thing” Archives - Business Intelligence Info

Tag Archives: thing”

One thing you can’t return

December 28, 2020 Humor

Let’s block ads! (Why?)

Political Irony

GOOD THING TIPPING IS NOT USED IN JAPAN

November 24, 2020 Humor

Or this guy would be screwed.

He claims to not have spent money except for rent and utilities.

He gets everything via coupons.

We all love coupons and vouchers, but can you imagine living on them almost exclusively for almost four decades? A Japanese man claims to have been doing it for the last 36 years, adding that he hasn’t spent a yen of his own money during that time.

71-year-old Hiroto Kiritani is a minor celebrity in his home country of Japan. His ability to live comfortably on coupons without spending any money unless he really has to is legendary, and he has been invited on numerous television shows and events over the years. Kiritani says that he gets by without spending real money except for utilities and rent. But he’s not as frugal as you might think. He just manages to live comfortably on the coupons he receives from companies he invested in over the years.

Kiritani, who used to be a professional shogi (Japanese Chess), got into stock investment when he was 35. He was invited to teach the staff of an investment company called Tokyo Securities Kyowakai about shogi, and was fascinated by the idea of owning parts of various companies. He bought his first stock in 1984 and quickly developed a taste for it, encouraged by the stock bubble of the 1980s.

Unfortunately, in December of 1989 the Nikkei Stock Average crashed and he lost 100 million yen. It was a terrible blow, but it also helped him discover the worth of investor benefits, an alternative to dividends. Basically, as long as the profitability of a company remains above a certain threshold, shareholders qualify for certain benefits offered in the form of coupons and vouchers.

During the troubled time of the Japanese stock exchange crash of 1989, these investor benefits helped Kiritani get by, allowing him to buy food and clothing without spending any real money. The same happened in 2011, after the Great East Japan Earthquake, when the stock market crashed once again. The coupons he earned were more than enough for him to get by, and as word got out about his ability to live almost exclusively on them, he became famous in Japan.

According to Hiroto Kiritani, if a business performance deteriorates, dividends will be reduced, so this system is advantageous for large investors. Minor shareholders are much better off with the investor benefits that more than 40 percent of large Japanese companies offer, as profitability need only remain over a certain threshold.

Moreover, dividends are dependent on the number of shares a person owns in a company, whereas investor benefits are often times the same regardless of the number of shares. So even owning a single share can qualify investors for various benefits.

Kiritani claims that he gets access to everything he needs with coupons alone. One coupon allows him to go to the cinema for free 300 times a year, another offers free gym membership. He can even buy vegetables with coupons. For example one coupon he gets from the ORIX Corporation allows him to choose a variety of food products from a very generous catalog, for free.

Even though he can get all sorts of groceries with his coupons, Hiroto Kiritani says that he prefers to eat out, which, of course, he can do with coupons. He owns stock in over 1,000 Japanese companies and corporations (of which about 900 are preferential stocks), so he basically has all kinds of coupons to use for everything he needs. He has become so good at living on these pieces of paper that he has been invited on several TV shows and has given interviews for magazines about it.

“I only use cash when paying rent or cover costs that are not 100% covered by my coupons. I don’t spend much cash and live on a special treatment, so in the end, I’m saving more and more money,” Kiritani said.

Let’s block ads! (Why?)

ANTZ-IN-PANTZ ……

AI Weekly: Multimodal learning is in right now — here’s why that’s a good thing

November 23, 2019 Big Data

Data sets are fundamental building blocks of AI systems, and this paradigm isn’t likely to ever change. Without a corpus on which to draw, as human beings employ daily, models can’t learn the relationships that inform their predictions.

But why stop at a single corpus? An intriguing report by ABI Research anticipates that while the total installed base of AI devices will grow from 2.69 billion in 2019 to 4.47 billion in 2024, comparatively few will be interoperable in the short term. Rather than combine the gigabytes to petabytes of data flowing through them into a single AI model or framework, they’ll work independently and heterogeneously to make sense of the data they’re fed.

That’s unfortunate, argues ABI, because of the insights that might be gleaned if they played nicely together. That’s why as an alternative to this unimodality, the research firm proposes multimodal learning, which consolidates data from various sensors and inputs into a single system.

Multimodal learning can carry complementary information or trends, which often only become evident when they’re all included in the learning process. Plus, learning-based methods that leverage signals from different modalities can generate more robust inference than would be possible in a unimodal system.

Consider images and text captions. If different words are paired with similar images, these words are likely used to describe the same things or objects. Conversely, if some words appear next to different images, this implies these images represent the same object. Given this, it should be possible for an AI model to predict image objects from text descriptions, and indeed, a body of academic literature has proven this to be the case.

Despite the many advantages of multimodal approaches to machine learning, ABI’s report notes that most platform companies — including IBM, Microsoft, Amazon, and Google — continue to focus predominantly on unimodal systems. That’s partly because it’s challenging to mitigate the noise and conflicts in modalities, and to reconcile the differences in quantitative influence that modalities have over predictions.

Fortunately, there’s hope yet for wide multimodal adoption. ABI Research anticipates the total number of devices shipped will grow from 3.94 million in 2017 to 514.12 million in 2023, spurred by adoption in the robotics, consumer, health care, and media and entertainment segments. Companies like Waymo are leveraging multimodal approaches to build hyper-aware self-driving vehicles, while teams like that led by Intel Labs principal engineer Omesh Tickoo are investigating techniques for sensor data collation in real-world environments.

“In a noisy scenario, you may not be able to get a lot of information out of your audio sensors, but if the lighting is good, maybe a camera can give you a little better information,” Tickoo explained to VentureBeat in a phone interview. “What we did is, using techniques to figure out context such as the time of day, we built a system that tells you when a sensor’s data is not of the highest quality. Given that confidence value, it weighs different sensors against each at different intervals and chooses the right mix to give us the answer we’re looking for.”

Multimodal learning won’t supplant unimodal learning, necessarily — unimodal learning is highly effective in applications like image recognition and natural language processing. But as electronics become cheaper and compute more scalable, it’ll likely only rise in prominence.

“Classification, decision-making, and HMI systems are going to play a significant role in driving adoption of multimodal learning, providing a catalyst to refine and standardize some of the technical approaches,” said ABI Research chief research officer Stuart Carlaw in a statement. “There is impressive momentum driving multimodal applications into devices.”

For AI coverage, send news tips to Khari Johnson and Kyle Wiggers — and be sure to subscribe to the AI Weekly newsletter and bookmark our AI Channel.

Thanks for reading,

Kyle Wiggers

AI Staff Writer

P.S. Please enjoy this video about Bill Gates discussing AI at Bloomberg’s New Economy Forum in Beijing, among other topics like climate change and nuclear power.

Let’s block ads! (Why?)

Big Data – VentureBeat

The only thing we have to fear…

November 4, 2018 Humor

Could this election get any sillier? Is the dominant topic really a small, ragtag bunch of refugees fleeing Honduras in Central America? They have just only crossed the border into the most southern part of Mexico, and their numbers are already decreasing. Apparently they are going to walk north across mountainous country full of dangerous criminal organizations. Hardly anyone thinks that many, if any at all, will even make it very far, let alone within spitting range of the US border. And yet Donald Trump is sending US troops to the border now, even though it is illegal for the military to operate as police inside the US.

This is not our greatest problem in the US. In fact, it is almost not worth any mention at all. But even Fox News is talking about it nonstop. Don’t believe me? Here’s actual footage from Faux News:

[embedded content]

Also published on Medium.

Let’s block ads! (Why?)

Political Irony

Data is Inherently Messy. Is That Really Such a Bad Thing?

September 1, 2018 Big Data

Harald Smith

August 28, 2018

Editor’s note: This article on data management written by Syncsort’s Harald Smith was originally published on Infoworld.

In an imperfect world, consider shifting your data quality mindset from “how do I clean all this up?” to “how do I make the most of this state of affairs?”

A data quality expert once told me that vendors providing data quality software solutions should always ensure 100 percent quality data, and if they didn’t, they should be liable for any ensuing issues. I disagreed with that harsh assessment then—and still do. The truth is, sometimes 100 percent data quality isn’t necessary and could even hinder an organization’s ultimate business goals.

As much as you would like our data to be perfect and pristine, to conform to your established dimensions of data quality, it isn’t. While there’s been renewed focus in recent years on the importance of data quality for achieving higher-value data and improving machine learning, data quality is not a new problem. Tools to address data quality have existed since at least the early 1990s, and MIT held its first International Conference on Information Quality back in 1996.

After 20 to 25 years, you might expect that we would have mastered data quality! So why is 100 percent complete, clean, consistent, and accurate data still so difficult to achieve?

The answer lies in changing your mindset: Data quality is contextual, not universal. It’s time for us to accept and expect that data is messy: incomplete, nonstandard, inconsistent, inaccurate, and out of date—but that’s not necessarily a bad thing. By understanding the contexts that make data messy, you can focus your efforts on addressing data quality issues where they are most critical, and to tolerate the rest where other factors are more important—in other words, put data quality in the right place at the right time.

Good data or bad? Context matters

Not all data is created equal. We all have names—identifiers by which we are recognized. In seminars I’ve given, I’ve asked the question: “Is ‘John Doe’ good data?” Almost unanimously, the answer is no because it is considered fictitious and often used as test data. Yet “John Doe” is common and valid in health care or police investigations as the name for an unknown male (someone who does or did actually exist), in legal cases, as part of a Twitter handle for more than 100 people last I checked—not to mention there are real people with that name. The name John Doe is complete, consistent, and can be accurate. But you need to understand the context before you can say whether it is good, bad, or simply needs additional processing logic.

Numeric values and dates can be equally challenging. Just think about a rating scale from 1 to 5. Is 1 the best rating, or is 5? Or a value of 100—is that a perfect grade, a high Fahrenheit temperature, an age, or an invalid credit rating? You need context (supplied via documentation, help, policies, metadata, etc.) to understand the data correctly, and to implement the right data quality checks and rules. You must then determine whether there is a data quality issue at all, and if so, whether it’s one around which you need data quality measurements and processes.

Consistent data? Keeping the systems running

How you incorporate data into your operations and systems is another factor impacting your consideration of data quality. Building custom applications for every organizational function is expensive. Over time, you’ve replaced many of these with software packages and even suites of systems such as enterprise resource planning (ERP) products. Each of these products, as well as your homegrown applications, have systemic requirements. Enforcing a single, consistent organization-wide standard, whether for dates (annual calendar vs. timestamp vs. Julian date), Boolean values (T/F vs. Y/N vs. 1/0), or other codes, would be quixotic at best and otherwise resource- and revenue-consuming. The same is true for third-party data, including the increasing variety of open data available.

The definitions and semantics of data impact consistency of data as well. The definition of “customer,” for example, may vary depending on whether you are in marketing, order fulfillment, or finance. These semantic variations are more challenging because the data may look the same but produce different and inconsistent results (particularly in aggregated content) depending on inclusion or exclusion. Business glossaries, data catalogs, and system or operational documentation are imperative to ensure communication and effective data use.

It’s when you attempt to merge or integrate these sources or systems that data quality issues around consistency emerge. When deciding how to make sense of data that appears contrary, remember that systems are created for different needs at different places at different times, and with differing semantics. As you work to bring this disparate data together for new purposes, you need to establish which source becomes your system of record for each piece of data, where you need reference data, and ensure that the integration processes put in place reconcile and resolve the data differences and inconsistencies.

When service matters more

An accident scene or an emergency room is the most obvious example of a time when data quality is of secondary importance to providing the necessary service. You don’t refuse medical attention because you don’t know the injured person’s name, address, and date of birth. Sometimes the issues and needs are subtler, though. A good example comes from Feeding America, an organization that uses data and analytics to facilitate food distribution to those most in need. To extend its services, it implemented pilot programs to improve data collection and data quality. As it assessed those pilots, it found that where the upfront data collection became too time-consuming; hungry people would actually leave the line without food even if waiting longer would mean getting food. The organization realized its quest to improve data quality conflicted with the organizational goal to best serve the underfed in America, and consequently scaled back the data collection effort to ensure optimal service.

GettyImages 528068934 300x200 Data is Inherently Messy. Is That Really Such a Bad Thing?

New and emerging needs

Other business-driven requirements can create other, often divergent needs around data. To ensure accuracy in matching customer data, the more pieces of related data you have, the better. But in machine learning, including multiple pieces of highly correlated data can skew findings rather than finding something new. A good example presented by Ingo Mierswa on a data set for the Titanic disaster sought to show what factors most predicted survival. Because the data set included whether the individual was in a lifeboat or not, that factor was selected as the predictive factor. But that is a highly correlated piece of data and therefore skews the data. You will find similar high correlations simply by including both geolocation and street address for a customer. The data may be complete, consistent, and accurate, but still produce data quality issues.

Constrained reality

Each step or process in an information supply chain includes factors or forces that must be addressed. The further you move along in that supply chain, the more constrained you are by prior decisions. There is a lot of freedom in what you initially collect and what requirements you put in place there. But as data is integrated and consolidated and then delivered to subsequent systems for reporting and analysis, you must account for the varied upfront requirements, and this is what makes information management challenging. For instance, does it make sense for an organization to forego a sale by requiring an online order to include every demographic detail about a customer? No, because only a few pieces of data are required to fulfill it. But for that same order, will you test and validate the quality of the delivery address information? Yes, because that data is critical to high-quality customer service and successful completion of the order.

Learning to dance with data

You’ve spent a lot of time and money building data warehouses, data marts, master data solutions, and now data lakes to facilitate data-driven insight that you can trust. There is no one-size-fits-all data quality solution—but in a way, that’s a good thing. As I’ve described, targeting 100 percent data quality across the board is an unproductive aim because organizations, and even different lines of business in organizations, have different goals and address differing needs and constraints.

But there are actions that you can take to make the most of your data and ensure your data quality resources are being directed to the right places:

Accept the reality that data is messy.
Build up a reference of what data is available and what is useful for what purposes (including identifying sources of record).
Make tools available for users to adopt right-time data quality practices and approaches, and ensure your employees are data literate by communicating data policies and practices.
Understand where you are in the information supply chain and how you can draw on common patterns of information management to best leverage (and protect) data you want to use.
Build up and use common objects that are clear and understandable for transforming, consolidating, and merging data to facilitate consistent preparation and outcomes.
Establish measurements of data that is critical for your needs and analyze and evaluate it against business requirements.
Assess the fitness of data for each purpose and recommend actions:
- Is this the right data with the right context?
- If so, what needs to be done to ensure it is of the right quality and fit for your purpose?
Communicate findings so that others do not have to repeat the process.

When developing your organization’s data quality strategy, knowing where it matters and where it doesn’t is half the battle. Understanding that means you’ll be deploying data quality resources in the right places, reaping the benefits of data quality in meaningful ways while not wasting time and energy where it doesn’t.

Check out our eBook on 4 ways to measure data quality.

Let’s block ads! (Why?)

Syncsort Blog

CRMDialer President Dimitri Akhrin: Raw Data Is No. 1 Thing

June 13, 2018 CRM News and Info

Dimitri Akhrin is president of
CRMDialer.

In this exclusive interview, Akhrin addresses

85368 300x300 CRMDialer President Dimitri Akhrin: Raw Data Is No. 1 Thing

CRMDialer President Dmitri Akhrin

CRM Buyer: What are some of the current trends you see in the CRM space?

Dimitri Akhrin: AI is giving the ability to gain insight, and the most important example is visitor tracking. That’s a big component — knowing how in real time to react to something a prospect is doing on a site.

It’s going to allow prescriptive recommendations about what to do in a particular moment, instead of having to crunch data after the fact. The key is having a massive amount of data and being able to leverage the different data points.

CRM Buyer: What’s the key to making data useful?

Akhrin: Look at the best person that you have in an organization and focus on enabling everybody else to be like that person. You have your best sales rep who has worked with you for five years — someone who’s encountered all kinds or rejections and pushback.

For a new person coming in, scripts can be hard to navigate. But if I have internal raw data that my best sales rep has had, I can parse that data in real time through transcription services to analyze it to suggest the types of questions and answers to give.

This enables each sales person to be like the best sales person, using real data from past experience.

CRM Buyer: What is the best way to display these suggestions to sales reps? Should they be a script, or a bulleted list, or some other format?

Akhrin: The suggested thing for display is what the CRM can handle. If the way the CRM system works is on the scripting side, it can pop up as a script, or it can be a bulleted list of possible items to say in response to an objection.

CRM Buyer: What are the keys to improving sales using CRM?

Akhrin: CRM is the equivalent of a supply chain or assembly line. The CRM system is a process that an entire company’s business is built on from the beginning. In a successful company, all of the processes will go through a single system. As soon as an agent goes out of that system — they automatically lose efficiency.

If there are external tools that constantly have to be used, it keeps the employee from being as successful as possible. You need a single system in order to have a steady and moving pipeline.

CRM Buyer: Why is it important to integrate a CRM system with sales and marketing?

Akhrin: You need a single application or tool to let you know everything about a prospect or customer in real time, without having to be super tech-savvy or to access the information elsewhere.

A good CRM system allows you to have a conversation based on what’s relevant to each individual. At a coffee shop, for instance, you will have a better relationship with that brand if it remembers what coffee you prefer, and it doesn’t recommend tea.

CRM Buyer: What’s the key to making sense of all the data — and making it actionable?

Akhrin: Artificial intelligence is being able to analyze the past to make decisions in the present. It really comes down to having quality data about what has happened before, so you can build the model.

It’s important to have the proper information and to set up the rule engine correctly. Having the raw data is the No. 1 thing, and you build your decisions on top of that.

CRM Buyer: What’s in the future for CRM? How is it evolving and changing?

Akhrin: We’re going to see CRM becoming more and more integrated into a single system — phone, email, helpdesk and various applications. Right now, we’re seeing a big drive to not just have it be optional, but required, that there’s an open-facing public API, which allows other systems to connect and to push and pull data.

With this open exchange, people won’t have to switch screens and use multiple tabs to do their job on a daily basis. Once you have everything built on a single system, companies will be able to create smart rules about what the next action should be. The key is to have all those processes and interactions done in a single system.

Vivian%20Wagner CRMDialer President Dimitri Akhrin: Raw Data Is No. 1 Thing
Vivian Wagner has been an ECT News Network reporter since 2008. Her main areas of focus are technology, business, CRM, e-commerce, privacy, security, arts, culture and diversity. She has extensive experience reporting on business and technology for a variety
of outlets, including The Atlantic, The Establishment and O, The Oprah Magazine. She holds a PhD in English with a specialty in modern American literature and culture. She received a first-place feature reporting award from the Ohio Society of Professional Journalists.
Email Vivian.

Let’s block ads! (Why?)

CRM Buyer

Hide your condoms in an old glass of milk… or this thing.

May 6, 2018 Humor

Posted by Krisgo

via

About Krisgo

I’m a mom, that has worn many different hats in this life; from scout leader, camp craft teacher, parents group president, colorguard coach, member of the community band, stay-at-home-mom to full time worker, I’ve done it all– almost! I still love learning new things, especially creating and cooking. Most of all I love to laugh! Thanks for visiting – come back soon

Let’s block ads! (Why?)

Deep Fried Bits

Is There Such A Thing As Over-Innovating?

December 13, 2017 BI News and Info

277357 l srgb s gl 300x200 Is There Such A Thing As Over Innovating? “Innovation distinguishes between a leader and a follower.” – Steve Jobs

As a part of the last wave of Millennials joining the workforce, I have been inspired by Jobs’ definition of innovation. For years, Millennials like me have been told that we need to be faster, better, and smarter than our peers. With this thought in mind and the endless possibilities of the Internet, it’s easy to see that the digital economy is here, and it is defining my generation.

Lately we’ve all read articles proclaiming that “the digital economy and the economy are becoming one in the same. The lines are being blurred.” While this may be true, Millennials do not see this distinction. To us, it’s just the economy. Everything we do happens in the abstract digital economy – we shop digitally, get our news digitally, communicate digitally, and we take pictures digitally. In fact, the things that we don’t do digitally are few and far between.

Millennial disruption: How to get our attention in the digital economy

In this fast-moving, highly technical era, innovation and technology are ubiquitous, forcing companies to deliver immediate value to consumers. This principle is ingrained in us – it’s stark reality. One day, a brand is a world leader, promising incredible change. Then just a few weeks later, it disappears. Millennials view leaders of the emerging (digital) economy as scrappy, agile, and comfortable making decisions that disrupt the norm, and that may or may not pan out.

What does it take to earn the attention of Millennials? Here are three things you should consider:

1. Millennials appreciate innovations that reinvent product delivery and service to make life better and simpler.

Uber, Vimeo, ASOS, and Apple are some of the most successful disruptors in the current digital economy. Why? They took an already mature market and used technology to make valuable connections with their Millennial customers. These companies did not invent a new product – they reinvented the way business is done within the economy. They knew what their consumers wanted before they realized it.

Millennials thrive on these companies. In fact, we seek them out and expect them to create rapid, digital changes to our daily lives. We want to use the products they developed. We adapt quickly to the changes powered by their new ideas or technologies. With that being said, it’s not astonishing that Millennials feel the need to connect regularly and digitally.

2. It’s not technology that captures us – it’s the simplicity that technology enables.

Recently, McKinsey & Company revealed that “CEOs expect 15%–50% of their companies’ future earnings to come from disruptive technology.” Considering this statistic, it may come as a surprise to these executives that buzzwords – including cloud, diversity, innovation, the Internet of Things, and future of work – does not resonate with us. Sure, we were raised on these terms, but it’s such a part of our culture that we do not think about it. We expect companies to deeply embed this technology now.

What we really crave is technology-enabled simplicity in every aspect of our lives. If something is too complicated to navigate, most of us stop using the product. And why not? It does not add value if we cannot use it immediately.

Many experts claim that this is unique to Millennials, but it truly isn’t. It might just be more obvious and prevalent with us. Some might translate our never-ending desire for simplicity into laziness. Yet striving to make daily activities simpler with the use of technology has been seen throughout history. Millennials just happen to be the first generation to be completely reliant on technology, simplicity, and digitally powered “personal” connections.

3. Millennials keep an eye on where and how the next technology revolution will begin.

Within the next few years Millennials will be the largest generation in the workforce. As a result, the onslaught of coverage on the evolution of technology will most likely be phased out. While the history of technology is significant for our predecessors, this not an overly important story for Millennials because we have not seen the technology evolution ourselves. For us, the digital revolution is a fact of life.

Companies like SAP, Amazon, and Apple did not invent the wheel. Rather, they were able to create a new digital future. For a company to be successful, senior leaders must demonstrate a talent for R&D genius as well as fortune-telling. They need to develop easy-to-use, brilliantly designed products, market them effectively to the masses, and maintain their product elite. It’s not easy, but the companies that upend an entire industry are successfully balancing these tasks.

Disruption can happen anywhere and at any time. Get ready!

Across every industry, big players are threatened — not only by well-known competitors, but by small teams sitting in a garage drafting new ideas that could turn the market upside down. In reality, anyone, anywhere, at any time can cause disruption and bring an idea to life.

Take my employer SAP, for example. With the creation of SAP S/4HANA, we are disrupting the tech market as we help our customers engage in digital transformation. By removing data warehousing and enabling real-time operations, companies are reimagining their future. Organizations such as La Trobe University, the NFL, and Adidas have made it easy to understand and conceptualize the effects using data in real time. But only time will tell whether Millennials will ever realize how much disruption was needed to get where we are today.

Find out how SAP Services & Support you can minimize the impact of disruption and maximize the success of your business. Read SAP S/4HANA customer success stories, visit the SAP Services HUB, or visit the customer testimonial page on SAP.com.

Comments

Let’s block ads! (Why?)

Digitalist Magazine

The Vision Thing

September 15, 2017 CRM News and Info

Salesforce earlier this year introduced its Einstein Vision capability, an idea with a lot of promise but not a great deal of precedent. Who had applications that could see, and how would this be used?

For decades, we’ve been content with scanning documents and analyzing them with optical character recognition tools, or we’ve used bar codes and QR codes — but it all came down to recognizing simple symbols.

Suddenly there was something much closer to human reading that needed explaining. Visual Search, introduced with Einstein Vision, gives customers the ability to photograph things and use them in searches for products and services. Vendors can use it to identify things in processes that didn’t have analogs before.

For instance, with Einstein Vision, marketers can quickly can analyze photos for presence of brand images and understand how brands are perceived and used. With a picture, you don’t have to rely on your gut or your experience, however faulty they might be.

Vision services also can be used in product identification to give service reps a way to evaluate possible service issues before dispatch, so that the right resources can be sent.

Marketplace Diffusion

All of this leverages existing customer technologies, mostly in handheld devices. This is important, because it reduces the time it takes to diffuse the solution throughout the marketplace.

If, for instance, these vision solutions required special cameras or a wired connection, it would take far longer to diffuse the solution through a customer base. Or maybe the solution, however useful it is, might never make it to market.

The Einstein Vision announcement whetted appetites with its ability to recognize photos and logos, and Salesforce anticipated it would be able to provide applications to support visual search, brand detection and product ID in short order — which it did last month for its Social Studio.

Recognizing that social itself has become a visually oriented medium, it was logical for Salesforce to add vision recognition to the mix, and that’s what it delivered by adding Einstein Vision to Social Studio.

Social Trendspotting

So what does this buy you? Well in marketing and service, it can mean a lot. Right now the solution is just available for Twitter — but with it, marketers and service people can search their streams for images that tell them something about their brand, products or customers’ experiences.

Finding your brand or logo in a stream, for instance, might give you reason to try to understand the context. What do the words that go with the pictures say? The sentiment, which also could be analyzed by Salesforce, might convey happiness or the opposite in each case, prompting different actions from a vendor.

Seeing a brand or product with a negative sentiment might kick off a service outreach. At the same time, unambiguous displays of logos or branding, say at an event, can tell a vendor how well sponsorship ads are performing.

Other insights are possible too. Sorting through a social stream, for instance, can provide basic research into potential trends.

If a noticeable percentage of your customers can be seen doing, eating or having something, it might indicate the early stages of a trend. Of course, none of this raw data is enough to make investments in, but it serves as level one research that you can test. This beats relying on your gut or thinking you know the customer. Everybody knows the customer… but well enough to make an investment?

So, the first version of AI-powered vision recognition from Salesforce is out there, and I expect it will be one of the many new ideas that get coverage at Dreamforce. I wouldn’t put it past Salesforce to announce more uses for it, or at least to announce a road map.

Going Into Overdrive

Two MIT professors, McAfee and Brynjolfsson, a few years ago alerted us to the reality that the tech revolution was about to go into overdrive. Their reasoning was simple: We build on top of prior successes, and at this point there’s a lot to build on.

Because of this organic growth, we really can’t forecast the uses and applications of these developments, the professors said, but we know uses can and will be invented.

To simplify with a concrete example, there were no computer programmers until there were computers. That makes sense to us today — but if you lived in the late 1940s, those words might have looked like modern English but they would have made no sense.

We’re at it again. Disruption is all around us, but history teaches us that we shouldn’t fear the future. Organic growth has a way of working itself out.

Denis%20Pombriant The Vision Thing Denis Pombriant is a well-known CRM industry researcher, strategist, writer and speaker. His new book, You Can’t Buy Customer Loyalty, But You Can Earn It, is now available on Amazon. His 2015 book, Solve for the Customer, is also available there. He can be reached at
denis.pombriant@beagleresearch.com.

Let’s block ads! (Why?)

CRM Buyer

Buzzword or not, AI is now a thing in corporate earnings calls

September 7, 2017 Big Data

In quarterly calls to discuss earnings with Wall Street, executives’ curated comments fall into two categories: information they are required by the SEC to divulge and things they think investors want to hear.

Aggregating trending topics from those calls, however, offers a glimpse into what’s on corporate managers’ minds. It was for this reason consulting firm Hamilton Place Strategies data-mined roughly 3,000 earnings calls every quarter for the past three quarters.

Following the inauguration of President Donald Trump in January, 41 percent of those earnings calls mentioned policy changes like tax reform and infrastructure spending. These policies were of interest to the tech industry because they could boost profits by repatriating overseas taxes or beefing up broadband infrastructure. Hamilton Place found that by July — with the Trump agenda stalling — such mentions appeared in only 16 percent of earnings calls in all industries.

So what kinds of buzzwords did executives instead use to get investors excited? One of the most frequently mentioned was artificial intelligence. So far this year, 360 earnings calls mentioned AI, according to Hamilton Place. Before 2017, only 181 calls had ever included the phrase — back then, most execs preferred terms like “robotics” and “automation.”

Tech giants like Amazon, Alphabet, Apple, Facebook, and Microsoft have been discussing AI nearly every quarter since 2016, with most pinning their futures on the technology. But they were not alone. Rocket Fuel, Cognizant, Nvidia, and Salesforce were also among the tech companies likely to mention artificial intelligence in conversation with investors.

Industries beyond tech are beginning to use the term, as well. AI was of particular interest to those in consumer goods and health care, including newspaper publisher Tronc, ad company MDC Partners, and health care management company Corvel.

Of course, it’s easy to toss AI around as a buzzword for future business plans. A survey of 3,000 business executives conducted by MIT Sloan in collaboration with the Boston Consulting Group found that 85 percent expect AI will offer them a competitive advantage, yet only 39 percent have an AI strategy in place, and just one in five actually use AI in some way.

AI remains a priority for tech giants that are already seeing their early investments in the technology pay off. For many others, it seems like an unwieldy strategy that demands expensive hires and is easier to jawbone about than implement. If and when AI delivers on its promise, those companies may wish they had treated AI as more than a trendy buzzword.

Let’s block ads! (Why?)

Big Data – VentureBeat

Tag Archives: thing”

One thing you can’t return

GOOD THING TIPPING IS NOT USED IN JAPAN

AI Weekly: Multimodal learning is in right now — here’s why that’s a good thing

The only thing we have to fear…

Related

Data is Inherently Messy. Is That Really Such a Bad Thing?

Harald Smith

Editor’s note: This article on data management written by Syncsort’s Harald Smith was originally published on Infoworld.

Good data or bad? Context matters

Consistent data? Keeping the systems running

When service matters more

New and emerging needs

Constrained reality

Learning to dance with data

CRMDialer President Dimitri Akhrin: Raw Data Is No. 1 Thing

Hide your condoms in an old glass of milk… or this thing.

About Krisgo

Is There Such A Thing As Over-Innovating?

Millennial disruption: How to get our attention in the digital economy

Disruption can happen anywhere and at any time. Get ready!

The Vision Thing

Marketplace Diffusion

Social Trendspotting

Going Into Overdrive

Buzzword or not, AI is now a thing in corporate earnings calls

Recent Posts

Categories

Archives

Tag Archives: thing”

Related

Editor’s note: This article on data management written by Syncsort’s Harald Smith was originally published on Infoworld.

Good data or bad? Context matters

Consistent data? Keeping the systems running

When service matters more

New and emerging needs

Constrained reality

Learning to dance with data

Like this:

About Krisgo

Millennial disruption: How to get our attention in the digital economy

Disruption can happen anywhere and at any time. Get ready!

Marketplace Diffusion

Social Trendspotting

Going Into Overdrive

Recent Posts

Categories

Archives