- 1.1 The Origin and Evolution of Business Analytics
- 1.2 Developing Analytical Thinking
- 1.3 Operationalizing Big Data from Global Perspectives
- 1.4 Extracting Useful Information from Big Data
- 1.5 Unique Challenges for Business Analytics
- 1.6 Capitalizing on Business Analytics for Building a Winning Global Strategy
- Bibliography
This chapter is from the book
This chapter is from the book
This chapter is from the book
1.5 Unique Challenges for Business Analytics
Since today’s competition is increasingly knowledge-based, knowledge obtained from the analysis of big data can be a key productive resource of the firm. Recognizing the opportunity to leverage information-driven knowledge as a competitive differentiator, many firms may be interested in learning more about their customer needs, demand patterns, purchase behaviors, market forces, industry dynamics, and technological advances through business analytics. Business analytics, however, can pose a number of unique challenges associated with the volume, velocity, and variety of data influx. First, a sheer volume of big data can not only overwhelm the capacity of data analysts and computer systems, but also create redundancy and inconsistency with conflicting information. Also, in the process of big data aggregation, some key information can be left out and then unintentionally withheld. In other words, the usefulness of big data rests on the quality of data input and thus business analytics can suffer from the GIGO (garbage in, garbage out) syndrome: The less trustworthy the data, the less trustworthy the data analysis result. Second, in today’s fast-changing business world, the rate at which big data flows into the organization will be much higher than before. That is to say, data comes in faster than the organization has a need for it. Thus, it would be challenging for data analysts to capture and digest quickly transmitted data in real time and diffuse information or knowledge gained from those data to multiple end users in codified forms (e.g., documented texts, tables, charts, figures, diagrams, scientific formulas, and mathematical expressions). Third, since big data typically come from different sources in different formats (e.g., texts from social media, graphical images from YouTube), the standardization and normalization of data (especially unstructured data) will be another challenge facing the data analysts.
In addition, every time data are collected and transmitted to multiple parties, the data security and privacy issues will arise. Especially in a global setting, varying privacy laws on data collection/sharing in different countries can make the use of business analytics extremely challenging for multination firms. For example, the European Union (EU) works to finalize sweeping regulations that set higher privacy standards for personal data collection and analysis in EU countries (Burns 2015). These kinds of new measures, which were released in draft form in 2012 and are expected to be finalized in late 2015 or in early 2016, will heighten already stringent EU privacy protections and subsequently limit the use of business analytics. Furthermore, given many different business analytics platforms and tools designed for particular functionalities (e.g., data discovery, data streaming, data processing, ad hoc reporting and querying), firms embracing business analytics will face the technical challenges of managing, consolidating, and unifying various incompatible business analytics platforms, tools, and database management systems.