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Criminals are looking for small businesses' data to sell on the dark web. Here, we look at how to be vigilant.

Most small businesses don’t give two hoots about the ‘dark web’, the part of the World Wide Web that is only accessible by means of special software, allowing users and website operators to remain anonymous or untraceable.

As far as many are concerned, the dark web is a murky place where dodgy criminals congregate to buy and sell things like weapons and drugs. It feels like a world apart from everyday business.

In today’s world, though, that’s a dangerous mindset to have. The truth is that, while criminals have been using the dark web for years to sell illegal items, they’re also using it more and more these days to sell something more valuable — stolen and leaked corporate data.

Today, every business has a wealth of valuable data, whether it’s employees’ personal details, corporate credit cards or sensitive client information. Criminals want to get their hands on that, so they can then sell it on the dark web to make some easy money. And it’s not just the criminals who want your data.

‘Hacktivists’ will happily steal from you and post your data online for free just to win kudos or because they want to damage your company reputation. Ex-employees can copy data to a USB or email it to themselves at home and then either deliberately leak it or suffer a breach themselves. And ‘script kiddies’ run automated scans to find vulnerable websites and servers for easy pickings.

The easiest victims to pick on are the small ones

And it’s not just the big firms who are targets. Small businesses are equally at risk, if not more so because they often lack the cybersecurity resources to deal with the problem. And every industry is equally at risk. The truth is that passwords, corporate credit cards, employee personal details, client information and so on are equally valuable whether they come from a big company or small, in manufacturing or in retail. The opportunistic nature of cybercrime makes the perpetrators blind to industry or size — and once perpetrators get hold of your data, they can wreak havoc with it. With corporate credit cards, criminals can buy what they want. With employee personal details, they can target victims with phishing attacks and fraud, and with client information, they can blackmail you.

Jeremy Hendy, CEO of cyber intelligence solutions company RepKnight, says he sees thousands upon thousands of dark web dumps every day of client login details (yes, with passwords). And most of the organisations to whom the data belongs have no idea these sales are happening because the dark web is, well, hidden. ‘The relatively low risk of getting caught (because the dark web affords strong anonymity) combined with the chance to make a lot of money (or at least show off) makes the dark web an incredibly attractive place for cybercriminals,’ Hendy says.

So, what can we do about it? First, we need to change the way we think about cybersecurity.

How AI and Big Data Impact the Structure of the Financial Industry

Protecting your network is a poor way to protect your data, Hendy says. ‘Protect your network, and your data’s safe, right? Wrong. Protecting your network is a poor way to protect your data.

‘Consider it from a parenting point of view. To protect your children (your data), you can install video cameras to the outside of your house and build a big fence around the perimeter of your property to deter kidnappers from getting in (expensive and complex).

‘But what about those times when your children need to leave your property, which will happen pretty much every day? Once your children have left the safety of the house, your house’s protection is useless.’

The same goes for data, he adds. The nature of modern business dictates that your data no longer live within the perimeter of your network protection. It has already flown the nest and has scattered into the online stratosphere through email and collaboration with third-party partners and suppliers.

“Even with the strongest network security, you’re still at risk of having a cybercriminal gain access to your network”

Hendy says that RepKnight recently did an audit of its own data and quickly found that there were around 35 partners, systems and places that were storing the data — all outside of its own network. ‘And we’re a small company, so imagine how that’s going to be magnified for larger organisations.’

Once that data leaves your network, its safety is well and truly out of your control. ‘But unlike children, once your data has left your perimeter it is at risk of being duplicated and leaked, so even if your data does return to the safety of your network, a copy will almost certainly exist elsewhere,’ Hendy says.

Even with the strongest network security, you’re still at risk of having a cybercriminal gain access to your network without your knowledge through the use of ‘compromised credentials’.

‘These kinds of attacks are on the rise because so many people use the same password across various accounts like banking, social media, online shopping and much more.

‘If one of those third parties suffers a breach, chances are they’ve unwittingly handed over the login credentials to your company network, giving criminals the chance to snoop around undetected and steal whatever they want. By the time you find out — which is usually after 450 days after the breach first happened — it’s too late to do anything about it.’

How to combat the threat of the dark web and protect your data

  • Change the focus from network protection to data protection — with an acceptance that your data has already ‘left the building’.
  • Weigh up your options. For most companies, combatting the threat of the dark web is not something that you can do manually. Not only is it hidden, it’s dangerous (rife with malware and phishing sites — there’s no honor amongst thieves) and horrifying (you’ll see things you wish you could un-see and perhaps earn yourself a surprise visit from law enforcement agencies). The dark web is definitely ‘not safe for work’.
  • Consider advanced, automated monitoring software that continuously looks for your data in places where it shouldn’t end up — like dark web marketplaces and bin and dump sites. If the monitoring system finds something it believes to be yours, it should tell you immediately, alerting you to a potential breach you might not even know about yet.
  • Be aware that data monitoring is like tracking your children through GPS. If they go missing, you’ll at least be able to see where they end up. So, if you can track your data in this way, you can do something about it when things go wrong. And so, with today’s technology, there’s no reason for the dark web to remain a hidden threat to small businesses.

 Source: This article was Published smallbusiness.co.uk By Ben Lobel

Published in Search Engine

Searching video surveillance streaming for relevant information is a time-consuming mission that does not always convey accurate results. A new cloud-based deep-learning search engine augments surveillance systems with natural language search capabilities across recorded video footage.

The Ella search engine, developed by IC Realtime, uses both algorithmic and deep learning tools to give any surveillance or security camera the ability to recognize objects, colors, people, vehicles, animals and more.

It was designed with the technology backbone of Camio, a startup founded by ex-Googlers who realized there could be a way to apply search to streaming video feeds. Ella makes every nanosecond of video searchable instantly, letting users type in queries like “white truck” to find every relevant clip instead of searching through hours of footage. Ella quite simply creates a Google for video.

Traditional systems only allow the user to search for events by date, time, and camera type and to return very broad results that still require sifting, according to businesswire.com. The average surveillance camera sees less than two minutes of interesting video each day despite streaming and recording 24/7.

Ella instead does the work for users to highlight the interesting events and to enable fast searches of their surveillance and security footage. From the moment Ella comes online and is connected, it begins learning and tagging objects the cameras see.

The deep learning engine lives in the cloud and comes preloaded with recognition of thousands of objects like makes and models of cars; within the first minute of being online, users can start to search their footage.

Hardware agnostic, the technology also solves the issue of limited bandwidth for any HD streaming camera or NVR. Rather than push every second of recorded video to the cloud, Ella features interest-based video compression. Based on machine learning algorithms that recognize patterns of motion in each camera scene to recognize what is interesting within each scene, Ella will only record in HD when it recognizes something important. The uninteresting events are still stored in a low-resolution time-lapse format, so they provide 24×7 continuous security coverage without using up valuable bandwidth.

Ella works with both existing DIY and professionally installed surveillance and security cameras and is comprised of an on-premise video gateway device and the cloud platform subscription.

Source: This article was published i-hls.com

Published in Search Engine

Big data is an incredibly useful platform for any business, big or small. It allows brands to delve deeper into the information and insights that fuel their products, services and processes.

For example, you can use data collected on past product performance to make a more informed decision about a future launch or development cycle.

That said, big data as a whole isn’t exactly what you’d call accessible. For starters, you need to deploy the systems and processes to collect useful data.

Then, you need to have a team of data analysts and scientists to sort through it all and find actionable intel.

Finally, you need someone to take that practical data and put it to good use. A company executive just might not have a clear plan for, or understand the applications of, a niche data set.

This doesn’t mean applying big data is impossible. It just means it’s a potentially involved and time-consuming process.

Naturally, this can give organizations and decision makers enough doubt to avoid big data systems. The number of companies using predictive analytics to drive processes and make decisions, for instance, remains low at 29 percent, according to a 2016 PwC press release.

Adoption for these technologies and systems is rising, but not at the rate it could be.

So if you’re reluctant to get involved with big data, you’re not alone. Luckily, there are tools and resources to help you manage the transition.

1. Marketing ROI: Kissmetrics

Big data isn’t just about the data itself. This means that even if you collect customer or visitor data and use it to your advantage, you’re not necessarily using big data fully.

In today’s highly digital landscape, collecting and analyzing data is par for the course. Tracking the number of visitors or traffic referred to your website isn’t necessarily “big data.” An alarming 61 percent of employees say their company is not using big data solutions despite collecting data regularly.

This all relates to marketing, because you’re constantly reviewing data to inform your decisions and actions. The performance of a new product launch will tell you whether or not the resources that went into it were worthwhile.

If it fails to catch on, you know not to waste more resources on similar products or services. But again, this is just surface information — not true insights.

Kissmetrics is a data-powered tool that can help you boost your marketing ROI and processes. It does more than just track information like pageviews, heatmaps, demographics and more.

It actually churns that data and spits out usable intel. You can use the platform to create triggers that resonate with your audience and further boost customer engagement and behaviors.

2. Sales Calling: PhoneBurner

Big data has become a key player in the evolution of modern sales and marketing departments. Few examples illustrate this point better than the ways in which big data has been incorporated into modern sales calling software.

PhoneBurner is a power dialing company that automates the sales calling process so that, when a potential customer answers the phone, they are connected to a live sales agent (with no annoying pause in between).

If the call goes to voicemail, the power dialers leaves your pre-recorded voicemail and integrates the call status directly into a software dashboard so you can easily follow up via email at a later date.

3. Third-Party Integration: InsightSquared

One key obstacle in big data is fragmentation. There are so many tools, platforms, third-party portals and information streams that combining and parsing everything can be incredibly daunting.

InsightSquared is a data-driven tool that solves this with solid integration with third-party platforms and services. It can connect to popular enterprise solutions you’re likely already familiar with, such as Google Analytics, ZenDesk, QuickBooks, Salesforce and more.

The information is then mined and analyzed, making it more accessible to you, your teams and even parties who don’t work with data regularly. If you connect a customer relationship tool or CRM, it syncs up the data to offer efficient lead generation, customer tracking, pipeline forecasting and even profitability predictions.

4. Machine Learning and Predictive Analytics: IBM’s Watson Analytics

Big data solutions can offer some pretty amazing insights into your business, customers and strategies. Machine learning and predictive analytics are the way to achieve this.

IBM’s Watson Analytics relies on the IBM Watson machine learning API to deliver remarkable analysis of your data. More importantly, it automates the entire process intelligently to leave you more time to focus elsewhere.

The best feature of Watson is that it unifies all data analysis projects into a single channel or source. It can be connected to marketing and sales tools, finance and human resources, customer data and performance and much more.

Watson also employs a unique AI system to deliver “natural language” insights, which is a fancy way of saying the data it returns is easy to understand.

5. Credit and Payment Analytics: TranzLogic

Love it or hate it, credit card transactions and related payment systems can deliver boatloads of invaluable and necessary data. Even so, associated data streams are not always accessible — especially to smaller businesses or teams — and they can be super complex and confusing.

TranzLogic is designed to process this information and extract actionable intel. Want to measure sales performance and customer patterns or improve promotions? What about using payment data to improve loyalty programs and boost engagement across your customer base?

Also, it’s a turnkey tool that doesn’t require additional knowledge or experience. Even if your specialty lies beyond IT or data, you can still make sense of everything reported through TranzLogic.

6. Customer Feedback: Qualtrics

Research can be invaluable to any business or brand. In fact, by conducting studies, surveys and simple questionnaires, you can extract highly useful insights about your audience and products. Why do you think polls and surveys are so popular on social media networks? People love to share their opinions. This is even a great way to discover and hear about new ideas or concepts.

Qualtrics is a customer feedback solution for related big data sources. With the tool, you gain access to three different types of real-time insights: market, customer and employee trends.

This includes things like customer satisfaction, exit interviews and market research. You can even unlock academic research and mobile studies, too. There’s a lot of data here you could put to use.

7. Long-Term Data: Google Analytics

Google Analytics probably needs no introduction. What’s special about Google’s toolset is that it can help you extract long-term information and stats. For instance, you get to see where traffic is coming from and how that fluctuates over time.

With information like this, you can fill in any gaps. You could, for instance, use this to your advantage during the holidays to target common referrals through marketing and promotions.

Social media traffic is another great source of data, which is also tied into Google’s platform.

It’s more of a robust, multi-platform toolset that can be used to track the kind of information you’d want to track anyway.

More importantly, it’s instantly accessible through your browser.

Source: This article was published bigdata-madesimple.com By Kayla Matthews

Published in Business Research

 Are you a liar? You bet you are but the real you is emerging through your online activities. What Big Data knows about the real you.

There are things about which we all lie. We lie about our innermost hopes, fears, and desires. We lie to our friends, spouses, doctors, pollsters, even to ourselves. But our truth is being discovered because we willingly reveal it every day through our activities online.

It’s all being tracked and through big data, a new picture about us is emerging which contradicts much of what we previously believed about each other.

 
Seth Stephens-Davidowitz, author of Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are

New York Times op-ed writer Seth Stephens-Davidowitz has studied this and reveals it all in his book, Everybody Lies. He says that data from the internet is like "digital truth serum," revealing how we really behave when no one's watching. Mike Collins talks with him about some of his findings.

Some highlights from the show:

On the events in Charlottesville

“There was a period when we thought we lived in a post-racism society. You could see online, even when people were telling pollsters politically correct truths, like they weren’t racists and didn’t care that Barack Obama was black, online they were telling a different story. They were making racists searches, usually for jokes mocking African-Americans with a shocking frequency. Stormfront is the biggest, most popular hate site in the United States. The demographics are young people, which you also saw at the events in Charlottesville. Neo-Nazis exist and I’ve known this for years because of internet research. Young people were becoming obsessed with neo-Nazis and the clear cause of it was Barack Obama.” 

For young adults age 19 to 21

“It’s a very impressionable group. It’s not a stupid or uneducated group. The most popular interest for Stormfront members is reading. They’re obsessed with philosophers, evolution and they’re political junkies. Many people say they join Stormfront because of a dating experience. Perhaps an African-American dated someone they wanted to date and it created this rage that led them to this material.”

Google reveals the most about the human psyche

“We’re in a habit of lying to make ourselves look better. That carries over to surveys, there’s no incentive to tell the truth. With Google there’s an incentive, you tell the truth, you get information you need. People are lying to surveys saying they aren’t racists. Compare that to the Google searches. It’s so clear there’s a very different truth about society that was being missed by the traditional way of understanding people. ”

“Google trends compare the rates of searches to different parts of the United States or world and when these searches are highest. You can learn interesting patterns. Anxiety has doubled in the last five years. It’s highest in Kentucky, Maine and rural areas. The recent rise in anxiety and panic attacks almost perfectly track rises in searches related to opioids.”

Guest Seth Stephens-Davidowitz - New York Times op-ed contributor, visiting lecturer at The Wharton School, and a former Google data scientist. He is the author ofEverybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are

Read an excerpt from Everybody Lies. 
"The power in Google data is that people tell the giant search engine things they might not tell anyone else. Google was invented so that people could learn about the world, not so researchers could learn about people, but it turns out the trails we leave as we seek knowledge on the internet are tremendously revealing."

Seth on NPR's Hidden Brain podcast: What Our Google Searches Reveal About Who We Really Are
"I think there's something very comforting about that little white box that people feel very comfortable telling things that they may not tell anybody else about their sexual interests, their health problems, their insecurities. And using this anonymous aggregate data, we can learn a lot more about people than we've really ever known."

Source: This article was published wfae.org By ERIN KEEVER

Published in Science & Tech

Big data has and will change how advertisers work and businesses market.

There are plenty of words online about how big data will change every facet of our lives, and a substantial chunk of those words are devoted towards how big data will affect advertising. But instead of haphazardly leaping on the change bandwagon, advertisers need to sit down and understand what big data has changed and yet what still remains the same.

At its core, advertising is about communication as it seeks to inform consumers about a business’s product and services. But different consumers want to hear different messages, which becomes all the more important as new customers join the internet thanks to the growing popularity of mobile.

Big data can refine those messages, predict what customers want to hear with predictive analytics, and yield new insights in what customers want to hear. All of this is certainly revolutionary and will change how consumers and marketers approach advertising. But it will still be up to advertisers to create messages in the name of their clients.

Algorithms and targeting

Some things which many people do not think about as advertising are in fact a conflation of big data and marketing. Netflix is a terrific example of this. Netflix obviously does not have advertisements, but it heavily relies on algorithms to recommend shows to its viewers. These algorithms save Netflix $1 billion per year by reducing the churn rate and marketing the right shows to the right customers.

Netflix’s efforts to target consumers with the right shows is hardly unusual, as websites and online stores like YouTube, Amazon, or Steam do this all the time these days. But the key here is the reliance on algorithms to make targeting more accurate.

These algorithms require a constant stream of data to stay up to date. But now that data is everywhere. Internet users leave a constant stream of data not just on social media websites, but anywhere they go in the form of digital footprints.

This represents new opportunities and challenges for advertisers. On one hand, the digital footprints which everyone creates offers new insights to advertisers into what we truly want which can be more accurate than what we say on social media. But at the same time, advertisers do have to worry about protecting consumer privacy and security. This is not just a moral thing; advertisers or websites that are flagrantly cavalier with their user data will spark a backlash that will hurt business.

Advertising targeting has already been in place for some time now. But as advertisers collect more data, targeting will become more personalized and thus effective. Advertisers will fight not just to collect as much data as possible, but to collect data which accurately represents individual customers to market to their individual tastes.

Changing forms of advertising

Big data can uncover new information about each individual customer, but the advertiser must craft a message to appeal to said customer. But with these new insights, advertisers can entirely change how they approach marketing as they craft entirely new strategies.

This is not completely new. The rise in content marketing is often cited as a major beneficiary of big data, but content marketing as a concept is older than the Internet. Nevertheless, the rise in content marketing as well as other strategies like native advertising or the endless dance around search engine optimization.

These rising advertising strategies are fascinating because just as advertisers rely on data to craft new strategies, they give data right back to the consumer. Content marketing is all about giving consumers details about a business such as how they make food, what it is like to work there, and so on. By sharing this data, the company makes the customers feel like they are part of a group which knows common information. And in turn the customer ends up giving up his data to the company which lets it construct new advertising strategies.

This symbiosis between consumer and company shows that data is not just about cold analytics, but is about creating a bond between the two groups like all advertising sets out to do. Similarly, businesses must take the complexity of big data, analyze trends, and then create simple guidelines which their customer staff can use. All the advertising in the world will not make as big of an impression on a customer as one surly or confused customer representative.

Big data has and will change how advertisers work and businesses market to consumers through more personalized and targeted advertisement as well as creating new forms of advertising. But big data is less important than smart data and strategy. Business leaders who can break big data down into small chunks, come up with a smart strategy, and formulate an effective message will still thrive just as much as they would have in the past. In this way, big data is not quite the revolutionary change that many think.

This article is published as part of the IDG Contributor Network. Want to Join?

Published in Search Engine

The world runs on data. Businesses are inundated by it. From information received from mobile phones, sensor networks and Internet of Things-enabled devices, industries have a world of information at their fingertips. But making sense of all this data is no easy endeavour.

According to research firm IDC, companies spent over $20 billion in big data technology and services in 2015. Those same companies are also expanding the number of data types and sources they analyze. But what is big data anyway? We break down the concept and answer some questions you may be afraid to ask, in partnership with Cisco.

data

What is it?

In simple terms, “big data” refers to large or complex data sets. This includes unstructured data (typically text-heavy database and IoT information including dates, numbers, and facts) and structured data (easily organized and searchable content such as relational database information).

Regardless of industry sector or vertical, the average organization stores data from a wide range of sources: regular business transactions, IoT devices, email, videos, Internet traffic and even social media. Take manufacturing, for example. Every machine on the factory floor is constantly generating vital data such as production output, equipment health, and inventory levels. All of this makes managing real-time information a challenge.

Big data is a big deal. And while the concept of big data may seem simple, it’s actually a technology phenomenon that has the potential to unlock business value and innovation on a global scale.

And make no mistake, the companies that are able to harness the power of big data via data analytics, preparation, and management tools will be the ones that innovate, thrive, and survive in an increasingly competitive marketplace. But the typical organization still relies on aging and/or outdated virtualization tools and data management systems — making the need for a new way of managing data a strategic concern.

Indeed, as the world of data changes before our eyes, today’s organizations need real-time technology tools that can quickly identify the value in the data to solve critical business problems.

data

Why does it matter? 


The rise of big data means that the organizations that embark on a process of digital transformation — adopting digital technologies such as analytics tools and services to help manage, streamline and unify data sets into business operations— will be the ones that will be better prepared to make faster business decisions.

This includes the use of advanced tools such as user behaviour or predictive analytics. Employing either of these will help you make data connections more quickly, help you identify business trends, and conduct complex calculations, transactions or simulations to better respond to customer needs and demands.

Understanding big data — and its intrinsic value and potential for future business growth — will help businesses get more data-driven insights, foster better customer experiences and establish stronger internal and external communications and interactions.

Working with an industry leader such as Cisco can help you develop intelligent, integrated, and agile technology environments that help swiftly curate, automate, and manage digital data in real-time. Simply put, taking advantage of big data to analyze both structured and unstructured information, enables companies to extract deeper market value and develop tomorrow’s business insights, today.

Cisco works with leading companies to make sense of big data, helping them prioritize the information that matters to make better business decisions. They help businesses develop analytics strategies to maintain a competitive edge in an increasingly connected, global world. To learn more about how your company can benefit from big data, download this free report.

Source : huffingtonpost.ca

Published in Others

Search and big data analytics have evolved significantly over the last few years, and organizations are increasingly using these technologies to meet their mission-critical needs.

At the beginning of 2016, we were talking a lot about machine learning and semantic search and how they would be key developments in this space. 

Those have certainly been hot topics and continue to be areas that companies are seeking to exploit for their data-driven applications. 

But what will we be talking about in 2017 as it pertains to this space?

Here’s a look at five areas you can expect to hear more of this year and beyond.

Open Source Rises to the Top

Open source technologies are becoming more prominent in a wide range of use cases, from traditional enterprise search to log analytics, e-commerce search, and even government document search.

In fact, current data shows open source search engines have gained significant popularity because of flexibility, cost and features. According to DB-Engines, Elasticsearch and Solr — two open source search engines based on Lucene — top the list of leading commercial and open source search engines.

Just last month I discussed the features and limitations of Elasticsearch and Solr in this article.

With its growing use in the commercial and government space, the move to open source will continue to be a hot topic as organizations seek greater features, costs savings, and more flexibility with their search and big data analytics solutions.

Life Without Google Search Appliance

In early 2016, Google announced its end of support for the Google Search Appliance (by March 2019) as part of its strategic move to a cloud-based platform. Since then, many have begged the question: What now?

In my article last summer, I provided some tips on moving from the GSA and looked at some of the replacement options available at the time.

As we enter 2017, there are still no concrete details from Google about a new cloud-based search solution, so users are forging ahead with seeking out and comparing their existing alternatives.  With the March 2019 deadline getting closer, we’ll be hearing a lot more about the alternatives, and maybe even get word on Google’s cloud-based plans. 

Analytics Powered by Enterprise Data Lakes

Enterprises have a lot of data but how well they use it to derive insights is key to success. Over the last year, we’ve been hearing a lot of hype around enterprise data lakes (or enterprise data hubs) to bring together data silos and make the right data available to the right users at the right time. 

There is a wide variety of structured and unstructured data in enterprise data lakes. That said, search engines are the ideal tool for storing, processing, accessing, and presenting this data because they are schema-free and can scale to billions of records.

Data lakes’ search and analytics capabilities are nearly endless when we combine search engines, big data techniques, and visualization dashboards in groundbreaking use cases such as bioinformatics, precision agriculture, and precision medicine.

As data lakes continue to gain popularity as a way to store massive amounts of data and analytics, we’ll see organizations continuing to have the conversation in 2017 about how to best exploit this.

Search Engines Become 'Insight Engines1'

Just like Google, Cortana and Siri, search is becoming much more than just keyword matching.

We’re now heading into the age of search results personalization. Search engines are becoming personal digital assistants or as Gartner calls it, Insight Engines. This was made possible with big data analytics techniques like machine learning and predictive analytics.

It’s pervading the modern business world in a multitude of use cases from intranet search, to e-commerce search, recruiting, medical research, media and publishing, and many others. Organizations are just beginning to skim the surface on how real-time personalization is significantly enhancing their operations so we expect a lot of talk about how to implement this in the coming year.

Search Engine Scoring

We know that analyzing statistically valid scores helps increase search engine relevancy over time. But, while this method can significantly increase business value and the bottom line, not many organizations have started engine scoring or are implementing it effectively.

With that said, we are observing proven success with newer, better algorithms used in the scoring process, some of which are discussed in this article.  This is, without a doubt, a solid technique that will continue to grow and be discussed in the coming years as organizations seek to improve their user’s search experience.

In conclusion, with the rise of open source, massive volumes of structured and unstructured data, and the need to do complex analytics, we will continue to hear a lot on these topics throughout 2017 as search and big data continue to converge. 

Source : cmswire.com

Published in Search Engine

Evolution of digital marketing

As a digital marketing agency with 10 years’ experience, we have witnessed the increasing adoption of digital marketing among advertisers in Hong Kong. Annual internet advertising spending in Hong Kong grew from HK$1.5 billion in 2011 to HK$5.9 billion in 2016, a 293% growth in five years. Our interaction with customers also evolved over the years
from our previously educating and persuading advertisers to place internet ads, to currently advertisers proactively asking us what’s new on the internet whilst we keep on sending the latest trends to customers, to help them keep in pace with the market.

The pull strategy in search engine marketing (SEM) contributes 56% (HK$3.3 billion) to total internet spending in 2016. Complemented with push advertising like display ad, social media ad and soft selling approach from social media content and KOL, all these demonstrate a holistic digital marketing landscape.

Targeting and goal setting

In the early days of digital advertising, advertisers used to review the ad campaign performance based on the number of clicks and impressions. Nowadays, big data is crucial for advertisers to focus and effectively spend advertising dollars on the right target customers, thus achieving better value for money in their advertising spending. In response to market needs, we, equipped with the latest technology and knowledge, utilise tracking and web analytics tools to enhance advertisers’ digital ads and help them reach the target customers by devices, by geographics, by behavior targeting, by demographics, by search terms, by specific dates and time… As a local agency, we have built a knowledge base with data related to keywords in the web, such historical data which is gathered daily throughout the year and from different industries will be transformed into valuable market insights for advertisers.

With so much data “bombarding” the advertisers, it is important for them to set up marketing goals for their campaign before determining the type of digital marketing channels to be adopted. These goals can be leads generation, online sales, website visit, phone calls, apps downloads, brand awareness and returning customers etc. With clear goal setting, the planning and execution of advertising campaigns can be better measured, reviewed and optimised.

There are no limits to being new for digital media which is ever-evolving. New iMedia strives to be the trusted digital marketing partner of our customers with a view to building and sustaining mutually beneficial collaborations. Through conceptualising and executing differentiated one-stop digital marketing solutions for our customers, riding on our experience, creativity and technological expertise, we work side-by-side with our customers to achieve their business goals while living our core values.

Source : http://www.marketing-interactive.com/features/big-data-key-understanding-customers-better/

Published in Others

Search and big data analytics have evolved significantly over the last few years, and organizations are increasingly using these technologies to meet their mission-critical needs.

At the beginning of 2016, we were talking a lot about machine learning and semantic search and how they would be key developments in this space. 

Those have certainly been hot topics and continue to be areas that companies are seeking to exploit for their data-driven applications. 

But what will we be talking about in 2017 as it pertains to this space?

Here’s a look at five areas you can expect to hear more of this year and beyond.

Open Source Rises to the Top

Open source technologies are becoming more prominent in a wide range of use cases, from traditional enterprise search to log analytics, e-commerce search, and even government document search.

In fact, current data shows open source search engines have gained significant popularity because of flexibility, cost and features. According to DB-Engines, Elasticsearch and Solr — two open source search engines based on Lucene — top the list of leading commercial and open source search engines.

Just last month I discussed the features and limitations of Elasticsearch and Solr in this article.

With its growing use in the commercial and government space, the move to open source will continue to be a hot topic as organizations seek greater features, costs savings, and more flexibility with their search and big data analytics solutions.

Life Without Google Search Appliance

In early 2016, Google announced its end of support for the Google Search Appliance (by March 2019) as part of its strategic move to a cloud-based platform. Since then, many have begged the question: What now?

In my article last summer, I provided some tips on moving from the GSA and looked at some of the replacement options available at the time.

As we enter 2017, there are still no concrete details from Google about a new cloud-based search solution, so users are forging ahead with seeking out and comparing their existing alternatives.  With the March 2019 deadline getting closer, we’ll be hearing a lot more about the alternatives, and maybe even get word on Google’s cloud-based plans. 

Analytics Powered by Enterprise Data Lakes

Enterprises have a lot of data but how well they use it to derive insights is key to success. Over the last year, we’ve been hearing a lot of hype around enterprise data lakes (or enterprise data hubs) to bring together data silos and make the right data available to the right users at the right time. 

There is a wide variety of structured and unstructured data in enterprise data lakes. That said, search engines are the ideal tool for storing, processing, accessing, and presenting this data because they are schema-free and can scale to billions of records.

Data lakes’ search and analytics capabilities are nearly endless when we combine search engines, big data techniques, and visualization dashboards in groundbreaking use cases such as bioinformatics, precision agriculture, and precision medicine.

As data lakes continue to gain popularity as a way to store massive amounts of data and analytics, we’ll see organizations continuing to have the conversation in 2017 about how to best exploit this.

Search Engines Become 'Insight Engines'

Just like Google, Cortana and Siri, search is becoming much more than just keyword matching.

We’re now heading into the age of search results personalization. Search engines are becoming personal digital assistants or as Gartner calls it, Insight Engines. This was made possible with big data analytics techniques like machine learning and predictive analytics.

It’s pervading the modern business world in a multitude of use cases from intranet search, to e-commerce search, recruiting, medical research, media and publishing, and many others. Organizations are just beginning to skim the surface on how real-time personalization is significantly enhancing their operations so we expect a lot of talk about how to implement this in the coming year.

Search Engine Scoring

We know that analyzing statistically valid scores helps increase search engine relevancy over time. But, while this method can significantly increase business value and the bottom line, not many organizations have started engine scoring or are implementing it effectively.

With that said, we are observing proven success with newer, better algorithms used in the scoring process, some of which are discussed in this article.  This is, without a doubt, a solid technique that will continue to grow and be discussed in the coming years as organizations seek to improve their user’s search experience.

In conclusion, with the rise of open source, massive volumes of structured and unstructured data, and the need to do complex analytics, we will continue to hear a lot on these topics throughout 2017 as search and big data continue to converge. 

About the Author

Kamran Khan is co-founder, president and CEO of Search Technologies, a company dedicated to developing, implementing and supporting search and big data solutions. He has been developing, supporting, selling and managing in the computer software/services industry for 25 years with a focus on search engine technology.

Author : Kamran Khan

Source : http://www.cmswire.com/big-data/search-big-data-analytics-in-2017-5-hot-topics/

Published in Science & Tech

As more personal information is collected up by ever-more-powerful computers, giant sets of data – big data – have become available for not only legitimate uses but also abuses.

Big data has an enormous potential to revolutionize our lives with its predictive power. Imagine a future in which you know what your weather will be like with 95 percent accuracy 48 hours ahead of time. But due to the possibility of malicious use, there are both security and privacy threats of big data you should be concerned about, especially as you spend more time on the Internet.

What threats are emerging? How should we address these growing concerns without denying society the benefits big data can bring?

The size of the potential problem

First of all, due to the sheer scale of people involved in big data security incidents, the stakes are higher than ever. When the professional development system at Arkansas University was breached in 2014, just 50,000 people were affected. That’s a large number, but compare it with 145 million people whose birth dates, home and email addresses, and other information were stolen in a data breach at eBay that same year.

From the perspective of a security professional, protecting big data sets is also more daunting. This is partly due to the nature of the underlying technologies used to store and process the information.

Big data companies like Amazon heavily rely on distributed computing, which typically involves data centers geographically dispersed across the whole world. Amazon divides its global operations into 12 regions each containing multiple data centers and being potentially subject to both physical attacks and persistent cyberattacks against the tens of thousands of individual servers housed inside.

Difficulties with access control

One of the best strategies for controlling access to information or physical space is having a single access point, which is much easier to secure than hundreds of them. The fact that big data is stored in such widely spread places runs against this principle. Its vulnerability is far higher because of its size, distribution and broad range of access.

In addition, many sophisticated software components do not take security seriously enough, including parts of companies’ big data infrastructure. This opens a further avenue of potential attack.

For instance, Hadoop is a collection of software components that allows programmers to process a large amount of data in a distributed computing infrastructure. When first introduced, Hadoop had very basic security features suitable for a system used by only a few users. Many big companies have adopted Hadoop as their corporate data platform, despite the fact that its access control mechanism wasn’t designed for large-scale adoption.

Consumer demand drives security and privacy

For consumers, then, it is critical to demand a heightened level of security through vehicles such as terms and conditions, service level agreements, and security trust seals from organizations collecting and using big data.

What can companies do to protect personal information? Countermeasures such as encryption, access control, intrusion detection, backups, auditing and corporate procedures can prevent data from being breached and falling into the wrong hands. As such, security can promote your privacy.

At the same time, heightened security can also hurt your privacy: it can provide legitimate excuses to collect more private information such as employees’ web surfing history on work computers.

When law enforcement agencies collect information in the name of improved security, everyone is treated as a potential criminal or terrorist, whose information may eventually be used against them. The authorities already know a lot about us but could ask companies such as Apple, Google and Amazon to provide more intelligence such as a decrypted version of our data, what search terms we are using and what we are buying online.

The fundamental security principle used to justify this type of blanket surveillance (which is now more affordable and feasible due to the use of big data technologies) is “nobody can be trusted.” Once collected, those data join the rest of the information in being susceptible to abuse and breaches, as demonstrated in snooping incidents involving National Security Agency employees.

And yet when used properly, big data can help enhance your privacy by allowing more information to be leveraged and eventually improve the quality (especially, the accuracy) of intelligence on potential attacks and attackers in cyberspace.

For example, in an ideal world we don’t have to worry about fraudulent emails (also called phishing) because a big data analytics engine would be able to pick out malicious emails with pinpoint accuracy.

How big data is used – for you or against you

There are also other privacy concerns about big data. Companies are eager to deliver targeted advertising to you and tracking your every online move. Big data makes this tracking easier to do, less expensive and more easily analyzed.

A service like IBM’s Personality Insights can build a detailed profile of you, moving well beyond basic demographics or location information. Your online habits can reveal aspects of your personality, such as whether you are outgoing, environmentally conscious, politically conservative or enjoy travel in Africa.

Industry representatives make benign claims about this capability, saying it improves users’ online experiences. But it is not hard to imagine that the same information could be very easily used against us.

For example, insurance companies could start questioning coverage to consumers based on these sorts of big-data profiles, which has already begun to happen.

Banning large-scale data collection is unlikely to be a realistic option to solve the problem. Whether we like it or not, the age of big data has already arrived. We should find the best way of protecting our privacy while allowing legitimate uses of big data, which can make our lives much safer, richer and more productive.

For example, when used legitimately and securely, big data technology can drastically improve the effectiveness of fraud detection, which, in turn, frees us from worrying about stolen identities and potential monetary loss.

Transparency is the key to letting us harness the power of big data while addressing its security and privacy challenges. Handlers of big data should disclose information on what they gather and for what purposes.

In addition, consumers must know how the data is stored, who has access to it and how that access is granted. Finally, big data companies can earn public trust by giving specific explanations about the security controls they use to protect the data they manage.

Author : Jungwoo Ryoo

Source : http://theconversation.com/big-data-security-problems-threaten-consumers-privacy-54798

Published in Internet Privacy
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