Business Intelligence & AI

Summary

Le script présente une nouvelle méthode d'analyse des entreprises, développée par Alis, qui utilise l'intelligence artificielle neuro-symbolique pour la prise de décision. Après cinq années de recherche et développement, cette approche permet de détecter des risques et des opportunités de manière automatisée, en identifiant des signaux d'alerte précurseurs. L'IA spot sept fois plus d'insights que l'exploration humaine, aidant ainsi les analystes à comprendre les causes profondes des problèmes et à élaborer des plans d'action correctifs. L'objectif principal est de repérer des opportunités même dans les unités commerciales en difficulté et des points d'amélioration chez les unités réussies. La solution d'Alis vise à résoudre les problèmes des outils de reporting actuels, en offrant une vision consolidée des données et en mettant en avant l'importance de l'analyse déconsolidée et de la dynamique des données pour les entreprises.

Takeaways

• 🚀 Une nouvelle méthode d'analyse des entreprises est présentée, basée sur une intelligence artificielle neuro-symbolique pour la prise de décision.
• 🔍 L'intelligence artificielle permet d'automatiser la détection des risques et des opportunités, ainsi que la découverte de signaux d'alerte précurseurs.
• 🌟 L'objectif principal est de repérer des opportunités même dans les unités commerciales en difficulté et des points d'amélioration dans les unités réussies.
• 📊 Les outils actuels de reporting sont critiqués pour ne pas offrir d'analyse multidimensionnelle ou de déconsolidation des données.
• 🤖 L'intelligence artificielle générative est combinée avec l'IA symbolique et les graphes de connaissances (KGE) pour identifier la cause profonde des problèmes.
• 🔑 L'algorithme de priorisation Mark of Shame est utilisé pour classer les problèmes business en fonction de leur impact et d'autres critères.
• 🧠 Une solution basée sur l'IA est proposée pour aider les analystes à construire des plans d'action, plutôt que de simplement générer des rapports.
• 📈 Des trackers spécialisés sont utilisés pour analyser les données financières d'entreprise de manière systématique et multidimensionnelle.
• 🗣️ Une interface de speech-to-text est envisagée pour simplifier l'interaction avec l'application et aider à identifier les problèmes non résolus dans les rapports.
• 🔄 L'IA est utilisée pour upskill les analystes et les aider à améliorer leur expertise en matière d'analyse financière.
• 🎯 L'objectif de la solution est de devenir un outil d'analyse complémentaire aux méthodes de reporting traditionnelles, offrant une vision plus large et une aide à la prise de décision.

Q & A

Quel est le nouveau modèle d'analyse des entreprises présenté dans le script ?

Le nouveau modèle d'analyse des entreprises présenté dans le script est la méthode d'analyse des risques et des opportunités. Elle utilise une intelligence artificielle neuro-symbolique pour la prise de décision, permettant de découvrir automatiquement les signaux d'alerte précoce dans les données de l'entreprise.

Quelle est la durée de recherche et développement pour cette nouvelle méthode d'analyse ?

La durée de recherche et développement pour cette nouvelle méthode d'analyse est de cinq ans.

Quelles sont les antécédents de l'orateur en matière de science des données ?

L'orateur a travaillé dans le domaine de la science des données pendant cinq ans avant de rejoindre Alis. Avant cela, il était Directeur Financier (CFO) dans le secteur public en France. Il a également publié beaucoup de contenu lié à la science des données sur LinkedIn et Medium.

Comment l'intelligence artificielle peut-elle aider dans l'analyse des performances d'entreprise ?

L'intelligence artificielle permet aux organisations d'automatiser la détection et la découverte des risques et des opportunités, ce que l'on appelle les signaux d'alerte précoce. En utilisant cette méthode, on peut identifier sept fois plus d'informations pertinentes que par l'exploration humaine.

Quel est le rôle d'un analyste dans le cadre de cette nouvelle méthode d'analyse ?

Le rôle d'un analyste est de comprendre la cause fondamentale du problème. Grâce à l'intelligence artificielle, les analystes peuvent automatiquement détecter les anomalies dans les données de l'entreprise, puis trouver la cause racine et bâtir un plan d'action correctif.

Quelles sont les principales limitations des outils de reporting actuels ?

Les outils de reporting actuels sont principalement basés sur une analyse consolidée des données, ce qui masque les anomalies et les insights pertinents. Ils ne permettent pas une analyse multi-dimensionnelle systématique et ne répondent pas aux questions sur la conversion d'insights en plans d'action.

Pourquoi les entreprises ne détectent-elles pas proactiquement les anomalies avec les logiciels de business intelligence actuels ?

Les logiciels de business intelligence actuels ne sont pas conçus pour détecter proactiquement les anomalies. Ils laissent aux analystes la responsabilité de faire une analyse multi-dimensionnelle manuelle, ce qui est impossible pour l'homme lorsqu'il s'agit de données à plusieurs dimensions.

Quelle est la solution proposée par Alis pour améliorer la détection d'insights ?

Alis propose une solution qui utilise de l'intelligence artificielle, combinant l'IA générative et l'IA symbolique sous forme de graphes KGE, pour trouver la cause racine et bâtir un plan d'action correctif. Cette méthode permet de repérer jusqu'à sept fois plus d'insights qu'avec les méthodes traditionnelles de reporting manuel.

Comment la solution d'Alis aide-t-elle les entreprises à réagir plus rapidement que leurs concurrents ?

La solution d'Alis permet de détecter les anomalies et les risques plus tôt que les concurrents, grâce à une analyse multi-dimensionnelle systématique et automatisée. Elle offre également des insights sur les actions correctives à prendre, permettant aux entreprises de réagir rapidement et de s'adapter à la situation.

Quelle est la vision globale de la solution proposée par Alis pour l'analyse des entreprises ?

La vision globale de la solution proposée par Alis est de trouver des opportunités même dans les unités d'affaires en difficulté et des points d'amélioration dans les unités prospères. Elle vise à aider les entreprises à répondre aux questions critiques et à suivre les KPIs pertinents grâce à une analyse plus approfondie et à une prise de décision plus éclairée.

Quelle est la méthode proposée par Alis pour améliorer la qualité des données et la pertinence des insights ?

Alis utilise une approche de traitement par lots pour détecter les insights chaque mois en utilisant des données financières de qualité supérieure. Ils ont également développé un algorithme de priorisation pour gérer le flot d'alertes et pour identifier les insights les plus importants en fonction de la stratégie de l'entreprise et de la situation urgente.

Outlines

00:00

📝 Présentation de la nouvelle méthode d'analyse d'entreprise

Anthony, le responsable des opérations d'Alis, présente une nouvelle méthode d'analyse d'entreprise basée sur l'intelligence artificielle. Après cinq années de recherche et de développement, cette méthode, appelée "reporting des risques et des opportunités", permet de détecter automatiquement les risques et les opportunités, ou les signes d'alerte précoce, dans les données d'une entreprise. Cette approche permet de découvrir sept fois plus d'informations que l'exploration humaine. Le rôle de l'analyste est alors de comprendre la cause profonde des problèmes. L'intelligence artificielle, en utilisant des techniques génératives et symboliques, aide à identifier la cause racine et à élaborer un plan d'action correctif.

05:00

🔍 Problèmes avec les outils de reporting actuels

Le script souligne les problèmes avec les outils de reporting actuels, qui sont principalement basés sur une analyse consolidée de données. Cela masque les anomalies et les insights importants qui ne sont pas visibles dans l'analyse consolidée. Les outils actuels ne permettent pas une analyse multidimensionnelle systématique, ce qui est essentiel pour identifier les problèmes. Les outils de reporting ne fournissent pas non plus de KPIs sur le nombre d'insights identifiés et convertis en plans d'action. L'intelligence artificielle peut jouer un rôle crucial pour résoudre ces problèmes en détectant proactivement les anomalies et en aidant à répondre aux questions critiques.

10:00

🤖 Solution Alis pour la détection d'insights

Alis propose une solution qui utilise l'intelligence artificielle pour une analyse multidimensionnelle des données. Leur outil peut identifier jusqu'à 85% de plus d'insights que les méthodes traditionnelles de reporting manuel. Cependant, l'afflux d'insights peut être overwhelming, nécessitant une algorithme de priorisation pour aider les utilisateurs à identifier les insights les plus importants. Alis a développé un algorithme de priorisation qui prend en compte la stratégie de l'entreprise, l'urgence et la faisabilité pour aider les utilisateurs à agir efficacement.

15:03

💡 Amélioration du rôle de l'analyste avec l'IA

L'IA permet de libérer les analystes de la tâche de générer des rapports et de les aider à trouver des informations pertinentes. Les analystes peuvent ainsi se concentrer sur la résolution des problèmes et l'élaboration de plans d'action. Alis utilise une combinaison d'IA générative et d'IA symbolique pour aider les analystes à comprendre les causes profondes des problèmes et à construire des plans d'action. L'IA sert à recommander des actions et à poser des questions, aidant ainsi les analystes à améliorer leur expertise et à prendre des décisions plus éclairées.

20:04

🌐 Vision de l'IA dans l'analyse d'entreprise

La présentation d'Anthony met en évidence la vision d'Alis sur l'IA dans l'analyse d'entreprise. Il souligne que l'IA peut aider à détecter des problèmes de manière systématique et à recommander des actions, mais que les humains sont mieux placés pour prendre des décisions. L'IA peut également aider à améliorer l'expertise des analystes, en particulier dans les entreprises où il y a un taux élevé de rotation. Le but d'Alis est de créer une application simple et facile à utiliser qui automatise la génération de rapports et permet aux analystes de se concentrer sur la résolution des problèmes.

Keywords

💡Intelligence artificielle neuro-symbolique

L'intelligence artificielle neuro-symbolique combine l'apprentissage profond (neuro) et l'intelligence symbolique pour traiter et interpréter des données complexes. Dans le script, cette méthode est utilisée pour automatiser la détection des risques et des opportunités, améliorant ainsi l'analyse de performance des entreprises. L'approche permet de découvrir des signaux précurseurs (early warning signs) avec une efficacité sept fois supérieure à l'exploration humaine, illustrant son importance dans la prise de décision basée sur les données.

💡Détection d'anomalies

La détection d'anomalies est le processus d'identification des écarts significatifs dans les données d'une entreprise, qui peuvent indiquer des problèmes ou des opportunités cachées. Le script met en évidence l'utilisation de l'IA pour automatiser cette détection, permettant ainsi une identification plus rapide et plus précise des anomalies dans les entrepôts de données des entreprises, par rapport à l'exploration manuelle par des humains.

💡Graphes de connaissances (Knowledge Graphs)

Les graphes de connaissances organisent et intègrent des informations sous forme de réseaux d'entités et de leurs relations interdépendantes. Dans le contexte du script, ils sont utilisés pour informer l'IA générative et améliorer la compréhension des causes profondes des anomalies détectées, facilitant la création de plans d'action correctifs.

💡Analyse multidimensionnelle

L'analyse multidimensionnelle permet d'examiner les données à travers plusieurs dimensions ou angles simultanément. Le script souligne son importance dans l'identification des anomalies et des opportunités d'affaires, en mettant en évidence que l'IA peut traiter de manière systématique et automatique plusieurs dimensions, ce qui dépasse les capacités humaines limitées à des analyses tridimensionnelles sur des feuilles de calcul.

💡Priorisation des alertes

La priorisation des alertes est le processus de classement des anomalies détectées selon leur importance ou leur urgence. Le script décrit comment l'utilisation de l'IA permet d'organiser les alertes générées par la détection d'anomalies, en s'assurant que les utilisateurs se concentrent sur les problèmes les plus critiques, en fonction de la stratégie de l'entreprise et d'autres critères pertinents.

💡Consolidation des analyses

La consolidation des analyses fait référence à l'agrégation de données provenant de différentes sources en un aperçu unifié. Le script critique cette approche pour masquer des anomalies importantes au niveau granulaire à cause des phénomènes de compensation. Il plaide pour une approche de déconsolidation permettant une analyse plus fine et précise des données.

💡IA générative

L'IA générative est capable de créer du contenu nouveau et original à partir de données existantes. Dans le script, elle est utilisée conjointement avec des graphes de connaissances pour générer des explications et des plans d'action correctifs, illustrant son rôle dans la compréhension des causes profondes des anomalies et la facilitation de la prise de décision.

💡Détection proactive

La détection proactive implique l'identification des problèmes potentiels avant qu'ils ne deviennent critiques. Le script questionne pourquoi les outils de business intelligence actuels ne détectent pas proactivement les anomalies, mettant en lumière la capacité de l'IA à remplir ce rôle et à anticiper les problèmes, améliorant ainsi la réactivité des entreprises.

💡Rapports de risques et opportunités

Les rapports de risques et opportunités fournissent une analyse ciblée des potentiels défis et avantages pour une entreprise. Le script introduit un nouveau moyen, développé par Alis, d'automatiser ces rapports grâce à l'IA, mettant en évidence l'amélioration significative dans la détection des anomalies et la génération d'insights pour une prise de décision éclairée.

💡Analyse granulaire

L'analyse granulaire fait référence à l'examen détaillé des données au niveau le plus fin. Elle est cruciale pour identifier des insights spécifiques qui peuvent être masqués dans des vues consolidées. Le script met en avant cette approche pour montrer comment l'IA peut révéler des anomalies et des opportunités cachées par des effets de compensation dans des analyses moins détaillées.

Highlights

提出了一种新的商业分析方法，通过多年的研究和开发，专注于风险和机会报告。

使用神经符号人工智能进行决策，通过自动化检测公司数据仓库中的异常，发现风险和机会的早期警告信号。

与人类探索相比，该方法能够发现7倍多的洞察力，转变分析师的角色，更注重理解问题的根本原因。

利用生成型人工智能和符号型人工智能的结合，通过知识图谱（KGE图）来发现问题的根本原因。

该方法不仅适用于问题业务单元寻找机会，也适用于成功业务单元的改进点分析。

提出问题：为什么2023年的商业智能软件还不能主动检测异常？强调人工智能在大规模并行处理中的作用。

指出当前报告工具的问题，如合并分析导致的信息补偿和隐藏，以及缺乏动态技术分析。

强调需要系统性多维分析，以及人工智能在自动执行此类分析中的必要性。

讨论了当前报告世界的状况，指出传统报告工具只能提供全局视角，而无法发现被隐藏的洞察力。

提出了解决方案，使用机器学习和监督学习进行异常检测，并结合生成型人工智能和知识图谱。

解释了如何通过人工智能帮助用户构建行动计划，而不是完全自动化决策过程。

讨论了数据质量问题对报告的影响，以及如何通过工具解决这些问题。

强调了人工智能在增强分析师技能和专业知识方面的应用，特别是在人员流动率高的领域。

提出了一种新的人工智能应用方法，即通过生成型人工智能辅助分析师提出问题和建议行动。

讨论了人工智能在决策支持中的角色，强调人类在最终决策中的重要性。

提出了一种新的报告工具，旨在通过人工智能自动化和系统化地提供风险和机会的早期警告。

强调了人工智能在帮助用户理解和应对复杂业务问题中的潜力。

讨论了如何将人工智能与业务分析和项目管理相结合，以提高决策效率。

Transcripts

started

recing hey Anthony welcome welcome to

this event and happy you could make it

um thank you so um I'll let you start

okay so today I would like to talk about

um a new method uh to do business

analysis uh at Alis we have been working

on it uh for many years five years of

research and development and uh it is a

new way to do um we call it risk and

opportunities

reporting uh to to talk more about me so

I am a chief IE officer at

Alis uh I have been uh doing data

science for five years uh before that I

was a CFO so Chief Financial Officer in

the public sector in France and I have

been uh uh publishing many data science

related content over LinkedIn and medium

and uh since then I it has been two

years that I I have joined Alis on this

project on this

adventure uh so uh what we are

using we are using neuros symbolic

artificial intelligence for decision

making uh what we have understood

is

that uh for for to do a business

performance analysis the artificial

intelligence uh allow an organization to

automate uh the uh detection the

discovering of risk and opportunities

what we call early warning signs and by

doing it with method that we use we spot

seven s times more insights than by

human exploration so the question is no

longer where to look

at it

is the why question the analyst and the

artificial intelligence sport Insight

needs to understand the root cause of

the problem so it is a new role for them

because because we use artificial

intelligence to automatically detect

anomalies within uh the companies data

warehouse uh the the new role for the

analyst is to find the root cause and

for that what we are

leveraging are artificial intelligence

in form of generative EI combined with

symbolic EI in form of kge graph uh so

it is it is to find the root cause and

on you you you spot the root cause uh

all method allow also to help the

analyst uh build a corrective action

plan so it is the old question or to

react quicker than your competitors and

to to to to act on those uh root cause

uh that uh our method allow to to detect

so our

approach its main goal is to find

Opportunities even in a trouble business

unit okay and

Improvement points in success successful

ones uh because even your best uh

business units uh have Improvement

points and I will show how how we are

doing

that okay what I ask our clients is

this uh why in

2023 no business intelligence software

is able to proactively detect

anomalies why is it is it the the end

user the analyst

to

manually uh that he has to manually uh

do a

multi-dimensional to do an an

analysis why we don't use artificial

intelligence with massively uh parallel

processing to detect proactively

anomalies this is the first question and

usually what I ask also to clients is

this from your reporting uh we can take

for example September do you know how

many insights were identified and

generally the answer is no because this

is uh a kpi it is not a kpi that is uh

um analyzed by

companies and even imagine if we we we

we we we respond to this question yes

uh how many of those insights did you

convert into corrective action plans and

we can go even further uh from those

corrective action action plans what is

the trend curve of Correction rate of

insight into action plans those those

question should be the uh kpi of any

Reporting System yet currently in many

companies they are not uh answered they

are not followed and it should be the

the main goal of any reporting tool

currently and we think at Al thisis that

artificial intelligence uh is a parament

is essential to answer those question

and help a business answer those

question and follow these uh main C

Capi so what is the main problem of

current reporting tools

the main issue is

Consolidated

analysis all current

reporting are based are built on

Consolidated analysis of

data why is it a problem I will show you

a simple example right here we have two

continents okay uh what you can see at

the solidated view is that continent two

it's in a worse situation than continent

one but but if you DEC consolidate okay

what you can see is that for continent

one that seem better off there is an

anomaly that is compensated by other

countries for this continent this is the

problem of consolidation because

consolidation you have multiple uh

mathematic IAL

phenomenon like Simpson Paradox

like mix when you mix there is a

compensation between negative value and

positive value but even in positive

value you have a compensation uh if you

have an extreme positive value and other

value are smaller in comparison this is

Extreme positive value will be hidden

will be masked by Consolidated uh data

by Consolidated value value this is this

this seem a simple problem but it is not

because right now every um reporting

tool uh um uh consolidates data and

don't have a a comprehensive and

systematic way to

do uh a deconsolidated

analysis and it is also true in time de

in time dimension

right here I am showing you an example

where um I am analyzing the current year

and I compared it to the previous year

so I am in 23 and I am comparing 22 I am

studying what we call in b b business

finance the year to date so I am

analyzing the

situation uh we are in October so until

until October and I and I comparing the

situation from the pre previous year so

right here in my reporting what I

observe is that I am better off in

203 comparing to 22 but this

Consolidated time analysis uh behind

that you can have multiple

situation

okay it might be that the situ the

current situation it explained by an

anomaly a positive anomaly uh uh right

here you can see an outlier a quarter

outlier that is explaining the current

better off situation of my current here

to date but it can also be that uh my my

my positive trend uh is degrading is is

decreasing okay but you can Al also have

a a tendency reversal so you wear better

off you were improving during uh until

the the Third quarter and after you are

degrading those multiple and I I only

took a three example but there is many

many configuration uh all those example

show that you need to

deconsolidate even time analysis so you

need to add Dynamic technical analysis

within your reporting to analyze the

trends uh and the dynamic of your data

so and another issue that is uh I am

stacking stacking up problems so for you

to understand what is the current issue

of

reporting another issue is that to have

a an Insight detection solution you need

to do a systematic multi-dimensional

analysis okay I will show you an example

right here I am taking the client uh

dimension in line I have product

but every of those line are

Consolidated okay so here the situation

appear appear to be good okay all all

are positive all is fine okay I am

taking the now the product dimensions in

every line of my client dimension for

the product Dimension all are

Consolidated and

positive but what

happen if I combine the two with those

same data

here with only two Dimension it is it is

only to illustrate the necessity of

multi-dimensional analysis with only two

Dimension I can see I can spot problems

and now imagine imagine to do that with

a four four five six seven eighth

Dimension uh right now every most of

multinational companies big company have

built uh olap cubes a multi-dimensional

data warehouse so they are able to do

those multi-dimensional analysis but it

is impossible for human Okay human can

can only

do

uh up to three dimension on a spre

spreadsheet spreadsheet so so you need

artificial intelligence to do

this in a systematic and automatic

manner

yet not many many companies not many

solution propose that okay and yet yet

it is necessary to sport

insights okay right now what is the

situation of reporting of the reporting

world what current reporting tool can

see is this it is a global vision a

reporting a Consolidated View Vision

what we call a top

approach so what they spot is what we

call flush head flush head are rotten

Branch they are problem that are so

important that they are coming

up the they are affecting the

Consolidated

view okay but when you spot them as a

Consolidated level it is too late

because what you you should have done is

to spot

them when they first appear before they

they were so big that they were

affecting the Consolidated View and what

those reporting doing Consolidated

analysis are not showing you what they

can't show

you are pocket of insights and golden

nuggets what we call them it is meth

methaphor metaphor but those are insight

that are impossible to spot at a

Consolidated view why because there is

there is compens compensation effect

they are they are concealed within your

reporting you can't spot them as a

Consolidated

View and be because because you don't

look at the at the most granular optimal

level of data analysis you can't spot

those insights and what we found with

our solution with with our experimental

uh uh work is that

85% of insights are currently not found

by traditional mean of reporting by

manual mean of

reporting when you use artificial

intelligence to do that in a systematic

way you find up to seven time more

insights more anomalies within uh for

example a business reporting uh for a

large corporation

so it is like we we are comparing it to

a Iceberg of urance I don't know if you

know the the management auor that told

about that but it is like uh you are

uring most of the problem that you have

okay

so how can you tell that the approach

that we are proposing and and I think we

are not the only one to propose um to uh

to propose a solution of inside

detection but how can you tell that our

solution is better than others

okay uh there there are three

criteria like I said I think that you

need a multi-dimensional analysis

because no not no human and and no human

are able to do it and you need

artificial intelligence to do that in a

systematic way because you need a

multi-dimensional analysis you need to

compare product geography a time uh

region uh Etc to spot anomalies uh to to

detect early warning sites to act on

them quick and and and quicker than your

competition but what happen when you do

that is that you are overflowed by alert

okay because you are sporting seven time

more Insight the user will be

overflowing by alerts and anomalies and

what you need to

introduce is some sort of priorization

so you need to build up a priorization

algorithm what what what what will be

within this priorization

algorithm uh are for example the

strategy of the

company uh some notion of urgency some

notion of um feasability

some notion of whatever we built we

built with an University uh a

priorization algorithm that we can

personalize to the company Focus or

strategy because you need to tell the

user with this uh what are insight that

are uh I high priority level what are

the most impactful insights

and what you do what you need also to do

you need to to find the most optimal

granular level of analysis and it is uh

more simple to say it than to find it so

you need a

method you need to find the right level

of analysis that you can

compare across time okay because if you

go too low to too um too below too below

certain

fesal you won't be able to compare it

over time so you need to find a perfect

granular level to do your

analysis and artificial intelligence can

help also with

that

so so those what I I just explained

those are the problem of current

reporting what we have building what we

have built at Alesis is the solution to

to solve those problem and to um to to

give tools to a company to have an early

warning side detection

tool

so this in this uh now I will talk about

our data quality

approach right now our solution is based

on the use case for business finance so

we use uh Finance

data to detect

anomalies so it is a a batch a batch

processing uh uh tool right now for to

detect Insight every month because

Finance data is every month uh and why

why we we chose this this this approach

it's because Finance data uh generally

is the mo is the most uh is um its

quality is better uh because there there

are lot of um regulation around Finance

data because you got the taxpayer it

wants it's money you want Auditors

Auditors want certain uh certification

around run Finance data shareholders

also want a good quality of Finance data

because they they want to know where to

put their money okay

so for the what for the first part of

detec detecting inside we use Finance

data and we found Insight what our tool

is

proposing is to find the causality of

what Insight because Insight are just

symptoms okay there will be anomalies

within uh data warehouse uh so an

anomalies of

symptoms is not possibly a problem you

need to find uh the causality the why

for you to know if you need to act or no

so right here we are helping the analyst

in in his new role because this is a new

role for analyst most analyst in large

corporation are only doing the first

part the what but the what like I just

said we automate it with artificial

intelligence so we need the user in in

his in his new role and I we think that

it it new role is is in more it has more

value much more value than the first one

the second one is much more complicated

and and right here what we use is

generative

a but not just only generative a we have

built rack system so retrial augmented

generation that is a system that use use

data to help the Genera VI generate its

content and we where we get our data

it's from operational data so the

problem with operational

data is that you you you got quality

issues some talk about data swamp in

many companies because you got e genous

data quality inconsistency in accuracy

uh you got because if you take also

external data you you will got biased

one-sided aliden data you are in a VCA

World okay VCA it means a complex word

in certain uh so generative VII will be

here only to to to give you

methodologies to give you uh what is

currently

available it will we are here in using

artifici icial intelligence to to build

a collective intelligence so it is to

help the user build an action plan and

not build for them the action plans

because here it is too complicated to

automate so artificial intelligence is

only able to help the user find

information and what we like to do is

that here we are more in the data

science size and here in this part we

are more in a decision science s and

decision science sites you got a

psychology you got uh information that

are not present in the data in the in

the in the internet in the data way

house in instructure data

Etc one question are you going deeper

into the algorithms so I can ask my

question here uh I I will uh in the

subse slide I I will go deeper yes so I

would be

patient I am doing uh in the step by

step uh

manner okay so what we use so I would

like to to show you our stack of

artificial

intelligence so we use machine learning

supervis and nonsupervised uh machine

learning uh for the detection sit okay A

Time series um we use Al EJ boost uh to

predict values from uh

the the detection in the detection part

of our

solution like I said we also use

generative VI so what we do but I I will

show

you right here we combine generate VI

with chge graph a chge graph that are

built uh on operational data from our

clients and we also use chge graph uh to

inform uh the generate VI of some sort

of methodologies

uh for me it is an alternative of

fine-tuning i i i i much more better

build U an effective sge graph uh that

gives uh that can uh when you have an an

ontology or operational data than fine

tuning a model because fine tune uh you

need with time you need to to retrain

your model Knowledge Graph is more

easier to up toate to to prove over time

and what we have also we have uh some

parts of reinforcement learning the

reinforcement learning part it's more

for the super priorization the

priorization algorithm that we use and I

what I will show I will show the actual

app just after this

line This is the data science side okay

and what we have on

top are symbolic artificial

intelligence

because our prediction from machine

learning supervis

nonsupervised use in the detection sites

a rule engineering expert system so we

combined a connectionist with a symbolic

artificial intelligence and I will show

you just after this

slide uh how how we do we we did that so

and the second part is decision

science we have a natural business

analysis explainable intelligence that

explain what is happening in orap but in

why this insight has been prioritized

Etc and we have what I have just said a

virtual collective intelligence what we

call a process intelligence that helps

the user build his action plan so right

here we have an escalation a hierarchy

of

explainability because those part of at

the bottom are not explain are not that

much

explainable and more you you come up

this uh line more they are more

explainable so we use symbolic EI to uh

to have an

explainability within our our stack of

artificial intelligence and I will show

you how how it it happen in the

solution so what are what what are the

symbolic EI that we use we call them

trackers okay trackers are specialized

analysis so like I just explained

earlier we have the maintain the M of

data what we do is that we explod this m

to have a a small granular small

granular slides of data and for each

slides of data we do a systematic

multi-dimensional analysis and to do

this systematic multi-dimensional an

analysis we

use some sort of rules because uh you

need to know where to look at and

currently our use case is uh is built

around business finance so our our

trackers are are here to simulate best

practice SOA a state-of-the-art practice

for business finance

analysis and what happen is that we have

two types of trackers we have trackers

that are mathematical and business

mathematical are here to spot a

mathematical anomalies okay for example

a gross revenue increasing

significantly above his objective okay

it is a threshold that we put that is

best practice to analyze corporate data

and it will it will spot this threshold

automatically within granular data

analysis but we get so business trackers

business tracker are more uh uh um um

specialized rule for to do corporate

finance right here I I put an example of

gross revenue increasing plus net

revenue decreasing it is an an anomaly

okay if your gross revenue is increasing

and that your net revenue is decreasing

is that you have a problem okay so we

spot how much automatically and and we

have 100 uh just here I just shown you

two example but we have hundred of those

trackers and we can with our client

build more

okay imagine that our client have some

sort of strategy or some sort of

specific problem we can build them with

them those type of

trackers so these are the symbolic uh

part

alongside Knowledge

Graph

so what I have just said until now seem

very complicated okay but what we aim

for is link

reporting we want our app to be in one

page

okay with two

parts we want a part that shows the risk

and opportunities found by artificial

intelligence right here in the first

part you got overview so we we show the

user what it it is used to see

Consolidated

data okay generally in pobi for example

reports you got Consolidated data and

the user can drilled on okay that's what

happen in most reporting here we show

the Consolidated data but below it we

show where to act okay what are the top

insights and right here you got a

list so you got a

list what are the most important

important

Insight individually and for each of

those

insights there there were there were a

multi-dimensional analysis and and those

are granular slice of data that were

systematically analyze and compare and

you got on the same line you got a train

analysis okay so we we do a dynamic

analysis we we show the user uh where

this product for example is going in the

next month we show a risk this is the

risk of not attaining the budgets the

budget Target okay we show the influence

of outlier

if this this uh result was explained by

outlier and we show

effects effects are uh the mix of price

and volume okay uh something can be

explain uh by an evolution in price but

also by an evolution in volume so we

show that in this visualization so this

is the first part of our application the

second part it is interpretability part

what we use we

use all fashion natural language

processing combined with Genera VI to

explain for each of those insights so I

have just to click on it and I will get

a full explanation of why this insight

has been

prioritized I will get also a

comparision with other product

etc etc for the analyst to to know why

this insect has been prioritized and to

be explainable because we are using

artificial

intelligence so what I am telling you is

that our

solution don't mean to erase the current

weight to do standard

reporting what we are building here is a

boost is a a complex mentary is a more

effective way to do traditional

reporting and it is complimentary in in

in many

ways so if we compare the uh the ability

to to give an overall picture A big

picture our reporting is not able to

give a big picture even even if we show

the Consolidated view but standard

reporting is more able to show uh an

overall picture or big picture okay

does the standard reporting do a

complete analysis no I I've just just

shown you uh standard reporting don't do

a multi-dimensional analysis don't de

consolidate so our solution is able to

detect early oneing sight uh to detect

uh problems when they happen and the

fastest fastest way possible and they

they do it it do it uh in a systematic

way and with a priorization

uh algorithm to do

that is standard reporting a guidance on

where to act no standard reporting in

most company are

Forest of reports and you don't know

where to act you need to do manual

digging it's like

Fishing manual

digging uh and human uh to do for

example a drill down approach to

deconsolidate manually it takes time and

it is a guessing it is an arbitrary way

of analyzing data what we do is that we

automate and systematically with

parallel uh processing algorithm with

Cuda calculus we automate the systematic

way of the aaging multi-dimension

uh the

analysis so in term of dimensional

analyze with with standard reporting you

can go to up to

three

dimension more you can't show it

visually you can't show it in a in a

pobi in whatever in a in in an

orac you can't show more than three

dimension with our solution this is why

we don't show visualization in this way

we we we do

unlimited amount of Dimension at the

same time in the

meantime as many as the management

account accounting for example for you

are use case currently

carries but what is useful with current

reporting is that it give

you where to

demobilize because on you spot uh an

Insight what high the high level

management will will want to do is to

move resources on those insights to dep

prioritize maybe some other project that

are not

uh a PRI priority thanks to our analysis

so this reporting can help you know

where to act in this regard where to

demobilize demobilize resources

so this

is the second part on you spot

Insight what we are building is Genera

VI to help the user build an action plan

so I have my insight okay I the Deep

detect we call it the first module

detect an insight and I want to build an

action plan with deep root this is the

second module that we we got and what we

we did is that we bu we built a

methodology to analyze a

problem okay what we are

spotting we do in a in a multi multi

multistep way a methodology to analyze

this problem and we are combining behind

the H A generate T VI with Knowledge

Graph to do that so the artificial

intelligence is here to to ask questions

to spot information available but also

to spot information that are necessary

but but they are not available so what

we what we would like to do with that is

to induce some sort of gumba walk some

sort of questioning some sort of inquiry

to prepare the analyst to do a business

plan and to show that at the actual

meeting that will decide prioritize

project so we are making a bridge

between the we reporting world and the

uh project management World in most

companies those two words don't talk

with each

others so we are building a two a bridge

between these two

words and we are building also a process

intelligence in the app uh you can uh uh

you have a view uh of what artificial

intelligence you use which which Step

because we are simulating what what I

didn't explain is that we are simulating

sparing sparring partners so I am

simulating a marketing director I am

simulating a financial director so

obviously each of those artificial

intelligence will have access to the

specific data of his domain and will be

able to help the user uh build some sort

of process intelligence Maybe in the

risk opportunity scope you you talk with

the the the

marketing generative AI uh and after the

context change when you analyze the con

that has been changing uh with the um

Supply uh uh Supply manager you want to

go back to the risk opportunity scope

and to talk with the uh Chief Financial

Officer okay what we are building here

is not to automate we are here to help

the user find information and build

something okay and I and we found that

generate VII is not here to help user uh

do for example text

tosql uh help them ask question to Data

Warehouse they are here to help to to to

ask to to have methodology to help them

get methodologies to help them find

information uh in this way they are far

more

effective so that's was it my

presentation I have lots of questions

yes no problem because

um how did you train the model yes did

you train a model or are you

using standard kind

of for for the generative AI part okay

uh for the model currently I am using um

because I I have no choice because I am

leveraging conage graph I am using uh

three 3.5 five uh model of open ey I am

I am uh it is a necessity because it is

the only model that is is not that much

costly and is able to quaring in um in

Cipher

but but what I I want uh and what I am

looking for currently in term of

development is to leverage for example

zier zier

7B or that sort of Open Source model to

do that but currently I didn't see in in

the research papers uh any evidence of

such capabilities I didn't have time to

find two an open source model of this oh

okay but then for the inside you're

using anomal detection model right

mostly inside no because what we are

using like like I said we are using uh a

layer of symbolic artificial

intelligence okay we are using

effectively uh a Time series to uh

calculate uh Trends and uh data

scientist will tell me that I don't have

enough data uh to do that but we have

some technique of data augment

augmentation mhm

uh and our solution overall is an

anomaly detection but we don't use uh an

anomaly detection uh we have an an

inhouse uh algorithm that is proprietary

we are doing a patency pat patency yes

claim we have a mark of Shain that is

doing the priorization of

insight so the priorization that you saw

behind the Ood is it is a complex Mark

of Shain to prioritize business problems

okay and when you said we we using I

mean this is interesting long long time

ago when I started studying artificial

intelligence it was still called expert

system yes that's why that's why we use

it yes and to see that expert system are

coming back after probably 30 years

there are I think right now uh recently

a stud a study showed that generate tvi

is more is more able to do text tosql

thanks toled graph where thege graph

have a database Shema in it so I think

we we are seeing am and I I am writing a

lot about it we are seeing uh some sort

of neuros symbolic I call neuros

symbolic system that are emerging

because the problem with a pure deep

learning approach is that deep learning

is not

interpretable uh or or it is very very

much difficult to interpret yeah and I I

believe that in most use case you want

you

need interpretability an

interpretation because artificial

intelligence its role is to if you want

artificial intelligence to be

prescriptive to prescribe uh

action you need a collaboration between

what the are the output of the

artificial intelligence and the human

and to have this collaboration you need

some layer of

interpretation

so and

uh but okay when you go to different

clients right then you might have

different rules business to you

different data quality you have

different semantics you have different

context you have different everything

right so yes so SCE you you very there

are two question in your

question for for data quality what

happens is that our tool is so granular

is so multi-dimensional that at

first what you are getting are lot of

data quality issues we are spotting

anomalies that are not based on a

business problem but anomalies that are

based on uh on the quality issue and and

we are we we help the client deal with

that because we are expert my company

has been working on on on this type of

reporting tool for almost 20 years and

we are expert in dealing with those

types of data quality issues so we help

them resolve their quality issues so it

is the first St of this is the first it

is the first tape and what you told me

is

that our current use case is Corporate

Finance so we uh like I said with

symbolic AI we built methodologies to

analyze Corporate Finance but even even

for this use case we can add specific

trackers okay currently we are working

with a large multidimens multinational

corporation in France and we are adding

uh specific trackers that are relevant

of what what we are doing right now with

their data so we can personalize our our

tool because uh the Deep learn the

machine learning part is is uh is

instrument to the symbolic part

the the main engine are symbolic the the

machine learning part is instrumental so

it doesn't it's not the main focus of

symbolic layer at the top you show

because our true Innovation if I I I I

put it blankly it's not an artificial

intelligence Innovation it is a

methodology method of De consolidating

multi-dimensional uh but it is only

possible with methodology

by uh parallel processing massively

parallel processing and with artificial

intelligence to do it in a systematic

and automated

way anyway this is a dream right to be

able

to um to get early warnings to get early

insights to take actions to make

decisions this has been a dream since

I'm do since I'm doing technology by the

way so it's not new

it is a dream but we talk about um my my

CEO talk about with high level figures

in the big

Corporation currently uh reporting tools

don't deal with Consolidated problem

because they don't have a

solution they they simply don't have a

solution or they just used uh

statistical kind of methods right to

find ins it doesn't yeah but then yeah

then you still need to like you said to

deconsolidate right now with a classical

statistical method there there are nine

nine methods to deconsolidate you can

use PCA Dimension reduction etc etc the

problem with those method is that uh you

can't do it

systematically and you need an a data

scientist to to to ask himself how here

there is maybe a problem and I I will do

uh some sort of analysis but it is too

late because when it SP the problem it's

taking too much time yeah it's taking

too much time yes so our our solution I

won't reveal or we we did

it obviously but but we we take a a

surprising

approach a Brute Force approach of

course I approach interesting I don't

know if there are more questions but

um so it's quite interesting because we

see the application of not only

generative AI but also standard machine

running uh you you spoke about xgb XG

boost right so which is kind of standard

machine

learning for database for symb combining

quite a lot of different yes and for one

page because what we want

is the Simplicity of it we want

something

U to be funny uh we are we are

incorporating in the app a speech speech

to interface but I can talk to the

application to show me what I want to

see yeah we want something that is

simple to understand because we want to

focus the Analyst job and they there

might be hundreds of them in in our

client to to focus on building a Sol

solution to the problem that has been

spot and not to build reporting because

the situation

currently most of the time of of of the

job day of those

analy are building

reporting dealing with data quality and

exact exactly we want to automate that

and we think that artificial

intelligence needs to be used to do that

because it is possible to do that in a

systematic way and we are not here to to

to to allow the user to ask any question

he wants right now all of lot of Builder

are speaking about text tosql unlimited

data on demand

Etc we think that artificial

intelligence is much better better than

than us to find problems in a systematic

way and to help making decisions based

on the experience yes but but to make

decision we think that the human it's

it's is it's more better to find

problems the EI is more better to make

decision the human is more better but it

can be helped with artificial intellig

but you can recommending you can

recommend actions

exactly you can also ask questions for

example you can have the we see it as

questions right it is interesting

because we see a generative as some sort

of recommendation engine it is here to

it is here to to recommend action but

not to make action for the user yes a

colleague of mine was showing me just

this morning um just a quick uh quick

and dirty app about reporting by the way

and then somebody was writing a report

and then the app was asking questions

that report was not answering right so

for example I don't know you have a

project stus report and then you forget

to men a Target date you forget to

mention this that then the the board of

the assistant will spot this and say

okay what about when do you expect this

to happen you need a Target blah blah

blah so this kind of thing is really

augmenting the intelligence yes because

I think gener is is really good to to to

to process a lot of data and to infly to

to to next token predict what is missing

uh what

what's present and yes it's really good

it's really good to do that like like

like you just just said I use it a lot

with u um with tabular with tabular data

frame and I I I ask it I ask it what is

missing from this

uh yes yes it's very good and we are

using it for that we are using it what

we are doing is that you you are using

it here in your solution for operational

data but not only for unstructural for

structured information but also for

unstructured information we are putting

in graph methodologies a way of doing

things a way of doing a problem a

particular problem Etc ET to help the

user to to upskill the level of

expertise of those analyst because uh

for our use case we have a problem of

turnover for those position and lots of

analyst don't stay a lot of time within

the company so we are helping them also

with their for their

expertise Mercy thank you very much

thank you thank you it was very

interesting good discussion hope

everybody enjoyed so I will I will

upload the video on YouTube and give you

the links on LinkedIn and every

everywhere possible thank you

Patrick and enjoy nice to talk to you

and enjoy the weekend

speak