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  1. Business Challenges of Our Digital Society

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    Digitization has revolutionized business in everything from how we work, to the way information is stored and transmitted. Telecommuting is a growing trend that would be impossible without digital technology. New ways of gathering information and analyzing that data to increase efficiency and profitability are enabling exciting new business trends like autonomous agents and things. But the digital revolution presents some challenges for business as well — here’s a brief overview of some of the most important issues.

    Piracy

    Our global society is a major contributor to piracy, which affects creators and everyone in the associated supply chains of digital media. Many of the pirate sites from which media are downloaded illegally are located in countries without adequate copyright laws, but anyone, anywhere can access these sites. Meanwhile, internet companies struggle to keep up with requests to take down links containing copyrighted material — as of 2016 Google receives more than 20 million such requests weekly.

    Fraud & identity theft

    Fraud and identity theft is another growing issue, affecting 6.15 percent of U.S. consumers in 2016, according to Javelin Strategy & Research. Efforts to combat fraud include the switch to chip-enabled credit cards, which successfully reduced cloning, where stolen account data is used to create counterfeit cards. But other types of fraud — such as fraudulent online purchases and new account fraud, where criminals open new accounts using a stolen identity — have increased to more than compensate for this reduction.

    Technological and regulatory challenges

    The amount of data being created and collected is truly mindboggling. According to IBM, every day we create 2.5 quintillion bytes of data, which means that 90 percent of the world’s data is less than two years old. The data, known as “Big Data,” is created by Internet of Things, purchase records, digital images, GPS signals, social media posts, sensors, and more. The challenge then becomes finding ways to use Big Data in order to draw meaningful conclusions.

    Interpreting Big Data is a multifaceted problem. For starters, there are not enough professionals with training in Big Data analysis. IBM has responded to this issue by launching Cognitive Class, a free set of online courses that are designed to train people for a career in the rapidly growing field of data science and cognitive computing. In fact, Information Age estimates the Big Data market to be worth $40 billion in 2016, and will reach $66.8 billion by 2021.

    There are technological issues as well, including the exhaustion of IP addresses. As we’ve seen, limitations on IPv4 space are real and becoming acute. Another major issue is security and DDoS attacks – a serious and growing problem for businesses, and a potential obstacle to the growth of the IoT.

    Lawmakers and regulatory agencies are challenged to keep up with technology that’s evolving at an incredibly rapid pace. Another regulatory issue is that our digital society is global. While specific digital laws govern a single country, they are hard to uphold and implement across country borders.

    Individual and business privacy

    Protecting individual and business privacy is a key challenge in our digital society, both in making sure data collected can’t be traced back to a single individual, and in prevention of data breaches of sensitive information. An incredible amount of personal information is transferred and stored online, notably financial data such as credit card records and bank account information.

    Data breaches are not always detected or reported, so exact figures can be tough to determine — but the Center for Strategic and International Studies estimated that annual global losses from cybercrime fell between $375-575 billion in 2014. The problem continues to grow. According to the Identity Theft Resource Center, data breaches hit a new high in 2016, and attacks on the business/financial sector were the most common. Medical records, education records, and governmental/military records are often targeted as well.

    Conclusion

    The business opportunities and challenges presented by our digital society will continue to evolve. But you can count on DQE to continue to evolve along with it, and offer scalable, customizable solutions for your business needs.

  2. Thickening the Data to Discover Customer Insights

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    Thickening the Data: How combining Big Data with Thick Data can lead to more accurate customer insights

    For over a decade, there has been great deal of buzz around the term “Big Data.” This term was coined in 2005 by Roger Magoulas from O’Reilly Media to refer to the mass amounts of information that businesses collect on a daily basis that cannot be easily processed and analyzed with traditional software. Companies aim to harness these large data sets in order to gain insights about their customers by investing copious amounts of money in Big Data and business analytics software. In fact, Forbes estimates that worldwide software revenues will grow from $122B in 2015 to $187B in 2019, growing by more than 50% over a five-year projection period. It is safe to say that Big Data has been viewed by some as a “genie in a bottle” that will allow a company to understand the customer, and lead to prosperity.

    However, while customer insights—especially those your competitors do not have—can lead to business success, Big Data does not deliver these insights on a silver platter. There is a step that can be easily overlooked—asking the question, “why?” Why are my customers behaving that way? Big Data can effectively explain consumer behavior, but it can easily miss the consumer motivations that drive the behavior. Put simply, Big Data only shows the tip of the iceberg—sometimes the insight can be hidden under the surface, and companies must seek it with qualitative research.

    A way to discover these insights that are hidden under the surface is through “Thick Data”. Thought leaders, Christian Madsbjerg and Mikkel B. Rasmussen popularized this term in their 2014 book, The Moment of Clarity. They explain that Thick Data is qualitative information collected when companies try to “understand the emotional, even visceral context in which people encounter their product or service.” This information can be gathered through observational research like ethnography, interviews, and surveys—research that looks at the customer on an individual level. In order for companies to effectively develop insights, they must collect Thick Data in combination with Big Data.

    Thick Data, Big Data

    One of the most common uses of Big Data is in the collection of sales numbers. As sales data is collected, it is not uncommon for a company to be surprised by the outcome. Sometimes companies will wonder why sales are lower (or higher) than expected, and will look for answers. This was the case with Samsung in Madsbjerg’s 2014 Wall Street Journal Article, “The Power of ‘Thick Data.’” Samsung knew that they had to find a way to differentiate their TVs from the competition on the shelf, but they lacked the insight to know what to change or improve. In order to gain this insight, Madsbjerg and the Samsung team aimed to answer the question, “What does the TV mean in the modern household?”

    After watching hundreds of interviews and videos, and looking at other artifacts (collecting Thick Data), the team found that people were commonly hiding their clunky TVs in corners, or mentioning how they wanted their television to look. Madsbjerg and team discovered a pattern; people did not think of their TV just as technology, but also as furniture. From this insight, Samsung redesigned their televisions to be more aesthetically pleasing to their consumers by creating a modern look with its speakers and wires out of view, leading to better differentiation. It is clear that this insight could have been generated by Big Data alone, but by also using ‘Thick Data’ they were able to understand their customers’ needs and serve them more effectively.

    In summary, before companies decide to invest millions of dollars into Big Data campaigns, they must realize that some resources will need to be allocated to Thick Data as well. Big Data is directional and shows consumer behavior, but it does not show the whole picture. To get to the heart of the customer and really understand their motivations, attitudes, and perceptions, companies must collect Thick Data. By using today’s Big Data technology in conjunction with Thick Data, organizations can more effectively develop the insights that they seek, and then utilize them to better satisfy their customers and lead their business to greater success.

  3. Technology/IT Trends for 2017

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    The field of information technology continues to evolve at a dizzying pace! Here are some thoughts on important trends and things to watch for in 2017 – many related to how increasing amounts of data are being used to improve automation and efficiency.

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