Data Analysis For Business
Data Analyses for Business
Three important best practices of this article
Thomas Davenport, the author of this article “Analytics 3.0” outlined three best practices concerning data analytics. To start with, there is a need to have a resolution towards applying powerful data-gathering mechanisms and analysis methods in an organization’s operations to set data smartness into the company’s products and services. According to the author, this is the ultimate course of any organization because competitiveness in the market keeps shifting and new players perennially emerge. Applying strong data analysis methods to the company's operations is a critical step towards aligning the organization’s operations around driving change as well as discerning market patterns in a more efficient way. It also equips companies with a substantial basis to build new capabilities and get more customers.
The second best practice disclosed in the article is investing in analytics to increase the competitiveness of customer-facing products, features, and services. This is not just for online companies and firms that analyze data for consumers, it is also for every organization that makes and moves products, and interacts with the customer. Every manager has the responsibility to analyze data for the benefit of the market, sales, and customers. They should embed analytics to optimize the performance of every sector of the firm. Thirdly, strategic change in data analytics should equip an organization to recognize, understand and respond to new trends, positions, and capabilities and priorities. Unlike previous analytics which used databases, new analytics integrates Artificial Intelligence (AI) to analyze multiple data in structured and unstructured formats to produce prescriptive and predictive models (Alsghaier, 2017).
Relationship between this article and topics covered
This article relates to the topics covered in class. First, this article articulates concepts and trends in data analysis, which we explored in class. Business data analysis involve activities that help firms to make informed strategic decisions, achieve goals, and mitigate complex challenges. As the article posits, every manager must strive to embed concepts of analytics around business operations to allow businesses to remain competitive. As we learned in class about the relevance of analytics, this article highlights the significance of data analytics to business survival and competitive advantage. Importantly, we learned that data analytics incorporates using technology and big data to identify patterns and correlations; this concept is also evident in this article. The author identified different companies including General Electric, Schneider Electric, Amazon and Google, which adopted big data and analytics to identify customer patterns and trends as well as integrate products and services with extra capabilities.
Alignment of concepts in this article with concepts described in class
The concept of data analytics and its impact on business and organizational performance has been ingeniously highlighted in this article. This is aligned to concepts we learned in class about the significance of data analytics in powering business performance. As noted in the article, we learned in class that there is much that businesses can do to create value data analytics, they can invest in new technologies, software and integrate big data into useful information. As described in the article, we learned that companies aiming to expand their businesses and prosper especially in the modern data economy need to invest significantly in data to create value for both the business and the customers. In conclusion, Thomas Davenport’s article Analytics 3.0 provides an in-depth analysis of the significance of modern data analytics and the importance of investing in data analysis in performance. The concept described coincides and aligned with those we learned in class.
Davenport, T. (2013). Analytics 3.0. Havard Business Review. Retrieved from <https://hbr.org/2013/12/analytics-30>
Alsghaier, H., Akour, M., Shehabat, I., & Aldiabat, S. (2017). The importance of big data analytics in business: a case study. American Journal of Software Engineering and Applications, 6(4), 111.
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