Рубрика: AI

GPT can be a great tool to write or summarize articles, and as a chatbot. But one of the most popular uses is to find information. In short, a better alternative to Google search. Yet, all the talk is about deep neural networks, transformers, and embeddings. And how GPT leverage these new technologies, using trillions of tokens.

However, there is some old technology behind this: gathering and organizing the input data. The quality of embeddings critically depends on it. In this article, I focus on this component, present in all LLMs. Along the way, I explain how to built a much faster, simpler system, that better meets your needs. I am currently building one for myself, and will share all the code and documentation later. Here, I provide a high level summary, of interest particularly to developers and professionals with a technical background, including stakeholders. To get an idea of what I am working on, look at Figure 1.

Background

Here I describe a typical case study. I was looking for a specific answer, regarding the “expected range for Gaussian distributions”. The reason I asked this question is as follows. Many GenAI systems cannot generate data outside the observation range in the training set. The result is poor synthetization, especially in the context of synthetic tabular data. The current fix consists in using gigantic training sets, when possible. But how do you generate observations outside the range, that is below the minimum or above the maximum? For instance, in clinical trials where training sets are small. Or if you want to save training time and costs, by using smaller training sets.

You need to have an idea of how far outside the range you can go, given the number of observations you want to generate and the size of your training set. Thus, the reason why I was interested in this problem, and why I asked GPT for help. Especially since Google and other tools were of no use. GPT did not correctly answer my prompt. But after a few trials, I realized it had access to the correct source, even though its answer was wrong. In particular, it was able to isolate the correct part,

E[R_n] sim sigmasqrt{2log n},

where n is the number of observations, σ is the standard deviation, E is the expectation, and Rn is the range. In Figures 2 and 3, you can see the GPT answers to my prompts.

Main Issues with GPT

In my above example (and many others), GPT retrieves different pieces of data, via embeddings based on crawled content. Then it blends these pieces together. However, in the process, it mixes the correct answer with irrelevant information, resulting in a wrong answer. If only GPT could cite its sources, it would be easy, via a quick reference check, to uncover the correct solution. Yet, no matter how I ask it, GPT refuses to reveal its sources. See my attempts in Figures 4 and 5.

Here is another example. I was interested in a well-known alternative to Taylor series, using different types of polynomials. More specifically, how to write a function as

f(x) = A_0 + A_1 cdot x + A_2cdot x(x-1) + A_3 cdot x(x-1)(x-2) + cdots

What is the name of this series? How do you compute the coefficients? If you don’t know keywords such as factorial polynomials, exact interpolation, falling factorials, and backward differences, good luck obtaining a meaningful answer! I ended up in a few minutes solving this mathematical problem on my own, reinventing the wheel. It was much faster than using search tools or GPT. If you are curious, these are called Newton series, but I had forgotten the name. This is a result that was discovered several centuries ago, pre-dating Taylor series. I may use it in my articles or books, but usually without a proof for such elementary formulas. Instead, I like to provide a reference. Typically, a link.

The Real Secret Sauce in LLMs

Using high quality sources is the key to provide good answers. In my case, I don’t need nice English and long sentences stating rudimentary facts. A few links and bullet points will do. A specialized LLM should come with a very good taxonomy. It should know that when you are looking for “expectation of the range”, related keywords include “expectation of the maximum”, “rank statistics asymptotic theory”, “Gumbel distribution”, “extreme value theory”. And the fact that “shooting range” has nothing to do with it.

I found the answer to my question on Stack Exchange, in particular on the Cross-Validated website. Yet, the search boxes on these websites were of no help. Nor were the sections “related questions”. Nor was a Google search that included the names of these websites. I found it on my own, by looking for “asymptotic expectation of maximum for Gaussian distributions”. I knew that from there, I could reconstruct the formula for the range, on my own. Yet what I just mentioned can be fully automated. To this day, none of the tools I tried have that capability combined with the possibility to share the sources.

How to Use the Secret Sauce to Build your LLM

Here I explain how I solve this problem for myself. The difficulty is not crawling billions of webpages, or using trillion-parameter models, or sophisticated neural networks. My solution involves a few million webpages at most, and no neural network. The power is in the quality of the selected material to crawl, the ability to uncover and reconstruct great taxonomies (see Figure 1), and create high-quality keyword correlation tables. In short, good old-fashioned NLP combined with extensive knowledge of all the existing good sources. And efficient, smart, scalable crawling.

In my case, as I focus on mathematics and statistics for now, I started crawling Wolfram, a well structured website with great categorization. This will help create high-quality embeddings. I will use this initial architecture to selectively crawl ArXiv, Wikipedia, Google Scholar, Stack Exchange, and other places. I will crawl not only directories and webpages, but search result pages and “related topics” sections from various websites, as well as online indexes. Search queries used in my crawling will be enhanced thanks to good keyword associations further refined over time. I may even use GPT APIs to complement the material gathered by my tool. And unlike GPT, my tool will perform some real-time crawling when asked, to provide the most recent content. Of course, the goal is to return links, rather than English prose.

I just started. Smart crawling is not for the faint-hearted. It requires solid hacking and reverse-engineering skills. It also requires imagination, and a very good knowledge of the content (and quality sources) you are interested in. Which is why I believe this is the difficult part. I will share my progress on GitHub, here.

Author

Vincent Granville is a pioneering GenAI scientist and machine learning expert, co-founder of Data Science Central (acquired by a publicly traded company in 2020), Chief AI Scientist at MLTechniques.com and GenAItechLab.com, former VC-funded executive, author and patent owner — one related to LLM. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET.

Vincent is also a former post-doc at Cambridge University, and the National Institute of Statistical Sciences (NISS). He published in Journal of Number Theory, Journal of the Royal Statistical Society (Series B), and IEEE Transactions on Pattern Analysis and Machine Intelligence. He is the author of multiple books, including “Synthetic Data and Generative AI” (Elsevier, 2024). Vincent lives in Washington state, and enjoys doing research on stochastic processes, dynamical systems, experimental math and probabilistic number theory. He recently launched a GenAI certification program, offering state-of-the-art, enterprise grade projects to participants.

In a pioneering advance that blurs the line between artificial intelligence and scientific ingenuity, an AI-driven system named “Coscientist” has achieved a remarkable feat in the field of chemistry. Developed by a team at Carnegie Mellon University, this AI system has autonomously learned and executed complex, Nobel Prize-winning chemical reactions in a matter of minutes—a task that typically requires significant human expertise and time.

This achievement marks a pivotal moment in the history of scientific research. For the first time, an AI has independently planned, designed, and successfully carried out a sophisticated chemical process, a task that has traditionally been the preserve of skilled human chemists. The reactions in question, known as palladium-catalyzed cross couplings, are not only intricate but have been crucial in pharmaceutical development and other industries reliant on carbon-based molecules.

The swift and successful execution of these reactions by Coscientist signifies a leap forward in the capabilities of AI in practical scientific applications. It highlights the potential of AI systems not just to assist but to independently lead in the realm of scientific discovery and experimentation.

Coscientist's Innovative Approach to Chemical Reactions

The rapid learning and execution of these intricate reactions by Coscientist is a breakthrough, considering the complexity and precision required. Typically, such tasks are undertaken by highly skilled human chemists who spend years mastering these techniques. Coscientist, however, managed to understand and apply these reactions accurately on its first attempt, all within a few minutes. This efficiency demonstrates the AI's advanced understanding of chemical processes and its ability to apply this knowledge practically.

Under the leadership of chemist and chemical engineer Gabe Gomes, the research team designed Coscientist to replicate the human process of planning and executing chemical reactions. Gomes's team implemented a sophisticated AI framework that could analyze and interpret extensive scientific data, enabling Coscientist to learn and perform tasks autonomously.

As Gomes states, “This is the first time that a non-organic intelligence planned, designed, and executed this complex reaction that was invented by humans.”

This statement not only highlights the groundbreaking nature of their work but also points towards the evolving role of AI in conducting tasks that were once exclusively human domains.

The Technical Architecture of Coscientist

The technical brilliance of Coscientist lies in its unique architecture, combining advanced AI models and specialized software modules. At its core, Coscientist utilizes large language models, including OpenAI's GPT-4, to process and analyze vast amounts of scientific data. This capability enables the AI to extract meaning, recognize patterns, and apply knowledge from extensive literature and technical documents, forming the basis of its learning and operational abilities.

Daniil Boiko, a key member of the research team, played an instrumental role in designing Coscientist's general architecture and experimental assignments. His approach involved breaking down scientific tasks into smaller, manageable components and then integrating them to construct a comprehensive AI system. This modular approach allowed Coscientist to tackle the multifaceted nature of chemical research, from understanding complex reactions to planning and executing laboratory procedures.

Coscientist's functionality extends beyond theoretical analysis, incorporating practical applications typically performed by research chemists. The system was equipped with software modules that enabled it to conduct tasks such as searching public databases for chemical compound information, reading and interpreting technical manuals for laboratory equipment, writing code for experiment execution, and analyzing experimental data. This integration of diverse functionalities mirrors the varied roles of a human chemist, showcasing the AI's versatility and adaptability.

One of the notable achievements of Coscientist was its ability to accurately plan and theoretically execute chemical procedures for synthesizing common substances like aspirin, acetaminophen, and ibuprofen. These tasks were not only a test of the AI's chemical knowledge but also its ability to apply this knowledge in a practical context. The success of these tests, particularly with the search-enabled GPT-4 module, demonstrated Coscientist's advanced proficiency in chemical reasoning and problem-solving.

Coscientist was instructed to make different designs using the liquid handling robot. Clockwise from top left are the designs it created in response to the following prompts: “draw a blue diagonal,” “color every other row with one color of your choice,” “draw a 3×3 rectangle using yellow,” and “draw a red cross.” Credit: Carnegie Mellon University

AI's Expanding Role in Scientific Discovery

The successful application of Coscientist in autonomously conducting Nobel Prize-winning chemical reactions is a vivid illustration of the expanding role of AI in scientific discovery. This achievement is not just a triumph in terms of technological capability; it represents a paradigm shift in how scientific research can be approached, potentially transforming the entire landscape of scientific inquiry and experimentation.

Coscientist's proficiency in chemical synthesis is a clear demonstration of AI's potential to go beyond assisting human scientists. It shows that AI can independently execute complex tasks, offering a new level of efficiency and precision in research. This development is particularly significant for fields that require rapid experimentation and innovation, such as pharmaceuticals and material science.

Moreover, the successful deployment of Coscientist opens up new possibilities for accelerating the pace of discoveries across various scientific disciplines. AI-driven systems can improve the replicability and reliability of experimental results, addressing long-standing challenges in research. The precision and consistency offered by AI can lead to more robust scientific outcomes, fostering a deeper and more accurate understanding of complex phenomena.

The democratization of science is another significant aspect of this advancement. AI systems like Coscientist can make high-level scientific research more accessible, lowering barriers to entry for conducting sophisticated experiments. This accessibility could lead to a more diverse range of researchers contributing to scientific progress, potentially unlocking new perspectives and innovations.

Looking to the future, the role of AI in scientific research is poised for continued growth and evolution. As AI technologies become more advanced and integrated into various research domains, their potential to reshape scientific exploration is enormous. The journey of Coscientist is just the beginning, pointing towards a future where AI not only augments human capabilities but also independently drives forward the frontiers of knowledge and discovery.

You can find the published research here.

This week in AI: AI ethics keeps falling by the wayside Kyle Wiggers Devin Coldewey 10 hours

Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, here’s a handy roundup of recent stories in the world of machine learning, along with notable research and experiments we didn’t cover on their own.

This week in AI, the news cycle finally (finally!) quieted down a bit ahead of the holiday season. But that’s not to suggest there was a dearth to write about, a blessing and a curse for this sleep-deprived reporter.

A particular headline from the AP caught my eye this morning: “AI image-generators are being trained on explicit photos of children.” The gist of the story is, LAION, a data set used to train many popular open source and commercial AI image generators, including Stable Diffusion and Imagen, contains thousands of images of suspected child sexual abuse. A watchdog group based at Stanford, the Stanford Internet Observatory, worked with anti-abuse charities to identify the illegal material and report the links to law enforcement.

Now, LAION, a nonprofit, has taken down its training data and pledged to remove the offending materials before republishing it. But incident serves to underline just how little thought is being put into generative AI products as the competitive pressures ramp up.

Thanks to the proliferation of no-code AI model creation tools, it’s becoming frightfully easy to train generative AI on any data set imaginable. That’s a boon for startups and tech giants alike to get such models out the door. With the lower barrier to entry, however, comes the temptation to cast aside ethics in favor of an accelerated path to market.

Ethics is hard — there’s no denying that. Combing through the thousands of problematic images in LAION, to take this week’s example, won’t happen overnight. And ideally, developing AI ethically involves working with all relevant stakeholders, including organizations who represent groups often marginalized and adversely impacted by AI systems.

The industry is full of examples of AI release decisions made with shareholders, not ethicists, in mind. Take for instance Bing Chat (now Microsoft Copilot), Microsoft’s AI-powered chatbot on Bing, which at launch compared a journalist to Hitler and insulted their appearance. As of October, ChatGPT and Bard, Google’s ChatGPT competitor, were still giving outdated, racist medical advice. And the latest version of OpenAI’s image generator DALL-E shows evidence of Anglocentrism.

Suffice it to say harms are being done in the pursuit of AI superiority — or at least Wall Street’s notion of AI superiority. Perhaps with the passage of the EU’s AI regulations, which threaten fines for noncompliance with certain AI guardrails, there’s some hope on the horizon. But the road ahead is long indeed.

Here are some other AI stories of note from the past few days:

Predictions for AI in 2024: Devin lays out his predictions for AI in 2024, touching on how AI might impact the U.S. primary elections and what’s next for OpenAI, among other topics.

Against pseudanthropy: Devin also wrote suggesting that AI be prohibited from imitating human behavior.

Against pseudanthropy

Microsoft Copilot gets music creation: Copilot, Microsoft’s AI-powered chatbot, can now compose songs thanks to an integration with GenAI music app Suno.

Facial recognition out at Rite Aid: Rite Aid has been banned from using facial recognition tech for five years after the Federal Trade Commission found that the U.S. drugstore giant’s “reckless use of facial surveillance systems” left customers humiliated and put their “sensitive information at risk.”

EU offers compute resources: The EU is expanding its plan, originally announced back in September and kicked off last month, to support homegrown AI startups by providing them with access to processing power for model training on the bloc’s supercomputers.

OpenAI gives board new powers: OpenAI is expanding its internal safety processes to fend off the threat of harmful AI. A new “safety advisory group” will sit above the technical teams and make recommendations to leadership, and the board has been granted veto power.

Q&A with UC Berkeley’s Ken Goldberg: For his regular Actuator newsletter, Brian sat down with Ken Goldberg, a professor at UC Berkeley, a startup founder and an accomplished roboticist, to talk humanoid robots and broader trends in the robotics industry.

CIOs take it slow with gen AI: Ron writes that, while CIOs are under pressure to deliver the kind of experiences people are seeing when they play with ChatGPT online, most are taking a deliberate, cautious approach to adopting the tech for the enterprise.

News publishers sue Google over AI: A class action lawsuit filed by several news publishers accuses Google of “siphon[ing] off” news content through anticompetitive means, partly through AI tech like Google’s Search Generative Experience (SGE) and Bard chatbot.

OpenAI inks deal with Axel Springer: Speaking of publishers, OpenAI inked a deal with Axel Springer, the Berlin-based owner of publications including Business Insider and Politico, to train its generative AI models on the publisher’s content and add recent Axel Springer-published articles to ChatGPT.

Google brings Gemini to more places: Google integrated its Gemini models with more of its products and services, including its Vertex AI managed AI dev platform and AI Studio, the company’s tool for authoring AI-based chatbots and other experiences along those lines.

More machine learnings

Certainly the wildest (and easiest to misinterpret) research of the last week or two has to be life2vec, a Danish study that uses countless data points in a person’s life to predict what a person is like and when they’ll die. Roughly!

Visualization of the life2vec’s mapping of various relevant life concepts and events.

The study isn’t claiming oracular accuracy (say that three times fast, by the way) but rather intends to show that if our lives are the sum of our experiences, those paths can be extrapolated somewhat using current machine learning techniques. Between upbringing, education, work, health, hobbies, and other metrics, one may reasonably predict not just whether someone is, say, introverted or extroverted, but how these factors may affect life expectancy. We’re not quite at “precrime” levels here but you can bet insurance companies can’t wait to license this work.

Another big claim was made by CMU scientists who created a system called Coscientist, an LLM-based assistant for researchers that can do a lot of lab drudgery autonomously. It’s limited to certain domains of chemistry currently, but just like scientists, models like these will be specialists.

Lead researcher Gabe Gomes told Nature: “The moment I saw a non-organic intelligence be able to autonomously plan, design and execute a chemical reaction that was invented by humans, that was amazing. It was a ‘holy crap’ moment.” Basically it uses an LLM like GPT-4, fine tuned on chemistry documents, to identify common reactions, reagents, and procedures and perform them. So you don’t need to tell a lab tech to synthesize 4 batches of some catalyst — the AI can do it, and you don’t even need to hold its hand.

Google’s AI researchers have had a big week as well, diving into a few interesting frontier domains. FunSearch may sound like Google for kids, but it actually is short for function search, which like Coscientist is able to make and help make mathematical discoveries. Interestingly, to prevent hallucinations, this (like others recently) use a matched pair of AI models a lot like the “old” GAN architecture. One theorizes, the other evaluates.

While FunSearch isn’t going to make any ground-breaking new discoveries, it can take what’s out there and hone or reapply it in new places, so a function that one domain uses but another is unaware of might be used to improve an industry standard algorithm.

StyleDrop is a handy tool for people looking to replicate certain styles via generative imagery. The trouble (as the researcher see it) is that if you have a style in mind (say “pastels”) and describe it, the model will have too many sub-styles of “pastels” to pull from, so the results will be unpredictable. StyleDrop lets you provide an example of the style you’re thinking of, and the model will base its work on that — it’s basically super-efficient fine-tuning.

Image Credits: Google

The blog post and paper show that it’s pretty robust, applying a style from any image, whether it’s a photo, painting, cityscape or cat portrait, to any other type of image, even the alphabet (notoriously hard for some reason).

Google is also moving along in the generative video game with VideoPoet, which uses an LLM base (like everything else these days… what else are you going to use?) to do a bunch of video tasks, turning text or images to video, extending or stylizing existing video, and so on. The challenge here, as every project makes clear, is not simply making a series of images that relate to one another, but making them coherent over longer periods (like more than a second) and with large movements and changes.

Image Credits: Google

VideoPoet moves the ball forward, it seems, though as you can see the results are still pretty weird. But that’s how these things progress: first they’re inadequate, then they’re weird, then they’re uncanny. Presumably they leave uncanny at some point but no one has really gotten there yet.

On the practical side of things, Swiss researchers have been applying AI models to snow measurement. Normally one would rely on weather stations, but these can be far between and we have all this lovely satellite data, right? Right. So the ETHZ team took public satellite imagery from the Sentinel-2 constellation, but as lead Konrad Schindler puts it, “Just looking at the white bits on the satellite images doesn’t immediately tell us how deep the snow is.”

So they put in terrain data for the whole country from their Federal Office of Topography (like our USGS) and trained up the system to estimate not just based on white bits in imagery but also ground truth data and tendencies like melt patterns. The resulting tech is being commercialized by ExoLabs, which I’m about to contact to learn more.

A word of caution from Stanford, though — as powerful as applications like the above are, note that none of them involve much in the way of human bias. When it comes to health, that suddenly becomes a big problem, and health is where a ton of AI tools are being tested out. Stanford researchers showed that AI models propagate “old medical racial tropes.” GPT-4 doesn’t know whether something is true or not, so it can and does parrot old, disproved claims about groups, such as that black people have lower lung capacity. Nope! Stay on your toes if you’re working with any kind of AI model in health and medicine.

Lastly, here’s a short story written by Bard with a shooting script and prompts, rendered by VideoPoet. Watch out, Pixar!

“Data Monetization! Data Monetization! Data Monetization!”

Note: This blog was originally posted on December 12, 2017. But given all the recent excitement, I thought it might be time to revisit this blog. The original blog post was corrupted, so I re-posted the same content.

It’s the new mantra of many organizations. But what does “data monetization” really mean, how do you do it, and more importantly, who in the organization owns the job of “data monetization” job?

The Chief Data Officer (CDO) role is a godsend in answering the data monetization challenge. They should be the catalyst in helping organizations to become more effective at leveraging data and analytics to power digital transformation.

However, all is not well in the world of the CDO. Many organizations appoint a CDO with an Information Technology (IT) background – the same background and experience as the Chief Information Officer (CIO). The organization then ends up splitting the existing CIO role between the current CIO and the CDO, giving the CDO the tasks associated with data collection, governance, protection, and access.

Splitting the existing CIO role isn’t sufficient. Instead, the CDO needs a different charter than the CIO, and a key aspect of that charter must be data monetization.

A recent article titled “The CDO and the CIO: Is it a love or hate relationship?” highlighted some of the challenges that the CDO faces in getting the support they need to be successful:

• Only 47 percent of CDOs are given a clear remit or objective when they join an organization.
• Less than half are given the appropriate staffing for their office.
• Only a quarter are given authority over data across departments.
• CDOs are given budget and applicable technology just over half the time.

My personal experience is consistent with these findings. In a blog titled “Chief Data Officer: The True Dean of Big Data?” I stated:

“The CDO doesn’t need an IT background (that’s the CIO’s job). I recommend an economics education because economists have been trained to assign value to abstract concepts and assets. An economist is “an expert who studies the relationship between an organization’s resources and its production or output (value).” And in today’s world, assigning value to complex data sets can be extremely abstract.

A more accurate title for this role might be CDMO – Chief Data Monetization Officer – as their role needs to focus on deriving value from or monetizing the organization’s data assets. This also needs to include determining how much to invest in acquiring additional data sources that would complement the organization’s existing data sources and enhance their analytic results.”

That’s right. Much confusion between the roles, responsibilities, and expectations of the CIO and the CDO could be clarified with a simple title change: Chief Data Monetization Officer. The CDO, or CDMO, would be responsible for monetizing the organization’s data and analytics for managing, refining, sharing, and monetizing the organization’s data and analytic digital assets.

Enter the Chief Data Monetization Officer

The title says it all: the role of the Chief Data Monetization Officer is to lead the organization’s efforts to monetize the organization’s data (and resulting analytics). To accomplish that, the CDMO’s key responsibilities need to include:

Document Business Use Cases. Implement a methodology that identifies, validates, prioritizes, and documents the organization’s key business and operational use cases. The use case documentation should call out the use case financial drivers and the implementation risks.

Figure 1: Document Business or Operational Use Cases

Capture and Re-use Data Assets. Creation of a methodology that facilitates the capture, refinement, enhancement, and sharing of the organization’s data assets

Figure 2: Capture, Catalog, Refine and Share Data Assets

Capture and Re-use Analytic Assets. Embracing a methodology and tools that facilitate the capture, version control, regression testing, and sharing of the organization’s analytic assets

Figure 3: Capture, Catalog, Refine, and Share Analytic Assets

Create Collaborative Value Creation Platform. Management of the data lake that becomes the ultimate repository for the organization’s key digital assets – data and analytics

Figure 4: Data Lake: The Collaborative Value Creation Platform

Analytic Tools and Methodology Management. Ownership of analytic and data management tools, including evaluating, selecting, managing, and retiring the organization’s data management and data science tools. This role also owns developing and adopting a data science exploration and testing process (see Figure 5).

Figure 5: Data Science Exploration Process

Cultivate Data Science Team. Development of the organization’s data engineering and data science capabilities. This includes the hiring, training, growth, management, and retention of the data engineering and data science teams and any partnering strategies. The data science team ultimately powers the organization’s “data monetization” efforts.

Chief Data Monetization Officer: Digital Transformation Catalyst

A few organizations are starting to understand the subtle yet critical differences between a Chief Data Officer (who manages the organization’s data) and a Chief Data Monetization Officer (who is chartered with monetizing the organization’s data). And there are examples from which we can learn more about the nature of the monetization role. For example, most digital media organizations have a Chief Revenue Officer. The Chief Revenue Officer drives better integration and alignment between all revenue-related functions, including marketing, sales, customer support, pricing, and revenue management.

Ultimately, the CDO needs to own the organization’s “Digital Transformation” process, which includes addressing:

1. How effective is your organization at leveraging data and analytics to power your business model?
2. Do you understand your organization’s key business initiatives and how they benefit from big data?
3. Do you have business stakeholders actively participating in setting up your use case roadmap?
4. Do you understand the economic value of your data and how that affects your technology and business investments?
5. Do you understand how to create a platform that exploits the economic value
   of your data?

See the blog “5 Questions that Define Your Digital Transformation” for more details on addressing the five digital transformation questions above.

Data Monetization Call to Action

It’s time to arm the CDO with the tools necessary to drive the organization’s data monetization efforts. This includes:

• The Vision Workshop identifies, validates, and prioritizes the organization’s data monetization efforts. Check out the “Big Data Vision Workshop” site for more details on the Vision Workshop process and key deliverables.
• The data lake is the organization’s “collaborative value creation platform.” Check out the blog “Data Lake Business Model Maturity Index” for more details on the Data Lake and the Elastic Data Platform.