The scientific method helps give a framework for the data analytics lifecycle (Dietrich, 2013). Per Khan et al. (2014), the entire data lifecycle consists of the following eight stages:
- Raw big data
- Collection, cleaning, and integration of big data
- Filtering and classification of data usually by some filtering criteria
- Data analysis which includes tool selection, techniques, technology, and visualization
- Storing data with consideration of CAP theory
- Sharing and publishing data, while understanding ethical and legal requirements
- Security and governance
- Retrieval, reuse, and discovery to help in making data-driven decisions
Prajapati (2013), stated the entire data lifecycle consists of the following five steps:
- Identifying the problem
- Designing data requirements
- Pre-processing data
- Data analysis
- Data visualizing
It should be noted that Prajapati includes steps that first ask what, when, who, where, why, and how with regards to trying to solve a problem. It doesn’t just dive into getting data. Combining both Prajapati (2013) and Kahn et al. (2014) data lifecycles, provides a better data lifecycle. However, there are 2 items to point out from the above lifecycle: (a) the security phase is an abstract phase because security considerations are involved in stages (b) storing data, sharing and publishing data, and retrieving, reusing and discovery phase.
Over time the threat landscape has gotten worse and thus big data security is a major issue. Khan et al. (2014) describe four aspects of data security: (a) privacy, (b) integrity, (c) availability, and (d) confidentiality. Minelli, Chambers, and Dhiraj (2013) stated that when it comes to data security a challenge to it is understanding who owns and has authority over the data and the data’s attributes, whether it is the generator of that data, the organization collecting, processing, and analyzing the data. Carter, Farmer, and Siegel (2014) stated that access to data is important, because if competitors and substitutes to the service or product have access to the same data then what advantage does that provide the company. Richard and King (2014), describe that a binary notion of data privacy does not exist. Data is never completely private/confidential nor completely divulged, but data lies in-between these two extremes. Privacy laws should focus on the flow of personal information, where an emphasis should be placed on a type of privacy called confidentiality, where data is agreed to flow to a certain individual or group of individuals (Richard & King, 2014).
Carter et al. (2014) focused on data access where access management leads to data availabilities to certain individuals. Whereas, Minelli et al. (2013) focused on data ownership. However, Richard and King (2014) was able to tie those two concepts into data privacy. Thus, each of these data security aspects is interrelated to each other and data ownership, availability, and privacy impacts all stages of the lifecycle. The root causes of the security issues in big data are using dated techniques that are best practices but don’t lead to zero-day vulnerability action plans, with a focus on prevention, focus on perimeter access, and a focus on signatures (RSA, 2013). Specifically, certain attacks like denial of service attacks are a threat and root cause to data availability issues (Khan, 2014). Also, RSA (2013) stated that from a sample of 257 security officials felt that the major challenges to security were the lack of staffing, large false positive amounts which creates too much noise, lack of security analysis skills, etc. Subsequently, data privacy issues arise from balancing compensation risks, maintaining privacy, and maintaining ownership of the data, similar to a cost-benefit analysis problem (Khan et al., 2014).
One way to solve security concerns when dealing with big data access, privacy, and ownership is to place a single entry point gateway between the data warehouse and the end-users (The Carology, 2013). The single entry point gateway is essentially middleware, which help ensures data privacy and confidentiality by acting on behalf of an individual (Minelli et al., 2013). Therefore, this gateway should aid in threat detection, assist in recognizing too many requests to the data which can cause a denial of service attacks, provides an audit trail and doesn’t require to change the data warehouse (The Carology, 2013). Thus, the use of middleware can address data access, privacy, and ownership issues. RSA (2013) proposed a solution to use data analytics to solve security issues by automating detection and responses, which will be covered in detail in another post.
- Carter, K. B., Farmer, D., and Siegel, C. (2014). Actionable Intelligence: A Guide to Delivering Business Results with Big Data Fast! John Wiley & Sons P&T. VitalBook file.
- Dietrich, D. (2013). The genesis of EMC’s data analytics lifecycle. Retrieved from https://infocus.emc.com/david_dietrich/the-genesis-of-emcs-data-analytics-lifecycle/
- Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z. Ali, W. K. M., Alam, M., Shiraz, M., & Gani., A. (2014). Big data: Survey, technologies, opportunities, and challenges. The Scientific World Journal, 2014. Retrieved from http://www.hindawi.com/journals/tswj/2014/712826/
- Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. John Wiley & Sons P&T. VitalBook file.
- Prajapati, V. (2013). Understanding the data analytics project life cycle. Retrieved from http://pingax.com/understanding-data-analytics-project-life-cycle/
- Richards, N. M., & King, J. H. (2014). Big Data Ethics. Wake Forest Law Review, 49, 393–432.
- RSA (2013). The big data security analysis era is here. Retrieved from https://www.youtube.com/watch?v=9dVN1fxeong
- The Careology (2013). Security for big data analytics and Apache Hadoop. Retrieved from https://www.youtube.com/watch?v=M_8S6ZPtU4w