Earlier marketers used to gather data to create enticing ads. And, that were targeted to wide range of demographics. But nowadays, personalization has become a core strategy for marketers and publishers. And, Big Data platforms are flawlessly helping companies figure out, analyze and manage enormous amount of information in new and exciting ways. In turn, this is allowing the marketers to create personalized ads for successfully targeting individuals.
According to a series of studies -When marketers effectively utilize the vast amount of data they can be collectively used to create personalized messages. And, these data-driven strategies can create substantial increase in conversion and engagement rate. Even marketers and media buyers are seeing tremendous opportunities, results and improvements in the performance of their ad campaigns, when they are compared with traditional display ads. In a recent study, which was conducted by the digital marketing software provider Signal – approx 92 percent of the media buyers showed that their clients are planning to accelerate their media buys, and 66 percent are planning to increase their investment in “addressable media,” which will be a much more targeted approach than programmatic branding and marketing.
Additionally, the study found that around 83 percent of marketers, who are currently using addressable media reported top-notch performance across their clients when compared with simple and easy display ads. Apart from that approx 60 percent experienced higher conversion rates, and 63 percent advertisers experienced higher click-through rates.
Big Data Challenges and Solutions
Despite of having a few challenges to the implementation of Big Data platforms, it has emerged as one of the leading platforms. According to the recent study done by Monetate – many marketers are already overwhelmed by the sheer volume of the data collected, while few of them know what to do with it. So, basically 94% of the marketers know how the valuable personalization is. And, when data gets real, 95% of them get stuck in the analysis paralysis.
Big Data, Big Challenges? – But solutions for marketers are being rapidly developed
This is where leading Big Data software vendors as Parangat Technologies comes into the picture. With the cost-effective and viable solutions, Parangat’s proven methodologies and solutions make it possible for companies to execute Big Data projects aptly that were previously considered too lengthy, intricate, complex and costly to work on. Parangat’s big data solutions help in simplifying the intricate and complex data sets into highly compelling Big Data analytics solutions, thus enabling marketing and advertising tech industries.
Wrapping it up
Nowadays, marketers are consistently and constantly looking for newer ways to increase customer loyalty and enhance user experience, especially in this fiercely competitive world. And, Big Data has emerged as the most important ways to create the best user experience that deals with customers in the way that they need to be treated – just like individuals.
In the world where an e-commerce experience is targeted by immediate signals of context like geo-location, site-searches, cookies, campaign referrals, true personalization. All these rely on the data that will drive the experience. And, that’s why for many e-businesses, Big Data has become a critical part of their equation.
1: They both perform different activities: Both Hadoop and Apache Spark are big-data frameworks. But one of the common facts is – they don’t really serve the same purposes. Hadoop is basically a distributed data infrastructure. So, it helps in distributing the massive data collections across various nodes, that too, within a cluster of commodity servers. This means that you don’t need to buy and maintain the expensive custom hardware. Hadoop also indexes and keeps close track of the data, thus enabling, big-data processing and analytics and making them far more effective than was possible previously. On the other hand Spark is a data-processing tool that easily operates on those distributed data collections. And, it doesn’t allow distributed storage.
2: You can use one without the other. Hadoop not just includes a storage component, which is known as the Hadoop Distributed File System, but also includes a processing component named MapReduce. Users don’t need Spark to get the processing done. But conversely, user can also use Spark without Hadoop. In-fact Spark does not come with its own file management system, though; it needs to be integrated with the one. If not HDFS, then another cloud-based data platform will be used. Spark was designed for Hadoop, however, many users agree that they’re better together.
3: Spark is comparatively speedier. Generally, Spark is a lot faster than MapReduce because of the method and way it processes data. Since MapReduce operates in steps and Spark operates on the whole data, which is set in one fell swoop. Kirk Borne, principal data scientist at Booz Allen Hamilton explained Spark, And said, “The MapReduce workflow looks like this: easily reads data from the cluster, performs an operation, writes results to the cluster, reads updated data from the cluster, easily performs next operation, writes next results to the cluster, etc”. While on the other hand, it completes the full data analytics operations in-memory and in the near real-time: “User can Read data from the cluster, easily perform all the requisite analytic operations, write results to the cluster,” Borne added. If we talk in terms of efficiency, Spark can be as much as 10 times faster than MapReduce for batch processing and can be up to 100 times faster for in-memory analytics.
4: User may not need Spark’s speed. MapReduce’s processing style can be just well if the data operations and reporting needs are static and you can wait a longer for batch-mode processing. But being a user, if you are looking for doing analytics on streaming data, like from sensors on a factory floor, or you have applications that need multiple operations, you probably want to go with Spark. A few of the applications for Spark include real-time marketing campaigns, cyber security analytics, and online product recommendations with machine log monitoring.
5: Failure recovery: However, different, but still good. Hadoop is naturally flexible to system faults or failures because data is written to disk after each and every operation. But Spark has very similar built-in resiliency by virtue of the fact that its data objects are usually saved and stored in resilient distributed data sets, which are distributed across the data cluster. According to Borne, “These data objects can be stored in memory or on disks, and RDD provides full recovery from faults or failures”.
Big data and business intelligence have always played a crucial role in mobile application development. According to consumers demand getting immediate access and insight into the “mobile moments”— for getting key points on time where context and real-time data ignites decision-making, prompts a buyer to purchase opportunities and allows companies to maintain brand consistency across devices. Enterprises need immediate data from a wide range of sources in order to fuel the processes by combining big data and analytics. In this piece of article, we will focus on global supply chain management (“SCM”) in order to explain how connection between big data and analytics results in building more efficient business processes. Let’s take a look at the scope of big data, as well as the challenges it brings with itself:
- Firstly, the size of big data itself is overwhelming. We are already speaking of petabytes and zettabytes, that means we are approaching the era of the yottabyte, which is 10 raised to the power of 24 bytes.
- Secondly, this data avalanche includes immensely huge unstructured data that cannot be stored in a conventional database. Examples include Word documents, videos, PowerPoint, telecommunications and presentations.
- Thirdly, the Internet of Things (“IoT”) contributes more big data than any other source. , the IoT is growing exponentially with user-centric sensors that can be wore in our wristbands, street corners, cars and geolocation systems.
Business Intelligence And Global Supply Chain Management
As we have discussed earlier as well in the title of this piece, that we will evaluate the big data and mobile app development in the context of SCM. The issues that it faces – its myriad, globally dispersed actors; and the requirement for data worldwide to make immediate and strong use cases.
We have heard stories about pre-CEO Tim Cook, who has mastered the Apple’s supply chain including Cupertino could develop and deliver any product within four days. Through this effort, Cook’s inspired and exceptional bilateral relationships helped in controlling the supply of key parts to competitors. And, finally, Cook got it right, and Apple profited outstandingly.
Impact of Big Data on Mobility
In recent years, number of smartphone and tablet users have increased tremendously and people are actually relying and depending upon the smartphone and other mobile gadgets for their day-to-day tasks. Now searching on the web has become an entire mobile activity. Even businesses and brands are counting on the mobility trends for meeting their objectives in order to to make a huge impact and find a niche to affect their marketplace enormously and positively.
In recent times, Big data has expanded enormously in this huge market. According to IDC stats – the big data market is growing enormously. However, the growth is a bit sporadic after 2015. But in the coming years, Big Data is expected to grow at an exponential rate. Mobility is offering numerous opportunities and driving the incidence of multiple innovations to efficiently manage data. IoT and automation too are also some of the technologies that are affecting gadgets and the products we use. In the world of smart mobility where about 5 billion people are calling, texting, and tweeting worldwide, we can expect numerous trends to spring out from the scenario.
Summing It Up
We can see the that in coming years, statistics and the burgeoning amount of data is going to be difficult to handle. And, that’s why it is required for businesses to wisely opt the ways to store data. As it is going to be quite cumbersome. Businesses are required to churn out the information with strict retention policies in place.
Today businesses are looking for a more connected world. As it enables them to enhance their brand awareness and empowers them to collect a lot of consumer data from smart devices. At present, many businesses and brands are already collecting scores of user data this way. But have you ever thought? – What happens to this large amount of data? And, that’s because many businesses are still unaware of the potential that ‘Big Data’ can bring to their business and change their marketing game. If we see from app marketers’ perspective, there is a tremendous scope of mining the large volume of data, that can be utilized for increasing revenues. To begin with, marketers can rummage through the data gathered from both conventional as well as digital sources so that consumers can be easily understood. Thus, they can use this to run digital campaigns and promotions in the near future. The same data proved to be enourmously useful for marketers because it helps marketers in doing in-depth predictive analysis, especially if we look for the success or failure of an app. By using Big Data, they can easily evaluate the end-to-end marketing startegy and performance, earn insights into their clients’ buying habits and learn more about different marketing trends, etc. This is in turn, will add value to their client-centric marketing strategies, thus, narrowing the gap between what the clients want and what they exactly get. Cloud Computing Enables App Marketers in Widening The App Marketers’ Horizon
Today cloud computing has become a key factor for any business especially for the future of digital media and has a major impact in terms of mobile app marketing. And, that’s why this technology is being adapted by various marketers to help their business grow. Today, various businesses already have hybrid cloud model strategies for their business growth.
One of the common benefits that Cloud Computing offers is – it provides marketers an option to store and access the data anywhere and everywhere, thus, this makes a clear winner over conventional storage mediums. By leveraging cloud computing technology, app marketers can use a single point of contact system for all their networks. If we talk about the Big Data, there is no limit on the amount of data tthat will be stored on the cloud. This paves the way for marketers to store huge data on cloud without any hurdle. Thus, this enables no more losing of crucial data stored on storage devices and no more freaking. The another key benefit is cost, which is a key area of concern for app marketers, especially for those who are working on a strict budget. Cloud Computing offers marketers a lot of key benefits and that too at a lower costs. With wide access to the internet and cloud-ready devices, marketers can achieve their goals smoothly. Enhanced security, improved connectivity, multiple-device connectivity, support, immense flexibility, convenience of use and higher productivity are a few of the other benefits that cloud technologies brings to the table for app marketers. Thus, we can say that it revolutionizes the way app marketers reach out to and engage with their potential customers and target audiences, which in turn, broadens the horizon of their businesses.
Wrapping it Up
Undoubtedly, the fact that a right combination of Big Data, IoT and Cloud Computing is going to take app marketing to the next level. Let’s see how it all gets implemented by the app marketers for various categories of apps.