There is a lot of talk about data science, big data, AI, and IoT these days, but what is the reality behind the hype? What do these terms actually mean, and what impact could these have on your business?

Let’s take a look at 10 of the highest-profile tech terms affecting businesses in 2018;

1. Data Science
Data Science is a very general term used for many modern business applications of data, technology and analytics.

It generally involves collection and processing of a wide range of data: customer, marketing, web, financial, third party data, etc. After collection, the data is either analyzed for meaningful insights or used in recommender algorithms, fraud detection, churn prediction or any of several dozen such applications.

Data scientists generally use techniques borrowed from statistics, numerical optimization or machine learning, implemented using programming languages such as R, python, Java, SAS, or C/C++.

2. Big Data
Big Data is data that is abnormally large, fast moving, or diverse. In general, it’s data which technologies of the 20th Century were poorly equipped to handle.

Examples of big data include searches over internet websites (1.3 billion and growing), videos stored on YouTube (100’s of hours of video uploaded each minute), and data processed at the CERN particle physics research laboratory (150 million sensors delivering data from experiments 30 million times per second).

For today’s businesses, the most relevant types of big data might include web analytics (accumulating at gigabytes or even terabytes per day), video data and IoT sensor data (see below).

3. Machine Learning (ML)
Machine Learning (ML) is the head-line grabber these days. It is when a program self-improves by continuously learning from training data. An example is an image recognition program trained to recognize cats by being shown pictures labeled as containing or not containing a cat. The more pictures used for training, the more accurate the program (hopefully) becomes.

Machine learning has applications in many different areas, including Google’s smart reply feature, which gives Gmail users several recommended replies after each email. These are based on what Gmail has learned by reading millions of earlier responses to similar emails.

Most of the hype around AI these days is related to machine learning. Small businesses can quickly tap into certain advanced ML technologies by using pay-per-use offerings from companies such as Google and Salesforce’s Einstein.

4. Artificial intelligence (AI)
Artificial Intelligence (AI) is a general term for a machine that can respond intelligently to its environment. Much of machine learning is also considered to be AI, and many people use the terms interchangeably.

To illustrate the distinction, consider the IBM computer Deep Blue, which in 1997 bested the reigning world chess champion. Deep Blue used a combination of massive computing power and user-supplied playing rules. If the computer seemed to be doing something wrong, the programmers would reprogram its playing strategy between games.

Deep Blue played chess in a way that was considered to be artificial intelligence but not machine learning. It didn’t learn by itself.

However, when the program Alpha Go beat the world champion in the game of Go nineteen years later, the program had taught itself to play so well by playing against itself over and over and over again.

In the end, even its own programmers didn’t understand why it made some of its winning moves. This was machine learning.

5. Cloud Computing
Cloud Computing involves renting space or running applications on a remote computer. Amazon allowed users to rent digital storage space in its data centers starting in 2006, but Salesforce.com had been running its applications as Software-As-A-Service, a form of cloud computing, since 1999.

Now X-as-a-Service offerings are everywhere, providing anything from hardware to platform to software (including Gmail).

6. Open-Source Software
Open-Source Software is software made freely available for use and modification (subject to some restrictions). One of the largest repositories of open-source software is the Apache Foundation, created in 1999.

Apache maintains much of the big data software used today, including Hadoop, Spark, and Kafka. Open-source has been extremely valuable in helping companies get up and running with data science and big data applications.

7. Deep Learning
Deep Learning is a powerful machine learning method which extends a method dating back to the 1950s.

Deep Learning uses carefully constructed networks of simple building blocks trained on massive amounts of data. These are then trained to do specialized tasks such as labeling images, playing games, or processing natural language.

8. Web Analytics
Web Analytics is the collection and analysis of the actions of visitors to your web sites and mobile applications. Because such a large portion of customer interaction takes place in a digital setting, web analytics plays a very important role in modern data science.

When we step past basic web analytics interfaces and APIs and begin collecting raw clickstream data, we are entering the world of big data and opening new opportunities to draw deeper insights into customer actions and product performance

9. Data Warehouses

Data warehouses are centralized data bases carefully constructed to allow companies to draw the most value from their data.

A data warehouse will collect data from multiple operational data bases (e.g. finance systems, marketing efforts, web analytics data, etc.) and make it easy to link this data and ask holistic questions, such as how marketing efforts link to online activity, and subsequent sales.

10. The Internet of Things (IOT)
The Internet of Things refers to the billions of connected processors and sensors that are spread out in our cars, household devices, field equipment, industrial machines, etc.

The IoT makes it possible for us to gather tremendous amounts of real-time data, harvest insights and improve industrial and business operations.

Two key applications are the predictive maintenance of machines and detailed monitoring of customer activity (e.g. for insurance or healthcare applications).

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A new password guessing technique takes advantage of machine learning technologies. Expert Michael Cobb discusses how much of a threat this is to enterprise security.

What happens when authentication and access control measures are attacked by adversaries equipped with machine learning? This question has been examined in a couple of recent university studies, and it’s worth taking a look at the potential impact of their findings on the security of real-world information systems.

Password guessing impacts system security in both online and offline attacks. An online password guessing attack can be found in the logs of every server that’s on the internet — a constant series of attempts to log in remotely using guessed credentials. Such attacks can be thwarted by having complex passwords, limiting the number of attempted logins and requiring two-factor authentication.

In an offline password guessing attack, the adversary obtains a set of system or application credentials, usernames and hashed passwords. They can then attempt to guess passwords on their own machine. This is done by checking to see if a hash of the guess, such as password, matches any of the hashes obtained from the target system.

Offline password guessing depends on having a large collection of plausible passwords, often called a password cracking dictionary. A real dictionary is used as the starting point, and then variations are added based on common tricks, like character swapping — D1sn3yW0rld — and adding special characters — password! Hackers also add actual passwords disclosed in breaches, such as LinkedIn and RockYou, to these dictionaries.

How machine learning increases the threat

A new approach to improving password guessing techniques is harnessing the power of machine learning algorithms. For example, researchers at the Stevens Institute of Technology and the New York Institute of Technology came up with something they call PassGAN, a novel technique that “leverages Generative Adversarial Networks (GANs) to enhance password guessing.”

Without going into the science of Generative Adversarial Networks, a GAN uses two neural networks, one of which tries to fool the other with fake data that is very close to actual data. What researchers found is that, by training a GAN on a list of leaked passwords, it can rapidly produce a large number of plausible password guesses, potentially outperforming password guessing tools such as Hashcat and John the Ripper.

What does this mean for information system security, apart from underlining the importance of protecting password hashes, given that cybercriminals are increasingly likely to apply machine learning to offline cracking? Password cracking tools are classic examples of the double-edged phenomenon: security technology that can be used for evil or good; and in this case, it can be adapted to measure the strength of passwords before users are allowed to use them based on an ease of guessing score.

Of course, this latest research also adds to the reasons why system security needs to use stronger authentication than passwords alone to protect access. One popular technology is a one-time passcode generated on a mobile device assumed to be under the control of the device owner.

However, the reliability of that assumption is somewhat undermined by another piece of research, this time from Newcastle University. Researchers there have developed a proof-of-concept attack called PINlogger that uses machine learning and a neural network to analyze sensor data on a mobile device to detect when a PIN is being entered, and then determine the actual PIN.

With several dozen sensors on a mobile device — from the touchscreen to sensors for motion, speed, orientation, rotation and more — it is perhaps not surprising that combined sensor output, when analyzed with machine learning, can reveal a lot about a user’s physical interaction with a device.

However, there are some constraints on this PINlogger attack. It requires the mobile device to have a web browser that supports JavaScript and web APIs that can access onboard sensors. Also, the user needs to be led to the attacker’s malicious webpage and must keep that page open during an attack. However, the use of JavaScript to access sensors via the browser means that the attack does not require users to download an app to become victims.

The researchers were not content just to create a proof of concept for this sensor-based attack; they actually studied how mobile device users perceived the risks from sensors typically found in these systems. The results showed that many people were not aware of all the sensors on their devices or the potential for information like mobile orientation and motion to be used to defeat security measures like a PIN. The researchers also noted a lack of granularity in sensor access control policies.

As more sensors are added to mobile devices, the potential for abuse is likely to grow, and the researchers concluded that the problem of sensor-based attacks is a hard one to solve, but needs to be addressed fairly urgently, before they start appearing in the wild. Update your security awareness training content now.

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In some regards, the term big data management can be viewed as an oxymoron. In fact, oxymorons abound in this industry and society — virtual reality, artificial intelligence, science fiction and awfully good, the latter of which can apply to the challenges encountered in managing the onslaught of big data from multiple sources. There are countless tools, techniques and practices available for the big data ecosystem to properly gather, mine, prep, store and analyze data and help smooth operations, build marketing campaigns, improve customer service and develop the next new product disruptor. As simplistic as this may sound, it’s up to data managers to sort it all out as their data lakes swell beyond capacity.

“The data lake isn’t where data goes to die,” Gartner analyst Merv Adrian said at the 2017 Pacific Northwest BI Summit, “it’s where data goes to live.”

October’s Business Information opens with our editor’s note and advice for data managers to move beyond traditional data control to the critical task of improving data quality and delivery — taking all that raw data and making it useful. Whether for internal or external business use, the demands for instantaneous data access continue to accelerate, spurred on by mobile apps, artificial intelligence (AI), machine learning and internet of things (IoT).

In that vein, our cover story examines companies that use their big data ecosystem to divert data lakes toward developing new strategies, products and revenue streams — in the process, smashing their old business patterns. In another feature, IoT and machine learning technologies help take the guesswork out of estimated times of arrival for transport companies whose businesses depend on shipping and receiving goods.

Also in this issue, a business intelligence project combined three data warehouses into one to reduce warehouse size by 80% and data load time from several weeks to just days while causing IT staffing problems in the process. In other features, learn how metadata programs can ease mega management woes; semantic technology could be a blessing or curse to AI; companies are gearing up for greater big data management deployments; and all data must be treated equally in the search to find value.

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