Data Analytics Techniques
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The report includes the different discussions based on the opportunities and the growth which is defined in the data and the data analytical techniques. It helps in focusing on the organisations that are working towards the improvement of the different tools and techniques. The selection of the industry has been done to determine about how the data analytics is important to work over the different forms and the business applications.
1. Opportunities that growth in data and data analytics techniques provide organisations.
Answer : The companies are adapting the optimal distribution of resources with incorporating the techniques of the data and data analytics. There is a need to focus on how the operations and the sales can boost depending upon the different opportunities which works on the extraction, acknowledging the different techniques and the patterns. The data analytics is a broader term which includes the data analytical lifecycle for the different operations. Some of them are:
a. Analysis of the business value chain: The increase in the customer responsiveness where B2C marketers are using the market for a greater insight into the customer behavior with using the data mining techniques.
b. Industry Knowledge: This is for the economic growth with availing benefits. Implementation of the price differentiation strategies where the companies are using the customer product level with the use of achieving targets. The correct pricing is important for better sales and marketing process (Chiang et al., 2018).
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c. Seeing the right opportunity: The dynamic trend is important with focusing on seeing the opportunities and working on unlocking options. The different forms include the people who are gravitating the profession and seeking to make a profit for the demand rise along with the higher rate growth.
d. Grwoing needs of the coordination of analysts with IT: The rapid demands are for the placing pressures on IT and handling the tools which could enable the users to work on the different answers and deal with the data. The companies are hiring and working over the Market Analytics for providing a better approach to the different skills and competitive footing.
e. The analysis is depending upon how the improved supplier management works on maximizing the customer value and then drive the down costs. The long-term relationship is built with the customers which helps in maximizing the values with the business set at the level of repeat.
2. Typical challenges facing organisations/managers trying to build a data analytics strategy.
Answer : Some of the challenges are about the volume of information where the opportunities need to expand depending upon the insights for combining the data. The sourcing of the data in a creative manner with working on the potential for the external and the new source of data. The social media helps in generating the different data and adding the data flow from monitored processes (Gandomi et al., 2015). Some of the challenges with the data analytics in industry are:
a. The Shortage of Professionals
b. Need to synchronise the data sources which is a major challenge. The models need to be built with predicting and optimizing the business outcomes which helps in planning and setting the different data analytical models which allow the managers to predict and work over the different approaches.
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c. Getting Meaningful Insights Through use of data analytics, where the challenge is about the widening gap. The hypothesis is about modelling which helps in focusing over the inherent risks. The transformation of the company capability is through the new approaches where the tools are designed for the experts in modeling.
d. Data Storage and Quality issues are major problems where the quantity of the data is unstructured. The managers need to work on sharpening the marketing, risk management and operations which will help in giving a frontline for the intuitive tools along with the interfaces of the jobs. The technology infrastructure requirements are considered important for sourcing and gathering the data which helps in creating a robust data strategy for working over the different infrastructure implications.
e. Security and Privacy Issues: The decisions need to be made for the data competencies and data governance that will help in working on the factoring of the ownership, privacy and the security issues.
Section B: Select an industry. Outline what kind of analytics techniques/tools are used in that industry and for what type of business applications.
The industry chosen is the mining industry which makes use of the different analytical tools. It includes the processes for the different patterns that holds the larger set of data for the transformation into the effective information. The techniques are able to make use of the specific algorithm and then planning over the statistical analysis or the artificial intelligence. Here, the focus is on the comprehensive approach where:
a. Rapid Miner: It is for the data science software platform which is one of the analytical tool that helps in providing with the integrated environment for the machine learning, deep learning and the text mining with predictive analyses. The sources are defined in Java programming language (Sun et al., 2016).
b. Oracle Data Mining: The Analytics are determined through the leading companies using the maximized potential with powerful data to target the customers at its best. It helps in identifying the anomalies and the cross-selling opportunities that enables one to apply to the predictive models. The customization of the customer profiles is done in a particular manner.
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c. IBM Modeler: The industry of the data mining includes the text analytics where there is a need to focus on the state of art which helps in finding the data valuable. It works on the minimal or no programming sectors where there are Bayesian networks and the Cox regression.
There are other tools like SaS and Tableau which are effectively used in the industry. The data analytics includes the different formats where the robust, versatile tools are used for working over the analytics and the midsize business (Rathore et al., 2016). The analytics helps in focusing over the use of R or Tableau which is important for working over the analytical team interface. This works on improving the forms and the standards.
The analytical tools like Weka is about the machine learning with visualization of the tools and algorithms where the algorithm is coupled with the GUI. Weka supports the standards defined for the data mining tasks with data pre-processing, clustering, classification, regression with the visualization and the feature sections. The Teradata analytical platform is helpful for working over enabling the users and planning over the larger scale of the data. The embedded forms of the analytics are defined through closed data forms, with the elimination of the users to run their analytics with larger datasets with high speed and accuracy (Akter et al., 2016).
Section C: Select one company in the industry described in Section B you would like to provide recommendations for and describe how they can use data analytics to improve business performance.
The company example taken is BHP which is a mining industry company and can make use of the data analytics for the improvement of the functionalities in the company. The data concepts are defined through collecting information with capturing the numbers with examining the numbers. The advancement of the software systems requires to focus on the speedy and the analytical procedures. This includes the ability to work on the offering of competitive advantage to the business with enjoying the lower costs, through the software analytics.
The organizations are working over the use of the data analytics where the planning is done for the customer acquisition and the retention. It helps in working over the different forms where the business is set to observe the customer related patterns and trends. The observations are based on how the business is able to collect more patterns and the trends for a better identification. The business requires to collect the information and then understand about the modern-day procedures as well. The strategies are defined to focus on the acquisition and retention (Singh et al., 2015). The use of the data is to work on the different forms, where there are advertisement technology is now able to embrace the data in a big manner. The example for the brand that makes use of thedata is the targeted adverts.
The data analytics could also be helpful for the risk management, where there are critical investments that are able to seek for the potential risks and then mitigating the problems to remain profitable. There are contributions which are available for allowing the business to quantify and then work over the diversity of statistics. The organizational standards are defined through implementing a structured approach with the clear insights about how one will be benefitted. The examples are related to the potential risks which are identified for the use of data and to drive the risk management. The data analytics are important for the innovations and the product development, where the ability is to help the companies in innovation and redeveloping the products. The data has an avenue for creating the data that has been for the re-designing of the existing products. The designing procedures are for the customers and then to identify the best approaches.
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