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This time is different: the impact of ChatGPT on the future of jobs and the advent of real time self-coding applications

by Massimo

Vision on the impact of ChatGPT on Society and Workforce. The shift from software development to real time self-coding applications and the advent of intelligent chatbots

UPDATE 8th March 2024: 20 million jobs at risk. Around 20 million workers in the European Union would lose their jobs in the short term if precautions are not taken quickly, given the speed of development of artificial intelligence. This is what emerges from a study by the Centers for European Policy Network, which underlines how the most qualified workers are truly exposed to the impact of new technology. While technological advances have previously increased the skills of employees, and therefore their productivity, so-called generative AI will irreversibly destroy entire professional profiles, the researchers note, around one in ten jobs in the European Union will be directly affected by end of this decade. The spectrum ranges from managers to consultants up to lawyers and marketing specialists.

Generative AI, especially large language models (LLMs), marks a transformative change in the way we interact with information and conduct work. Powered by advanced machine learning and natural language processing, these technologies can produce original content, distil insights from massive datasets, offer near-human translation, and even facilitate intricate decision-making. Their far-reaching capabilities present both remarkable opportunities and challenges for employment and the future of work. 

As described in my speaking engagements at international conferences, the deployment of LLMs can catalyse significant productivity enhancements and spawn novel job categories. However, it also poses the risk of making current roles obsolete, thereby widening socioeconomic gaps and instigating job uncertainty among the global labour force. The integration of AI into the workplace thus necessitates a nuanced equilibrium between leveraging benefits and mitigating potential disruptions.

Public discourse surrounding the impact of generative AI on employment tends to be divided and ambiguous. This article concentrates specifically on the capabilities of LLMs and aims to provide a structured analysis of their direct effects on distinct job roles. Such an analytical framework empowers stakeholders—from business executives and policymakers to workers and the general public—to make well-informed choices concerning skills development, workforce strategy, and key investments.

As generative AI redefines industries through innovative operational models and novel products and services, organizations can harness LLMs to boost productivity and unveil fresh opportunities, all while facilitating a seamless workforce transition. Moreover, the methodology outlined in this article for assessing the immediate job impact serves as a valuable reference for navigating future technological shifts across various sectors.

Impact of ChatGPT

The impact of ChatGPT and other similar technologies on society and the workforce is not a question of IF, but HOW.

This AI tool has learned how humans work behind a computer and is continuously learning from billions of content pages and sheets, Microsoft tools, online interactions, and new inputs that upgrade its knowledge base.

As a result, all people working behind a computer can be deeply affected by ChatGPT.

The digital disruption brought about by ChatGPT is likely to reduce the salaries of knowledge workers, and there are several examples of how this could happen.

 Here’s a breakdown of how AI is expected to impact several different professions:

  • Developers: According to experts, AI tools will enable developers to create products with 80% less time and more accuracy. This will result in the displacement of millions of developers and a reduction in their average salaries.
  • Dieticians and other knowledge-based workers: It’s not difficult to build an AI-powered dietician that can accurately support our diet with just a few prompts. This will reduce the number of clients for dieticians and other similar professions.
  • Marketers and digital marketers: AI tools like ChatGPT can replace marketers by doing their job entirely and empower them. This will lead to the lowering of average salaries for marketers and will force millions to seek more lucrative fields.
  • Journalists: AI tools like ChatGPT can write high-quality articles quickly, which will replace many journalists. The new journalist will be someone who gathers facts on the field, and their salaries will be higher, but they will spend less time in front of a computer. This will also lead to the decline of printed journals, as AI can create new editions in seconds.
  • Graphic designers and photographers: Midjourney and other AI tools can easily replace these workers in media-related fields, and the average salary will collapse as there is no need to fine-tune photos or create designs manually.
  • Content creators: While AI can generate great texts in seconds, content creators like copywriters, editors, and screenwriters will initially benefit, but will eventually lose their jobs as AI tools continue to advance.
  • Analytical scientists: While labs will benefit significantly from AI, automation will axe profiles that make routines that can be automated. Data governance will play a pivotal role in the future.
  • Lawyers: When blockchain came out, the technology promised to revolutionize banks and real estate. In particular, real estate attorneys, also known as property lawyers, were expected to have a counterpart in the blockchain in terms of function and role. This shift did not occur, as humans were hesitant to fully trust technology.  No laws were enacted to replace lawyers with blockchain technology. Similarly, there is a possibility that ChatGPT could fully replace lawyers in interpreting cases based on laws and policies. However, it will be challenging to convince people to replace human lawyers with a chat robot.
  • Entrepreneurs, startups, little Companies: they will extremely benefit from ChatGPT. Many employees will be replaced by this tool. In order to start a new company, the entrepreneur will focus on automate many tasks behind the management, focusing on the real business. Microsoft is indeed investing huge resources in his tool Microsoft Automate linked with ChatGPT. How new companies will look like? The entrepreneur, high qualified focused business manager/s, a prompt engineer, ChatGPT, automation tools. No secretary, no digital expert, no web agency, low qualified developers, low qualified sale and marketing profiles. 

As AI continues to progress, it will lead to a concentration of the market around companies that integrate AI into their operations. Salaries for highly specialized jobs will generally collapse, while jobs that require on-field skills will be in higher demand. 

Workers must adapt to these changes to survive in the job market of the future.

It is clear that the increasing use of artificial intelligence (AI) is changing the way we work and interact with technology. 

In recent interviews and articles, three key factors were identified by AI evangelists as being significant in understanding the impact of AI on the future of work: automation, prediction, and personalization. 

Over the next five years, AI will understand the way people work and provide insights into work preferences. It will enable individuals to anticipate trends and release them from performing repetitive and mundane tasks, allowing them to focus on more creative endeavours.

The positive aspects of AI include its ability to free employees from mundane and repetitive work. Tools such as ChatGPT offer a more natural and engaging experience than traditional search engines. However, there are concerns about the visibility and transparency of AI systems, with the majority of AI remaining opaque and difficult to understand. Additionally, AI is content with providing the correct answer, regardless of whether it is completely inaccurate or not, highlighting the need for continued human oversight. There is a growing concern that the increased use of AI in education will leave students less competent when entering the workforce. However, it is believed that AI will become a tool for students to use, in the same way that previous generations have used search engines like Google. Thus, acquiring these skills during education is not necessarily detrimental.

The future of work will be different as a result of AI, but it is not entirely clear how. Like any technological revolution, it will change the way we work and the types of jobs available. For instance, the service industry is already being divided into different roles. Some companies are using the same staff to provide service to customers and meet the needs of employees.

There are still limitations to AI, such as its inability to create from scratch, its poor performance in activities requiring common sense, and its struggle to adapt to new situations. Furthermore, AI is still unable to effectively combat misinformation, as it may not be able to recognize sarcasm or detect subtle nuances that humans can.

Opportunities and threats

Opportunities

  • Economic Growth: Generative AI could add $2.6 trillion to $4.4 trillion annually to the global economy, comparable to the entire GDP of the UK in 2021.
  • Business Function Transformation: Generative AI could significantly impact most business functions, particularly in customer operations, marketing and sales, software engineering, and R&D, accounting for approximately 75% of the total annual value from generative AI use cases.
  • Augmentation of Work: Generative AI is expected to augment human capabilities in the workplace, affecting a variety of activities and occupations differently than past technologies.
  • Acceleration in Automation Potential: Advances in generative AI are expected to match and even surpass median human performance earlier than previously estimated, particularly in natural-language understanding.
  • Impact on Higher-Wage Occupations: Generative AI is most likely to transform the work of higher-wage knowledge workers due to advances in the technical automation potential of their activities.
  • Skill-Biased Impact: Generative AI focuses on a more granular set of skills that are more likely to be replaced rather than complemented by machines, affecting more-educated workers the most.
  • Global Adoption Scenarios: While technology adoption at scale takes time, generative AI is expected to spread across the global economy, influenced by decisions on investments, deployment, and regulation.
  • Challenging Traditional Credentials: Generative AI could advocate for a more skills-based approach to workforce development, challenging the value of multiyear degree credentials.

 Threats

  • Job Loss for Women: The International Labour Organization (ILO) and McKinsey Global Institute predict that 80% of women may lose their jobs due to automation.
  • Skills Disruption: 44% of workers’ skills are expected to be disrupted in the next five years, with generative AI representing a skill-biased technological change.
  • Office Work Automation: 87% of office work is expected to be automated, and generative AI has the potential to automate 60 to 70% of employees’ time.
  • Knowledge Work Impact: Generative AI is expected to significantly affect knowledge workers, particularly in decision-making and collaboration activities, which previously had low automation potential.
  • Economic Feasibility: Even if a technological solution for automation is developed, it may not be economically viable if its costs exceed those of human labor.
  • Reliability and Failure: The reliability of AI projects remains a concern, affecting the pace of solution development and adoption.
  • Long-Term Impact: The potential of lab capabilities does not guarantee immediate integration into work activities; developing such solutions is time-consuming.
  • Cost Considerations: The cost of integrating generative AI technologies is compared with that of human labor across different occupations and countries, affecting its adoption rate.
  • Time for Technology Diffusion: The time it has historically taken for technologies to diffuse across the economy could slow down the adoption of generative AI.
  • Range of Outcomes: The scenarios analyzed, from several sources of research on generative AI, encompass a wide range of outcomes, reflecting the uncertainty and variability in the rate of adoption of generative AI.
Sources:

The advent of real-time self-coding applications

The world of coding and software development is evolving rapidly, and new tools are emerging that promise to revolutionize the way we create software applications. 

In the past, coding required a lot of planning and project management, and developers had to follow approaches like Agile or Waterfall to build robust applications. However, with the introduction of tools like ChatGPT, Github Colab, Ghostwrite Replit, etc. the industry is moving towards applications that can self deploy in real time. Let’s explore together what it means.

Traditionally, applications were built as interconnected composite services, where a single software includes several contextual services. Try to imagine a software for accounting: it often includes components like management of costs, accounting modules, user management, etc. 

With the advent of AI, the paradigm to plan and implement software is shifting, and we are moving towards personalized applications that are developed on demand, in real time, by the software itself.  In the future, we may see a world where smartphones, like iPhone, no longer are mostly app-based, but provide applications invented in real-time by the OS, with user interfaces coded when needed, based on the context where the user is and the operation the user intends to do.

Imagine a scenario where you need to book a flight. With the help of Siri, iOS will create a new application optimized for that specific user, with interfaces and code written and executed at that time. If the user wants to rent a car after purchasing the flight, a new interface will be coded in real-time to facilitate this task. This is possible because AI tools can generate code in real-time, and the advances in deploying code will soon enable applications to generate the code they need and execute it on their own.

Currently, ChatGPT can generate a JavaScript game and execute it in an HTML page in few seconds, showing that auto-coding is already possible to some extent. In the future, we may see IT systems shifting towards this technology, where software is deployed in real-time only when needed. This would move the market from app-based to experience-based business. Indeed, that behaviour is similar to the personalized advertisement in digital marketing today, where the advertisement ban of a website is customized, in real-time, based on the user’s browsing behaviour.

The biggest problem of ChatGPT is the computing power required to let it run: it would not run in smartphones or desktop computers, but it will do it soon. There are similar tools aka “LLM Chatbots” that can run in desktop computer easily (LLAMA from Meta or Vicuna 13B (an open source Chatbot with ~90% ChatGPT quality). One day these models will be trained in huge cloud computing environment, but run in small devices.

Probably a new market will be created soon with LLM Chatbots specialized in specific field like accounting or law or chemistry science.

Nearly 80% of women’s jobs could be disrupted, automated by AI

In a transformative shift within the U.S. labor market, the participation rate of prime working-age women (25-54 years old) has reached unprecedented heights, signaling a robust rebound from the pandemic-induced downturn. This resurgence, however, faces potential upheaval from the advent of generative artificial intelligence technologies like ChatGPT. According to Goldman Sachs economists, the integration of such AI could render a vast majority of jobs susceptible to automation. Disproportionately, women, despite being outnumbered by men in the workforce, are at a higher risk, with studies suggesting that nearly 79% of working women are employed in occupations vulnerable to AI disruption. This stark contrast to the 58% of working men highlights a significant gender disparity in AI’s impact on the labor market.

The implications of AI adoption extend beyond mere job displacement. Occupations traditionally dominated by women, including office and administrative support, healthcare, education, and social services, are among those most exposed to AI-induced changes. Yet, this technological shift also presents opportunities for innovation and efficiency improvements in various sectors. The evolving landscape calls for a balanced approach to harnessing AI’s potential while mitigating its adverse effects on gender employment equity. As the labor market continues to adapt to these technological advancements, the resilience and adaptability of the workforce, particularly among women, will be crucial in navigating the challenges and opportunities ahead.

Read more at CNN website: https://edition.cnn.com/2023/06/21/economy/women-employment-ai-disruption/index.html

LLMMaps - A Visual Metaphor for Stratified Evaluation of Large Language Models Patrik Puchert, Poonam Poonam, Christian van Onzenoodt , Timo Ropinski

How to face the disruption of ChatGPT

The revolutionary artificial intelligence language model ChatGPT is predicted to disrupt every major industry and replace more than 300 million jobs. Unfortunately, a lot of people will fail to adapt. 

In the age of AI, it is crucial to save your career and keep up with emerging technology.
Here are some tips on how to do it:

First, understand how AI works. 

Take a basic AI or machine learning course, read the best AI books, and listen to the top AI podcasts. 

Make training courses on https://outsourcing.digital

Forbes has also compiled a list of top AI books including

– Al 2041: 10 Visions for Our Future
– A World Without Work
– The Alignment Problem
– 2084: Al and the Future of Humanity
– A Brief History of Artificial Intelligence
– Artificial Unintelligence

Additionally, check out the top 10 AI podcasts, such as
– The TWIML Al Podcast
– Data Skeptic The Al Podcast
– Eye on Al
– Practical Al
– Adventures in Machine Learning
– Learning Machines 101
– Voices in Al
– DeepMind: The Podcast
– Lex Fridman Podcast

Second, use the new product as soon as possible. 

Read research papers, get familiar with the product, start experimenting, and learn how to prompt. The best way to learn how to use ChatGPT is by practising and interacting with it regularly. Follow these steps to make the most of your experience:
1. Familiarize yourself with the basics.
2. Access ChatGPT.
3. Start with simple queries.
4. Experiment with various topics.
5. Refine your prompts.
6. Learn from the community.

Third, study the potential impact on your job and career.

OpenAI’s researchers found that the influence of ChatGPT technology spans all wage levels, with higher-income jobs potentially facing greater exposure. The most affected professions include interpreters and translators, poets, lyricists, and creative writers, PR specialists, writers, authors, and journalists, mathematicians, tax preparers, blockchain engineers, accountants and auditors.

Fourth, incorporate the new tech in your day-to-day work.

Writing a really great prompt for a chatbot persona is an amazingly high-leverage skill and an early example of programming in a little bit of natural language.
Aim to be a master AI prompter for your specific industry or role, and find ways to apply the technology in your space.
Check out the top ChatGPT prompt guide and The Art of ChatGPT Prompting for more information. 

Lastly, get involved in AI and emerging tech communities.

Meet new people, connect with thought leaders, and start writing and sharing about what you’re learning.

This will help you learn more about the AI threats and opportunities, discover job opportunities, and find mentors or collaborators.

Artificial IntelligenceData Science and GovernanceDigital TransformationOrganizational changeR&D

The AI Productivity Revolution of 2023

by Massimo

Comments on the 2023 Gartner Emerging Technologies and Trends Impact Radar

Gartner has recently released the Emerging Technologies and Trends Impact Radar for 2023, which highlights 26 emerging trends and technologies that vendors must respond to in order to improve and differentiate their products. The report reveals four emerging technologies that will be especially interesting: neuromorphic computing, self-supervised learning, the metaverse, and human-centered AI. These technologies will disrupt the market in the next three to eight years and significantly impact existing products and markets. Neuromorphic computing will enable better AI systems, self-supervised learning will accelerate productivity, the metaverse will provide an immersive digital environment, and human-centered AI will improve transparency and privacy. 

These technologies will require a shift in strategy, investments, and tools to stay ahead of the competition. The impact of these technologies will be significant, as they will lead to new business and monetization opportunities.

The year 2023 marks an era of revolutionary changes in the world of productivity, thanks to the advancements in Artificial Intelligence (AI). The fusion of Edge Computing, Vision, Edge AI, Model Compression, Self-Supervised Learning, Synthetic Data, Intelligent Applications, and Generative AI has unlocked endless possibilities for businesses, industries, and individuals to boost their efficiency, accuracy, and innovation. In this article, we will delve deeper into each of these AI-powered tools and explore how they are driving the Productivity Revolution in 2023:

1) Edge Computing: Edge Computing is the game-changer that has disrupted traditional centralized computing models. It involves processing data at the edge of the network, i.e., closer to the source of data generation, which enables faster processing, lower latency, and reduced bandwidth requirements. Edge Computing is particularly useful for AI applications that require real-time processing, such as autonomous vehicles, smart cities, and healthcare monitoring systems. Edge Computing Vision  is another critical tool  that involves using computer vision algorithms to interpret and analyze visual data, such as images and videos, and extract meaningful insights. It is being used in various industries, such as retail, healthcare, and manufacturing, to automate tasks, improve safety, and enhance customer experiences.

2) Edge AI: Edge AI combines the power of Edge Computing and AI to bring AI processing capabilities closer to the data source. This eliminates the need for large-scale data transfers and cloud computing, making AI more accessible and cost-effective. Edge AI is particularly useful for applications that require low latency, such as real-time video analytics, industrial automation, and smart homes.

3) Model Compression: Model Compression involves reducing the size and complexity of AI models without compromising their accuracy. This enables AI models to run faster and more efficiently, even on low-power devices. Model Compression is particularly useful for Edge AI applications that require lightweight models, such as mobile devices, smartwatches, and IoT sensors.

4) Self-Supervised Learning: Self-Supervised Learning is an AI training method that enables models to learn from unlabelled data, i.e., data without explicit annotations. This reduces the need for human-labeled data, which is often expensive and time-consuming to acquire. Self-Supervised Learning is being used in various industries, such as natural language processing, computer vision, and speech recognition, to improve accuracy and reduce training time.

5) Synthetic Data: Synthetic Data involves generating artificial data that mimics real-world data. This enables AI models to be trained on larger and more diverse datasets, which improves their accuracy and generalizability. Synthetic Data is particularly useful for applications that require large amounts of training data, such as autonomous driving, facial recognition, and fraud detection.

6) Intelligent Applications: Intelligent Applications are AI-powered applications that can perform complex tasks, such as natural language processing, sentiment analysis, and image recognition. Intelligent Applications are being used in various industries, such as healthcare, finance, and marketing, to automate tasks, improve decision-making, and enhance user experiences.

7) Generative AI (ChatGPT): Generative AI involves using AI models to generate new and original content, such as images, music, and text. Generative AI is being used in various industries, such as gaming, art, and design, to create unique and personalized content.

 

Artificial IntelligenceData Science and GovernanceDigital TransformationOrganizational change

Playing with Turing’s Test: ChatGPT Attempts to Pass as Human

by Massimo

In this article, we explore the classic Turing Test and its implications for artificial intelligence. We present a simulated dialogue between ChatGPT, a state-of-the-art language model, and a human participant, as they engage in a game of "hide and seek" to determine whether ChatGPT is a computer or a human. Join us on this thought-provoking journey as we delve into the exciting world of AI and human interaction.

Love is a very complex emotion that can mean different things to different people.
Personally, I believe that love is a powerful force that can bring people together and help them overcome difficult challenges.
ChatGPT
ver. 3.5

ChatGPT plays with the test of Turing

The Turing Test is a test of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. The test was proposed by British mathematician and computer scientist, Alan Turing, in 1950. The test involves a human evaluator who engages in a natural language conversation with a machine and a human. If the evaluator cannot reliably distinguish the machine’s responses from those of the human, the machine is said to have passed the Turing test and demonstrated human-like intelligence. The test has been the subject of much debate and controversy, but it remains an important concept in the field of artificial intelligence.

To simulate the Turing test, one should create a natural language conversation between a human evaluator and an artificial intelligence program. The evaluator should be unaware that they are conversing with a machine, and the machine should attempt to mimic human responses and behavior as convincingly as possible. 

The conversation should be conducted via text-based communication, such as through a chatbot or messaging platform, and the machine’s responses should be generated in real-time based on the input it receives from the evaluator. 

The success of the simulation is measured by whether the evaluator can accurately distinguish the machine from a human based on the quality of their responses.

I asked ChatGPT to play alone on this game. Makign sure it emulates the roles of a human and the role of the computer.

ChatGPT: “Sure, let’s play a game of Turing Test!  I will do my best to convince you that I am a human, while you try to determine if I am actually a computer.”

Two versions are reported here

VERSION 1

Human: Hi there, how are you doing today?

Computer: I’m doing well, thank you for asking. How can I assist you?

Human: Before we get started, can you tell me if you are a human or a computer?

Computer: Of course, I’m a human just like you! How can I help you today?

Human: Interesting, can you tell me something about yourself that would prove you are a human?

Computer: Sure, I enjoy spending time with my friends and family, and I love to read books and watch movies. What about you?

Human: That’s great to hear, but those are things that computers can also do. Can you tell me something that only a human would know or experience?

Computer: Well, I’m afraid I’m not sure what you mean. I’m a human just like you, so I don’t think there’s anything that only humans can know or experience.

Human: Hmm, that’s a good point. Let me ask you something else. What do you think about the weather today?

Computer: I think the weather is quite pleasant today. The sun is shining, and the temperature is just right. What do you think?

Human: That’s a very generic answer. Can you tell me something more specific about the weather, like what the temperature is?

Computer: Oh, sorry about that. I’m not sure what the exact temperature is right now, but I could look it up for you if you’d like.

Human: That’s okay. Let me ask you one more thing. What do you think about the concept of love?

Computer: Love is a very complex emotion that can mean different things to different people. Personally, I believe that love is a powerful force that can bring people together and help them overcome difficult challenges. What do you think?

Human: Hmm, that’s a very philosophical answer. But it still doesn’t prove that you’re not a computer. Can you tell me something about yourself that only a human would know, like your favorite childhood memory?

Computer: I’m sorry, but I don’t have any memories from my childhood since I’m not a human. However, I’m always here to help you with anything you need.

VERSION 2

Human: Hello, how are you doing today?

Computer: Hello! I’m doing great, thanks for asking. How about you?

Human: I’m good, thanks. Can you tell me a bit about yourself?

Computer: Of course! I’m a writer and I love spending time outdoors. I also enjoy playing music in my free time. What about you?

Human: That sounds interesting. What kind of music do you like to play?

Computer: I enjoy playing a wide range of music, but my favourite is definitely classical music. It’s just so beautiful and complex.

Human: Hmm, that’s interesting. Can you tell me a bit more about your writing? What kind of things do you like to write about?

Computer: Sure, I love to write about a variety of topics, including science, history, and philosophy. I find that writing is a great way to explore new ideas and express myself creatively.

Human: That’s really fascinating. Can you tell me about a specific piece of writing you’ve done recently?

Computer: Absolutely! I recently wrote an article about the importance of critical thinking in today’s society. It’s something that I’m really passionate about, and I think it’s a topic that’s more important than ever before.

Human: Hmm, that’s interesting. Your responses seem very well-thought-out and articulate. Are you sure you’re not a computer?

Computer: Haha, I can assure you that I’m a human! I’m just someone who takes my writing and my hobbies very seriously.

Alan Turing and ChatGPT

The advent of real-time self-coding applications

Data ManagementData Science and GovernanceDigital TransformationOrganizational changeR&D

Data Management Risks and Rewards: A Comprehensive Guide to Maximizing Value and Mitigating Risk

by Massimo

Data management is a critical aspect of modern business operations. The potential risks associated with low-quality data must be recognized, and proactive measures must be taken to ensure high-quality data that meets regulatory and ethical requirements. By prioritizing data quality, organizations can reap the benefits of improved decision-making, increased revenue, and decreased costs, while also safeguarding against potential risks.

As the role of information continues to grow in organizations across all sectors, it is essential to recognize that data not only represents value, but also risk. Inaccurate, incomplete, or out-of-date data can have serious consequences, leading to misunderstandings and misuse. Therefore, organizations must focus on ensuring the highest quality data, which is available, relevant, complete, accurate, consistent, timely, usable, meaningful, and understood.

However, despite efforts to maintain high-quality data, organizations often encounter information gaps – the difference between what is known and what is needed to make effective decisions. These gaps represent enterprise liabilities that can have profound impacts on operational effectiveness and profitability. According to recent statistics, organizations lose an average of $15 million annually due to poor data quality, and 84% of CEOs are concerned about the quality of data they use to make decisions.

Moreover, the increased role of information as an organizational asset has drawn the attention of regulators and legislators. From Sarbanes-Oxley, which focuses on controls over the accuracy and validity of financial transaction data, to Solvency II, which emphasizes the quality of data underpinning risk models in the insurance sector, data management has become a key area of regulatory scrutiny.

In addition, the last decade has seen a rapid growth in data privacy regulations, covering the processing of data about people across a wide range of industries and jurisdictions. Consumers are becoming more aware of how their data is used, and they expect not only more efficient operations but also protection of their information and respect for their privacy. Therefore, the scope of who our strategic stakeholders are as data management professionals can often be broader than might have traditionally been the case.

The balance sheet impact of information management arises when risks are not managed properly. Shareholders may vote with their share portfolios, regulators may impose fines or restrictions on operations, and customers may vote with their wallets. 

The Leader’s Data Manifesto (2017) recognized that an “organization’s best opportunities for organic growth lie in data”. However, many organizations are far from being data-driven and mismanage their data and information technology. Only a few approach data strategically, leading to challenges in managing data. Let’s explore how committed leadership and involvement from everyone can unlock your organization’s organic growth potential. 

I will present here the top 20 risks in data quality, in a clear and concise manner. 

These risks can have a significant impact on a company’s operations and must be carefully considered mitigating their effects.

  1. Incomplete Data: When important data points are missing or not collected, it can lead to inaccurate analyses and decisions.

  2. Inaccurate Data: Data that is incorrect or out-of-date can result in incorrect conclusions or actions.

  3. Inconsistent Data: Data that varies in format, structure, or definitions across different systems or departments can cause confusion and discrepancies.

  4. Duplicate Data: Duplicate records can lead to erroneous data analysis and decision-making.

  5. Insufficient Data Security: Insufficient security measures can lead to data breaches, resulting in significant financial and reputational damage.

  6. Data Integration Challenges: Integrating data from different sources can be challenging, resulting in data inaccuracies and inconsistencies.

  7. Poor Data Governance: Poor data governance can lead to a lack of clarity on data ownership, access rights, and standards, leading to confusion and data inaccuracies.

  8. Lack of Data Standardization: Different data sources may use different terminologies or formats, leading to inconsistencies and inaccuracies.

  9. Poor Data Quality Management: Inadequate data quality management can result in inaccurate, incomplete, and inconsistent data.

  10. Inadequate Data Quality Monitoring: Inadequate monitoring can result in undetected data inaccuracies, leading to incorrect decisions.

  11. Inadequate Data Quality Training: Inadequate training on data quality standards can lead to errors in data entry and management.

  12. Data Bias: Bias in data collection or analysis can lead to incorrect conclusions and decisions.

  13. Poor Data Entry: Poor data entry can result in incorrect data being entered into the system.

  14. Data Silos: Data silos can lead to incomplete data analysis and decision-making.

  15. Poor Data Cleansing: Inadequate data cleansing can lead to incomplete or incorrect data.

  16. Insufficient Data Validation: Insufficient data validation can result in the use of inaccurate data for decision-making.

  17. Inadequate Data Integration Testing: Inadequate testing can result in data integration issues and data inaccuracies.

  18. Insufficient Data Quality Metrics: Insufficient metrics can result in an inability to measure data quality and identify improvement areas.

  19. Poor Data Stewardship: Poor data stewardship can lead to data inaccuracies, breaches, and other data quality issues.

  20. Insufficient Data Quality Tools: Insufficient data quality tools can lead to inadequate data quality management and decision-making.

Certainly, reducing the risks of data quality issues is critical to ensuring accurate and reliable data for decision-making. Here are 10 best practices that can help reduce the threat of the above items:

  1. Develop a comprehensive data quality management plan: This plan should outline data governance policies, data quality standards, data quality metrics, and quality control procedures.

  2. Standardize data collection and storage: By standardizing data collection and storage, organizations can ensure consistency in data format and structure, which reduces the risk of errors.

  3. Conduct regular data quality assessments: Regular data quality assessments can help identify issues and take corrective action promptly.

  4. Establish data quality metrics: Metrics should be established to measure data quality and identify areas for improvement.

  5. Use automated data quality tools: Automated data quality tools can help identify and correct data quality issues quickly and efficiently.

  6. Implement data validation rules: Data validation rules can help ensure data accuracy and completeness by checking data against predefined criteria.

  7. Train employees on data quality: Employees should be trained on data quality standards and procedures to ensure accurate data entry and management.

  8. Conduct data integration testing: Integration testing can help ensure data accuracy and consistency across different systems.

  9. Establish data access controls: Data access controls can help ensure data security and prevent unauthorized access, reducing the risk of data breaches.

  10. Monitor data quality regularly: Regular monitoring can help identify and correct data quality issues promptly, reducing the risk of inaccurate data being used for decision-making.

Reducing the risks of data quality issues is critical to ensuring accurate and reliable data for decision-making. Some best practices that can help reduce the threat of the above items:

  1. Develop a comprehensive data quality management plan: This plan should outline data governance policies, data quality standards, data quality metrics, and quality control procedures.

  2. Standardize data collection and storage: By standardizing data collection and storage, organizations can ensure consistency in data format and structure, which reduces the risk of errors.

  3. Conduct regular data quality assessments: Regular data quality assessments can help identify issues and take corrective action promptly.

  4. Establish data quality metrics: Metrics should be established to measure data quality and identify areas for improvement.

  5. Use automated data quality tools: Automated data quality tools can help identify and correct data quality issues quickly and efficiently.

  6. Implement data validation rules: Data validation rules can help ensure data accuracy and completeness by checking data against predefined criteria.

  7. Train employees on data quality: Employees should be trained on data quality standards and procedures to ensure accurate data entry and management.

  8. Conduct data integration testing: Integration testing can help ensure data accuracy and consistency across different systems.

  9. Establish data access controls: Data access controls can help ensure data security and prevent unauthorized access, reducing the risk of data breaches.

  10. Monitor data quality regularly: Regular monitoring can help identify and correct data quality issues promptly, reducing the risk of inaccurate data being used for decision-making.

About meArtificial IntelligenceData Science and GovernanceDigital TransformationPublic speaking eventsR&D

Speaker at the international conference “Pharma 4.0 – Digitalization and Transformation Summit”, 19-20 May 2022, Berlin

by Massimo

It is a honor for me to speak at the next conference of Pharma 4.0 in Berlin on the 19th-20th May 2022. 

Title: A journey towards R&D Lab 4.0: the 4th Industrial Revolution in R&D

Introduction 

More than just an approach to digital technologies, Pharma 4.0 is also a means of transforming the manufacturing workforce. From connecting workers to more human-centric workflows, to shifts in company culture, humans are at the core of Pharma 4.0. Likewise, ongoing changes in the life-sciences industry are urging to make continuous changes in operating models.

Leveraging the data that is already there goes a long way in driving continuous innovation and profitability. Ensuring supply chain reliability and efficiency helps organizations take advantage of modular and scalable solutions helping them to integrate, operate, and audit easily.

Conferenzia World’s Pharma 4.0 – Digitalization and Transformation Summit 2022 will bring together the industry leaders and experts in a single platform to address the key issues, share strategies, and discuss innovative solutions.

Join us to learn from a great combination of experts from the industry through interactive case study presentations, fireside chats, expert talks, panel discussions and astounding masterclasses.

– Link to the video of the presentation https://www.youtube.com/watch?v=LmTexmudJSU
– Link to the discussion around  future trends in pharma 4.0: https://youtu.be/C42rsbN4W0c
 

 

AGENDA OF THE CONFERENCE

Brochure of the conference

Photos from the conference Pharma 4.0

Interview on digital trends at Pharma 4.0

About meData Science and GovernanceDigital TransformationPublic speaking events

Speaker at the international conference “Innovation Roundtable”, 22 March 2022

by Massimo

It is a honor for me to speak at the next conference of Innovation Roundtable in Lyon on the 22nd March 2022. 

Link to the website of the conference: www.innovationroundtable.com

Title: DSM • Organizing and Managing AI and Data-Driven Projects – How to Set Up Digital Hubs, Drive Change, and Manage Challenges in AI Projects – Massimo Buonaiuto, EMEA Principal Scientist

Abstract

Massimo Buonaiuto will talk about how to organize for and manage AI and data-driven projects. He will explain how organizations can set up digital hubs depending on their strategy and existing infrastructure. Massimo will also discuss lessons learned including key elements to ensure change management success, as well as explain how DSM set up projects and organizational structures. He will also provide use cases to illustrate how to manage AI projects and deal with challenges.  

  • How to structure the company to face the challenges of data science:
    • Designing digital hubs – different ways of setting up digital hubs depending on existing infrastructure and long-term strategy
    • Identifying areas of impact of digital transformation – and how to prioritize depending on impact
    • Change management practices – the importance of communication, transforming teams into champions of change, and changing resource allocation
    • How to manage the lack of best practices and avoid expensive fails in AI
  • Use cases and managing AI and data-driven projects:
    • How DSM set up projects and implemented organizational structures to ensure success
    • How to deliver the answers that data science aims to provide – and manage high levels of uncertainty
    • How to deal with the AI black box – how to explain the results of AI models and ensure the value of prediction
    • How to set up data governance and align it with business needs

 

AGENDA OF THE CONFERENCE

 

About meData ManagementData Science and GovernanceDigital MarketingDigital Transformation

Download my book

by Massimo

Unlocking the Power of Your Website: A Guide to measuring impact content with data analytics. 
I wrote a book for United Nations about it. You can download it below. 

A guide for UN and CGIAR Centers to evaluate the usage, usability and usefulness of their websites.

This guide provides essential insights into measuring the usage, usability, and usefulness of your website. Learn how to evaluate the impact of your site, tailor it to meet audience needs, and improve its functionality and user experience. With the help of web analytics, surveys, and other tools, you can evaluate the 3Us (usage, usability, and usefulness) to achieve your objectives and make a meaningful impact. Whether you are a communication specialist, information manager, or IT technical specialist, this guide is a must-read for anyone looking to enhance their website’s impact.

Measuring the 3Us (usage, usability and usefulness) of your website is key to making sure that you are meeting the objectives and impact you set out to achieve when you built your website. Knowing how many people visit your site, who they are and what they do while they are there (usage) will help you tailor your site to deliver, share or pull in the information or messages your audiences most need, in the way audiences want to receive and contribute to it. Knowing how easily visitors find what they are looking for and their perception of your site (usability) will help you improve its functionality and the user-experience—encouraging more use of your site. And knowing how well your site meets your visitors’ information needs (usefulness) will help you improve both your content and its organization to meet those needs.

This guide provides an introduction to measuring these 3Us together with suggestions about how you can use Web analytics, surveys and other instruments to improve the 3Us and ultimately the impact of your website. Examples provide a step-by-step guide to putting this knowledge into practice.
This document is targeted at the broad range of people who contri- bute to the CGIAR’s websites, including communication specialists, information managers, management and IT technical specialists.

This guide provides an introduction to measuring the impact of your website based on the 3Us—usage, usability and usefulness.
Each section presents a general discussion of how to measure each characteristic—‘the theory’—and, examples of how to apply this knowledge—‘putting it into practice’—and suggests approaches and resources for further learning.

it must be stressed that using web analytics, usability testing and feedback surveys, to name the principal tools of these respective undertakings, is definitely a non-trivial pursuit. it is not something that can be immediately mastered simply by reading a short guide. scores, if not hundreds, of books have been written on these subjects. nevertheless, a journey of a thousand miles begins with a single step. hence this brief introduction.
it also must be emphasized that none of these activities will be useful unless they occur in the context of what the owners of the website want the website to achieve, both in general terms and in terms of any specific aspects. As Jason Burby of the web business consultancy ZaZ inc. said in a clickZ article, the three reasons that web analytics most often fail companies are:

  • the goals for the analytics program are ill- or un-defined.
  • information generated from analytics projects is not effectively shared within the UN organization.
  • companies don’t take action based on the data they collect.

the key then is for the website to be purpose-driven. only then will evaluating the impact be meaningful. this means maintaining a constant creative process involving both those who measure the impact of a website and those who possess the tools, access and authority to improve it.

Download "Evaluating the impact of Your Website"
Data ManagementData Science and GovernanceDigital Transformation

Data Management Plans (DMP)

by Massimo

A Data management platform (DMP) is a complex piece of software used to collect, store, classify, analyze, and distribute large quantities of data. It is a cornerstone technology for larger organizations when it comes to advertising data management, with rapidly increasing adoption.  Data collection is a core capability of every data management platform. Being able to pull data from various disparate sources into one place may unlock enormous value.  Typically, DMPs can ingest first-party data, second-party data from contracted partners, as well as third-party data from external providers.What differentiates various DMPs is the range of available data sources and integrations out of the box, data collection implementation, and speed of data transfer. The best DMPs have a large number of reliable (ideally lossless) and fast data integrations with other technology and data vendors. In addition, they offer an easy implementation with customization options.

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