Introduction to Business Analytics

1           Part A. Case Study Analysis

1.1         Industry in which analytics has been applied

The global energy industry has been addressed in the underlying case study with regard to the implementation of data analytics. The specific emphasis has been maintained on the case of GE powers, which is said to support the 30% demand for the world’s power and electricity. The key focus of GE with regard to the implementation of data analytics is on using big data, machine learning, and the Internet of Things (IoT) to transform the global energy industry.

1.2         Business problem to be solved

The industry is compelled to move from a linear, one-way model of energy delivery to the networked model of energy delivery. The challenge is to implement a networked model along with grid-based infrastructure while ensuring the smooth flow of data.

1.3         Type of analytics used to solve a business problem

The industry has implemented advanced analytics and machine learning which are considered as well aligned with networked infrastructure requirements of the industry. It is implemented by using features of predictive maintenance and power optimization. Moreover, the two categories of data powered applications are being implemented including; operations optimization and business optimization.  Operations optimization is applied across whole enterprise, while the business optimization help in improving the profitability by optimizing large scale data from internal as well as external domain of business. These chosen types of data analytics implementation have helped in the generation of digital power plant. The predix platform of GE supports its data driven energy distribution.

1.4         Challenges in analytics implementation

The key challenge for using business analytics to achieve goals and objectives of business is about achieving the effectiveness of organization to use data analytics for targeting strategic goals of organization. Monitoring and integration of most useful and relevant data is also challenging, as datafication is highly complex process.

1.5         Recommendations for stakeholders

In order to encourage stakeholders to develop adaptability for the stated changes, they should be explained about the resulting benefits of data analytics for the industry and business. The open communication and clear understanding of process are key aspects for developing willingness of stakeholders.

2           Part B. Role of Analytics in Solving Business Problem

Among different types of business analytics opportunities being used to solve the business problem, predictive analytics is being wide which helps in capturing the insight of future trends by assessing the historical data and trends. Being in retail sector, where buying behavior of consumers is of huge importance, Carrefour (A French Retailing chain) can use predictive analytics to learn about its focal customers. Carrefour is facing an issue of fluctuating demand of consumers which has increased shelf-stocking of many of its products, which are not purchased by consumers. The solution might encompass the identification of actual consumer demand, such that products in demand are stocked by retail stores.

It is noted that sales of specific retail products are likely to raise based on different events or seasons. The analysis of historical data can help Carrefour to understand which items are in more demand at which time of year (Duan & Xiong, 2015). The stocking up of shelves can become an easy task to do while identifying and assessing the historical trends about the demand of customers. The predictive analytics will help Carrefour to pile up all the historical data and then using aggregating and statistical operations. The aggregation of data will not only help in assessing that what has happened in the past but factors that have caused any event will also be revealed in depth (Grover et al., 2018). The revelation of facts and behavioral patterns of consumers in the past will serve as a basis for foreseeing the upcoming happenings and buying patterns of business. Carrefour can mainly define the scope of predictive analytics for short-run, whereby it can predict the sales of items in one season. The use of predictive analytics will solve the issue related to the stocking of products. The company can thus use predictive data to stock products in different locations according to the demand of products. The products will no longer go out of stock and thus consumers can be provided with their desired items.

3           Part C. Developing and Sourcing Analytics Capabilities

3.1         Ingraining Analytics into decision-making processes

In order to effectively ingrain analytics into the decision-making process, Telefonica can reengineer the decision-making process. The data analytics can be linked with all operations of the company, whereby integration of data will be carried out across multiple departments. The data-driven culture will be created, whereby each decision will be taken by considering the insight of data (Accenture, 2013). For instance, in order to drive innovation in Telefonica, the market trends, as well as customers’ demands, can serve as the purpose of highlighting the gaps in technology. The company can identify the changes in its existing product base while recognizing the desired features by the customers. This insight will then drive the innovation process, which will be made more customer-centric by gaining insight into analytics. Additionally, in order to continuously improve the processes as well as products and operations of Telefonica, descriptive as well as predictive data analytics will serve the key purpose. For instance, descriptive data analytics will help in recognizing the ongoing response of consumers towards the products, while predictive analytics will help in the identification of future trends and demands of customers. Therefore, the knowledge about the market as well as customers can be obtained and continuous improvement in business processes and practices can be made through the integration of data into business decisions. Thus the improvements in production and innovation, as well as sales and marketing, can be guided through data-driven decision making.

3.2         Organization and coordination of analytics capabilities

In order to organize and collaborate analytics capabilities across Telefonica, the reliance can be maintained on developing a centralized analytics organization. The benefit of implementing centralized analytics lies in the notion that whole organization becomes willing to take ownership of the data analytics activities and thus management of activities is carried out ineffective way (Accenture, 2013). The resources are allocated by different departments and divisions, which allows to ensures that the analytics organization is sufficiently funded. The pay-to-play model of funding will be implied in centralized analytics organization and thus analytics will be integrated in daily decision making processes and actions. Through this organization of data analytics, Telefonica will become able to cascade vision of data analytics into whole organization and thus data analytics will be regarded as central to all operations of organization.

Moreover, the analytics will reside at central position. The integration of data will become easy across different units, functions as well as divisions of organization. The capabilities will be developed through which each division’s action will be data driven and thus decision making can be done predictively.

Finally, this model will allow high level of coordination by the central analytical unit. The decisions across Telenfonica will be interlinked and thus coordinated data analytics will serve as the way of making highly adaptable decisions. By developing the capabilities of data analytics through centralized organization, Telefonica will be highly flexible and responsiveness to external environment can be developed.

3.3         How to Source, train and deploy analytics talent?

The sourcing of analytics talent for Telefonica can be done best by making partnership with third party provider at initial stages. The third party can help in gaining access to analytics talent and workforce, which is capable enough to meet the analytics requirement of the organization. Gradually, the organization can directly hire from the market, which is not an easy task to do.

Further, the training and development prospects are crucial for ensuring that capabilities of analytics talent are improved and they are being retained in long run. The training rules applied by Google can also serve as guiding principle for Telefonica. Given this instance, Telefonica can offer opportunity to talent workers to invest their time in developing their capabilities further (Accenture, 2013). It will not only help in retaining talent but will also serve as the way of fostering innovation potential of the organization. Moreover, the retention of talent analytics is also an important concern for the organization. The retention is best possible while devising the differentiated career path for analytics. The creation of analytics leadership position and progression of talent analytics on that path will be an opportunity to retain the staff in long run. Therefore, Telefonica needs to innovate its existing human resource management system for acquiring and retaining the talent analytics staff in long run.

References

Accenture (2013). Building and analytics driven organization. Retrieved from; https://www.accenture.com/us-en/~/media/accenture/conversion-assets/dotcom/documents/global/pdf/industries_2/accenture-building-analytics-driven-organization.pdf.

Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal of Management Analytics, 2(1), 1-21.

Grover, V., Chiang, R. H., Liang, T. P., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), 388-423.

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