The Nation has experienced economic growth in various sectors of the economy. The few companies in each sector have had to compete for the market share. Strategic plans are put in place to take the most beneficial course of action regarding various factors in a company. One of the most effective ways to arrive at a certain decision is by use of data analytics (Hurwitz, & Nuget, 2017) .
What is Data Analytics?
Data analytics or simply data analysis, explains the elaborative qualitative and quantitative process that is used to examine sets of data. These sets of data may be grouped together according to the needs of an organization. By use of software and other detailed technological techniques, valuable behavioral data patterns can be identified. Data analytics is mostly used in business enterprises as well as in scientific research to ascertain certain processes. Some of the economic giants using data analytics include Amazon Inc. The global electronic, e-commerce company serves many countries with different varieties of goods and services (Hurwitz, & Nuget, 2017) .
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Advantages and Disadvantages of Using Data Analytics.
Electronic monetary transactions have been a major attraction for hackers and fraudsters. Amazon has a large volume of transactions that are carried out per unit time. By use of data analytics, cases such as attempts to defraud customers can be quickly detected and appropriate preventive measures carried out. Errors in transactions or orders can also be detected easily and early enough. Big data analysis also helps businesses such as Amazon to find out the moves of competitors before they are implemented and act accordingly. These measures may include, price reductions or new product launches. Amazon can, for instance, be able to counter in the same way. More revenue can also be added to the company. For instance, Amazon can be able to detect a product that sells best and therefore increase this product in stock. Predictive algorithms can also be used to detect customer behavior, such as the times they visit and the products or services they look for most (Abramow, 2014).
On the downside, data analytics comes with a few disadvantages. In its very nature, data analytics involves the use of immense volumes of data. This alone brings in many complex logistical issues. Data analytics is also expensive to implement. Amazon, for instance, must ensure that they have a set of highly qualified staff who handle their sensitive information. The company must also invest in costly IT infrastructure such servers. There is also a growing concern for privacy and security. Hackers could leak vital bank information. Close competitors might also stumble upon customer information (Prajapati, 2013) .
Obstacles Faced When Implementing Data Analytics and Strategies to overcome them
Implementation of business intelligence solutions is a complex and intricate procedure. It begins by designing. In this phase, every team player must clearly define their roles and fulfill their obligations adequately. Failure in doing so means that the final solution will not deliver the results as expected. Strategies must be put in place to guarantee the team works smoothly (Prajapati, 2013) .
User acceptance is another obstacle to overcome. New methods are not readily absorbed into the working environment. Staff and other users of business intelligence systems were used in doing certain tasks in a certain way. The introduction of new systems, no matter how effective might be met with little resistance. Training programs should, therefore, be implemented before the data analytics systems are officially launched. Earlier this year, for instance, Amazon Inc launched a pilot program to train its underperforming employees who are in danger of being fired. Amazon also has dedicated tutorials and training materials for new and existing customers/businesses. In some instances, the quality of feedback received from business intelligence may be in doubt. It is a common occurrence given the complexities and intricacies of big data. To overcome this, software testing lifecycle should be implemented (Abramow, 2014).
There are high amounts of costs that are involved in the implementation of business intelligence and big data analytics solutions. These costs fall into the categories of new IT infrastructure and hiring expertise. Servers and other high-end technological infrastructure would be a long-term and one-time, investment that the firm should not overlook (Prajapati, 2013) . Eventually, the returns brought about by these IT advancements would supersede the costs. During the phase of implementation, outsourcing data engineers would be the most viable approach. Data engineers are very expensive professionals and therefore hiring them permanently, would result in the firm incurring high recurrent expenditure to sustain them in the organization (Abramow, 2014).
Customer Responsiveness and Satisfaction
In the modern era, technology has been integrated into our daily lives. For instance, virtual reality and wearable technology. Social media is also another technological evolution that has seen businesses and other organizations set up their pages with followers and customers. From these sites, companies can gather valuable information such as customer shopping patterns and preferences. In the case of Amazon Inc, relevant customer information can be derived from their debit and credit cards ( Hurwitz & Nuget, 2017).
Customer satisfaction and responsiveness have been improved in some ways. By use of predictive algorithms, a product can be recommended to a client based on the previous items bought. A precise description of a product a customer searches for can be sent to his personal email address account. The potential client does not have to purchase the product. Based on his searches on the Amazon website, it can be determined what kind of product the customer is looking for, and it can, therefore, be sent to the email address. Amazon is also known to recommend items such as books, music and kitchen utensils to customers (Abramow, 2014).
When it comes to customer satisfaction, big data and business analytics have also been valuable. For instance, complaints and questions by customers are handled quickly and in real time by customer care representatives. The representatives also do not have to ask too many questions as they already know who the customers are based on data gathered initially. The customer care representatives can, therefore, respond accordingly. Other platforms have enabled clients and agents to chat in real time for quicker responses ( Hurwitz & Nuget, 2017).
Big Data and Business Intelligence in the Next Ten Years
Many scientific theories have been put forward to try and explain the exponential growth of technology and its different areas of application. It is evident compared to the yesteryears where automation was not implemented. The next ten years will definitely see more growth in big data. More data and data manipulating procedures will require more servers to store data gathered. More sophisticated and complicated technological know-how will require more training for the personnel. By the year 2020, technological growth is estimated to have grown by a staggering 4,300 percent (Abramow, 2014).
Business intelligence has been used to mine and interpret data in different ways. Biometrics and demographic changes are the two types of data that could be gathered by use of wearable technology. Such information is useful in tracking down criminals as well as restricting access in some areas. The collaboration of technology and mathematics has been imperative formulating decisions that have had positive effects in their areas of application (Abramow, 2014).
Abramow, M. (2014). Improving the Customer Experience through Big Data Analytics . Retrieved from http://www.oracle.com/us/corporate/profit/big-ideas/030614-mabramow-2163930.html
Hurwitz, J., & Nuget, A. (2017). Improve the Customer Experience with Big Data Analytics . Retrieved from http://www.dummies.com/programming/big-data/data-science/improve-the-customer-experience-with-big-data-analytics/
Prajapati, V. (2013). Big Data Analytics with R and Hadoop (1st ed.). Birmingham: Packt Publishers.