Big Data
SHAHD NASSIER
MSC MARKETING 2013-2014
CUSTOMER RELATIONSHIP MARKETING
Customer Relationship Marketing (CRM) is one of the models of customer management, where the business focuses on building its client relationships, customer loyalty as well as brand value through various marketing techniques and activities (Minelli et al., 2013). CRM plays a crucial role to a business in developing long-term positive relationships with its existing and potential customers as well as helping in streamlining corporate performance. CRM involves not only commercial strategies but also client-specific techniques through such activities as employee training, relationship building, marketing planning and advertising. In this regard, therefore, CRM serves to facilitate the gaining of valuable insight by the business from customer feedback, which in turn enables creation of improved, greater and targeted marketing as well as brand awareness. Developing an effective marketing plan requires one to describe or advertise products or services as unique and to figure out how to communicate the same to potential clients.
Big Data as a concept refers to the swift acceleration of increasing volume of complex, high velocity, and various types of data which require complex strategies and technologies in aiding the procurement, storage, distribution, organization, and scrutiny of the data (Minelli et al., 2013). There are three major sources of Big Data: (1) Conventional enterprise data (e.g. customer information as retrieved from CRM systems, business transactional enterprise resource planning (ERP) data, general ledger data, web store transactions); (2) machine-generated or sensor data (e.g. Call Detail Records, smart meters, weblogs, equipment logs/digital exhaust, manufacturing sensors, trading systems data); (3) Social-related data (e.g. customer feedback streams, social media platforms, micro-blogging sites (Minelli et al., 2013).
There are four defining features that distinguish big data from traditional data types. The most obvious feature of Big Data is its volume. As compared to non-conventional data, machine-generated data is often retrieved in larger quantities. For instance, can generate about 10TB of data can be generated in 30 minutes from a single jet engine. Considering that in excess of 25,000 airline flights are operational in a day, the total volume of a single data source can easily surpass the petabyte threshold. Heavy industrial equipment and Smart meters found in such places as drilling rigs or oil refineries also produce similar large amount of data, exacerbating the problem.
The second defining feature of Big Data is its velocity. This is especially evident in data gathered from social media sources. Data from social media streams may not be as voluminous as that generated by a machine. However, it gushes out as a vast inflow of opinions and relationships valuable to customer relationship management. For instance, though a single tweet has a maximum of 140 characters, the cumulative high velocity of the data results into large volumes in excess of eight terabytes per day.
Another distinguishing feature of Big Data is its variety. Conventional data systems are often relatively well-defined through an elaborate data plan though they are less flexible to change. On the other hand, contemporary data formats have greater rate of change. The addition of new services results in deployment of new sensors, execution of new marketing campaigns and collection of new data types to obtain the resultant information.
The final distinctive attribute of Big Data is value. Different data types have different economic value. Naturally, there is always large volume of vital information that is hidden within a larger bundle contemporary data. As such, it is always challenging to isolate the useful data, transform and extract it for thorough analysis. To take advantage of opportunities presented by Big Data, organizations need to transform their IT infrastructure in order to handle the new voluminous, high-velocity, multiple sources of data to assimilate them with pre-existing corporate data for necessary analysis.
With the increase of volume of data within various data stores and in different formats, an organization may accumulate huge terabytes of data containing numerous varied data combinations (Minelli et al., 2013). This makes finding a resolution to the Big Data problem rather challenging. This is especially the case because Big Data usually demands high-performance analytics for processing and establishing important data and less significant data. By definition, Big Data analytics refers to a process of probing large amounts of data for the purpose of discovering hidden patterns, anonymous correlations as well as other useful information. These information can then be utilized by a business to gain significant competitive advantages in the marketplace, resulting in such business benefits as more effective marketing, increased revenue, and customer satisfaction.
The main objective of Big Data analytics is therefore to assist organizations reach more informed business decisions. It makes use of data professionals and other users in analyzing large amounts of transaction data and other data sources which may have been missed by traditional business intelligence (BI) programs. The other data sources include Internet-click stream-data, web server logs, mobile-phone call records social-media activity reports, and sensor-captured information (Minelli et al., 2013). Distillation and analysis of Big Data in conjunction with conventional enterprise data enables a business to create a more thorough and astute comprehension of their organization, resulting into improved productivity, increased competitive advantage together with greater innovation. All these have significant positive impact on the bottom line.
Infrastructure needed to successfully analyze Big Data must have the ability to sustain greater analytics i.e. data mining and statistical analysis of varying types of data stored in mixed systems; scaling extremely large data volumes; delivering faster response times due to changes in behavior, as well as automating decisions using analytical models (Minelli et al., 2013). Fundamentally, the infrastructure needs to have the capacity to incorporate analysis on the blending of Big Data and conventional enterprise data. Greater understanding results not only from analysis of the new data, but also from analysis of the data within the context of the former to offer new viewpoints on old challenges.
Research conducted by the Economist Intelligence Unit (2011) reveals that most business managers see a positive correlation between effective Big Data management practices and better financial performance. Figure 1 shows the responses of managers to how they rate their financial performance in the context of their Big Data practices. Those managers whose organization had strategic data use practices reported more favorable financial performance than their counterparts who were data wasters.
Figure 1 (Source: Economist Intelligence Unit)
With new data and new data practices comes the need to acquire new skills. Sometimes the existing skill set will determine how and where analysis can and should be done. When the requisite skills are lacking, a combination of training, hiring and new tools will address the problem. Since most organizations have more people who can analyze data using SQL than using Map Reduce, it is important to be able to support both types of processing (Minelli et al., 2013). This implies that customer relation managers need to wake up to the new reality and upgrade their skill set to survive in this new era of Big Data and Big Data analytics.
Considering that modifications in the volume and potential data in the modern business environment are happening almost all the time, businesses are forced to adopt more than simplistic, incremental improvements in information management. Strategically and operationally, organizations have to review their entire approach to management of data, and make meaningful decisions on the type of data to be utilized as well as how they decide to use them.
Research by Economist Intelligence Unit (2011) shows that majority of business organizations are yet to make rapid progress in making value out of Big Data. It is worth noting that the concept of Big Data is only picking up in the corporate world. We will continue to witness acceleration in the amount of data produced. Though there is still much to learn, those who will resist the tide of Big Data analytics will ultimately perish. On the other hand, organizations that blend their long-term vision with cutting-edge data management techniques will be able to gain competitive advantage in their respective industries (Minelli et al., 2013).
While many organizations have achieved proficiency in exploiting their data through data analysis, they are still at the early stages of creating an analytic model that can deliver real business value from Big Data. The main obstacles are the slow and arcane processes to enable direct and timely access to corporate data. However, new technologies are collapsing the old walls between information technology and data analysis by enabling advanced analytics within the database itself, alleviating the need to move large volumes of data around. For example one may have no idea whether or not social data sheds light on sales trends. The challenge comes with figuring out which data elements relate to which other data elements, and in what capacity. The process of discovery not only involves exploring the data to understand how one can use it, but also determining how it relates to the traditional enterprise data. New types of inquiry entail not only what happened, but why. For example, a key metric for many companies is customer churn. It’s fairly easy to quantify churn. But why does it happen? Studying call data records, customer support inquiries, social media commentary, and other customer feedback can all help explain customer defections? Similar approaches can be used with other types of data and in other situations. Why did sales fall in a given store? Why do certain patients survive longer than others? The trick lies in finding the right data, discovering the hidden relationships, and analyzing it correctly (Economist Intelligence Unit, 2011).
Data can be termed to be an organization’s proprietary asset. Individual corporations have unique data, which, if utilized strategically, may result in significant competitive advantage for the firm. These data can deliver valuable insights relating to customer behavior or preferences, informing the product/service catalogue of the company and their selling strategies. The same data usually contain such private and proprietary information as customer records or financial records which require that the organization complies with rules and regulations established by industry bodies or government. As such, it is crucial for corporate decision makers to readily access data together with necessary tools for analyzing, securing, and managing data on the basis of its organizational value (Economist Intelligence Unit, 2011). Organizations need to make an effort towards achieving an befitting balance between data availability on the one hand and data security on the other, based on information sensitivity.
In the modern business environment, data is also being reserved for relatively longer periods of time. This is because of strict retention requirements by regulating bodies about the amount of data and degree of data detail which must be stored. Furthermore, there is growing interest in utilizing data analytics for all decision making, which is making organizations to retain data for longer periods for historical trending. This need has seen an increasing number of business adopt cloud computing services. However, in embracing cloud computing, organizations must first gain understanding the level of security they require in the cloud computing environment, the degree of security offered by cloud computing service providers, and the potential additional security (Economist Intelligence Unit, 2011). For example, a business in the financial services industry would be required to offer security to customer data according to federal regulations, but such compliance may be challenging to achieve in cloud-computing environment. This would necessitate the addition of own security applications by the company.
Essentially, adopting a cloud computing services translates into entrusting critical customer data to a third party. Consequently, ensuring data safety both at rest and in transit is greatly important. The key lies in knowing what data one allows into the cloud and which type of cloud is suitable for that data. Sensitive data, for instance, should only be stored and processed at specified data centers in a private or appropriate community cloud that is fully auditable. And, of course, data stored in any kind of cloud model must to be securely backed up – this can be done in-house or through a back-up service provider.
With public cloud computing, an organization may not know where its data is being physically stored – the cloud may even be outsourced outside the country. This can pose a problem when the organization is subject to data protection and governance laws and policies that require it to retain control over data (Economist Intelligence Unit, 2011).
The great paradox is that Big Data is only a recent concept which organizations are grappling to match. For the time being, many organization may still find it particularly challenging to comprehend, and leverage the capabilities it presents for the greater benefit of the business at large. Most organizations will continue struggle with mitigating the large volume of data being made available with the growth of IT in the business environment. Therefore, businesses are obliged to find an effective way of managing the large streams of Big Data required by their people, how best to manage the information, as well as extract new and useful insights.
References
Economist Intelligence Unit Limited. (2011). Big Data: Harnessing a game-changing asset. Retrieved from: http://dssresources.com/news/3480.php.
Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big data, big analytics: Emerging business intelligence and analytic trends for today’s businesses. Hoboken, New Jersey : John Wiley & Sons, Inc.
