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- Is There a Difference Between Analytics and Analysis?
- Where Does Data Mining Fit In?
- Why the Sudden Popularity of Analytics?
- The Application Areas of Analytics
- The Main Challenges of Analytics
- A Longitudinal View of Analytics
- A Simple Taxonomy for Analytics
- The Cutting Edge of Analytics: IBM Watson
- References
This chapter is from the book
This chapter is from the book
This chapter is from the book
This chapter is from the book
The Main Challenges of Analytics
Even though the advantages of and reasons for analytics are evident, many businesses are still hesitant to jump on the analytics bandwagon. These are the main roadblocks to adoption of analytics:
- Analytics talent. Data scientists, the quantitative geniuses who can convert data into actionable insight, are scarce in the market; the really good ones are very hard to find. Because analytics is relatively new, the talent for analytics is still being developed. Many colleges have started undergraduate and graduate programs to address the analytics talent gap. As the popularity of analytics increases, so will the need for people who have the knowledge and skills to convert Big Data into information and knowledge that managers and other decision makers need to tackle real-world complexities.
- Culture. As the saying goes, “Old habits die hard.” Changing from a traditional management style (often characterized by intuition as the basis of making decision) to a contemporary management style (based on data and scientific models for managerial decisions and collective organizational knowledge) is not an easy process to undertake for any organization. People do not like to change. Change means losing what you have learned or mastered in the past and now needing to learn how to do what you do all over again. It suggests that the knowledge (which is also characterized as power) you’ve accumulated over the years will disappear or be partially lost. The culture shift may be the most difficult part of adopting analytics as the new management paradigm.
- Return on investment. Another barrier to adoption of analytics is the difficulty in clearly justifying its return on investment (ROI). Analytics projects are complex and costly endeavors, and their return is not immediately clear, many executives are having a hard time investing in analytics, especially on large scales. Will the value gained from analytics outweigh the investment? If so, when? It is very hard to convert the value of analytics into justifiable numbers. Most of the value gained from analytics is somewhat intangible and holistic. If done properly, analytics could transform an organization, putting it on a new and improved level. A combination of tangible and intangible factors needs to be brought to bear to numerically rationalize investment and movement toward analytics and analytically savvy management practice.
- Data. The media is taking about Big Data in a very positive way, characterizing it as an invaluable asset for better business practices. This is mostly true, especially if the business understands and knows what to do with it. For those who have no clue, Big Data is a big challenge. Big Data is not just big; it is unstructured, and it is arriving at a speed that prohibits traditional collection and processing means. And it is usually messy and dirty. For an organization to succeed in analytics, it needs to have a well-thought-out strategy for handling Big Data so that it can be converted to actionable insight.
- Technology. Even though technology is capable, available, and, to some extent, affordable, technology adoption poses another challenge for traditionally less technical businesses. Although establishing an analytics infrastructure is affordable, it still costs a significant amount of money. Without financial means and/or a clear return on investment, management of some businesses may not be willing to invest in needed technology. For some businesses, an analytics-as-a-service model (which includes both software and the infrastructure/hardware needed to implement analytics) may be less costly and easier to implement.
- Security and privacy. One of the most common criticisms of data and analytics is the security. We often hear about data breaches of sensitive information, and indeed, the only completely secured data infrastructure is isolated and disconnected from all other networks (which goes against the very reason for having data and analytics). The importance of data security has made information assurance one of the most popular concentration areas in information systems departments around the world. At the same time that increasingly sophisticated techniques are being used to protect the information infrastructure, increasingly sophisticated attacks are becoming common. There are also concerns about personal privacy. Use of personal data about customers (existing or prospective), even if it is within legal boundaries, should be avoided or carefully scrutinized to protect an organization against bad publicity and public outcry.
Despite the hurdles in the way, analytics adoption is growing, and analytics is inevitable for today’s enterprises, regardless of size or industry segment. As the complexity in conducting business increases, enterprises are trying to find order in the midst of the chaotic behaviors. The ones that succeed will be the ones fully leveraging the capabilities of analytics.