Comparison of Biz Intelligence Tools

Via friend Walter Riker, useful. 

Power BI vs. Tableau: Business intelligence tools comparison

by William J. Francis in Software in Techrepublic.  on March 23, 2022, 3:07 PM PDT

Power BI and Tableau are business intelligence tools. Which top BI tool best fits your needs? We compare features and more.  ... '

Re-Defining Decision Making with AI and BI

 Interesting claim, but it does assume we understand how business decision making is made in our enterprise.  Something we attempted to understand over many years.   It still makes sense to study the processes being used and how they are influenced today by analytical methods as a starting point.

How AI-powered BI tools will redefine enterprise decision-making

Steve Sloane, Menlo Ventures   @bad2thesloane    April 2, 2021 8:05 AM

Value-creation in business intelligence (BI) has followed a consistent pattern over the last few decades. The ability to democratize and expand the addressable user base of solutions has corresponded to large value increases. Enterprise BI arguably started with highly technical solutions like SAS in the mid-’70s, accessible only to a small fraction of highly specialized employees. The BI world began to open up in the ’90s with the advent of solutions like SAP Business Objects, which created an abstraction layer on top of query language to allow a broader swath of employees to run business intelligence. BI 3.0 came in the last decade, as solutions like Alteryx have provided WYSIWYG interfaces that further expanded both the sophistication and accessibility of BI.

But in many cases, BI still involves analysts writing SQL queries to analyze large data sets so that they can provide intelligence for non-technical executives. While this paradigm for analysis continues to increase, I believe that a new BI paradigm will emerge and grow in importance over the next few years — one in which AI surfaces relevant questions and insights, and even proposes solutions.  ... ' 

Monitoring in-production ML models

Useful and detailed look at real world problems with Sagemaker Model Monitor.  Good graphical views.   Somewhat but practically technical.

Monitoring in-production ML models at large scale using Amazon SageMakerModel Monitor  | AWS Machine Learning Blog

by Sireesha Muppala, Archana Padmasenan, and David Nigenda | on 17 DEC 2020 | in Amazon SageMaker, Artificial Intelligence  

Machine learning (ML) models are impacting business decisions of organizations around the globe, from retail and financial services to autonomous vehicles and space exploration. For these organizations, training and deploying ML models into production is only one step towards achieving business goals. Model performance may degrade over time for several reasons, such as changing consumer purchase patterns in the retail industry and changing economic conditions in the financial industry. Degrading model quality has a negative impact on business outcomes. To proactively address this problem, monitoring the performance of a deployed model is a critical process. Continuous monitoring of production models allows you to identify the right time and frequency to retrain and update the model. Although retraining too frequently can be too expensive, not retraining enough could result in less-than-optimal predictions from your model.

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy ML models at any scale. After you train an ML model, you can deploy it on SageMaker endpoints that are fully managed and can serve inferences in real time with low latency. After you deploy your model, you can use Amazon SageMaker Model Monitor to continuously monitor the quality of your ML model in real time. You can also configure alerts to notify and trigger actions if any drift in model performance is observed. Early and proactive detection of these deviations enables you to take corrective actions, such as collecting new ground truth training data, retraining models, and auditing upstream systems, without having to manually monitor models or build additional tooling.

In this post, we discuss monitoring the quality of a classification model through classification metrics like accuracy, precision, and more.  ... " 

Difference Between Business Intelligence and Data Science

Nicely done piece.  Good charts at the links below.   Though in some ways I have to ask if there should be a difference in practice?   Depending on Goals and potential value?   I always say you should start any 'advanced'  analytics/science with a descriptive examination.    Or you should have the experts of that description closely accessible to your team.  Attitude should be the same:  Achieve the business goal.

Updated: Difference Between Business Intelligence and Data Science

Posted by Bill Schmarzo in DSC.

I'm reposting this blog (with updated graphics) because I still get many questions about the difference between Business Intelligence and Data Science. Hope this blog helps.

I recently had a client ask me to explain to his management team the difference between a Business Intelligence (BI) Analyst and a Data Scientist.  I frequently hear this question, and typically resort to showing Figure 1 (BI Analyst vs. Data Scientist Characteristics chart, which shows the different attitudinal approaches for each)...  " 

AI, Mixed Reality and Analytics Announced at Microsoft

Strong push at AI business applications and notably also their 'mixed reality' methods.   I still have not seen enough MR applications for the typical business. 

Announcing new AI and mixed reality business applications for Microsoft Dynamics  Alysa Taylor - Corporate Vice President, Business Applications & Industry

Today, I had the opportunity to speak to press and analysts in San Francisco about our vision for business applications at Microsoft. In addition, I had the privilege to make two very important announcements: the upcoming availability of new Dynamics 365 AI applications, and our very first mixed reality business applications: Dynamics 365 Remote Assist and Dynamics 365 Layout.

Our vision for business applications at Microsoft

We live in a connected world where companies are challenged every day to innovate so they can stay ahead of emerging trends and repivot business models to take advantage of new opportunities to meet growing customer demands.

To innovate, organizations need to reimagine their processes. They need solutions that are modern, enabling new experiences for how they can engage their customers while making their people more productive. They need unified systems that break data silos, so they have a holistic view of their business, customers and employees. They need pervasive intelligence threaded throughout the platform, giving them the ability to reason over data, to predict trends and drive proactive intelligent action. And with adaptable applications, they can be nimble, allowing them to take advantage of the next opportunity that comes their way.

Two years ago, when we introduced Dynamics 365 we started a journey to tear down the traditional silos of customer relationship management (CRM) and enterprise resource planning (ERP). We set out to reimagine business applications as modern, unified, intelligent and adaptable solutions that are integrated with Office 365 and natively built on Microsoft Azure.

With the release of our new AI and mixed reality applications we are taking another step forward on our journey to help empower every organization on the planet to achieve more through the accelerant of business applications. Specifically, today we are making the following announcements:  ... "
(more details here)

Business Intelligence vs Operations Intelligence

Had heard the Operational Intelligence term, but was not often mentioned in the enterprise.   This piece defines both methods and how they interact.   OI could be a place to start when you are trying to define and model specific business process.    But more detail of the kind in BPM can be very useful, and get you to more detail you can improve.

In iiAnalytics Blog:

BI versus OI, A Distinction with very big difference    By Geoffrey Moore

As a reader of this blog, you are likely quite familiar with BI (Business Intelligence). It has been a foundational element of enterprise computing for over thirty years, the mainstay of iconic companies like SAS, Cognos (now IBM), and BusinessObjects (now SAP). And I expect you may also have heard of OI (Operational Intelligence), but I am willing to bet you do not have a clear sense of what precisely that latter term refers to.

Looking up Operational Intelligence in Wikipedia does not help much. The definition there blurs the distinction between BI and OI by combining attributes from each. I have reprinted it below with what I consider to be the BI attributes in blue bold and all the OI ones in red italics:

It is not that this definition is wrong. It is just that it suppresses the differences between OI and BI, differences that are key for enterprise executives to understand. I think we would all be better served, therefore, if we first began by defining Operational Intelligence in direct contrast to Business intelligence, along the following lines:

Convergence of Analytics Products, Services

Below contains a link to a complimentary report. Inescapable direction.  Few would buy a single capability.

Continued Convergence Of Analytics Products And Services

By Boris Evelson,  Vice President, Principal Analyst
I’ve been watching this trend with great curiosity (first wrote about it in 2007).

On the one hand, software product vendors are slowly but surely migrating from just selling products to selling solutions. And solutions always require professional services. IBM led the trend when it acquired PwC in 2002. For the last few years GoodData has also been concentrating on building embedded BI solutions with a strong emphasis on professional services. Most recently MicroStrategy (and I hope other BI/analytics/big data vendors will follow soon) made a significant investment into its management consulting capabilities hiring hundreds of consultants and coming out with a “BI/analytics maturity assessment” service offering – formerly solely the realm of management consultants.  80% of BI projects success depends on people/process part of the equation and that’s why strong management consulting capabilities are key. .... " 

Microsoft: from Excel, to BI, to Advanced Analytics?

Excel is a legitimate starting place, that is used by many if not most enterprises.  So can it be a reasonable path for industry, when linked to right resources?   Good thoughts in linked piece.

Microsoft BI: From Excel to Power BI
Posted by Ravi Madhavan

Gartner's latest Magic Quadrant for Business Intelligence software ranks Microsoft as the Leader. Here's how their Power BI solution emerged as the evolution of Excel, along with some clarity on the various components and product names which Microsoft has been using. 

In 1985, Madonna was everywhere- on the radio, in the movies, and inside record stores on that new disruptive technology called compact discs. Back to the Future was the biggest movie of the year. Each Thursday night nearly 30 million households spent the evening watching the Cosby Show, Family Ties, Cheers and Night Court. It was also the year a ten-year-old Microsoft introduced a humble little program called Excel to the world. The story of Microsoft's rise to the top of the Business Intelligence software game with their Power BI product begins with Excel.

If there were a Mount Rushmore for software, Excel would make the cut. Of course, the real Mount Rushmore is a monument for past great, long dead presidents. So one may think BI, Big Data and Analytics would have made Excel little more than a flashback to the 80's and 90's. Think again. Estimates for total Excel users worldwide range from 400 to 750 million. Almost every organization around the globe still does some portion of their data analysis on Excel. It's fair to say even companies providing the most advanced BI and Analytics tools today still probably use Excel internally, although they may not want to mention it. ..... "

Intelligence and Analytics Case Studies

A nice set of broad examples that are insightful.

5 Business Intelligence & Analytics Case Studies Across Industry
Last updated on April 4, 2017 by Daniel Faggella

When businesses make investments in new technologies, they usually do so with the intention of  creating value for customers and stakeholders and making smart long-term investments. This is not always an easy thing to do when implementing cutting-edge technologies like artificial intelligence (AI) and machine learning. Business intelligence case studies that show how these technologies have been leveraged with results are still scarce, and many companies wonder where to apply machine learning first (a question at the core of one of TechEmergence’s most recent expert consensuses.)

Artificial intelligence and machine learning have certainly increased in capability over the past few years. Predictive analytics can help glean meaningful business insights using both sensor-based and structured data, as well as unstructured data, like unlabeled text and video, for mining customer sentiment. In the last few years, a shift toward “cognitive cloud” analytics has also increased data access, allowing for advances in real-time learning and reduced company costs. This recent shift has made an array of advanced analytics and AI-powered business intelligence services more accessible to mid-sized and small companies.

In this article, we provide five case studies that illustrate how AI and machine learning technologies are being used across industries to help drive more intelligent business decisions. While not meant to be exhaustive, the examples offer a taste for how real companies are reaping real benefits from technologies like advanced analytics and intelligent image recognition.  .... " 

Linking Echo to Business Data Queries

Does get back to when is a voice interaction with your data most useful?  An added human channel to data.    Another means to deliver business intelligence beyond the dashboard.

Sisense-Amazon Echo integration lets you ask Alexa questions about your business data  by Ron Miller (@ron_miller)

Sisense, a company which helps customers link multiple data sources and summarize them in a single dashboard view, has been working on ways to understand the data outside of the context of a computer.

Today, the company announced an initiative called Business Intelligence Virtually Everywhere. That could involve asking questions to a voice-driven system like the Amazon Echo or linking important Key Performance Indicators (KPIs) to an intelligent lightbulb that changes colors to represent the current state of the KPI — for example, green for meeting the metric, orange for being in danger of falling short, or red for missing it.   ... " 

Amazon Echo and Business Intelligence

Been looking at potential business applications of the Amazon Echo.  Like many virtual advisory applications, you can think of potential 'hands free'  scenarios, but these are relatively rare in the back office.  And rarer yet in stationary situations.     Sisense, a business intelligence supplier, believes there is engagement value in such an advisor.  CWeek is skeptical, as I am, but am looking forward to a demonstration:

" .... So what is Sisense actually doing here?
In the case of Amazon Echo, Sisense is allowing business users to ask questions and hear results in real time. Apparently, that is driving an increased efficiency and engagement. The idea being that a business user won't run a query about, say, regional Midwest sales this year versus last in a visualization tool. Instead, a user will ask Amazon Echo to perhaps play a song that matches the sentiment of salespeople in the U.S. versus those in Europe. Or something like that. Apparently, the ease of conversation drives increased engagement with data and opens access to BI insights. Or something. It's all about humanizing data consumption, apparently  ....  " 

SAP and Roambi

Forrester view with further comment.

Forrester Quick Take: SAP Acquires Roambi, Opens New Chapter In Mobile BI ... Posted by Martha Bennett

Thoughts on Gartner BI Conference Next Week

Brought to my attention.  The Gartner BI conference next week.  Noted in particular the list of exhibitors.   Wide view of corporate needs there.   Who do you bet on now?   And how does BI relate to data science?  As an introduction or prerequisite for every project?   I think visualization should always be a pre requisite and a key delivery item.   Its BI, maybe, but essential.

Looking forward at BI

Forward view of Business Intelligence. PDF eBook by IBM:

" ... Making smarter business decisions quickly and confidently drives business value across an entire organization. To accomplish this
in today’s competitive global marketplace, leading organizations are complementing their current approaches to business intelligence (BI) and analytics. ... 

In particular, top organizations are:
• seeking to incorporate deeper insight into their reports and dashboards
• making that information available to more people who may interact with it on the device of their
choice
• expanding the use of analytics to solve more business challenges.

This eBook discusses how to drive BI deeper into an organization with easier to use descriptive analytics solutions and how to transform an organization from being reactive to being pro-active by embedding predictive and prescriptive analytics in existing BI solutions. ... " 

Dashboards that Persuade

Whitepaper by Tableau Software.  Well done.  Registration information required.   " .... Do your dashboards tell the story you want to get across or does your data get lost in a sea of pixels? Tableau strives to keep our users in the flow with software centered on principles of design, cognition, and perception. The same principles apply to great dashboards. .... " 

Amazon Announces AWS Quicksight

Have subcribed to AWS for a time,  and the beta of this, good effort:

Amazon Announces QuickSight - Business Intelligence for Big Data on AWS by Matt Kapilevich  
Throughout the years, Amazon has introduced a number of solutions to make it easier for developers to collect, process, and store data, such as Amazon RDS, Amazon Redshift, Amazon Aurora, Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3. By introducing a fast, scalable BI service that caters to business users and can pull data from any of AWS data stores, as well as from on-premise data stores, Amazon takes an important step to becoming a full-service shop for all data needs. ....  " 

Demystifying Self Service Data to Gain Insight

Looking at the state of service Business Intelligence. Time to insight is an interesting concept.

" .... Based on a concept of centralized data analysts, traditional BI creates a system where IT specialists create deep-dive reports, typically for C-suite executives because analysts’ time is too valuable to spread any thinner.

Traditional BI is better than no BI, but business happens outside the C-suite, too, and traditional BI can’t hope to meet the breadth and volume of data demands from every business user or keep pace with the competitive data landscape. The “time to insight” is just too slow. Users can’t afford to send requests and wait days or weeks for a report that could already be outdated. To get the most out of the data your company collects, relevant data points need to be available to all business users when they need it. .... " 

Future of Excel in Business Intelligence

Via Paulina Gibson from Investintech:

" .... An interview with 27 Excel experts, where they talk about the future of Excel in Business Intelligence. ... "    Some good thoughts here.    Every enterprise uses Excel, so you can't ignore it as an existing consolidation place for data in the enterprise.  So also a place where business intelligence will live.   It will take some time to evolve away from this situation.   And effort's like Microsoft's Power BI will further slow the change.

(Update) I have added the comments on the interviews of my Excel Expert, Walter Riker below:
....

Content Analytics Re-Emerges

This was an approach we called 'Content Analytics' a long time ago, when the data we analyzed came from big spools of tape.  I had thought the term had gone away, to be replaced by 'Bigger' things.   So was glad to see this piece.  It now has many more tools, visual and analytical you can attach to get better results.   Now as then it has a more focused business direction.    Aimed at unstructured data, but even that term changes as we can better add our own structure.  In Datafloq:

" .... Content analytics can be defined as unlocking business value from unstructured content via semantic technologies to find answers to important questions or discover causes to certain trends. Companies can use content analytics to understand the content that is created, how it is used, the context it is in and the nature of that content. Content analytics is all about unstructured data and it can be used to explain trends in structured data and provide valuable insights to organisations.

Content analytics is especially relevant for organisations where knowledge is at the core of their business and in that case ordinary business intelligence is not sufficient anymore. Knowing who read what content, when, how often it was shared, number of clicks, location of visitors, etc. is not sufficient anymore. You should also care about whether the content is actually useful to your audiences and that it serves the objective it was intended for. Content related trends or insights revealing information such as what content caused a drop in sales can help make better content and thus drive growth or revenue. It is not about having more content; it is about having better content. ... " 

Windows Business Intelligence Automation

In CWorld: Mentions the integration of business intelligence and voice interaction with an operating system.  We could all use a personal assistant with business intelligence advisory and delivery skills. Assuming all the data would be easily available and understandable. This is probably more deeply integrated with Microsoft's Power BI. (See below for that, currently using it)  This is not automating Data Science, but the more mundane need for data visualization and basic statistics.    My guess is that is a big percentage of business intelligence needs.  BigData/Data Science can be added for more innovative solutions.   Yet voice interaction with something detailed like spreadsheet data can be a problem. Looking forward to see what this might look like in practice.