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by Divakar Mysore, Shrikant Khupat, Shweta Jain | Updated September 16, 2013 - Published September 17, 2013
Big data can be stored, acquired, processed, and analyzed in many ways. Every big data source has different characteristics, including the frequency, volume, velocity, type, and veracity of the data. When big data is processed and stored, additional dimensions come into play, such as governance, security, and policies. Choosing an architecture and building an appropriate big data solution is challenging because so many factors have to be considered.
This “Big data architecture and patterns” series presents a structured and pattern-based approach to simplify the task of defining an overall big data architecture. Because it is important to assess whether a business scenario is a big data problem, we include pointers to help determine which business problems are good candidates for big data solutions.
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If you’ve spent any time investigating big data solutions, you know it’s no simple task. This series takes you through the major steps involved in finding the big data solution that meets your needs.
We begin by looking at types of data described by the term “big data.” To simplify the complexity of big data types, we classify big data according to various parameters and provide a logical architecture for the layers and high-level components involved in any big data solution. Next, we propose a structure for classifying big data business problems by defining atomic and composite classification patterns. These patterns help determine the appropriate solution pattern to apply. We include sample business problems from various industries. And finally, for every component and pattern, we present the products that offer the relevant function.
Part 1 explains how to classify big data. Additional articles in this series cover the following topics:
Business problems can be categorized into types of big data problems. Down the road, we’ll use this type to determine the appropriate classification pattern (atomic or composite) and the appropriate big data solution. But the first step is to map the business problem to its big data type. The following table lists common business problems and assigns a big data type to each.
Utility companies have rolled out smart meters to measure the consumption of water, gas, and electricity at regular intervals of one hour or less. These smart meters generate huge volumes of interval data that needs to be analyzed.
Utilities also run big, expensive, and complicated systems to generate power. Each grid includes sophisticated sensors that monitor voltage, current, frequency, and?other important operating characteristics.
To gain operating efficiency, the company must monitor the data delivered by the sensor. A big data solution can analyze power generation (supply) and power consumption (demand) data using smart meters.
Telecommunications operators need to build detailed customer churn models that include social media and transaction data, such as CDRs, to keep up with the competition.
The value of the churn models depends on the quality of customer attributes (customer master data such as date of birth, gender, location, and income) and the social behavior of customers.
Telecommunications providers who implement a predictive analytics strategy can manage and predict churn by analyzing the calling patterns of subscribers.
Marketing departments use Twitter feeds to conduct sentiment analysis to determine what users are saying about the company and its products or services, especially after a new product or release is launched.
Customer sentiment must be integrated with customer profile data to derive meaningful results. Customer feedback may vary according to customer demographics.
IT departments are turning to big data solutions to analyze application logs to gain insight that can improve system performance. Log files from various application vendors are in different formats; they must be standardized before IT departments can use them.
Retailers can use facial recognition technology in combination with a photo from social media to make personalized offers to customers based on buying behavior and location.
This capability could have a tremendous impact on retailers? loyalty programs, but it has serious privacy ramifications. Retailers would need to make the appropriate privacy disclosures before implementing these applications.
Retailers can target customers with specific promotions and coupons based location data. Solutions are typically designed to detect a user’s location upon entry to a store or through GPS.
Location data combined with customer preference data from social networks enable retailers to target online and in-store marketing campaigns based on buying history. Notifications are delivered through mobile applications, SMS, and email.
Fraud management predicts the likelihood that a given transaction or customer account is experiencing fraud. Solutions analyze transactions in real time and generate recommendations for immediate action, which is critical to stopping third-party fraud, first-party fraud, and deliberate misuse of account privileges.
Solutions are typically designed to detect and prevent myriad fraud and risk types across multiple industries, including:
Categorizing big data problems by type makes it simpler to see the characteristics of each kind of data. These characteristics can help us understand how the data is acquired, how it is processed into the appropriate format, and how frequently new data becomes available. Data from different sources has different characteristics; for example, social media data can have video, images, and unstructured text such as blog posts, coming in continuously.
We assess data according to these common characteristics, covered in detail in the next section:
It’s helpful to look at the characteristics of the big data along certain lines — for example, how the data is collected, analyzed, and processed. Once the data is classified, it can be matched with the appropriate big data pattern:
Figure 1, below, depicts the various categories for classifying big data. Key categories for defining big data patterns have been identified and highlighted in striped blue. Big data patterns, defined in the next article, are derived from a combination of these categories.
In the rest of this series, we’ll describes the logical architecture and the layers of a big data solution, from accessing to consuming big data. We will include an exhaustive list of data sources, and introduce you to atomic patterns that focus on each of the important aspects of a big data solution. We’ll go over composite patterns and explain the how atomic patterns can be combined to solve a particular big data use cases. We’ll conclude the series with some solution patterns that map widely used use cases to products.
The authors would like to thank Rakesh R. Shinde for his guidance in defining the overall structure of this series, and for reviewing it and providing valuable comments.
April 23, 2019
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