Artificial Intelligence Codex

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This is a brief blog to help understand the synergies and similarities between AI & BI We will start our at what drives the process, which is the business goals I will be publishing sevral guides and templates\ to help you in this but is important this is written for your team and others to evaluat

Businemss Intelligence

This path will involve recording and providing the deliverables so let your development team translate your requirements into business and technical deliverables

Equally we will use these deliverables as a springboard in defining, creating and documenting interesting business and technical deliverables into functional deliverables from the business that the AI development team can use

BI Dimensionns

BI dimensions is it area we will cover quite a bit. It is similar here distinctly different from AI use of the term in PI. A dimension is a high-level category or a category of glossary terms or attitude from a file or table, bottom line in BI. It’s a grouping of separate fields or an AI each field is called equally a feature, or a dimension, or a variable. In AI it is around the algorithm selected. We will cover this in detail.

BI Metrics

AI measure a KPI or metric that is piece part of many data elements and several parts of dimensions which is the name of the dimension or category in business terms

AI Features

In AI features is interesting, and a bit different than the grooving of separate fields or data elements or attributes, which is called business dimensions. A feature has several definitions. The primary definition is a field that that in part of an algorithm chosen and impacts the algorithm based on the input in AI. There are many techniques and tools to.A high features is interesting, and a bit different than the in AI there are many tools and techniques for feature selection. They are very similar and based on the tools and techniques used in BIA to define attributes who will discuss in detail later.grooving of dittos, which is called business dimensions. A feature has several definitions. The primary definition is a field that can choose or impacts the model based on the input in AI. There are many techniques and tools to.in AI there are many tools and techniques for feature selection. They are very similar and based on the tools and techniques used in BI to define attributes who will discuss in detail later.

AI predicted Content

Here too, we have not standardized on description. The predicted content is commonly known as the generated content. this is important in both models types, both expert AI, and knowing that there are features, however, LLM specifically creates generated content, or its prediction of your contest based on your questions or prompts

Practical artificial intelligence and it’s application to traditional business intelligence

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AI + BI

There are many books and photos that talk about the so-called revolution of artificial intelligence or AI I appreciate their enthusiasm however, I don’t think that is really being recognized that much of what they have invented i can be used in reality to greatly, reduce the time and effort to creat the kind of things we’ve had a creat for decades And they are essential to the creation of AI, which is predicated on learning and contact

One of the things that we will address is, there is a proclivity to use much language much of it is not very semantic. Today folks are describing architecture that already have descriptions. We have a mix of marketing terms and creative terms that mean the same thing, but they use words from the past. It’s causing confusion.

Just one example, maybe the term dimensionis one thing it meant to me thing 10 years ago come today it’s being used in a different context, in AI is it’s dimension or parameter or a feature There are many people of many years in a IT language is important it’s semantic

Another is the term similarity . It is used completely different in AI versus traditional, fuzzy matching. true the concept is the same but the Technical use Is different

There is no doubt of the benefit of what’s been created through the use of neural networks and transformers tthat hey can have tremendous positive impact on delivering business intelligence with the aid of artificial intelligence, machine, learning, and Deep Learning subsequently

I have been deeply involved in business intelligence, data quality, data profiling, and MDM and Data Governance for several decades.

I would like to take you on a journey and be able to help you exploit all these capabilities today and yesterday we are experience in the evolution of what we’re doing it is not a revolution. It is an evolution. if anything I hope to help achieve a basic understanding and terminalnology used in information architecture and various techniques that we have that will help , frankly nobody has a corner on the best approach it has all been done before at the logical level I want to be a part of helping us leverage, reuse, and apply what we were doing for decades, to what is now being introduced in the last several years you have to judge among the three goals, cheaper faster, better or we can guarantee is cheaper and faster. It’s up to you to make it better not necessarily the technology.

More to come . . .p

AI LLM New Project Roles including Prompt Engineering and Role AI Scientist and integrated data and information

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I’d like to offer some advice on transitioning skills and knowledge skills and knowledge who worked hard to retainto include some of the new AI and NLM developments it’s actually less impactful, and better than you may think

Well, I dressed up in a little bit for now let’s talk about prompt engineers You most likely have currenb SME’s or expert on your current data and requirements

First, do you notice I used the term AI scientist Instead of data scientist , A data scientist Currently is actually a AI model scientist and they will help you apply. We are concerned here with how a lot of folks have opinions heuristic no not necessarily fact-based already we are going to suggest some techniques, and provide some mentoring to explore this important factor in AI is proper training we specialize in providing techniques and mentoring in separate information which is Formulated and an opinion and facts or Data, which, cannot change

There is a series of steps involved in preparing for the use of Data inAI and Chat in the form of LLM models. This is not much different and you may have most of the information already gathered in order to properly design the requirements for your model we would collect the phone.it is important to realize the steps are critical, for you have confidence in your models putput which will be your result of integrating, your Word documents, your Presentations,, spreadsheets, and, of course your actual data.

We wiKeaton, Billy, and modeling of information words versus modeling and requirements for data preparation . There is a difference that is extremely important and in line with what you’ve been doing.I know that Data preparation is not glamorous, but in my 20+ years you’ll get nowhere without proper data preparation you can’t AI it you can’t impute it you need to discuss requirements with people and write them down and then execute it The AI will make the legwork, faster. but in the end you’ll have to review it otherwiseit otherwise you may end up needlessly retracing your steps based on Improper preparation I know that Data preparation is not glamorous, but in my 20+ years you’ll get nowhere without proper data preparation you can AI it you can’t imputed you need to discuss requirements with people and write them down and then execute a I will do it. Faster time is the legwork, but in the end you’ll have to review with Stuck you may end up needlessly retracing your steps based on.I know that Data preparation is not glamorous, but in my 20+ years you’ll get nowhere without proper data preparation you can AI it you can’t imputed you need to discuss requirements with people and write them down and then execute a I will do it. Faster time is the legwork, but in the end you’ll have to review with Stuck you may end up needlessly retracing your steps based on. improper preparation. This can be at Floydd by phone, the proper steps.

1. Word document

2. Presentations

3. Spreadsheets

4.Data reports

5. Data quality report for AI preparation

6.Internet

7.Other Sources (Network,Internet or Local)

We have suggested tools/techniques/open source.and suggestions for each of these. Don’t let that bother you, however, is important with today’s capabilities in AI integrate your words your thoughts, your abstraction, and your actual data together in order for you. They’re trustworthy results from your AI.

We will be providing a separate post on each of these and then finally how they come together our point is that the what you’ve been doing to understand and form requires for tradition BI can be reutilized and extend it for AI

AI and ChatGPT Real Impact to Business Process and Cost

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The graphic I created for this post has come from my experience in BI and AI AI is being positioned message as a revolution of What we have learned and delivered for decades. Intelligence is built overtime Knowledge is acquired and changes with new discoveries

Businesses need to quickly understand it is not a panacea or really anything new at a high-level AI is simply using rules and logic and words to draw conclusions. AI can just do it faster and we can know better.

If we want to stick with the intelligence. I remember that when a baby is born. It has the ability to learn and create, but that is based on what is fed, both intellectually and nutritionally we seem to forget that.intelligence is acquired it is not spontaneous

I have realized for many years there is a certain methodology and strategy we follow in BI whether it’s still data quality MDM, or Data. Governance the goal to get to is that we can digest information at a high-level and not try to consume the detail.

The future of a business and also AI will be based on what it is said it is not magic What is explosive growth of open source AI many of these models are specialized for domains. They are not specialize for your business. That knowledge only comes from you. It has to be properly organized cleaned and fed to AI in essence. AI is the new Data Warehouse House. Data quality, MDM, Data Governance is more important than ever all AI has done is replace the manual work, and make it easier to establish standards, and the logic behind metrics, and anything that AI produces will have to be curated by humans, real intelligence versus artificial intelligence

While, you’re a busy daydreaming about AI I challenge anyone to read the actual definition of intelligence not a single AI is capable of all of these just a few specific things, and in that case, it is solely based on what it was spcified and the definition, too establish its paradigm of automated decision, making not  thinking

1. Intelligence https://en.wikipedia.org/wiki/Intelligence

Finally, I stress this is not a revolution. It is an evolution. Well, we may reduce the cost an effort to create many of the deliverables. We need to create AI. Ultimately will need much review oh, deliverables as to review that output.this is in no way anti-AI post a ice fantastic, and can be extremely beneficial when used correctly and realistically this is in no way anti-AI post AI is fantastic, and can be extremely beneficial when used correctly and realistically. AI will actually revolutionize BI and Data Governance It is a natural evolution of the capability. Everything that has been stressed or we continue to be stressed. Wait for it. Keep in mind the new term for wrong answers is my AI is hallucinainting this may come as a surprise, but for decades that is has been identified as data quality. AI’s recent is Astors have been explained it to produce additional information however, we have known Eva data quality issues how to resolve their relation to information for decades. we simply need to apply, in addition to the newly released AI capabilities thtu LLM

Data Governance and AI.

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With a little guidance, you can actually chat with the information you’ve got in caliber, or any data governance tool and integrate that with your PI Data Warehouse and the Data Marts

This would be pPossible by leveraging new capabilities, not necessarily new vendors no doubt new vendor features are on the horizon but this ability to chat with your data governance information is here now if you have already implemented the quality or MDM, or even Data Governance early adoption and prototyping of AI is possible today. We can enable this very quickly and easily by leveraging, our current, capabilities, and knowledge and tools. Who is B) in vomiting and Luigi current Lenckee or Microsoft copilot capabilities along with LLM and provide you the ability to create your own. LLM privately and insecure, basically, LM is what provides chat capability but this time with your personal data in addition, we have exceptional data quality capabilities, which can also be enhanced for you This capability will be taking traditional BI, and data governance, as well as data quality and MDM to explosive to new heights Finally, we have a decade of experience. This is merely extension of the Information Value Chain methodologies. Which we can gladly help you take advantage of