AlphaSense Downloads Some Bucks

May 3, 2022

AlphaSense is a market intelligence platform utilized by businesses to extract insights from 10,000 business sources, including SEC filings, news sources, analyst research, and transcripts. AlphaSense prides itself on assisting companies to save time and discover important market information that is translated into profits. Alley Watch details the business platform’s latest round of fundraising: “AlphaSense Raises $180M For Its Market Intelligence And Search Platform For Businesses To Be In The Know.”

The article is an interview with AlphaSense’s CEO Jack Kokko, who stated that the investors in the Series C fundraising were Goldman Sachs Asset Management, Morgan Stanley, AllianceBernstein, Viking Global Investors, City, Cowen Inc., Barclays, Wells Fargo Strategic Capital, Bank of America, as well as their past investors. He also highlighted the services AlphaSense offers and what makes it different from its competition:

“AlphaSense is a market intelligence and search platform for businesses. It leverages AI and natural language processing technology to extract relevant insights from an extensive universe of public and private content, including over 10,000 premium business sources. Without AlphaSense, people would lack a reliable way to find the mission-critical information that matters, given how disparate and inaccessible much of it is. We enable professionals to make critical decisions with confidence and speed, improving their business performance and outcomes.”

What appears to make AlphsaSense different is its AI-enabled tech teamed with NLP that locates pertinent information and then delivers it in a user-friendly manner.

Kokko started AlphaSense because when he worked at Morgan Stanley he found the current search technology lacking. He partnered with Rag Neervannan to launch AlphaSense in 2011 to create a platform that would fill a niche. Kokko was proud that during this round of investing many of his clients invested in AlphaSense. With the funding, he plans to invest in product development, expand in new territories and languages, and hire more employees.

Whitney Grace, May 3, 2022

DarkCyber for January 18, 2022 Now Available : An Interview with Dr. Donna M. Ingram

January 18, 2022

The fourth series of DarkCyber videos kicks off with an interview. You can view the program on YouTube at this link. Dr. Donna M. Ingram is the author of a new book titled “Help Me Learn Statistics.” The book is available on the Apple ebook store and features interactive solutions to the problems used to reinforce important concepts explained in the text. In the interview, Dr. Ingram talks about sampling, synthetic data, and a method to reduce the errors which can creep into certain analyses. Dr. Ingram’s clients include financial institutions, manufacturing companies, legal subrogration customers, and specialized software companies.

Kenny Toth, January 18, 2022

Business Intelligence: Popping Up a Level Pushes Search into the Background

January 17, 2022

I spotted a diagram in this Data Science Central article “Business Intelligence Analytics in One Picture.” The diagram takes business intelligence and describes it as an “umbrella term.” From my point of view, this popping up a conceptual label creates confusion. First, can anyone define “intelligence” as the word is used in computer sectors. Now how about “artificial intelligence,” “government intelligence,” or “business intelligence.” Each of these phrases is designed to sidestep the problem of explaining what functions are necessary to produce useful or higher value information.

Let’s take an example. Business intelligence suggests that information about a market, a competitor, a potential new hire, or a technology can be produced, obtained (fair means or foul means), or predicted (fancy math, synthetic data, etc.) The core idea is gaining an advantage. That is too crude for many professionals who are providers of business intelligence; for example, the mid tier consulting firms cranking out variations of General Eisenhower’s four square graph or a hyperbole cycle.

Business intelligence is a marketing confection. The graph identifies specific “components” of business intelligence. Some of the techniques necessary to obtain high value information are not included; for example, running a fake job posting designed to attract employees who currently work at the company one is subject to a business intelligence process, surveillance via mobile phones, sitting in a Starbucks watching and eavesdropping, or using analytic procedures to extract “secrets” from publicly available documents like patent applications, among others.

Business intelligence is not doing any of those things because they are [a] unethical, [b] illegal, [c] too expensive, or [d] difficult. The notion of “ethical behavior” is an interesting one. We have certain highly regarded companies taking actions which some in government agencies find improper. Nevertheless, the actions continue, not for a week or two but for decades. So maybe ethics applied to business intelligence is a non-starter. Nevertheless, certain research groups are quick to point out that unethical information gathering is not the dish served as conference luncheons.

Here are the elements or molecules of business intelligence:

  • Data mining
  • Data visualization
  • Data preparation
  • Data analytics
  • Performance metrics / benchmarking
  • Querying
  • Reporting
  • Statistical analysis
  • Visual analysis

Data mining, data analytics, performance metrics / benchmarking, and statistical analysis strike me as one thing: Numerical procedures.

Now the list looks like this:

  • Numerical procedures
  • Data visualization
  • Data preparation
  • Querying
  • Reporting
  • Visual analysis

Let’s concatenate data visualization and visual analysis into one function: Producing charts and graphs.

Now the list looks like this:

  • Producing charts and graphs
  • Data preparation
  • Numerical procedures
  • Querying
  • Reporting.

Querying, in this simplification, has moved from one of nine functions to one of five functions.

What’s up with business intelligence whipping up disciplines? Is the goal to make business intelligence more important? Is it a buzzword exercise so consultants can preach doom and sell snake oil? Is it a desire to add holiday lights and ornaments to distract people from what business intelligence is?

My hunch is that business intelligence professionals don’t want to use the words spying, surveillance, intercepts, eavesdrop, or operate like a nation state’s intelligence agency professionals.

One approach is business intelligence which seems to mean good, mathy, and valuable. The spy approach is bad and could lead to an on one Lifetime Report Card.

The fact is that one of the most important components of any intelligence operation is asking the right question. Without querying, masses of data, statistics software, and online experts with MBAs would not be able to find an online ad using Google.

Net net: The chart makes spying and surveillance into a math-centric operation. The chart fails to provide a hierarchy based on asking the right question. Will the diagram help sell business intelligence consulting and services? The scary answer is, “Absolutely.”

Stephen E Arnold, January 14, 2022

TikTok: Innocuous? Maybe Not Among Friends

January 5, 2022

Short videos. No big deal.

The data about one’s friends are a big deal. A really big deal. TikTok may be activating a network effect. “TikTok Tests Its Own Version of the Retweet with a New Repost Button” suggests that a Twitter function is chugging along. What if the “friend” is not a registered user of TikTok? Perhaps the Repost function is a way to expand a user’s social network. What can one do with such data? Building out a social graph and cross correlating those data with other information might be a high value exercise. What other uses can be made of these data a year or two down the road? That’s an interesting question to consider, particularly from the point of view of Chinese intelligence professionals.

China Harvests Masses of Data on Western Targets, Documents Show” explains that China acquires data for strategic and tactical reasons. The write up doses not identify specific specialized software products, services, and tools. Furthermore, the price tags for surveillance expenditures seem modest. Nevertheless, there is a suggestive passage in the write up:

Highly sensitive viral trends online are reported to a 24-hour hotline maintained by the Cybersecurity administration of China (CAC), the body that oversees the country’s censorship apparatus…

What’s interesting is that China uses both software and human-intermediated systems.

Net net: Pundits and users have zero clue about China’s data collection activities in general. When it comes to specific apps and their functions on devices, users have effectively zero knowledge of the outflow of personal data which can be used to generate a profile for possible coercion. Pooh pooh-ing TikTok? Not a great idea.

Stephen E Arnold, January 5, 2022

Amazon: Lobbying Is a Component of the Model Of Course

November 23, 2021

Small news item from the trusted source Thomson Reuters. The title of the item is “Amazon Wages Secret War on Americans’ Privacy, Documents Show.” What’s interesting is that the trusted outfit has tapped into Amazon “internal documents.” These content objects reveal to the intrepid trusted real news folks that

“ has killed or undermined privacy protections in more than three dozen bills across 25 states, as the e-commerce giant amassed a lucrative trove of personal data on millions of American consumers.”

In my lectures about this online bookstore I described some of Amazon’s public documents about its data wrangling, data stores, and data analytics capabilities. Sure, my lectures were directed at law enforcement and intelligence professionals.

How can an old person like myself using open source intelligence capture the scope, capabilities, and functionality of Amazon’s capabilities without resorting to the use of company confidential information.

If a person were to reveal company confidential information about Thomson Reuters or any of its subsidiaries, how might the Thomson Reuters “trust” brigade react to this situation?

I am no cheerleader for Amazon. I have been critical of leakers, including the cutesy Edward Snowden person.

Lobbying is an established component of many business organizations processes. Let’s think about big pharma, shall we? No, let’s not. What about those Beltway Bandits? No, let’s not.

“Trust” is an interesting concept, and I am disappointed that sensationalism and confidential information is what helps define “trust.”

Yep, real journalism. Why not rely more on open source information and good old fashioned analysis, interviews, and research? Is “too good to pass up” a factor? Blocking and tackling, right?

Stephen E Arnold, November 23, 2021

The Business Intelligence You Know Is Changing

November 11, 2021

I read “This Is the Future of Intelligence.” I have been keeping my researchers on their toes because I have an upcoming lecture about “intelligence,” not getting grades in schools which have discarded Ds and Fs. The talk is about law enforcement and investigator centric intelligence. That’s persons of interest, events, timelines, and other related topics.

This article references a research report from a mid tier consulting firm. That may ring your chimes or make you chuckle. Either way, here are three gems from the write up. I leave it to you to discern the wheat and the chaff.

How about this statement:

Prediction 1: By 2025, 10% of F500 companies will incorporate scientific methods and systematic experimentation at scale, resulting in a 50% increase in product development and business planning projects — outpacing peers.

In 36 months half of the Fortune 500 companies! I wonder how many of these outfits will be able to pay for the administrative overhead hitting this target will require. Revenue, not hand waving strike me as more important.

And this chunky Wheaties flake:

By 2026, 30% of organizations will use forms of behavioral economics and AI/ML-driven insights to nudge employees’ actions, leading to a 60% increase in desired outcomes.

If we look at bellwether outfits like Amazon and Google, I wonder if the employee push back and internal tension will deliver “desired outcomes.” What seems to be delivered are reports of management wonkiness, discrimination, and legal matters.

And finally, a sparkling Sugar Pop pellet:

By 2026, advances in computing will enable 10% of previously unsurmountable problems faced by F100 organizations to be solved by super-exponential advances in complex analytics.

I like the “previously unsurmountable problems” phrase. I don’t know what a super-exponential advance in complex analytics means. Oh, well. The mid tier experts do, I assume.

Read the list of ten findings. I had a good chuckle with a snort thrown in for good measure.

Stephen E Arnold, November 11, 2021

About TikTok and Privacy: $92 Million Catch Your Attention

March 4, 2021

I have commented about the superficial understanding shared among some “real” and big time journalists of data collection. What’s the big deal about TikTok? Who cares what kids are doing? Dismissive attitude flipped off these questions because “real” news knows what’s up?

ByteDance Agrees to US$92 Million Privacy Settlement with US TikTok Users” suggests that ignoring the China-linked TikTok may warrant some scrutiny. The story reports:

The lawsuits claimed the TikTok app “infiltrates its users’ devices and extracts a broad array of private data including biometric data and content that defendants use to track and profile TikTok users for the purpose of, among other things, ad targeting and profit.” The settlement was reached after “an expert-led inside look at TikTok’s source code” and extensive mediation efforts, according to the motion seeking approval of the settlement.

My view is that tracking a user via a range of methods can create a digital fingerprint of a TikTok user. That fingerprint can be matched or cross correlated with other data available to a specialist; for example, information obtained from Oracle. The result is that a user could be identified and tracked across time.

Yep, today’s young person is tomorrow’s thumbtyper in one of the outfits compromised by the SolarWinds’ misstep. What if the TikTok data make it possible to put pressure on a user? What if the user releases access information or other high value data?

TikTok, TikTok, the clock may be ticketing quietly away.

Stephen E Arnold, March 4, 2021

Is Business Intelligence the New Silver Bullet for Managers Who Want to Be Even More Intelligent?

February 15, 2021

Survey results from an outfit called Reveal contains some interesting market data. “Demand for Business Intelligence Solutions Increases by 41 Percent” provides some data about the sample size (120 developers and information technology Napoleons) but zero information about how these individuals were selected, the confidence level Reveal considers just peachy for the analysis, and other now old fashioned facets of a “survey” once taught in Statistics 101. Those YouTube videos about survey methods are good enough, thank you.

Here is one of the findings from the study:

A 41 percent increase in demand for business intelligence solutions in 2020.

Okay, financial, social, and political excitement, the Covid Rona thing, and the implosion of once boring business sectors like airlines, real estate, and local small businesses.

Business intelligence to the rescue with real time analyses of data, predictive analytics, and nifty charts and graphs.

Were there other findings from this remarkably amusing sample? Absolutely. To wit:

  • 50 percent had budgets chopped
  • 23 percent had projects killed
  • 14 percent lost staff and funding.

Popular buzzwords include the aforementioned predictive analytics, edge analytics which I assume means cloud based services like Amazon AWS, and “natural learning” which I possibly machine learning, AI, et al.

Net net: Let’s bet on business intelligence. Better, faster, and cheaper. Plus, managers will be able to make better decisions based on actionable intelligence. Sounds good, right?

Stephen E Arnold, February 15, 2021

Business Intelligence, Expectations, and Data Fog

February 10, 2021

Business intelligence and government intelligence software promises real time data intake, analysis and sense making, and outputs with a mouse click. Have you heard the phrase, “I have the Brooklyn Bridge for sale”? Sure, sure, I know I don’t want to own the Brooklyn Bridge, but that super spiffy intelligence software (what I call intelware), count me in.

The marketing pitch for business intelligence and general intelligence software has not changed significantly over the years. In my experience, a couple of nifty outputs like a relationship diagram and a series of buttons set up to spit out “actionable intelligence” often close the deal. The users of the software usually discover three points not making up a large part of the demos, the discussions, and the final contract for the customer’s requirements.

I read “The Age Of Continuous Business Intelligence.” The idea is appealing. Lots of information and no time to read, review, digest, analyze, and discuss the available information. In my opinion, the attitude now is “I don’t have time.”

Yep, time.

The write up asserts:

we [an outfit called KX] know that shortening the time it takes to ingest, store, process, and analyze historic and real-time data is a game changer for businesses in all sectors. Our customers in finance, manufacturing, automotive, telecommunications and utilities tell us that when processes and systems are continuously fed by real-time data that is enriched by the context of historic data, they can automate critical business decisions resulting in significant operational and commercial benefits.

The write up contains a diagram which lays bare “continuous business intelligence.”


The write up concludes:

As the research clearly shows, real-time data analytics is a critical area of investment for many firms. To ensure maximum value is derived from these investments, it is imperative that organizations – regardless of size and sector – challenge their understanding of what real-time means. By implementing a strategy of continuous business intelligence, firms can dramatically reduce the time it takes to uncover and act on insights that can materially change the game in terms of growth, efficiency and profitability.

I love that “research clearly shows.” The challenges for the continuous thing include:

  • Defining real time. (According to research my team did for a project years ago, there are numerous definitions of real time, and there is a Grand Canyon sized gap among these.)
  • Making clear the computational short cuts necessary to process “fire hoses”. (Yep, these compromises have a significant impact on costs, validity of system outputs, and the mechanisms for issuing meaningful outputs from sense making.)
  • Managing the costs. (Normalizing, verifying, processing, storing, and moving data require human and machine resources. Right, those things.)

Net net: Software whether for business or government applications in intelligence work only if the focus is narrow and the expectations of a wild and crazy MBA are kept within a reality corral. Otherwise, business intelligence will shoot blanks, not silver bullets.

Oh, KX is hooked up with a mid tier consulting firm. What’s that mean? A sudden fog has rolled in, and it is an expensive fog.

Stephen E Arnold, February 10, 2021

An Existential Question: What Do Business Intelligence Tools Do?

February 10, 2021

Business intelligence tools are integral for enterprise systems to achieve their optimum performance, but without technology expertise it is difficult to understand their importance. Towards Data Science explains how BI tools can help a business in the article, “What BI Tools Can Do—The Six Different BI Artifacts You Should Know.”

According to the article, the six BI artifacts are spreadsheets, OLAP cube, visuals (reports and dashboards), stories, graphs, and direct access. Most BI tools do not feature all six BI tools and neither do companies. This does not allow end users to work at their best. There are work arounds and smart end users know how to utilize them.

Each artifact has its weaknesses and the only way to solve them is work around them like when there is a lack of tools:

“We basically have to do the same thing we do in the rest of our software architecture. We can build modular things, architectures where we can quickly exchange the EL in our EL (T). Where we can quickly exchange our storage, our reporting tool for a notebook based architecture. We can build evolutionary architectures, where we are perfectly clear on our fitting function, the quality of our answers to current problems. Where we know we will take small iterative steps towards providing better answers.”

It helps to be versed in all tools to improve BI structure, but it is even better to have access to the entire toolbox. Developers and workers are only as good as their tools.

Whitney Grace, February 10, 2021

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