|By Bob Gourley||
|December 13, 2014 12:00 PM EST||
Since the birth of Hadoop in 2005-06, the way we think about storing and processing information has evolved considerably. The term “Big Data” has become synonymous with this evolution. But still, many of our customers continue to ask, “What is Big Data?”, “What are its use cases?”, and “What is its business value?”. The Internet is overloaded with definitions, characteristics, and benefits; however, few discussions synthesize all three of these topics in one place. This paper answers these questions, and proposes a total cost calculation framework for CTOs and CIOs that are evaluating solutions for their organization’s use case(s). In the text below, I examine an on-premise Hadoop ecosystem as a general purpose Big Data solution in relation to alternative commercial purpose-built storage technologies (-e.g. Oracle, Teradata, IBM, SAP, Microsoft, EMC, etc). It may be difficult to determine the exact point at which you should leverage one over the other. It is my contention that when the total cost of using all your data exceeds what you are able to spend using purpose-built technologies, it is time to consider using a general purpose solution like Hadoop for process offloading.
What is Big Data?
According to Edd Dumbill, a well respected thought leader and VP of Strategy for Silicon Valley Data Science, a big data and data science consulting company, Big Data is “data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or does not fit the structures of your database architectures. To gain value from this data, you must choose an alternative way to process it.” This definition was published in an article entitled “What is Big Data?” in Big Data Now: 2012 Edition by O’Reilly Media, and touches on the three primary characteristics of data :
- Volume: The size of your data. Data has mass, and there is a cost to moving it around the network.
- Velocity: The speed with which the data either arrives or is created, and how quickly it needs to be consumed in order to make use of it.
- Variety: The differences in structure between all the types of data within an enterprise.
Scour the internet and you will find that there are other, less commonly discussed but relevant characteristics associated with Big Data to include Veracity and Volatility . Veracity refers to the truthfulness of the data or your degree of trust in what it is conveying. Volatility is how often your existing data changes or is updated by the new data you are receiving/creating. There are certainly other characteristics as well. I recently began using the term Viscosity to describe the degree of data fragmentation in client environments, and the level of effort required to reassemble it into a coherent view. In this context, organizations with low viscosity have significant fragmentation, and duplication throughout their enterprise.
The term “Big Data” has come to focus on these characteristics and imply that traditional database architectures, such as on line transaction processing (OLTP) and on line analytic processing (OLAP) purpose-built technologies simply will not scale to meet your data capacity needs. However, massively parallel processing (MPP) database architectures are one example of purpose-built technologies that have been developed to support both OLTP and OLAP data structures at enormous scales up into the petabytes (PB). For example, there is a 50 PB MPP cluster at eBay . Certainly this size conforms to any logical definition of Big Data.
Depending on your use case, it is possible that a purpose-built technology may suite your needs at scale. While MPP systems remain most effective with structured, tabular and transactional data sets, it is possible to store most everything except massive files in relational structures. However, this may not be the best fit for your use case(s) in terms of appropriateness or cost. There is limited published pricing data for commercial offerings, but MPP systems are notably expensive. When including the cost of software, hardware, and licensing/support, the cost per terabyte (TB) of an MPP system is estimated at tens of thousands of dollars . At these prices, a one PB system can cost tens of millions of dollars. In contrast, the equivalent cost of Hadoop is roughly $2,000 per TB, leaving a one PB Hadoop cluster to cost roughly $2 million. That is a significant initial cost savings; however, use case(s) will always drive the total cost of any solution.
Coincidentally, eBay has also released information on their production 50 PB Hadoop cluster, one of the largest such clusters in the world . The fact that eBay uses both types of systems demonstrates that there is a place for each, and that the difference may come down to price and purpose. Given the relative lower cost of Hadoop, I submit that it is easier to identify Big Data if we add cost to our definition. Therefore, Big Data is the result when (a) the sum of all your data’s characteristics coupled with (b) the resources required to achieve your use case exceeds (c) the cost you are willing/able to spend using traditional approaches. When that inflection point is reached, it is clearly time to consider other, non-traditional approaches for process offloading. Each unique situation warrants a cost/benefit analysis to determine if a general-purpose solution like Hadoop is right for your use case.
What are the Use Cases for Big Data?
Process offloading refers to the act of moving workloads from one implementation to another to achieve better suitability, performance, availability, etc., at a lower price point. Both traditional and non-traditional solutions have advantages and disadvantages given a particular workload, and they should be leveraged accordingly to maximize cost efficiencies. Let us examine Hadoop’s use cases for process offloading.
Hadoop is comprised of two major components: the Hadoop Distributed File System (HDFS) and MapReduce, a framework for writing applications to process large amounts of content over multiple nodes (servers). Hadoop is often referred to as a schemaless system because data is not forced into a schema upon ingest. Ultimately, there is a structure known as the key/value pair in which data is expressed as a collection of [key]->[value] tuples or records. This is the most fundamental data structure in computer science. Hadoop uses the key/value pair because nearly any data can be expressed, stored, processed and retrieved using this minimal structure. Because key/value is so rudimentary, a schema can be applied at query time based on the question being asked. This adds tremendous flexibility and differs significantly from traditional approaches like OLTP and OLAP, which require you to know/define the data model up front, and have an understanding of the questions you intend to ask. Figure 1 illustrates these different process flow models. Having to know what questions you intend to ask, and constructing a pre-defined schema will add artificial constraints to the answers you are able to get from the data.
Another issue with schema-based systems is scalability. Traditional relational architectures scale vertically with ease, but are difficult to design for horizontal scaling due to their rigid data structures (tables, table relationships, rows, columns, indices) which must be sharded or split across multiple nodes. The integrity of these structures must be maintained while offering near-real-time (on line) create, read, update and delete (CRUD) operations on data. This is not trivial, and it requires commercial companies to make significant financial investments to do it well, which drive up the cost of those solutions. As a schemaless system, the latest release of Hadoop (2.x) scales horizontally to 10,000+ nodes without the added complexity inherent to traditional MPP systems .
Many organizations have purpose-built solutions for asking business intelligence questions, providing disaster recovery/backup, etc., but scaling these solutions beyond an initial, narrowly defined usage for structured data usually involves significant cost increases. As a schemaless computational file system, Hadoop can be applied to an almost endless set of challenges at a lower cost. Below we walk through six higher-order use cases to illustrate how these savings can be realized:
1. Raw Storage/Data Lake: Backing up all the data your enterprise collects and creates daily, to include its historical holdings, for continuity of operations (COOP) and disaster recovery (DR) has previously been too expensive, and therefore unfeasible. Instead, businesses make difficult tradeoffs as to what will and will not be recoverable should disaster strike. Imagine the possibilities if you were able to economically store everything in your enterprise for the price of traditional commodity hard disks. Fortunately, Hadoop makes this dream a reality with its internally redundant data structure that by default makes three copies of all data written to HDFS. This scalable, schemaless raw storage lends itself conceptually to what is now being called a “data lake”. A data lake is based on the notion that data can be tagged with metadata about its source, contents, structure and other characteristics. These properties stay with the data as it is minimized into key-value pairs and written to the Hadoop file system. To process the data, all one needs to know is what data they wish to process leveraging these properties. This allows many different types of data to exist side-by-side within the simple structures of the Data Lake. The amount of pre-processing is minimal, as data is no longer fit into specific schemas up-front, making the data accessible to a wider variety of purposes. This would not be cost effective using traditional commercial systems.
2. Multi-Format Data Analysis: There are many different types of data beyond structured and unstructured text, to include audio, video, and images. Analyzing structured and unstructured text at scale can be an expensive and difficult challenge, but analyzing large collections of digital media is not even possible using traditional relational systems. Many businesses have previously been unable to unlock the potential of their data holdings due to an inability to process digital content, such as the ability to analyze and track objects in video, or to identify and extract biomarkers in healthcare images. HDFS accepts all these formats for analysis without the need for a schema. Hadoop’s ability to work with unstructured text and binary data (audio, video, imagery) extends well beyond the native capabilities offered by existing storage solutions, providing an enormous capability advantage.
3. Data Cleansing/Transformation Businesses often contend with multiple relational data models, unstructured text and streaming data. You likely need to correlate, cleanse, de-duplicate, synchronize and normalize/de-normalize these data sets as they move between databases and tools to create a complete, clean operating picture for downstream analysis. The vast majority of work in conducting analytics is often preparing the data for use. In addition, new initiatives to leverage autonomous self-reporting devices and sensors provide continuous streams of data, creating explosions in the amount of information if used in their raw form. Purpose-built technologies present challenges when attempting these types of tasks due to their reliance on schemas. General purpose solutions, like the Hadoop ecosystem, deliver an economical way of storing, pre-processing and/or summarizing these data sets and streams, thereby minimizing the unchecked growth in commercial licensing investments within your enterprise.
4. Data Exploration: When new questions arise, the relevant variables and their relationships must be identified from within your data before you can begin to calculate definitive answers. However, these elements are not always understood, nor are the best algorithms for analyzing the data. Exploration is often required in order to build a model that will answer the questions being asked. Traditional relational architectures with pre-defined schemas are not likely to provide a platform for discovery. In these cases, identifying key variables and useful analytic methods is a trial/error process. Hadoop provides a flexible, schemaless environment that reduces the friction associated with the iterative process of exploring and analyzing data when the model is unclear. Hadoop provides a sandbox for exploring data without having to increase commercial capacity or spend the time building new schemas.
5. Data Science & Personalization: Data science leverages tools and techniques from many different areas of study, to include statistics, machine learning, mathematics, probability/uncertainty modeling, etc., to surface meaning from data, and generate data-driven products. This is essentially the art of making data actionable, either by a user or a machine. Data science is not exclusive to Big Data, but there is tremendous knowledge potential in large data sets. One use of data science is for personalization, the act of exhaustively analyzing large quantities of related data, such as the online behaviors of millions of Internet users to in order to calculate recommendations for a specific individual. The results are then presented in the form of “you might also like” books, movies, and other targeted advertisements. These techniques are also being applied to healthcare where symptoms, genetics, treatments, and outcomes are being analyzed to optimize treatment for specific individuals to optimize treatments. Hadoop is a perfect platform for data collecting, synthesizing, munging, cleaning and joining disparate data sets for analysis to achieve decision-relevant insight.
6. Data Anonymization: Certain industries, perhaps healthcare more than any other, require anonymized data for research. Rules governing the release of such data to the public generally require the information contain no personally identifiable information (PII). In the case of healthcare specifically, the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule, released in 2003, governs the use and disclosure of protected health information (PHI). The Privacy Rule does not give a specific algorithm for achieving the level of de-identification required, and there are many ways to approach anonymization in general, which depend greatly on how the data will be used. If a portion of your business model relies on providing anonymized data to internal or external groups for analysis, you want that process to be as clear, efficient, and repeatable as possible. Hadoop provides a solution for codifying and institutionalizing these algorithms for your enterprise. This increases the speed and effectiveness of all groups depending on anonymized data, providing them with an approved, documented process, and an authoritative source from which to receive data.
This list is not meant to be exhaustive, and there are definitely other use cases. Each use case is applicable to a wide variety of domains, to include finance, cyber, healthcare, defense, and scientific research.
What is the Business Value of Big Data?
Our new definition of Big Data (when the cost of using all your data for your use case exceeds what you are able to spend using purpose-built technologies) lends itself to a cost/benefit analysis. Figure 2 establishes a rubric through which to express the decision calculus of Big Data for process offloading. This framework illustrates the components of cost, discussed below, that every CIO and CTO should take into account when evaluating solutions for their use cases.
Projects should always start with gathering and analyzing requirements. In an analytic context, these are the questions you want to ask of your data. Or more generally, how you intend to use the data you would like to store in Hadoop. These requirements have obvious implications for leveraging the relevant data assets.
The Data / Characteristics (AS-IS) corner of the triangle refers to all data related to your requirements, and all the attributes discussed earlier, to include the amount, how quickly it grows/changes, differences in type/structure, where it resides on the network, etc.
Once the associated data has been identified, a solution is identified and designed. In the case of data analysis, the solution often involves models and techniques to change and analyze the data to find answers. Overall, this step includes any processes, human or machine, that are necessary to get the results you are looking for.
The Purpose / Answers (TO-BE) corner is your end-state vision, which is sometimes expressed in terms of success criteria and/or key performance indicators. In the case of data science, this corner represents the answers you want from your data, in addition to how users should expect to access those answers, and how frequently the answers need to be updated (real-time, hourly, daily, monthly, etc).
Lastly, there are often numerous ways for this solution to be physically implemented. Each possible implementation requires specific people, intellect (expertise, experience), technology (licenses, support), time, and physical capital (power, space, cooling) to assemble and extend (write algorithms, or build solutions on top of) the desired end-state. There are many factors here to consider. For example, certain software licenses will charge by the number of users, which may limit your derived business value (in terms of productivity) if that cost prevents your entire team from leveraging the software. As well, the more data you have, the more physical or virtual compute resources you may need.
Together, these elements influence the total cost of the solution. Ultimately, cost is the tipping point that can cause you to change the scope of your requirements and timeline, the data you use, the models/techniques you employ, the answers you are able to achieve and the algorithms/technologies you implement. Often, it is necessary to find an affordable balance to achieve the organization’s goals and objectives. However, these trade-offs may cause you to compromise certain business objectives, and reduce the business value derived from the solution.
The business value of Hadoop is the result of overcoming the functional limitations established by the cost of scaling purpose-built technologies, and having to make fewer compromises to achieve your data-driven business objectives. This relationship between cost and business value is illustrated in Figure 2. By managing (containing or reducing) cost, it becomes possible to maintain or broaden your scope and implement the solution that is right for you. Hadoop may allow you to get more from your data, with a significantly lower cost investment, resulting in tangible economic value. If Hadoop is able to satisfy your use case, then it is likely you will benefit from cost containment (and possibly savings) by preventing or reducing the expansion of more expensive purpose-built technologies.
It is important to choose the right technology for your particular use case. Hadoop continues to mature as a widely supported open source solution nearing its ten year anniversary. It is also supported by several commercial vendors offering on-site support. Depending on your particular use case(s), Hadoop may or may not be the best solution. Some Big Data is consistent, known, structured, and aligns well to the use cases best served by purpose-built technologies. However, when you do not have that, or cost constraints limit your business value, it is time to consider using a general purpose solution like the Hadoop ecosystem for process offloading. The formula presented in this paper provides a lens for CIOs/CTOs to examine potential solutions, business objectives, and cost constraints. Hadoop’s low cost and broad applicability are definitely worth exploring. I recommend you conduct your own cost/benefit analysis to determine if Hadoop is right for you and your use case(s). You may find that relative to commercial products, Hadoop will allow you to achieve greater business value and substantial cost savings.
 O’Reilly Media, Inc., “What is Big Data?”. Big Data Now: 2012 Edition. Sebastopol, CA. October 2012. Found Online at: http://www.oreilly.com/data/free/big-data-now-2012.csp
 Normandeau, Kevin. “Beyond Volume, Variety and Velocity is the Issue of Big Data Veracity”. Inside BigData. September 2013. http://inside-bigdata.com/2013/09/12/beyond-volume-variety-velocity-issu...
 Harris, Derrick. “Teradata pluges 17% on Q3 warning: Is it economics or Hadoop?” Gigaom. October 2013. https://gigaom.com/2013/10/15/teradata-plunges-17-on-q3-warning-is-it-ec...
 Barth, Paul; Bean, Randy. “Get the Maximum Value Out of Your Big Data Initiative”. Harvard Business Review Blog Network. February 2013. http://blogs.hbr.org/2013/02/get-the-maximum-value-out-of-y/
 Ma, Ming. “Hadoop @ eBay Marketplaces”. Slideshare. June 2013. http://www.slideshare.net/Hadoop_Summit/ma-june27-140pmroom212v2
 Murthy, Arun. “Apache Hadoop YARN – Concepts and Applications”. Hortonworks. August 2012. http://hortonworks.com/blog/apache-hadoop-yarn-concepts-and-applications/
About the author:
Jeremy Glesner is the Chief Technology Officer of Berico Technologies. Jeremy’s background is in information science and software engineering. Find him on Twitter at @jglesner (https://twitter.com/jglesner) and on Linkedin (http://www.linkedin.com/in/
Who are you? How do you introduce yourself? Do you use a name, or do you greet a friend by the last four digits of his social security number? Assuming you don’t, why are we content to associate our identity with 10 random digits assigned by our phone company? Identity is an issue that affects everyone, but as individuals we don’t spend a lot of time thinking about it. In his session at @ThingsExpo, Ben Klang, Founder & President of Mojo Lingo, discussed the impact of technology on identity. Sho...
Jan. 18, 2017 03:45 AM EST Reads: 3,993
Technology vendors and analysts are eager to paint a rosy picture of how wonderful IoT is and why your deployment will be great with the use of their products and services. While it is easy to showcase successful IoT solutions, identifying IoT systems that missed the mark or failed can often provide more in the way of key lessons learned. In his session at @ThingsExpo, Peter Vanderminden, Principal Industry Analyst for IoT & Digital Supply Chain to Flatiron Strategies, will focus on how IoT depl...
Jan. 18, 2017 02:30 AM EST Reads: 1,819
Data is an unusual currency; it is not restricted by the same transactional limitations as money or people. In fact, the more that you leverage your data across multiple business use cases, the more valuable it becomes to the organization. And the same can be said about the organization’s analytics. In his session at 19th Cloud Expo, Bill Schmarzo, CTO for the Big Data Practice at Dell EMC, introduced a methodology for capturing, enriching and sharing data (and analytics) across the organization...
Jan. 18, 2017 02:15 AM EST Reads: 3,215
With all the incredible momentum behind the Internet of Things (IoT) industry, it is easy to forget that not a single CEO wakes up and wonders if “my IoT is broken.” What they wonder is if they are making the right decisions to do all they can to increase revenue, decrease costs, and improve customer experience – effectively the same challenges they have always had in growing their business. The exciting thing about the IoT industry is now these decisions can be better, faster, and smarter. Now ...
Jan. 18, 2017 01:30 AM EST Reads: 4,202
WebRTC is about the data channel as much as about video and audio conferencing. However, basically all commercial WebRTC applications have been built with a focus on audio and video. The handling of “data” has been limited to text chat and file download – all other data sharing seems to end with screensharing. What is holding back a more intensive use of peer-to-peer data? In her session at @ThingsExpo, Dr Silvia Pfeiffer, WebRTC Applications Team Lead at National ICT Australia, looked at differ...
Jan. 18, 2017 01:15 AM EST Reads: 4,890
The cloud market growth today is largely in public clouds. While there is a lot of spend in IT departments in virtualization, these aren’t yet translating into a true “cloud” experience within the enterprise. What is stopping the growth of the “private cloud” market? In his general session at 18th Cloud Expo, Nara Rajagopalan, CEO of Accelerite, explored the challenges in deploying, managing, and getting adoption for a private cloud within an enterprise. What are the key differences between wh...
Jan. 18, 2017 01:00 AM EST Reads: 6,045
"ReadyTalk is an audio and web video conferencing provider. We've really come to embrace WebRTC as the platform for our future of technology," explained Dan Cunningham, CTO of ReadyTalk, in this SYS-CON.tv interview at WebRTC Summit at 19th Cloud Expo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
Jan. 18, 2017 12:00 AM EST Reads: 2,259
In 2014, Amazon announced a new form of compute called Lambda. We didn't know it at the time, but this represented a fundamental shift in what we expect from cloud computing. Now, all of the major cloud computing vendors want to take part in this disruptive technology. In his session at 20th Cloud Expo, John Jelinek IV, a web developer at Linux Academy, will discuss why major players like AWS, Microsoft Azure, IBM Bluemix, and Google Cloud Platform are all trying to sidestep VMs and containers...
Jan. 17, 2017 11:00 PM EST Reads: 636
IoT is at the core or many Digital Transformation initiatives with the goal of re-inventing a company's business model. We all agree that collecting relevant IoT data will result in massive amounts of data needing to be stored. However, with the rapid development of IoT devices and ongoing business model transformation, we are not able to predict the volume and growth of IoT data. And with the lack of IoT history, traditional methods of IT and infrastructure planning based on the past do not app...
Jan. 17, 2017 10:30 PM EST Reads: 747
The many IoT deployments around the world are busy integrating smart devices and sensors into their enterprise IT infrastructures. Yet all of this technology – and there are an amazing number of choices – is of no use without the software to gather, communicate, and analyze the new data flows. Without software, there is no IT. In this power panel at @ThingsExpo, moderated by Conference Chair Roger Strukhoff, Dave McCarthy, Director of Products at Bsquare Corporation; Alan Williamson, Principal ...
Jan. 17, 2017 10:30 PM EST Reads: 2,373
WebRTC has had a real tough three or four years, and so have those working with it. Only a few short years ago, the development world were excited about WebRTC and proclaiming how awesome it was. You might have played with the technology a couple of years ago, only to find the extra infrastructure requirements were painful to implement and poorly documented. This probably left a bitter taste in your mouth, especially when things went wrong.
Jan. 17, 2017 09:15 PM EST Reads: 7,566
SYS-CON Media announced today that @WebRTCSummit Blog, the largest WebRTC resource in the world, has been launched. @WebRTCSummit Blog offers top articles, news stories, and blog posts from the world's well-known experts and guarantees better exposure for its authors than any other publication. @WebRTCSummit Blog can be bookmarked ▸ Here @WebRTCSummit conference site can be bookmarked ▸ Here
Jan. 17, 2017 08:00 PM EST Reads: 11,649
A critical component of any IoT project is what to do with all the data being generated. This data needs to be captured, processed, structured, and stored in a way to facilitate different kinds of queries. Traditional data warehouse and analytical systems are mature technologies that can be used to handle certain kinds of queries, but they are not always well suited to many problems, particularly when there is a need for real-time insights.
Jan. 17, 2017 06:45 PM EST Reads: 6,232
Providing secure, mobile access to sensitive data sets is a critical element in realizing the full potential of cloud computing. However, large data caches remain inaccessible to edge devices for reasons of security, size, format or limited viewing capabilities. Medical imaging, computer aided design and seismic interpretation are just a few examples of industries facing this challenge. Rather than fighting for incremental gains by pulling these datasets to edge devices, we need to embrace the i...
Jan. 17, 2017 05:15 PM EST Reads: 3,573
Web Real-Time Communication APIs have quickly revolutionized what browsers are capable of. In addition to video and audio streams, we can now bi-directionally send arbitrary data over WebRTC's PeerConnection Data Channels. With the advent of Progressive Web Apps and new hardware APIs such as WebBluetooh and WebUSB, we can finally enable users to stitch together the Internet of Things directly from their browsers while communicating privately and securely in a decentralized way.
Jan. 17, 2017 04:45 PM EST Reads: 3,057
Fifty billion connected devices and still no winning protocols standards. HTTP, WebSockets, MQTT, and CoAP seem to be leading in the IoT protocol race at the moment but many more protocols are getting introduced on a regular basis. Each protocol has its pros and cons depending on the nature of the communications. Does there really need to be only one protocol to rule them all? Of course not. In his session at @ThingsExpo, Chris Matthieu, co-founder and CTO of Octoblu, walked through how Octob...
Jan. 17, 2017 04:30 PM EST Reads: 2,911
The Internet of Things can drive efficiency for airlines and airports. In their session at @ThingsExpo, Shyam Varan Nath, Principal Architect with GE, and Sudip Majumder, senior director of development at Oracle, discussed the technical details of the connected airline baggage and related social media solutions. These IoT applications will enhance travelers' journey experience and drive efficiency for the airlines and the airports.
Jan. 17, 2017 04:15 PM EST Reads: 1,992
SYS-CON Events announced today that Catchpoint, a leading digital experience intelligence company, has been named “Silver Sponsor” of SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Catchpoint Systems is a leading Digital Performance Analytics company that provides unparalleled insight into your customer-critical services to help you consistently deliver an amazing customer experience. Designed for digital business, C...
Jan. 17, 2017 02:30 PM EST Reads: 1,749
With major technology companies and startups seriously embracing IoT strategies, now is the perfect time to attend @ThingsExpo 2016 in New York. Learn what is going on, contribute to the discussions, and ensure that your enterprise is as "IoT-Ready" as it can be! Internet of @ThingsExpo, taking place June 6-8, 2017, at the Javits Center in New York City, New York, is co-located with 20th Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry p...
Jan. 17, 2017 02:15 PM EST Reads: 3,644
In his General Session at 17th Cloud Expo, Bruce Swann, Senior Product Marketing Manager for Adobe Campaign, explored the key ingredients of cross-channel marketing in a digital world. Learn how the Adobe Marketing Cloud can help marketers embrace opportunities for personalized, relevant and real-time customer engagement across offline (direct mail, point of sale, call center) and digital (email, website, SMS, mobile apps, social networks, connected objects).
Jan. 17, 2017 02:00 PM EST Reads: 5,384