Cloud systems have continued to gain momentum due to their mobility, scalability, flexibility and low costs. As companies awaken the potential of big data cloud popularity will only increase. Utilising a cloud system is the most effective way to manage big data as it reduces storage capability and performance issues. Integrating big data and cloud systems also has long term advantages to ROI. This is due to the cost effective benefits of cloud solutions, streamlining data analysis and storage. It allows marketers to excavate behavioural trends quickly, ensuring rapid responses and advantages in real world scenarios.
The data revolution is steadily influencing companies’ marketing. It is a new kind of knowledge, with the potential to illuminate previously hidden insights into consumer behavior. Consequently big data is increasingly being utilized for making choices about real time situations. Essentially data is driving decision-making.
Big data has emerged in response to the variety of devices and software receiving and sending information within our daily lives. Brown et al (2011) highlight, ‘products ranging from copiers to jet engines can now generate data stream that track their usage.’ Social media, online processes and retail transactions are strong contributors to the avalanche of data cascading through the Western world. Additionally the growth of mobile devices floods businesses with new types of structured and unstructured data.
Big data is characterized by its sheer size and the high volume of unstructured data sets it includes. Consequently the most common problems with big data are understanding the data (analysis) and filing it (storage). Managing and analyzing big data is essential in optimizing its value. Data without analysis is worthless. Big data requires advanced analytic techniques to deal with the extensive amounts of data. Traditional analysis methods are not always adequate.
Marketers are increasingly realizing the value of big data, but struggle to manage and analyse the data sets. A study by Rogers and Sexton (2012) on marketers indicated that all respondents considered data one of the most important drivers in measuring marketing ROI yet ‘over half [of marketers] say that a lack of sharing customer data across their own organization poses an obstacle to their effectively measuring marketing ROI.’ Unsurprisingly a rise in big data analytics systems has increased, the most common being Hadoop. Analysis of big data includes processing transactional, logistics, sales and market research material. These unstructured data sets have the potential to provide a competitive edge to businesses by reducing excess costs, improving data transparency and revealing customer behaviour. Consequently companies have established data mining studies on social networking websites to store huge amounts of personal information, and attempt to identify subtle behavioural trends (Bollier, 2010).
The intersection between cloud and big data is still relatively untapped. Yet utilizing a cloud system to store big data has long term benefits to both the insights yielded and the performance of the IT environment, as discussed further in detail.
The primary objective of this research is understanding the value of integrating cloud and big data systems to optimize ROI and marketing opportunities for companies. With this purpose, the study examines the importance of a cloud system to process and store data remotely, and intersects this with the use of big data analysis, which allows for real time responses to marketing opportunities, thereby offering a competitive advantage. As such, the study emphasizes the potential competitive value of utilizing both big data and cloud systems for maximizing ROI.
Defining Cloud systems
Cloud computing, despite the buzz surrounding it, is not a new concept. In its most basic form cloud systems refer to virtual servers to host data available over the Internet. The servers are generally stored remotely in an offsite data centre. Being stored remotely ensures higher performance on computers (as large amounts of data don’t weigh down individual computers).
Cloud computing can be divided into three different service options: Software, Platform and Infrastructure.
Software as a Service: refers to cloud-based software and applications run on a host company’s server, and accessible via the Internet.
Platform as a Service: cloud-based IT environment which supports management and delivery of cloud applications without the overhead costs of managing and hosting online software.
Infrastructure as a Service: provides business with IT resources such as storage space in a data centre and servers.
Cloud can be deployed through a public, private or hybrid cloud system.
Private: These systems, as the names suggests, are privately owned and operated. This means the hosting company can offer more control of resources and customization for their clients.
Public: Such systems are multi-tenanted. Users don’t pay for supporting infrastructure which may be ideal for small businesses, but support and privacy are key elements within a public cloud which require consideration.
Hybrid: combines elements of both private and public cloud systems. Typically utilizes a private cloud company hardware and storage infused with public cloud services.
The flexibility of cloud systems is appealing. This allows the IT environment to grow with the business, without the large upfront installation and management costs. It is a convenient model which enables multiple computers to share resources on demand with reduced management efforts, service provider interactions and costs’ (USA NIST). The benefits of a cloud system include infinite capacity, scalable performance (ensuring minimal downtime) and mobility. This can be better understood through the Capacity-Utilization Curve.
The Capacity-Utilization Curve
The iconic Amazon web services graph illustrates capacity versus utilization curve. This is a key feature in Cloud Computing, as businesses seek to balance between over-provisioning and under-provisioning. The models represents the balance between on demand provision and actual usage through a Cloud system.
Understanding Big Data
As the name suggests big data refers to large collections of data. Microsoft refers to big data as the process of applying extensive computing power to large, complex sets of information. However due to its sheer size traditional data management and analysis tools fail to process the data. Big data rests upon the five V’s.
Typically big data spans three dimensions- variety, velocity and volume.
-Variety: can include structured and unstructured data sets
-Velocity: the pace of the data coming in and increasing demand for real time monitoring
-Volume: extensive amounts of data
But its value is defined by the remaining two V’s-
Viability: the usefulness of the insights drawn from the data
Value: using data insights to solve business problems
The defining feature separating big data from traditional information is the amount of unstructured data sets. It is the unstructured data, combined with its sheer volume, which makes synthesising the information so difficult. Traditional data analytics are insufficient. In order to withdraw information from the data, and thereby yield potential marketing opportunities, data management needs stronger consideration.
The value of big data to marketing and ROI
The hype surrounding big data can be summed up in the following:
Big Data is like teenage sex: Everyone talks about it, nobody really knows how to do it; everyone thinks everyone else is doing it, so everyone claims they are doing it too.
Big data continues to be the marketing buzz word, yet supporting information on its effectiveness is minimal. However now emerging academic research indicates companies utilising data and business analytics in decision making are more productive with higher returns than their competitors not using big data (Brown et al 2011, 2). Another study at the McKinsey Research Institute (Gordon et al 2013) found that companies incorporating data and analytics into their operations have 5-6% higher productivity rates than their competitors. Research indicates big data profits will only grow. The Data Driven Marketing Economy (DDME) alone added $156 billion in revenue to the U.S. economy.
Moreover the value of big data is understood in the ability to analyse consumer and behavioural patterns as never before. Social data is an example of big data valuable to marketers. Social data contributes to the 360 degree view of the customer. It accounts for lifestyle choices, social preferences, attitudes, values and hobbies. Facebook, being the dominate player in the social landscape, is ideal for excavating habits and behavioural data. This is ideal for B2C companies. This individual level consumer data can assist marketers in optimizing ‘expenditure on interactive and direct response marketing, both offline and on. It reduced inefficiency in matching producers and customers, or increased effectiveness, or both’ (Miller 2013).
Such data has the potential to yield the following benefits:
- Amplifying knowledge of current customers.
- Accelerating the intelligence used in rapid product/service development.
- Responding to customer inquiries and needs in (near) real-time.
- Rapidly adapting messaging and offers to reflect shifts in consumer sentiment and activity.
- Assessing the need for customized printed production.
- Measuring the value of each media spend (Miller 2013)
Previously extensive data sets were overlooked due to their extreme size, difficulty in analysis and management and storing the data. However this data is increasingly becoming valued. Ohlhorst (2013) explains, ‘there is a growing consensus that both semi structured and unstructured data sources contain business critical information and must therefore be made accessible.’ Once synthesized big data has potential to increase marketing ROI. For this reason it is unsurprising 34% of companies are already utilizing big data and more than 80% predict they will be using it in the next three years (Gens 2012). Big data will become a marketing advantage, distinctively benefiting those companies who take on the big data challenge.
Background: The intersection between Cloud systems and Big Data ROI
The amount of data being generated now means that traditional storage paradigms are insufficient. (source) explains that, ‘exponential growth and the complexity of digital data beyond that which traditional data processing tools can handle, termed big data, makes it increasingly difficult to store, process and access streams of data arising from’ various platforms. Rapid and flexible infrastructure is needed to support the levels of data generated through transactions, logistics and social media. Hence cloud environments are optimal for analyzing data. Cloud systems can be scaled to fit an environment. They are able to deal with large quantities of information (whether seasonal or regular).
Cloud systems are typically based on remote servers, which are able to handle extensive amounts of data with rapid response time for real time processes. Utilizing Infrastructure-as-a-Service cloud systems further reduces data centre rental costs whilst providing high performing software solutions. In doing so Cloud computing infrastructure enables more efficient use of hardware and software investments. Pooling these resources forces costs down and improves utilization. Boss et al (2007) agrees stating that, ‘a cloud infrastructure can be a cost efficient model for delivering information services, reducing IT management complexity, promoting innovation, and increasing responsiveness through realtime workload balancing.’ This is the beginning of cloud and big data utilization for most companies- storing data within the cloud for analysis to feed real time data into business solutions. Cloud system performance means that marketers can respond in real world time to trends and insight gained from big data
Problems of analysis and storage can be solved through a hosted cloud system which provides secure, scalable solutions for managing data. Elasticity, pay-per-use, low upfront investment, and low security risks are some of the major enabling features that make cloud computing an ideal paradigm big data analysis, which would not have been economically viable on traditional infrastructure. Consequently cloud service providers are able to reduce costs whilst offering improved performance. Siewert (2013) explains that, ‘big data is an inherent feature of the cloud and provides unprecedented opportunities to use both traditional, structured database information and business analytics with social networking, sensor network data and far less structured media.’
The value of Cloud: A Case Study
A US Retailer marketing executive recently was confused by the sales reports. Despite a strong strategy- involving merchandizing improvements and online promotions- a major competitor continued to gain a larger market share. Upon further investigation it became clear that the challenges were far more complex than first thought. Their competitor had the ability to collect, analyse and integrate data from every sales unit and store to run real world experiments. This made it possible to adjust prices in real time, reorder hot selling items automatically and easily shift items between stores. By testing, bundling and synthesizing this information repeatedly the rival had become a nimbler, savvier type of business. (Brown et al 2011)
Maximising Big Data Gains through Cloud Systems
As highlighting creating a Big Data marketing strategy that utilizes a cloud system has the potential to maximize big data gains. It reduces technical problems of the WAN and delivers high performance data migration and management when needed. Combined with high performance analysis software (stored on cloud servers) this offers real time opportunities for marketers. The benefits include:
-Better targeted social influencer marketing
-More numerous and accurate business insights
-Easy segmentation of customer base
Additionally a privately hosted cloud system offers data security and sovereignty.As big data value rises the need for data protection simultaneously grows. Big data requires secure, safe, transparent storage solutions. An offsite data centre is ideal for companies interested in preserving their valuable information. These privately hosted cloud systems are securely hosted behind a firewall to protect data, whilst ensuring rapid delivery and recovery of data.
As discussed utilizing a cloud system is advantageous for marketers wishing to reap the benefits of big data. Utilising cloud computing and storage for big data analysis and storage provides rapid, real time responses. It removes the limitations on storage infrastructure, time delay for data migration and processing and, when combined with analysis software, offers real time insights for marketers on customer trends and hence maximize ROI.