Data Analytics

Data Analysis

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Data Analysis

Data analytics is the process of evaluating several sets of data for purposes of drawing out a summary of conclusions on the information with the help of using the desired system or software. Data analytics is used mostly by researchers, scientists and commercial industries. Most industries use it in order to get detailed information that would enable them makes better business judgment or investment. Scientists and researchers however use it to prove whether the existing models or theories and hypothesis are true or false.(Chen, Chiang & Storey, 2012).

According to (Chen, Chiang & Storey, 2012), evolution of data analytics started with a phase called analytics 1.0. This phase had people work in teams to process data and create a summary of previous or historical data that was important to come up with future analysis. The teams spent most of their time trying to get the data of the business or organization instead of analyzing the data. Internal data analysis was used while giving the final feedback on the summary. The reason behind this phase was to make business or organizations be able to make good judgements. Thus it achieved internal decision making.

The second phase of evolution happened in the 1990s and was called analytics 2.0. In this phase, they started using unstructured information like the environment. They focused on the important factors in the company or business that are unstructured. This phase experienced new changes because database products and services emerged. The implementation process was still in its early stages.(Chen, Chiang & Storey, 2012)

The third phase was called the analytics 3.0 which was now the combination of the other two phases into one. The traditional phase of getting data and analysis and using the big data. This phase relied mostly on technology where it generated the insights instantly in real time. The management however made decisions on the insights that were favourable for the company or business.

Data analytics influences the businesses to generate revenue. The frequent data analytics on the company or business tends to point out on the failures either as a product or a service. Thus any upcoming failure in terms of product or service will be solved before it occurs. Thus the money that was to be used in solving the upcoming failure will be invested back to the company generating more revenue.(Watson, 2014)

Data analytics makes operations in a company become efficient. This is achieved through data analytics where errors that are likely to affect the operation are detected. Detecting the errors save the operation from encountering minors problems or from failing completely. This is also an advantage because failure in the operation risks the company their customer base. An efficient operation would assure customers that the business and company is doing well.

According to (Watson, 2014), data analytics puts a business at a better competitive advantage. Through the gathered information from the analysis, a company is able to draw out its weaknesses and find ways they can solve them. The company can also tell the position they are at in the market and find ways they can use to stay ahead of their competitor. They can also gather analysis from customers, find out how they are doing and the expectations of the customer. Consulting customers also lead to improving the trends in the market.

Disadvantages

According to (Watson, 2014), data analytics need to use computers with a good power. Some companies do a real time data analysis, thus for this to be successful the computer ought to have a special power. One that is suitable to carry out analytics in real time. The computer should be in a position to allow new tools that aid analytics be purchased and used. Thus in cases where the tools purchased don’t integrate with the computer then it will create a problem to the data analytics.

Most companies receive predictions andtheir analytics once in a week. This might be a disadvantage to the companies that use real time data analytics because they might lose the information in a week’s time. (Watson, 2014) also shares that the information given to them might be urgent and needs to be worked on immediately thus company might lose the significant prediction. This would have a negative impact on the culture of the organization and business.

According to (Najafabadi & Villanustre, 2015), business management should overcome a challenge on communication. Communication with employees and stakeholders on the need for transformation and the importance of business intelligence will be a challenge.Gathering data of the company and being able to prepare employees for the process can be difficult. Thus it is essential that before transformation to the use of data analytics, the company’s data should be arranged well. Preparing the employees is also key. Training employees on how to use the systems or tools is very important.

The business management should be prepared to invest their money on technology and the new tools that will be used in data analysis as well as funds for training employees or hiring new one. This might need the company to spend on a lot of money for the transformation thus it needs to be financially prepared. The business management should look into finances make sure that they are in a good position.(Najafabadi & Villanustre, 2015)

The business management should be able to face some loses to the competition. The transformation process is likely to take long, delaying operational processes in a company. This change will alter with the company’s success for a short period of time. This is a challenge the company’s business management should be prepared to make and go through. Thus the company should be prepared early before transforming to using data analytics.

Adidas prioritise on the customer thus they use technology in order to understand and be closer to them.Adidas company used data analytics to come up with new brands. The new brands have been driven by their Analysed dataand their feedback from their customers by using an actionable analytics. Data analytics transformed the company such that they find it essential to communicate with the customers by using data to identify the expectations of customers.

Over the years, Adidas have been rivals with Nike and reebok in the sporting industry. Through the use of data analytics, Nike and Adidas have been very aggressive companies such that they have been able to come up with new creative products that have been loved by the customers putting them at an upper hand. Adidas however discovered a competitive strategy that they gathered from the use of analytics. They were able to buy Reebok and use it to their advantage in capturing many athletes as possible. This transformed the success of Adidas sporting business industry.

In their marketing strategy, adidas was able to use data analytics to find more ways which can make them global. They identified the weaknesses of its competitors and used it to their advantage. They were able to go into construct with celebrities like Kanye West, which they have been successful in releasing their Yeezy’s collection which was tapped in European countries. Adidas also invested in Asian and African countries where they partnered with manufacturing companies to market their brands at a local level. They were also able to sponsor matches and marathons in favour of their company.

Adidas will shift and focus more on training its employees because in the next ten years, the technology will have evolved so will data analytics. The company will have to train its employees to bridge the gap created by analytical skills. There will be a shortage on not only data analysts or data engineers but also data scientists. (Kambatla, Kollias, Kumar & Gama, 2014) shares that a great effort in training, hiring or retaining the data professionals will be felt in the company in the next ten years.

The Adidas Company will have to invest in upcoming technology for it to meet the evolving data analytics. They will have to purchase for new tools for data analytics. The new tools will be more likely to give real time insights for adidas company and they will have to introduce faster, better computers for the job. The company will also invest more money on data analytics.There are The market for data analytics is expanding and in the next ten years it might require more money to be spent on it.(Kambatla, Kollias, Kumar & Gama, 2014).

A type of data that can be collected using data analytics is secondary data. This is the kind of data that are generated from books, Internet or any other source of information. This data is readily available thus can be accessed easily. It is also an inexpensive data. It is usually accessed by researchers to back up their primary data and gather more useful information concerning their research. Thus data analytics can also collect this data because it can be accessed from several sources that are readily available.

Reference

K Kambatla, G Kollias, V Kumar, A Gama. (2014), Trends in big data analytics

HJ Watson. (2014), Big data analytics, Concepts, technologies, and applications

MM Najafabadi, F Villanustre. (2015), Deep learning applications and challenges in big data analytics

H Chen, RHL Chiang, VC Storey. (2012), Business intelligence and analytics: From big data to big impact

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