How to Ruin your Business with Data Science & Machine Learning by Ingo MierswaData Con LA
Abstract:- Everyone talks about how machine learning will transform business forever and generate massive outcomes. However, it's surprisingly simple to draw completely wrong conclusions from statistical models, and correlation does not imply causation is just the tip of the iceberg. The trend of the democratization of data science further increases the risk for applying models in a wrong way. This session will discuss. How highly-correlated features can overshadow the patterns your machine learning model is supposed to find this leads to models which will perform worse in production than during model building. How incorrect cross-validation lead to over-optimistic estimations of your model accuracy, especially we will discuss the impact of data preprocessing on the accuracy of machine learning models. How feature engineering can lift simple models like linear regression to the accuracy of deep learning but comes with the advantages of understandability & robustness.
5 Big Data Visualization Maps that Will Make Your HEAD EXPLODEBI Brainz
From BI Brainz Analytics on Fire
Original Blog Post: http://bit.ly/1Dab2JG
Written by Ryan Goodman - @rmgoodm
Posted on Analytics on Fire - @analyticsonfire
Not all data visualizations can be simplified to a speedometer or bar chart. Big data visualizations require more sophisticated visualization tools and more brainpower. Here are some big data visualizations examples that will blow your mind!
How to Ruin your Business with Data Science & Machine Learning by Ingo MierswaData Con LA
Abstract:- Everyone talks about how machine learning will transform business forever and generate massive outcomes. However, it's surprisingly simple to draw completely wrong conclusions from statistical models, and correlation does not imply causation is just the tip of the iceberg. The trend of the democratization of data science further increases the risk for applying models in a wrong way. This session will discuss. How highly-correlated features can overshadow the patterns your machine learning model is supposed to find this leads to models which will perform worse in production than during model building. How incorrect cross-validation lead to over-optimistic estimations of your model accuracy, especially we will discuss the impact of data preprocessing on the accuracy of machine learning models. How feature engineering can lift simple models like linear regression to the accuracy of deep learning but comes with the advantages of understandability & robustness.
5 Big Data Visualization Maps that Will Make Your HEAD EXPLODEBI Brainz
From BI Brainz Analytics on Fire
Original Blog Post: http://bit.ly/1Dab2JG
Written by Ryan Goodman - @rmgoodm
Posted on Analytics on Fire - @analyticsonfire
Not all data visualizations can be simplified to a speedometer or bar chart. Big data visualizations require more sophisticated visualization tools and more brainpower. Here are some big data visualizations examples that will blow your mind!
Featured in the August edition of Qantas magazine, find out in this interview with Glenn McPherson, managing director of NetApp Australia & New Zealand, why it's important to unlock the value of your data through a Data Fabric approach.
2015 is knocking on the door and will be an exciting and surprising year for the BI industry. However, not everything will be a surprise for Panorama as we are always on top of the latest trends influencing the Business Intelligence community.
• What will the future hold for the industry?
• What are our BI experts thoughts, predictions and internal assessments on what new directions the Business Intelligence community will see in the coming year?
• Countdown of the most important trends in the industry
10 signs you should invest in Web ScrapingPromptCloud
The web is like an endless ocean of unstructured data, and with this data comes unexplored possibilities. So when's a good time to start acquiring data with web scraping? Here are 10 sings to look for.
IBM Virtual Finance Forum 2016: Top 10 reasons to attendIBM Analytics
Explore the top 10 reasons to attend IBM's Virtual Finance Forum 2016 for insights and best practices on performance management in the cognitive era. Attend your choice of three broadcasts of IBM's Virtual Finance Forum 2016: http://bit.ly/oct5am, http://bit.ly/Oct512Noon or http://bit.ly/oct5eve.
How do i export data from google maps to excel Alisha Raza
Google Map Extractor is a powerful scraping tool that helps you find leads from Google Maps easily. Google Maps Extractor is a tool that captures contact information like business name, address, phone number, Google Maps link, website link, zip code, and other important information from Google Maps.
Data as a Service (DaaS): The What, Why, How, Who, and WhenRocketSource
Data as a Service (DaaS) is one of the most ambiguous offerings in the "as a service" family. Yet, in today's world, data and analytics are key to building a competitive advantage. We're clearing up the confusion around DaaS and helping your company understand when and how to tap into this service.
Top Business Intelligence Trends for 2016 by Panorama SoftwarePanorama Software
10 top BI trends for 2016 – by Panorama
Its all about the insight
Visual perception rules
The learning suggestive system - AI gets real
The data product chain becomes democratized
Cloud (finally)
“Mobile”
Automated data integration
Interned of things data accelerating into reality
Hadoop accelerators are the last chance for Hadoop
Fading of the centralized on–premise DWH
freeDatamap whitepaper - data visualization BI & GIS -free datamap
Mind Mapping + Business Intelligence = freeDatamap.
Unchain your data with the lightest and most intuitive self-service BI platform. Try a new data browsing experience thanks to a holistic and organization-wide dashboard to understand all the key aspects of your business in a unified data map.
With freeDatamap, access the right data, share the knowledge, break silos, help data to go “social”, make data available and collectively enriched.
• Find your way in an overwhelming amount of information.
• Visualize your data in a centralized trusted map.
• Display your business process across your organization.
• Navigate into the map and drill down to find the root cause of an indicator.
• Find any atomic data thanks to a powerful and immediate search engine.
• Reduce time to make fact based decisions.
Suburbia, Alternative Data Expert (FinTech), asked me to design their sales booklet. This is the outcome. The booklet was meant for their stakeholders.
A framework that discusses the various elements of Data Monetization framework that could be leveraged by organizations to improve their Information Management Journey.
KEY CHALLENGES FOR MONETIZING BIG DATA POWERED AI: AN OVERVIEWTyrone Systems
YOU’RE NOT THE ONLY ONE FACING THIS PROBLEM
according to recent articles in the Harvard Business Review and McKinsey. But don’t blame big data for that. It’s all your fault
How Enterprises Can Incorporate Big Data And AnalyticsPromptCloud
The proliferation of mobile computing and social media has led to enterprises having to deal with large amounts of data of varying types. Here is how organizations can incorporate big data to reap its benefits.
Data is the New Oil: Presented By Naveen Narayanan, Global Client Partner of ...InterCon
InterCon is a premier technology conference that brings together like-minded people on a common platform to share knowledge, present ideas, get recognition, and network. InterCon Dubai will offer knowledgeable sessions, informative content, extraordinary speakers, and an overall memorable experience.
Follow us:
Facebook: https://www.facebook.com/InterConWorld
Linkedin: https://www.linkedin.com/showcase/int...
Twitter: https://twitter.com/InterConWorld
Instagram - https://www.instagram.com/interconworld/
Most of what companies know is typically held
in a data warehouse – a database that collects transactions and looks at customer transaction activity over time to understand who is buying what through which channel.
Know Your Market - Know Your Customer: What Web Data Reveals if You Know Wher...Connotate
In this presentation, Connotate will share expertise gained from years of experience extracting data from the Web and making it usable. Connotate’s experts will explain why certain Web data sources are easy to tap into, why others aren’t – what to consider when scoping out a project.
Featured in the August edition of Qantas magazine, find out in this interview with Glenn McPherson, managing director of NetApp Australia & New Zealand, why it's important to unlock the value of your data through a Data Fabric approach.
2015 is knocking on the door and will be an exciting and surprising year for the BI industry. However, not everything will be a surprise for Panorama as we are always on top of the latest trends influencing the Business Intelligence community.
• What will the future hold for the industry?
• What are our BI experts thoughts, predictions and internal assessments on what new directions the Business Intelligence community will see in the coming year?
• Countdown of the most important trends in the industry
10 signs you should invest in Web ScrapingPromptCloud
The web is like an endless ocean of unstructured data, and with this data comes unexplored possibilities. So when's a good time to start acquiring data with web scraping? Here are 10 sings to look for.
IBM Virtual Finance Forum 2016: Top 10 reasons to attendIBM Analytics
Explore the top 10 reasons to attend IBM's Virtual Finance Forum 2016 for insights and best practices on performance management in the cognitive era. Attend your choice of three broadcasts of IBM's Virtual Finance Forum 2016: http://bit.ly/oct5am, http://bit.ly/Oct512Noon or http://bit.ly/oct5eve.
How do i export data from google maps to excel Alisha Raza
Google Map Extractor is a powerful scraping tool that helps you find leads from Google Maps easily. Google Maps Extractor is a tool that captures contact information like business name, address, phone number, Google Maps link, website link, zip code, and other important information from Google Maps.
Data as a Service (DaaS): The What, Why, How, Who, and WhenRocketSource
Data as a Service (DaaS) is one of the most ambiguous offerings in the "as a service" family. Yet, in today's world, data and analytics are key to building a competitive advantage. We're clearing up the confusion around DaaS and helping your company understand when and how to tap into this service.
Top Business Intelligence Trends for 2016 by Panorama SoftwarePanorama Software
10 top BI trends for 2016 – by Panorama
Its all about the insight
Visual perception rules
The learning suggestive system - AI gets real
The data product chain becomes democratized
Cloud (finally)
“Mobile”
Automated data integration
Interned of things data accelerating into reality
Hadoop accelerators are the last chance for Hadoop
Fading of the centralized on–premise DWH
freeDatamap whitepaper - data visualization BI & GIS -free datamap
Mind Mapping + Business Intelligence = freeDatamap.
Unchain your data with the lightest and most intuitive self-service BI platform. Try a new data browsing experience thanks to a holistic and organization-wide dashboard to understand all the key aspects of your business in a unified data map.
With freeDatamap, access the right data, share the knowledge, break silos, help data to go “social”, make data available and collectively enriched.
• Find your way in an overwhelming amount of information.
• Visualize your data in a centralized trusted map.
• Display your business process across your organization.
• Navigate into the map and drill down to find the root cause of an indicator.
• Find any atomic data thanks to a powerful and immediate search engine.
• Reduce time to make fact based decisions.
Suburbia, Alternative Data Expert (FinTech), asked me to design their sales booklet. This is the outcome. The booklet was meant for their stakeholders.
A framework that discusses the various elements of Data Monetization framework that could be leveraged by organizations to improve their Information Management Journey.
KEY CHALLENGES FOR MONETIZING BIG DATA POWERED AI: AN OVERVIEWTyrone Systems
YOU’RE NOT THE ONLY ONE FACING THIS PROBLEM
according to recent articles in the Harvard Business Review and McKinsey. But don’t blame big data for that. It’s all your fault
How Enterprises Can Incorporate Big Data And AnalyticsPromptCloud
The proliferation of mobile computing and social media has led to enterprises having to deal with large amounts of data of varying types. Here is how organizations can incorporate big data to reap its benefits.
Data is the New Oil: Presented By Naveen Narayanan, Global Client Partner of ...InterCon
InterCon is a premier technology conference that brings together like-minded people on a common platform to share knowledge, present ideas, get recognition, and network. InterCon Dubai will offer knowledgeable sessions, informative content, extraordinary speakers, and an overall memorable experience.
Follow us:
Facebook: https://www.facebook.com/InterConWorld
Linkedin: https://www.linkedin.com/showcase/int...
Twitter: https://twitter.com/InterConWorld
Instagram - https://www.instagram.com/interconworld/
Most of what companies know is typically held
in a data warehouse – a database that collects transactions and looks at customer transaction activity over time to understand who is buying what through which channel.
Know Your Market - Know Your Customer: What Web Data Reveals if You Know Wher...Connotate
In this presentation, Connotate will share expertise gained from years of experience extracting data from the Web and making it usable. Connotate’s experts will explain why certain Web data sources are easy to tap into, why others aren’t – what to consider when scoping out a project.
As the strategic importance of data has increased, new approaches to customer analytics have emerged as well. As customer interactions with companies grow and diversify, the need to integrate data faster and deliver real-time insights is critical. This presentation explores the underlying trends driving companies to become more data-driven and invest in customer analytics. And, it outlines three types of approaches to capturing, managing, analyzing, and activating customer knowledge and insights.
PresentationThe capability of enormous information - or the new .pdfaradhana9856
Presentation
The capability of enormous information - or \"the new oil,\" as a few CIOs and industry
specialists have named it - appears as perpetual as it is subtle. Huge information battles are in
their early stages, with endeavors of all stripes making sense of how to utilize new, old,
unstructured and outer information to make a focused procedure.
Despite the fact that the standard procedures for get-together information and investigating its
value are as yet coming to fruition, organizations know they have to get in the diversion. They
are gathering and mining information on clients, workers, market flow, the climate, and so on,
with instruments going from conventional business insight (BI) frameworks to more trial ones,
for example, geospatial and constant versatile following innovations, online networking
investigation and NoSQL databases.
SearchCIO isn\'t remaining on the sidelines, either. Our Essential Guide on enormous
information incorporates a preliminary for beginning with information social affair and
investigation, true contextual investigations from the CIO and business viewpoints, tips on the
best way to beat hindrances experienced by the huge information pioneers, and expectations on
the following huge information boondocks and what it implies for aggressive methodology.
This aide on the development of huge information is a piece of SearchCIO\'s CIO Briefings
arrangement, which is intended to give IT pioneers vital administration and basic leadership
guidance on opportune themes.
The most effective method to Collect Big Data ?
1 year agoby Ayush1 Comment
The most effective method to Collect Big Data ? : Yes we knoe you would have various inquiries
in your psyche like Collection of Big Data, How organizations gather Big Data, how to gather
information for quantitative research so don\'t stress, in the event that you are here to scan for
these inquiries here then you are on the right website page as here we are going to give you a
complete article on Collection of Big Data techniques quickly.
Astounding Facts about Rise of Big Data Collectection
Consistently buyers make around 11.5 million installments by utilizing Paypal
Consistently, Walmart (chain of rebate retail chains) handles more than 1 million client
exchanges
510 remarks, 293000 status and 136000 overhauls are posted on Facebook consistently
Consistently, ~7000 tweets are made on Twitter
Simply picture the measure of information created if the above details are figured for 24 hours?
Whoa! That is huge.
The term \'Enormous Data\' is ordinarily connected with 4V\'s to be specific, Velocity, Volume,
Variety, Veracity. These 4V\'s appropriately speaks to the genuine way of Big Data. Each \"V\"
has a noteworthy part to play in the presence of Big Data. On the off chance that consolidated,
these 4V paints a wonderful clarification of Big Data which can be comprehended as \" Big Data
as an idea alludes to high speed gathering of information in expansive volumes which radia.
Data is being generated at a feverish pace and forward thinking companies are integrating big data and analytics as part of their core strategy from day one. However, it is often hard to sift through the hype around big data and many companies start with only a small subset of data. Can smaller companies benefit from big data efforts? We will discuss several use cases and examples of how startups are using data to optimize their operations, connect with their users, and expand their market.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
Virtual Data Steward: Data Management 3.0CrowdFlower
Every company that is serious about data governance needs data stewards. Data stewards connect business information requirements and processes with information technology capabilities. This function is essential to bridging data management policies and standards to day-to-day operational practices.
The presentation includes the introduction to the topic, the various dimensions of big data, its evolution from big data 1.0 to bid data 3.0 and its impact on various industries, uses as well as the challenges it faces. The concluding slide gives a brief on the future of big data.
Using Data Mining Technique, Loginworks is offering the web data mining solutions. One of the leading Data mining companies delivering data mining services.
https://www.loginworks.com/data-mining/
Is Dirty Data Clogging your Marketing Engine? Do what high performance companies do; implement a data management program with InsideView. The sooner you do, the lower the cost.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Analysis insight about a Flyball dog competition team's performance
Data science in the noc and beyond
1. Data Science in the NOC and Beyond
Clayton. A. Hollister
May 29, 2018
2. Introduction
• We are a small friendly company with the same
big challenges of any corporation.
• Although the scale may differ, we both collect
analytic data for our web site, we both have
data centers to manage and so on.
• Like the early telescopes which gave humans a
new perspective of our universe; viewing your
organization through a data lens provides a
new way to understand your business.
3. What Do We Do
• Our Data Science team combines raw
data from several sources for analysis.
• We convert the data to a matching format
and perform extensive mathematical
testing resulting in a graphical
representation.
Taking this …………………… to this
4. Real World Data Science
• You know your business and we know data
science. We work with you to imagine the
possible, find the feasible, and develop the
execution plan. In many situations humans find
themselves like a tooth on a gear too close to
the process to see the whole machine.
• A famous example recounts the birth of the
early automobile by saying if Henry Ford had
done a marketing survey and asked people
what they wanted; their answer would have
been a faster horse!
5. Real World Data Science Example
• One of our customers had problems managing their
volume of trouble tickets which we can file under big data
causes big problems.
• Our team was able to analyze their data and found a
problem with the way they had configured their network
hardware causing a port storm. We found other problems
that their in house team had not found as they had never
seen all of the data in the same format and in one place.
• The customer was able to make a simple configuration
change to remove the port storm issue which improved
their network operations data management efficiency over
30%. Other recommended changes were made resulting
in more improvements.
6. Web Site Analytics
• Our experience in this area both in house and for
customers has shown it to be very easy to miss important
data due to a localized maximum hiding other data.
• For example when you look at a chart which categorizes
all of your site visitors it will indicate a large number of
visitors from .com sites. This is because in part .com sites
are the most prevalent on the web.
• This will make the other categories look like insignificant
numbers, however as we found on our own web site when
we removed the large outlier .com numbers we realized a
lot of non-profit organizations were interested in a group of
services we offer. This triggered us to change our
marketing campaign.
7. Raw Web Analytics Data
Humans find it easier to understand their
data pictorially in charts – not like this
8. Hidden Data
The larger numbers for .com, net and unknown made the
“non-profit” bar seem insignificant in comparison.
9. Revealed Data
Once the larger values were filtered out the drill down
revealed a large number of downloads were being made by
non-profit organizations in Germany.
10. Can We Help You?
• If you are tired of searching through raw
data and want meaningful repeatable
graphic reports we can help.
• We would love to help you uncover the
hidden data in your organization. Please
use our contact form at this link.
• https://www.sinenomine.net/contact-us