Big Data/Data Mining

  • Big data

    Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. Its what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

    Big Data History and Current Considerations

    While the term “big data” is relatively new, the act of gathering and storing large amounts of information for eventual analysis is ages old. The concept gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three Vs:

    Volume. Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden.

    Velocity. Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.

    Variety. Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.

    We consider two additional dimensions when it comes to big data:

    Variability. In addition to the increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. Is something trending in social media? Daily, seasonal and event-triggered peak data loads can be challenging to manage. Even more so with unstructured data.

    Complexity. Today's data comes from multiple sources, which makes it difficult to link, match, cleanse and transform data across systems. However, it’s necessary to connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out of control.

    Although there are more dimensions:

    Big data’s big potential

    The amount of data that’s being created and stored on a global level is almost inconceivable, and it just keeps growing. That means there’s even more potential to glean key insights from business information – yet only a small percentage of data is actually analyzed. What does that mean for businesses? How can they make better use of the raw information that flows into their organizations every day?

    Why Is Big Data Important?

    The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable

    1) cost reductions,

    2) time reductions,

    3) new product development and optimized offerings, and

    4) smart decision making.

    When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:

    • Determining root causes of failures, issues and defects in near-real time.
    • Generating coupons at the point of sale based on the customer’s buying habits.
    • Recalculating entire risk portfolios in minutes.
    • Detecting fraudulent behavior before it affects your organization.

    Cloud Computing Basics

    Whether you are running applications that share photos to millions of mobile users or you’re supporting the critical operations of your business, a cloud services platform provides rapid access to flexible and low cost IT resources. With cloud computing, you don’t need to make large upfront investments in hardware and spend a lot of time on the heavy lifting of managing that hardware. Instead, you can provision exactly the right type and size of computing resources you need to power your newest bright idea or operate your IT department. You can access as many resources as you need, almost instantly, and only pay for what you use.

    How Does Cloud Computing Work?

    Cloud computing provides a simple way to access servers, storage, databases and a broad set of application services over the Internet. A Cloud services platform such as Amazon Web Services owns and maintains the network-connected hardware required for these application services, while you provision and use what you need via a web application.

    Six Advantages and Benefits of Cloud Computing

    Trade capital expense for variable expense

    Instead of having to invest heavily in data centers and servers before you know how you’re going to use them, you can only pay when you consume computing resources, and only pay for how much you consume.

    Benefit from massive economies of scale

    By using cloud computing, you can achieve a lower variable cost than you can get on your own. Because usage from hundreds of thousands of customers are aggregated in the cloud, providers such as Amazon Web Services can achieve higher economies of scale which translates into lower pay as you go prices.

    Stop guessing capacity

    Eliminate guessing on your infrastructure capacity needs. When you make a capacity decision prior to deploying an application, you often either end up sitting on expensive idle resources or dealing with limited capacity. With cloud computing, these problems go away. You can access as much or as little as you need, and scale up and down as required with only a few minutes notice.

    Increase speed and agility

    In a cloud computing environment, new IT resources are only ever a click away, which means you reduce the time it takes to make those resources available to your developers from weeks to just minutes. This results in a dramatic increase in agility for the organization, since the cost and time it takes to experiment and develop is significantly lower.

    Stop spending money on running and maintaining data centers

    Focus on projects that differentiate your business, not the infrastructure. Cloud computing lets you focus on your own customers, rather than on the heavy lifting of racking, stacking and powering servers.

    Go global in minutes

    Easily deploy your application in multiple regions around the world with just a few clicks. This means you can provide a lower latency and better experience for your customers simply and at minimal cost.

    Types of Cloud Computing

    Cloud computing has three main types that are commonly referred to as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Selecting the right type of cloud computing for your needs can help you strike the right balance of control and the avoidance of undifferentiated heavy lifting

     

  • Data Mining Solutions

    is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes? Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more.

    Data Mining History and Current Advances

    The process of digging through data to discover hidden connections and predict future trends has a long history. Sometimes referred to as "knowledge discovery in databases," the term "data mining" wasn’t coined until the 1990s. But its foundation comprises three intertwined scientific disciplines:

    • Statistics (the numeric study of data relationships),
    • Artificial intelligence (human-like intelligence displayed by software and/or machines)
    • And machine learning (algorithms that can learn from data to make predictions). What was old is new again.

    As data mining technology keeps evolving to keep pace with the limitless potential of big data and affordable computing power.

    Over the last decade, advances in processing power and speed have enabled us to move beyond manual, tedious and time-consuming practices to quick, easy and automated data analysis. The more complex the data sets collected, the more potential there is to uncover relevant insights. Retailers, banks, manufacturers, telecommunications providers and insurers, among others, are using data mining to discover relationships among everything from pricing, promotions and demographics to how the economy, risk, competition and social media are affecting their business models, revenues, operations and customer relationships.

    Why is data mining important?

    So why is data mining important? You’ve seen the staggering numbers – the volume of data produced is doubling every two years. Unstructured data alone makes up 90 percent of the digital universe. But more information does not necessarily mean more knowledge.

    Data mining allows you to:

    • Sift through all the chaotic and repetitive noise in your data.
    • Understand what is relevant and then make good use of that information to assess likely outcomes.
    • Accelerate the pace of making informed decisions.

    Data mining is a cornerstone of analytics, helping you develop the models that can uncover connections within millions or billions of records. Learn how data mining is shaping the world we live in.

     

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