May i get a positive respond about the comment below.

As Data Analytics enabled businesses to optimize their supply chains, there are barriers that create complexities in regards to implementation. Execution depends heavily on the analysis of the information obtained, mostly through consumer-driven data. Examples of barriers that businesses may face in implementation would be obtaining the data in general, cost-effectiveness and the unusual case of IT systems built on Legacy systems. Another barrier that challenge businesses looking to capitalize off data analytics would be a multi-layered issue regarding organizational culture and work environment. Overcoming these barriers in implementation are essential for utilizing the substantial amount of data these businesses collect.

The process of simply obtaining the information is a barrier in itself. Businesses have to continue to adapt in terms of technological capabilities, especially in this era in time. The advancement in technologies such as RFID, which stands for Radio Frequency Identification Device, which enabled businesses better capabilities in tracking and managing inventories. In instances where business trying to optimize their systems by utilizing new technology are faced with another barrier, which old legacy IT systems prevent businesses from seamless adaptation. An example from the textbook, regarding RFID tags, shows how the incorporation of new technology can backfire as well. Errors in reliability in its early roll out of RFID, affected its adoption rate and cost remained high as a result.

Organizational culture and capabilities are another barrier that get in the way of implementation. An organization that isnt necessarily equipped with the correct talent to handle the workload is just as ineffective, regardless of how much information a company has to analyze. As long as a culture exists that supports data-driven decisions, this barrier is easily resolved. As more businesses began to utilize these data analytics, outsourcing of this process enabled third party companies to emerge whose sole purpose are to data mine. These third party data mining companies became a profitable business which in turn, increased stakes between businesses competitively. This scenario enabled businesses who would rather pay big money to have access to these other huge databases that are being mined. Another barrier may emerge as more businesses look to outsource this data analysis.

In conclusion, Data Analysis is not an overnight solution for businesses looking to optimize their logistics and supply chain. There are unique barriers that each business must identify and overcome to achieve successful data analytics implementation.