aggregating and finally loading large volume of data into warehouses. Recently Data virtualization has been used to speed up the data integration process. Data virtualization and ETL often serve unique and complementary purposes in performing complex, multi-pass data transformation and cleansing operations, and bulk loading the data into a target data store. In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.
Business intelligence (BI) services provide companies with the tools and expertise they need to collect and analyze data, turning it into actionable insights that can drive better decision-making. Tarams’ team of experts works closely with clients to understand their specific needs and develop tailored solutions that meet their unique requirements. With a commitment to excellence, Tarams is dedicated to delivering the highest quality Business Intelligence services to its clients.
Cognitive Solutions combine the power of mathematical algorithms and computing in collaboration with digital knowledge reasoning to enable intelligent insights and actions.
Semantic 'Radar' Steers Users to Insights in the Data LakeCognizant
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
Leverage Sage Business Intelligence for Your OrganizationRKLeSolutions
Learn how Sage Business Intelligence provides the insight you need to make better decisions faster! This informative presentation explores Sage Intelligence and Sage Enterprise Intelligence solutions for Sage 100, Sage 500 and Sage X3.
Data as a Service (DaaS): The What, Why, How, Who, and WhenRocketSource
Data as a Service (DaaS) is one of the most ambiguous offerings in the "as a service" family. Yet, in today's world, data and analytics are key to building a competitive advantage. We're clearing up the confusion around DaaS and helping your company understand when and how to tap into this service.
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
Few companies realize the full benefits of analytics initiatives to improve the customer experience. Here's a six-step guide for moving beyond operational reporting to enabling predictive insights.
This whitepaper aims to assist Chief Data Officers in promoting a data-driven culture at their
organization, helping them lead the enterprise on a digital transformation journey backed by
analytical insights.
Semantic 'Radar' Steers Users to Insights in the Data LakeThomas Kelly, PMP
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
BIG DATA has to be the hottest topic in the boardrooms of blue chip companies - organizations with access to vast amounts of data that promises to have a massive impact on their businesses... But if you're not Amazon, Google, Walmart and Tesco what does it mean to your business? What about MOTOR DEALERS for example?
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATAijseajournal
Data analytics and Business Intelligence (BI) are essential components of decision support technologies that gather and analyze data for faster and better strategic and operational decision making in an organization. Data analytics emphasizes on algorithms to control the relationship between data offering insights. The major difference between BI and analytics is that analytics has predictive competence which helps in making future predictions whereas Business Intelligence helps in informed decision-making built on the analysis of past data. Business Intelligence solutions are among the most valued data management tools whose main objective is to enable interactive access to real-time data, manipulation of data and provide business organizations with appropriate analysis. Business Intelligence solutions leverage software and services to collect and transform raw data into useful information that enable more informed and quality business decisions regarding customers, market competitors, internal operations and so on. Data needs to be integrated from disparate sources in order to derive valuable insights. Extract-Transform-Load (ETL), which are traditionally employed by organizations help in extracting data from different sources, transforming and aggregating and finally loading large volume of data into warehouses. Recently Data virtualization has been used to speed up the data integration process. Data virtualization and ETL often serve unique and complementary purposes in performing complex, multi-pass data transformation and cleansing operations, and bulk loading the data into a target data store. In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.
Business intelligence (BI) services provide companies with the tools and expertise they need to collect and analyze data, turning it into actionable insights that can drive better decision-making. Tarams’ team of experts works closely with clients to understand their specific needs and develop tailored solutions that meet their unique requirements. With a commitment to excellence, Tarams is dedicated to delivering the highest quality Business Intelligence services to its clients.
Cognitive Solutions combine the power of mathematical algorithms and computing in collaboration with digital knowledge reasoning to enable intelligent insights and actions.
Semantic 'Radar' Steers Users to Insights in the Data LakeCognizant
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
Leverage Sage Business Intelligence for Your OrganizationRKLeSolutions
Learn how Sage Business Intelligence provides the insight you need to make better decisions faster! This informative presentation explores Sage Intelligence and Sage Enterprise Intelligence solutions for Sage 100, Sage 500 and Sage X3.
Data as a Service (DaaS): The What, Why, How, Who, and WhenRocketSource
Data as a Service (DaaS) is one of the most ambiguous offerings in the "as a service" family. Yet, in today's world, data and analytics are key to building a competitive advantage. We're clearing up the confusion around DaaS and helping your company understand when and how to tap into this service.
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
Few companies realize the full benefits of analytics initiatives to improve the customer experience. Here's a six-step guide for moving beyond operational reporting to enabling predictive insights.
This whitepaper aims to assist Chief Data Officers in promoting a data-driven culture at their
organization, helping them lead the enterprise on a digital transformation journey backed by
analytical insights.
Semantic 'Radar' Steers Users to Insights in the Data LakeThomas Kelly, PMP
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
BIG DATA has to be the hottest topic in the boardrooms of blue chip companies - organizations with access to vast amounts of data that promises to have a massive impact on their businesses... But if you're not Amazon, Google, Walmart and Tesco what does it mean to your business? What about MOTOR DEALERS for example?
Future Trends in the Modern Data Stack LandscapeCiente
As we embrace the future, staying abreast of emerging technologies will be crucial for organizations seeking to harness the full potential of their data.
Exploring Different Funding and Investment Strategies for SaaS Growth.pdfCiente
In the competitive landscape of SaaS, securing adequate funding and implementing effective investment strategies are essential for driving growth, scalability, and long-term success.
Embracing autonomous testing is no longer merely an option but emerges as a strategic necessity for organizations committed to delivering superior software solutions within the dynamic contours of the contemporary tech landscape.
Securing Solutions Amid The Journey To Digital Transformation.pdfCiente
Innovation thrives on openness and accessibility, and security requires caution and control. Learn to navigate these challenges for successful digital transformation.
CRM Best Practices For Optimal Success In 2024.pdfCiente
CRM in 2024 is much more than just managing contacts. Read along to know how it is impacting businesses today and how to best implement it to achieve great success.
In this blog, we’ll delve into the importance of cybersecurity incident response planning and provide a guide for building a resilient response strategy.
PostHog is an open-source product analytics platform designed to help businesses understand user behavior on their websites or applications.
Read this Article here: https://medium.com/@ciente/what-is-posthog-and-its-pros-and-cons-05d8dff13194
Learn more: https://ciente.io/blog/
Explore more: https://ciente.io/
Top Technology Trends Businesses Should Invest In This Year.pdfCiente
As we enter 2024, it brings to light a platform ready for more innovation and progress.
Read this Article here: https://ciente.io/blogs/top-technology-trends-businesses-should-invest-in-2024/
Learn more: https://ciente.io/blog/
Explore more: https://ciente.io/
In the fast-paced realm of software development, the integration of security measures is paramount to safeguarding applications and data against an ever-expanding landscape of cyber threats.
Exploring the Applications of GenAI in Supply Chain Management.pdfCiente
Stay ahead of the curve with GenAI's capacity to learn, adapt, and generate insights, revolutionizing traditional supply chain processes for enhanced efficiency and innovation.
Benefits of implementing CI & CD for Machine LearningCiente
Implementing CI & CD in Machine Learning is a strategic move toward optimizing development workflows, enhancing collaboration, and accelerating the deployment of robust and reliable ML models
7 Elements for a Successful Hybrid Cloud Migration Strategy.pdfCiente
The world of IT infrastructure is evolving rapidly, and businesses are increasingly turning to hybrid cloud solutions to strike the perfect balance between on-premises and cloud-based environments.
Read this Article here: https://medium.com/@ciente/7-elements-for-a-successful-hybrid-cloud-migration-strategy-0b2a9dfbff85
Learn more: https://ciente.io/blog/
Follow for more Articles here: https://ciente.io/
In this blog post, we will explore what Ethical Technology is, why it is important, the benefits it brings, and its potential role in shaping our future.
Top Social Selling Tools For Your Business In 2024.pdfCiente
Brands tap into Gen-Z’s world by leveraging social media. But it’s the social selling tools that transform this digital engagement into real-world revenue.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
Data Analytics And Business Decision.pdf
1. Data Analytics And Business Decision-Making
Have you ever been in a meeting, witnessing crucial choices being based purely on gut feelings? While
there’s merit in intuition, today’s intricate business ecosystem demands more. Across the globe,
companies are wondering, “How does data analysis integrate into our decision-making process?”
Grasping and utilizing the intricacies of informed “decision-making in business management” through
data analytics might very well distinguish thriving enterprises from those struggling to keep up.
According to a study conducted by Deloitte, nearly half of the participants, tallying up to 49%,
expressed the belief that analytical tools enhanced their decision-making prowess. In this article, we
will explore the role of data analytics for today’s businesses and how analytics can help businesses
make better decisions.
The Data Revolution in Today’s Business Landscape
Think of data analytics as the heartbeat of modern business intelligence. It’s analogous to piecing
together a jigsaw. Individual data fragments might seem inconsequential, but combined, they weave
a narrative, spotlighting customer behaviors, driving informed decisions, and occasionally, hinting at
future trends. It’s not merely about crunching figures; it’s deciphering the tales they narrate.
2. The Genesis of Big Data:
Wondering where this avalanche of information originates from? That’s the realm of “big data”.
There was a time when data analytics was a niche domain, mostly navigated by tech giants and
affluent enterprises. It was a feather in a company’s cap but not foundational. Times have evolved.
With tech democratization and intensifying market rivalry, data interpretation has transitioned from
being a luxury to a cornerstone for businesses of every scale.
According to recent reports, over the next few years, the big data market is anticipated to experience
substantial growth, with projections of over 650 billion dollars by 2029 from 240 billion dollars in
2021.
Our everyday activities, from online shopping sprees to casual internet surfing, began churning out
data trails. However, the sheer volume of data causes an issue. The existing toolkits at the time were
ineffective and could only either process a small amount of data at a time or take a very long time to
do so, making the process lengthy and inefficient. This predicament spurred innovation, turning this
3. data deluge from an impediment into a goldmine of possibilities. From understanding consumer
behaviors to anticipating industry shifts, the use of data analytics has started shaping strategies and
decisions.
Analytical Tools and Techniques
The top analytical tools that stand out are platforms such as Tableau, Power BI, and SAS. Let’s look
closely at what they do:
• Tableau: Emerging at the forefront of visual data interpretation, Tableau offers user-
friendly dashboards that enable companies to sift through and comprehend their data
reservoirs. Its easy-to-grasp interface democratizes data understanding, making it
accessible to a wider audience. By amalgamating data from diverse sources and forming
intricate visual representations, it offers firms a detailed peek into their operations and
consumer landscapes.
• Power BI: As Microsoft’s contribution to the analytics domain, Power BI emphasizes real-
time data processing. Leveraging its cloud-centric infrastructure, corporations can
oversee their functions via live panels, craft extensive reports, and distribute key
findings throughout their network effortlessly. Its tight-knit integration with other
offerings from Microsoft, notably Excel, ensures that data remains interconnected
across tools.
• SAS: An anchor in the realm of evolved analytics, SAS presents a suite of functionalities
for data orchestration, visual interpretation, and insights powered by artificial
intelligence. Rooted in statistical methodologies, it equips corporations with models that
forecast future trends, allowing them to not merely respond to market shifts but also to
shape them actively.
4. The Role of a Business Intelligence Analyst
Not only tools, but specific roles need to be assigned in organizations to be able to navigate data
analytics into specific strategic decisions. This is mediated by a critical entity, known as the “business
intelligence analyst.” These individuals function as the conduit between unprocessed data and tactical
business maneuvers. Their skill resides in deconstructing complex data assemblies, ensuring the
insights extracted are in concordance with the company’s aspirations. They customize the analytical
tool’s functionalities to meet their organization’s specific needs, certifying the maximum utility of
every data unit.
The Integration of Data Analysis in Business Domains
• E-commerce: Virtual retail entities are increasingly dependent on analytical strategies to
individualize consumer interactions. Scrutinizing variables such as navigational habits,
historical acquisitions, and consumer inquiries allows these entities to engineer bespoke
product suggestions, enhance digital storefront configurations, and forecast forthcoming
consumer predilections.
• Virtual Healthcare Services: The increasing traction of web-based health consultation
platforms underscores the imperative for comprehensive studies. Utilizing data analytics
can assist these platforms in monitoring aspects such as user interactions, drug
consumption patterns, and subsequent responses. The chief objective of this initiative is
to elevate the caliber of medical attention given to individuals in digital domains,
harmonizing appointment mechanisms, and refining the user experience.
• Fintech: Digital finance portals harness analytical procedures to fortify investment
advisories, identify duplicitous endeavors, and craft tailored fiscal instruments. Through
a detailed inspection of expenditure trends, market oscillations, and client feedback,
these platforms can fine-tune their service repertoire and bolster protective measures.
5. • SaaS Enterprises: Businesses in the Software as a Service sphere capitalize on data
scrutiny to gauge client interaction with their digital offerings, pinpoint high-value
functionalities, and recognize potential enhancements. Analyzing utilization trends,
iterative feedback, and engagement indicators is pivotal for cyclical product
optimization.
Advantages of Data Analysis
• Customized Interaction Paradigms: Analytical insights afford digital businesses the luxury
of deciphering distinctive user predilections, and facilitating the customization of
content, commodities, or assistance correspondingly.
• Proactive Analytical Forecasting: By foreseeing client necessities, market realignments,
and nascent inclinations, digital enterprises can perpetually maintain a vanguard stance
in their propositions.
• User Engagement Amplification: Comprehending the catalysts behind user captivation
enables platforms to hone their substance and structural design, fostering maximal user
allegiance.
• Marketing Endeavor Refinement: Initiatives grounded in data-derived cognizance assure
that promotional activities are concentrated, germane, and yield superior investment
returns.
• Threat Containment Protocols: In the virtual arena, perils such as cyber intrusions or
deceptive acts are more predictably neutralized through astute data analytical practices.
• Informed Digital Product Evolution: Continuous product and service refinement in the
digital space leverages instantaneous feedback and behavioral analytics from users.
6. • Conversion Optimization Strategies: Discerning and reacting to behavioral archetypes
enables digital establishments to fortify their consumer procurement funnel, driving
enhanced transactional outcomes.
Data Management: Essential Foundation for Analytics
Within the intricate sphere of data analytics, a fundamental truth persists: without rigorous “data
management,” even the most cutting-edge analytics can guide more towards confusion than clarity.
The significance of the way data is assembled, stored, and retrieved is analogous to the importance of
a robust foundation for a high rise. If the base is compromised, the entire structure, regardless of its
aesthetics, is susceptible.
The realm of data management is both broad and complex. Here’s a closer look at its key elements:
• Storage: It’s not merely about allocating space for accumulating extensive data. It
involves guaranteeing that the storage facility is capable of expansion, durability, and
security. As enterprises expand, they generate more data. Revolutionary solutions like
Amazon S3 or Google Cloud Storage provide businesses with the ability to scale without
investing in substantial physical data warehouses.
• Retrieval: Efficient data management transcends storing information; it encompasses
the ability to extract it promptly and effectively when necessary. Tools like MySQL and
MongoDB have changed the landscape of data access, offering structured approaches
that enhance speed and efficiency. The pace at which data is accessed can directly
influence the agility of decision-making processes.
• Cleaning: Unprocessed data is frequently disorganized and cluttered. It could contain
redundancies, contradictions, or outright errors. Rigorous data cleaning guarantees the
dependability of data. Various manual or automated strategies are employed to sift
through datasets, ensuring uniformity and accuracy. This stage, although demanding, is
7. pivotal for the success of subsequent analytical processes, as analytical algorithms are
only as efficient as the data they process.
Data Management & Decision-Making:
The intertwined nature of data management and decision-making is unmistakable. Consider a
scenario where a decision-maker, perhaps a CEO, bases a strategic decision on analytics derived from
flawed data. The outcome, regardless of the decision’s rationale, could misdirect the organization.
This underscores the necessity for robust data management protocols. The “analysis of data” is
heavily dependent on its quality, upheld through comprehensive data management practices.
Challenges and Considerations in Data Analytics
Challenges
1. Concerns Over Data Integrity:
• Applicability: Filtering the data ocean to pinpoint germane pieces is a task fraught with
challenges.
• Precision: Inaccuracies in datasets can spin a web of misinformed inferences. Hence, it’s
paramount to establish stringent vetting mechanisms.
• Currentness: Decisions based on stale information can deviate from the real-time pulse
of the marketplace.
1. Decoding Data Rightly:
• Evading Biased Views: A lurking challenge is the tendency to perceive data through the
lens of preconceived notions.
8. • Balancing Tech with Touch: While technology streamlines the analytics, it’s vital to
ensure the human touch remains, capturing the subtleties that machines might miss.
Considerations
1. Navigating Ethical Landscapes:
• Openness in Gathering: Businesses ought to elucidate their methodologies and intents
behind data accumulation.
• Safeguarding Information: With cyber threats on the rise, fortifying data reservoirs
against potential breaches becomes indispensable.
• Respecting Individual Spaces: It’s a cardinal duty to cherish and uphold the privacy
sanctum of individuals, especially when their personal details are at stake.
1. Being Aware of Changes:
• Persistent Evolution: Data insights exist in a dynamic ecosystem where ongoing
development is required to keep up.
• Adapting to Tool Progressions: Modern data analysis methods are a necessity for any
successful organization.
• Upholding Gold Standards: Data must be managed in the most effective, efficient, and
well-informed way possible, thus staying up-to-date on best practices is essential.
9. Conclusion
In the nexus of business and advancing technology, it’s unmistakable that contemporary trailblazers
capitalize on the robustness of analytical insights derived from extensive data. Success now hinges on
extracting nuanced intelligence from data rather than simple intuitive judgment, propelling
actionable, strategic frameworks.
Nonetheless, this path comes with its unique set of trials encompassing the maintenance of data
integrity, navigating the intricacies of confidentiality, and adapting to the continuous evolution within
the analytical instrumentation.
The shift towards a paradigm rooted in data transcends operational change; it’s a core strategic
metamorphosis influencing a company’s competitive trajectory and resilience. Fundamentally,
neglecting the critical pivot toward a data-anchored methodology doesn’t merely represent an
oversight — it risks undermining the enterprise’s enduring relevance and prosperity.
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