Big Data Analytics in a Connected World

Transcription

1 Big Data Analytics in a Connected World Kurt Stockinger, Frank van Lingen, and Marco Valente Kurt Stockinger is an associate professor for the Zurich University of Applied Sciences, Switzerland. stog@zhaw.ch Frank van Lingen is a technology strategist for Cisco Systems International, Switzerland. fvanling@cisco.com Marco Valente is a technology strategist for Cisco Systems International, Switzerland. marcvale@cisco.com Abstract Enterprises often struggle with the design of big data architectures. Some argue that the traditional data warehouse is the silver bullet for solving most data-intensive enterprise problems; others claim that the new big data ecosystem based on Hadoop is a much better solution. In this article we discuss industry analytics use cases with a particular focus on the Internet of things (IoT), describe the challenges and opportunities of each case, and provide guidelines and best practices for choosing the most appropriate technology. Introduction Modern applications and the sensorization of society are driving unprecedented data growth and fueling the need for big data and analytics. Price/performance improvements in network, storage, and computing, together with the rise of cloud computing, make it more cost effective to deploy large IT infrastructures and capture large amounts of data. Another driver is the IoT. Cheaper sensors and improved connectivity are bridging the gap between the physical and digital worlds, enabling us to collect data from more devices and environments than ever before. We are now able to gather enormous amounts of data about almost everything, and it is challenging the conventional approach of analytics. Many enterprises are deploying (or at least evaluating) big data 1 technology in their efforts to become data-driven, meaning that decision making is based on data rather than feelings. Early excitement about big data technology especially among the most technology-savvy 1 Big data is a common term with several meanings. We refer here to the software and hardware infrastructure needed to handle more data, the increasing rate at which it is produced, and the increasing range of formats and representations employed. This is often referred to as volume, velocity, and variety. A detailed discussion on the different definitions appears in Ward and Barker (2013). 44 BUSINESS INTELLIGENCE JOURNAL VOL. 20, NO. 2

2 employees is often followed by unexpected disillusion once the highly praised technology does not fulfill its promises (such as enabling new business opportunities or improving scalability). An equal challenge for enterprises is determining the best time to begin and deploy a big data strategy. Technology changes rapidly, and the enterprise value of new technology is not always fully understood from the beginning. Several iterations between IT and business departments are required to develop promising use cases and sound business models. In the following sections, we present an overview of big data architectures, including traditional data warehouses, Hadoop-based technology, and stream processing systems. This is not an exhaustive discussion of all big data architectures. We have chosen a few key technologies that are currently popular. Next, we discuss several analytics use cases to demonstrate the potential of big data across different verticals, and we explore the relationship between use cases and the architectures. Finally, we advocate for a highly interoperable analytics platform that allows for seamless processing of structured and unstructured data as well as for batch and real-time data analysis. Big Data Architectures In this section we will discuss four approaches to handling big data workloads: Classical data warehouses Batch processing (e.g., Apache Hadoop) Real-time processing (e.g., Twitter Storm) Edge computing We also discuss a modern big data architecture that integrates both batch and real-time processing capabilities. The Classical Data Warehouse The classical approach for analyzing large amounts of structured data in an enterprise is the data warehouse. According to the Inmon architecture (Inmon, 2005), this system typically consists of three layers: the staging area, the integration layer, and the analysis layer (data marts), as depicted in Figure 1. Staging Integration Enrichment Analysis Staging Data Universe Data Marts Analysis Services Presentation Front End File Data Sources XML DB SOAP File... Historization I Historization II Applications: Aggregation, Calculation, Event Detection, Reusable Entities Selection, Aggregation, Calculation Reporting OLAP Mining Web/App Servers GUI Metadata Management Figure 1: A typical data warehouse architecture. BUSINESS INTELLIGENCE JOURNAL VOL. 20, NO. 2 45

3 Data is ingested via ETL (extract, transform, and load) processing into the data warehouse in batches, with daily snapshots of various online systems. After ingestion, data quality checks (data types, fields, business keys, etc.) are performed in the staging area. The integration layer harmonizes the data sets from a variety of systems by loading the data into a common data model. In addition, data is historized, i.e., records with different time stamps are stored to allow historical data analysis (Ehrenmann et al., 2012). Finally, the actual analytics takes place in data marts, where data is physically reorganized to optimize query performance. The pros and cons of traditional data warehouse architectures are summarized in Table 1. PROS Established technology Large user community, midsize to large enterprises Enables an enterprisewide view of the business and enhances factbased decision making CONS Expensive to build and maintain Typically long development cycles from data source to final business intelligence report Building an enterprisewide data model requires a mature enterprise with backing from all involved business groups Table 1: Pros and cons of a traditional data warehouse architecture. Batch Processing Hadoop is an open source software framework for storing and processing petabytes of unstructured data across highly distributed commodity hardware. It is a proven, viable extension or alternative to more traditional relational databases and centralized file systems. map phase. Partial results are gathered and aggregated to compose the final output the reduce phase. One of the main advantages of Hadoop is that it makes handling data at extreme scale economical by utilizing commodity hardware. Unlike data warehouses, where data is first cleaned and integrated, Hadoop does not require data integration. The mantra is often: load first, think about how to analyze later. Hadoop provides different interfaces for analyzing data. The most basic one is based on the MapReduce paradigm, which allows data to be analyzed with common programming languages such as Java or Python. Users who want to analyze their data in a higher-level language can use Pig Latin a statement-based query language with SQL-like features that can be extended by user-defined functions. If a user wants to leverage data warehousing features, the best choice is Hive, which is even more similar to SQL. For more complex tasks such as finding clusters and correlations in log files of customers or applications the machine learning library Apache Mahout provides a rich set of algorithms. The Hadoop ecosystem shows good scalability for analyzing large data sets in batch mode. Adding more nodes to a problem typically reduces the processing time linearly by the number of nodes. However, the disadvantage of Hadoop is that it is not well suited for real-time processing due to the high latency of the system. Systems such as Impala have been introduced to overcome the latency problems. These systems trade off performance Hadoop consists of a wide selection of components for data modeling, processing, management, and development. The core pieces are HBase, a non-relational database to store large volumes of sparse data, and HDFS (Hadoop Distributed File System), a distributed file system optimized for very large data sets. On top of these runs Hadoop MapReduce (see Figure 2), a software framework for large-scale parallel processing across compute clusters. A MapReduce processing job is first split into subtasks to be executed in parallel on different nodes known as the Map Node 1 Reduce Figure 2: The Hadoop architecture. Hadoop Job Management and Scheduling HDFS Map Node N Reduce 46 BUSINESS INTELLIGENCE JOURNAL VOL. 20, NO. 2

4 against fault tolerance. For an overview of how various companies have integrated or plan to integrate Hadoop into data warehousing and business intelligence, see Russom (2013). The pros and cons of a Hadoop-based architecture are listed in Table 2. Real-Time Processing Systems such as Apache Storm are much better suited to processing large amounts of streaming data than are Hadoop-based systems. These systems were designed from the ground up for real-time processing of data streams rather than fault-tolerant batch processing. The advantage of these systems is that they can process much more data per second. However, Storm does not provide high-level query languages for data analysis such as Pig or Hive; a strong knowledge of programming languages such as Java or Python is required. See Figure 3 for a topology (data flow) for real-time processing of data streams in Storm. The figure shows two so-called spouts (i.e., data sources such as Twitter feeds or streams of stock prices) and various bolts (i.e., real-time processing units). The pros and cons of a Storm-based architecture are listed in Table 3. An interesting recent development is SAMOA (Scalable Advanced Massive Online Analysis), a distributed machine learning framework mining big data streams. PROS Highly scalable at low costs Flexible, reliable, and distributed Due to open source software, the total cost of ownership to build an analytics solution is significantly lower than a commercial BI/DW solution CONS Because it is a fairly new technology, Hadoop-knowledgeable workers may be hard to find No established enterprisewide security concept yet in place Technology is rapidly evolving, making it difficult for enterprises to choose the right moment for adoption Table 2: Pros and cons of a Hadoop-based architecture. Figure 3: Overview of real-time processing system with Apache Storm. Edge Computing With the rise of the IoT and increased distribution of data sources, systems will have to scale and integrate many more applications, devices, and data sources, deal with data in motion, and handle data with increased volume, velocity, and variety. Edge computing (applications, data, and services residing in, or extending into, the network rather than residing uniquely in a traditional cloud or data center) will, therefore, become more relevant. Real-time requirements (fast feedback or response to devices) and limited or ad hoc connectivity for example in cities (smart traffic lights), wind farms, agriculture, and energy production such as oil and gas will increase the need for filtering, forwarding, and processing data in the network or gateways (Bessis and Dobre, 2014). Data will be moving from the edge (sensors and devices) with filtering, analysis, triggering, transformation, and reduction performed along the way coming to rest in more traditional data sources such as data warehouses PROS Highly scalable architecture for processing streaming data Enabling technology for real-time applications CONS Currently no visual programming model available as in data flow systems or ETL tools Fairly new technology; lack of knowhow Table 3: Pros and cons of an Apache Storm-based architecture for real-time processing. Spout: Data Source Bolt: Realtime Process Data Flow BUSINESS INTELLIGENCE JOURNAL VOL. 20, NO. 2 47

5 Decisions & Feedback or Hadoop clusters, hosted in clouds or data centers (Figure 4). Edge computing covers a wide range of technologies such as distributed data storage, mobile data acquisition, and fog computing, which extends the cloud computing paradigm to the edge of the network (Bonomi, 2012). Content delivery networks (CDNs) such as Akamai or Level3 are examples of edge computing we all frequently use, as content viewed in a Web browser is typically not retrieved from the data center or cloud but rather cached on so-called edge servers. The edge servers are globally distributed to ensure consistent low latency when you request a page. The use cases in this article focus on other roles edge computing can play in data management and processing. Table 4 lists the pros and cons of an edge-processing architecture. PROS In-motion At-rest Scalable data gathering across multiple gateway and network components The edge is closer to devices and suitable for low-latency feedback and automation toward devices, machines, etc. Optimizes bandwidth utilization by preventing unnecessary data transfers (especially important with limited connectivity) Business Decisions Cloud & data-center-based storage (warehouse, Hadoop, etc.) Edge (network, gateways, devices) People, Processes, Things CONS Real-Time Batch Figure 4: The role of edge processing within data processing. Processing should be simple, as it runs (within the context of the IoT) on relatively constrained devices Early stage technology Table 4: Pros and cons of an edge-processing architecture. Data & Insight A Modern Big Data Architecture To process a variety of data sets and workloads, such as structured and unstructured data as well as batch- and real-time-oriented workloads, an enterprise needs an architecture that enables the convergence of batch and real-time processing technologies into unified data fabrics. For example, Figure 5 shows the reference architecture of a major European bank (Brändli, 2013). In this architecture, the data warehouse is used for analyzing structured data that is fully integrated, harmonized, historized, and cleaned. Semi-structured and unstructured data such as log files or text documents are processed with Hadoop. The advantage is that data can be loaded when it arrives and can be analyzed in an ad hoc way later. Once specific features in the data are discovered, the data can be structured and stored in the data warehouse and made available for more traditional standard reports (for example). Real-time data analyses such as Twitter stream analysis or online risk calculations are performed by stream-based systems such as Storm. The key of a modern big data architecture is that the three platforms should not be seen as independent silos but rather as allowing interoperability and hence easy data exchange. For instance, it must be feasible to correlate results from Twitter streams with the data from a data warehouse or Hadoop, perhaps by using data virtualization techniques. Big Data Analytics Use Cases We will now introduce three specific use cases and discuss which of the big data architectures and technologies just described are best suited for each. The first two use cases focus on the increasing connectivity of our physical world (the IoT). The third use case focuses on the service provider side of managed IT. Managed IT is already a reality, but with the proliferation of connected devices, the IoT, and enterprises increasing dependency on technology, this area is likely to grow substantially. 48 BUSINESS INTELLIGENCE JOURNAL VOL. 20, NO. 2

6 Use Case #1: Smart Factories The Internet of things has the potential to establish the fourth industrial revolution, following those triggered by mechanization, electricity, and information technology. In manufacturing environments, businesses will establish global networks spanning connected machinery, data warehouses, and production facility systems to construct so-called cyber-physical systems or CPS (Kagermann et al., 2013). Companies will connect their manufacturing plants to form global, integrated manufacturing platforms (so-called smart factories) that provide a virtualized view of the physical supply chain as well as production statistics, maintenance or failure prediction, downtime management, and safety and security management (e.g., who is allowed to access or operate which machine). This view will enable companies to optimize operations, identify inefficiencies, and increase safety while lowering operational costs. In tomorrow s smart factories, manufacturing structures and processes will not be fixed and predefined. Instead, factories will be dynamically reconfigured on a case-by-case basis to automatically create a specific infrastructure for every situation and product, including all the associated requirements in terms of models, data, communication, and algorithms. Companies will increasingly combine their respective logistics and factory infrastructures to create products together. Good examples of product collaboration are the airplane construction processes of Boeing and Airbus, which involve complex supply chain models and many suppliers. The success of a smart factory will depend on many factors, ranging from automated and integrated decision and logistics models to communication protocols to sensors inside machines and robots. Most important in the evolution toward smart factories will be the ability to dynamically interconnect data sources, devices, and sensors, as well as to reconfigure factories and production processes to optimize the complete manufacturing process. Factory downtimes and delays whether related to machine failures, accidents, tool unavailability, or Traditional Big Data Capabilities New Big Data Capabilities Standard Reporting Ad hoc analysis Cognos Business Objects SAS Hadoop Analytics Real-Time Alerting Ticker Focused Storage Data Mart & Cubes General Storage (Data Warehouse) Oracle IBM DB2 Data Transformation & Integration File Staging & Storage Proprietary Framework Informatica Hadoop Middleware Hadoop File System Messaging Streaming Structured Click Stream Figure 5: Modern big data architecture of a major European bank. text data text data text data text data text data text data Text Social Semi- & Unstructured BUSINESS INTELLIGENCE JOURNAL VOL. 20, NO. 2 49

7 Rules and Processing Predict, Configure Control/Intervene/Locate Scale-Out Data Management (e.g., Hadoop) Reporting, Analysis Data Warehouse Edge: fast data processing (filtering, feedback) Network Infrastructure Sensors Update Edge Rules and Triggers Machines Tools Inventory Factory Factory Factory People Figure 6: Smart factories. inventory shortage are costly. Analytics can play a key role in reducing downtime and achieving higher operational efficiency through accurate monitoring of resources and data-driven predictive models. For example, with the help of location-based technologies, enterprises can track how long it takes for a worker to obtain a given tool, then use that data to optimize tool placement. Similarly, location-based technology can be useful to pinpoint where accidents happen and what machines and tools are involved. As illustrated in Figure 6, embedded sensors can monitor the health of machines and report to the control system, potentially monitoring multiple factories. Scale-out systems such as Hadoop can be leveraged to analyze the large and varied amount of data to predict maintenance needs and detect outliers (machines that consistently have problems). These analysis results, along with smaller, more structured data, can be stored in traditional data warehouses. Data can be filtered or aggregated at the edge to avoid sending large amounts of unnecessary data to the processing facility. Similarly, on-premises, fast data analysis can be used to detect whether machine parameters are under or over a given threshold and intervene as appropriate (for example, stopping a machine or alerting a supervisor). Sensors and asset tracking technology can also be used for inventory management, determining which goods need to be procured and when orders must be placed. Through reporting and analysis, the information gathered by different sensors along the workflow can be combined in a data warehouse to identify inefficiencies in the overall supply and production processes. This insight into efficiencies can then be propagated through updates to rules, triggers, and configurations in the edge layer. Once goods have left the factory to customers and retailers, manufacturers can leverage social media data to monitor consumers feedback and use the information to make product adjustments and improve customer support. 50 BUSINESS INTELLIGENCE JOURNAL VOL. 20, NO. 2

8 Use Case #2: Webification of Brick-and-Mortar Retail As with online stores, brick-and-mortar stores are becoming increasingly digitally enabled and connected, capable of tracking and understanding their customers behavior. You could say that a webification of these stores is taking place. Through sensors in stores, connectivity to mobile devices, and digital loyalty cards, stores can personalize and optimize individual shopping experiences. Opportunities include customers flow monitoring, Retailers want to tailor the customer experience for individuals or groups. The system needs real-time capabilities to accomplish this. analysis of buying habits, targeted ad placement, location-based coupons (Kosner, 2013; Dato, 2013), stock management, and offline customer engagement. A deeper understanding of customer behavior can help retailers drive more foot traffic. Retailers can aggregate individual customer movement patterns to optimize the layout of the store or shopping mall and better understand customers needs and habits. Similarly, vending machines can be equipped with sensors to track inventory as well as provide a better customer experience and improve engagement (Macsai, 2009). The customer experience is further improved through customer service. Enterprises can actively mine social networks for customer sentiment analysis and to identify customer service issues (Vizard, 2012). Technology makes it possible to correlate such data with an enterprise s internal databases and build more interactive sites while communicating with customers more proactively. Direct customer feedback is also important for retailers. As in Web environments, retailers want to tailor the customer experience for individuals or groups. The system needs real-time capabilities to accomplish this. Figure 7 shows how different components might be combined in a connected store. Connected cameras, shelf tags, and location-based sensors monitor in real time the way people move through a store and what customers choose from the shelves and put into their carts. Based on this data, the system can suggest particular items or point out discounted products that are of interest to a particular customer. The real-time infrastructure could connect sensing devices (such as cameras, motion detectors, and location tags) to a rules engine and an associated profile from the customer and could operate in-memory (data is not stored on disk for this) for faster processing. Depending on the actions of the customer in the store, the system would send alerts (feedback) to the customer s mobile device or a screen attached to the shopping cart, or present specific, targeted advertising on digital screens in the store when the customer approaches a particular location. The data remains useful even when a customer has left the premises. On the back end, the enterprise can perform historical purchase analysis and customer profiling. Customers entering a store of the same chain can be identified and their profiles retrieved from the back end. Hadoop can be a cost-effective option to store and analyze customers behavior data; the analysis results can be stored in more traditional data stores alongside customer profile data. Although there is great potential for brick-and-mortar retail stores, customer data privacy must be addressed when building such systems. Use Case #3: Outsourced Infrastructure Management Companies do not always have in-house expertise for infrastructure management or IT operations, often because of the cost of hiring the experts. Infrastructure in this case can mean physical infrastructure or services that are hosted within a company or deployed in a public or private cloud. Instead, enterprises outsource management to a third party (for example, their hardware, software, or services vendors) and sign contracts with specific service-level agreements (SLAs) regarding uptime, security, and maintenance with specific penalties if the SLAs are not met. BUSINESS INTELLIGENCE JOURNAL VOL. 20, NO. 2 51

9 Customer Profile (Incrementally Updated) Rule Optimization Rules Engine Real-Time Feedback (targeted ads, promotions, etc.) In-Memory Store and Streaming Compute Network Mobile Devices, Shopping Carts, Screens Cameras, Locations, etc. Historical Analysis Scale-Out Data Management (e.g., Hadoop) Results (Data Warehouse) Compute Storage Network Centralized Data Center (Back End) Store (Front End) Figure 7: A front-end and back-end retail infrastructure. The advantage of the infrastructure management company is that it can leverage technical, operational, and configuration data from multiple clients. If the management company detects that a certain version of an Apache Web server and a certain configuration cause problems at one client, it can leverage this knowledge with other clients and, through preventive maintenance, reduce the risk that an SLA will be violated. As illustrated in Figure 8, customers typically install so-called data collectors that send data back to a data center, where the data is stored and analyzed (model A). The security of this data is important because it is moved outside the customer premises. Traditionally this data was relatively small, but today, with increasing network capacity and decreasing infrastructure costs, the variety and the volume of the data are increasing (for example, large amounts of log files are transferred). Scale-out systems enable infrastructure management companies to analyze this data in a relatively fast and cost-effective way and determine appropriate actions (feedback from the analysis) for their clients infrastructures. A scale-out infrastructure such as Hadoop is suitable for this kind of workflow. However, analysis results are more structured and can be stored (together with the actions taken at the customer side) in a more traditional data warehouse. Although this particular use case is very well suited to a scale-out architecture, many enterprises want to reduce the time between data collection (while the data size is increasing) and feedback to the system being managed. This becomes particularly important when dealing with security-related incidents. One approach (model B) is to put an in-memory front end before the scale-out infrastructure and stream and process certain time-sensitive data directly in memory. An even more streamlined approach is to push rules from the data center to the infrastructure of the clients and provide the data collectors with the functionality to quickly preprocess the data collected based on the rules distributed by the infrastructure manager. This creates another security challenge because the rules and algorithms of the infrastructure management company (in some sense, part of the bread and butter of the company) are transferred to the customers premises. This potentially leads to a three-step process: at the edge (data collector level), in-memory in a data center, and scale-out in a data center. The final management model is that 52 BUSINESS INTELLIGENCE JOURNAL VOL. 20, NO. 2

10 Model A, on premises (low volume/velocity) Model B, on premises (high volume/velocity) Model C, managed cloud (high volume/velocity) Historical Results (Data Warehouse) Historical Results (Data Warehouse) Rules and Processing Rules and Processing Rules and Processing Results (Data Warehouse) Scale-Out Data Management (e.g., Hadoop) In-Memory Store and Streaming Compute Storage Network Feedback Upload/Update Scale-Out Data Management (e.g., Hadoop) In-Memory Store and Streaming Data Feedback Upload/Update Compute Storage Network Data Collectors Data Feedback Fast Data Processing Infrastructure Network Compute Storage Services Collectors Collectors Infrastructure Network Compute Storage Services Globally Distributed Infrastructure Infrastructure Network Compute Storage Services Globally Distributed Infrastructure Figure 8: Different models of outsourced infrastructure management. the customer infrastructure is managed in a cloud of the infrastructure manager (model C). Summary In this article, we presented three use cases to provide examples of different big data challenges: real-time (velocity), scale-out (volume), and edge processing (variety and low latency combined with connectivity challenges). In reality, most use cases deal with more than one challenge. Many actually require data ingestion from multiple sources (variety) and at the same time especially within the IoT domain involve some real-time aspect or a direct feedback component. In addition, data can be stored for historical analysis to extract business insights or to optimize decision making. Data models can vary considerably from one domain (manufacturing, retail, and so on) to another, and legacy applications and infrastructure may impact how and where data is stored and integrated. Different classes of problems can drive different requirements (such as distribution, scalability, and performance) and require different platforms: data warehouse, Hadoop, edge, and so on. Analytics and a proper data infrastructure are becoming key competitive differentiators. Technology and talent (data scientists) are equally important to unlocking this potential. More non-technical (or non-software) enterprises are acquiring tech startups (Rao, 2013), which are attracting funding thanks to trends in big data, analytics, and sensorization. The traditional warehouse has been omnipresent during the last four decades, and it will not be phased out soon by new technologies such as Hadoop or real-time systems such as Storm. Traditional data warehouses perform many vital functions within enterprises and can be quite suitable for highly structured data. BUSINESS INTELLIGENCE JOURNAL VOL. 20, NO. 2 53

11 Replacing traditional data warehouses with newer technology (where appropriate) will take time. Yet, several classes of problems are better served by newer technologies, particularly when dealing with high data volume, velocity, and variety. A classical data warehouse will not always be sufficient to capture and integrate all the data. Instead, enterprises will focus on data virtualization strategies. Finally, in many cases, the results of Hadoop processing can be stored in structured databases and could, therefore, be suitable for the classical data warehouses. The importance of traditional data warehouse technology should not be neglected with the rise of these new technologies. SQL-based query engines are today being developed on top of Hadoop, lowering the barrier to adoption for traditional database users, and at the same time acknowledging that SQL is still very important and widely used. However, we are seeing an evolution from the classical data warehouse to a logical data warehouse that combines the strengths of the traditional warehouses with alternative data management and access technologies such as those discussed in this article. References Bessis, Nik, and Ciprian Dobre, eds. [2014]. Fog Computing: A Platform for Internet of Things and Analytics, Big Data and Internet of Things: A Roadmap for Smart Environments, Studies in Computational Intelligence, Volume 546, pp Beyer, Mark [2011]. Does the 21st-Century Big Data Warehouse Mean the End of the Enterprise Data Warehouse? Bonomi, Flavio et al. [2012]. Fog Computing and Its Role in the Internet of Things Proceedings of the first edition of the MCC Workshop on Mobile Cloud Computing, pp Brändli, Pius [2013]. When Big Data Meets Reality, TDWI Conference, Zurich, Switzerland, November. Dato, Siraj [2013]. In-Store Advertising Will Soon Look You Straight in the Eye, Mashable/Quartz, November. Ehrenmann, Markus, Roland Pieringer, and Kurt Stockinger [2012]. Is There a Cure-All for Business Analytics? Case Studies of Exemplary Businesses in Banking, Telecommunications, and Retail, Business Intelligence Journal, Vol. 17, No Inmon, William H. [2005]. Building the Data Warehouse, Wiley. Kagermann, Henning, Wolfgang Wahlster, and Johannes Helbig [2013]. Recommendations for implementing the strategic initiative Industrie 4.0, National Academy of Science and Engineering (Germany), April. Kosner, Anthony W. [2013]. The Internet of ithings: Apple s ibeacon Is Already In Almost 200 Million iphones and ipads, Forbes. 12/15/the-internet-of-ithings-apples-ibeacon-is-alreadyin-almost-200-million-iphones-and-ipads/ Macsai, Dan [2009]. Douwe Egberts Bemoved Vending Machine Will Make You Jump for Joy... For Coffee, Fast Company, October. douwe-egberts-bemoved-vending-machine-will-makeyou-jump-joy-coffee Rao, Leena [2013]. As Software Eats The World, Non- Tech Corporations Are Eating Startups, TechCrunch, December Russom, Philip [2013]. Integrating Hadoop into Business Intelligence and Data Warehousing, TDWI Best Practices Report, Q2. tdwi-best-practices-report-integrating-hadoop-intobusiness-intelligence-and-data-warehousing.aspx 54 BUSINESS INTELLIGENCE JOURNAL VOL. 20, NO. 2

12 Ward, Jonathan S., and Adam Barker [2013]. Undefined By Data: A Survey of Big Data Definitions, arxiv.org. Vizard, Michael [2012]. FedEx CIO Sees Analytics Driving a World of Enterprise Change, Slashdot, October 4. Instructions for Authors The Business Intelligence Journal is a quarterly journal that focuses on all aspects of business intelligence, data warehousing, and analytics. It serves the needs of researchers and practitioners in this important field by publishing surveys of current practices, opinion pieces, conceptual frameworks, case studies that describe innovative practices or provide important insights, tutorials, technology discussions, and annotated bibliographies. The Journal publishes educational articles that do not market, advertise, or promote one particular product or company. Visit tdwi.org/journalsubmissions for the Business Intelligence Journal s complete submissions guidelines, including writing requirements and editorial topics. Submissions For complete submission guidelines and suggestions, visit tdwi.org/journalsubmissions Materials should be submitted to: Marie Gipson Managing Editor journal@tdwi.org Upcoming Deadlines Volume 20, Number 4 Submission Deadline: August 7, 2015 Distribution: December 2015 Volume 21, Number 1 Submission Deadline: November 20, 2015 Distribution: March 2016 Volume 21, Number 2 Submission Deadline: February 19, 2016 Distribution: June 2016 BUSINESS INTELLIGENCE JOURNAL VOL. 20, NO. 2 55