As you lead the newly-formed team that has been appointed to create a strategic plan for utilizing data analytics to help drive the marketing and sales of the company, you identify that there are several specific components missing from the current proposal. In this revision of the plan, you will need to incorporate an additional 2 pages and 8-10 shooting points(Bold font on each shooting points） including speaker notes that address the points below. You can start up new as documents to upload your file ( do not overwrite the previous work) Utilize the readings in Topics 2-4 to support points of the plan and conduct further research as needed. Include the following: State effective strategies for validating and reporting data. Identify methods for managing data that ensure accuracy, efficiency, and integrity. Identify the correlations between big data, marketing, and sales. Describe the situations when each of the various types of analysis should be utilized. Topics 2 Computer Networks, Data Storage, and Management 3 Data Warehousing 4 Analytics Process Lifecycles The additional materials are the previous work. It is an extended work
Strategic Plan for Becoming an Analytics-Driven Organization
As noted in the previous proposal, low-quality transactional data is problematic at scale and can lead to high erroneous data costs. This phenomenon is likely to occur because the low-quality raw information utilized by the firm may not be reliable to help firms make informed decisions. Therefore, as the team conducts strategic planning, it will also delve into strategies of validating and reporting data; the techniques of managing data that ensure accuracy, efficiency, and integrity; and exploration of situations when various types of analysis should be utilized, to avoid pitfalls that may constraint the entity’s competitive advantages.
Among the effective strategies of validating data used in data analytics is the utilization of enterprise tools. Most notably, enterprise tools such as FME can be used to create the required parameters for each transformer, which is then connected to a model representing a data flow pipeline (“Data Validation and Quality Assurance” 4). Once the transformation is set to run, the entered data can be validated based on the initially established transformers.
As soon as the data is validated, imported, and processed, it should be reported to its users using effective strategies such as visual analytics. Today, visual analytics, such as visual dashboards, have become popular with impressive statistical analytic benefits (Carrascosa 250). Most notably, this strategy can help build on the user’s cognition of the data because it constitutes of words and graphical representation of the processed data.
Besides data validation and reporting, proper management can also be conducted through a data warehouse to facilitate the accuracy, efficiency, and integrity of data. Scholars aver that a data warehouse is a system that allows the integration of various types of data from multiple areas within an organization (Yulia 94). Data warehousing may enable a firm to integrate data from different departments, or create a sub-set data for an entity’s subsidiary, thus promoting accuracy as all the organization information is verified in a central system. Besides, the data warehouse can foster efficiency and consistency by enabling firms to store data in a central system that is accessible to multiple stakeholders.
Correlation Between Big Data, Marketing, and Sales
From a business perspective, big data has a ripple effect on marketing and sales. As the name suggests, big data is an information asset characterized by high volume, velocity, and diverse information that requires specific analytical methods to transform the data into value (De Mauro et al 7). Studies reveal that big data impacts companies in depth, forcing them to reconsider the organization of their business processes in light of the availability of new information (McAfee and Brynjolfsson; Pearson and Wegener 2). Similarly, big data enables a firm to reorganize its marketing techniques based on available data about consumers purchasing behavior.
Consequently, the reorganization of marketing strategies in light of available information enables an entity to record higher sales, with the opposite also being accurate. For example, if an organization has data sets that reveal a significant shift in a particular trend in consumer taste and preference, it can develop marketing strategies that fit the identified pattern. In turn, the marketing strategy can help the organization target consumer preference and trigger more purchases.
Different methods of analysis can be used in data analytics, depending on the existing situations. On the one hand, descriptive analysis is suitable in scenarios that involve past incidents that do not require in-depth exploration. For example, a company can use existing raw data to analyze its profits and revenue in previous years. On the other hand, a diagnostic analysis should be utilized in situations that require in-depth insights. For example, if a firm wants to understand the cause of its losses, it should use diagnostic analysis. Predictive analysis, on the other hand, should be used in situations that require future forecasts. Most notably, it should utilize future and current raw data to predict forthcoming trends and patterns in an organization.
In summary, the use of enterprise tools such as FME, visual analytics, and data warehousing can help a firm avoid pitfalls in data analytics. Also, entities should apply appropriate data analysis techniques in light of the existing situations. Overall, big data can either have a positive or negative ripple effect on marketing and sales.
Works Cited
“Data Validation and Quality Assurance with FME.” Safe Software. cdn.safe.com/resources/technical-briefs/Data_Validation_and_Quality_Assurance_with_FME.pdf
Carrascosa, Ivan Palomares et al. Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications. Springer, 2017.
De Mauro et al. “A Formal Definition of Big Data Based on its Essential Features.” Library Review, vol.65, no.3, 2016, pp. 1-12.
McAfee, Andrew and Brynjolfsson, Erik. “Big Data: The Management Revolution.” Harvard Business Review, 2012. hbr.org/2012/10/big-data-the-management-revolution
Pearson, Travis and Wegener, Rasmus. “Big Data: The Organizational Challenges.” Bain and Company, 2013. www.bain.com/contentassets/25c167a5149c42168994338f9dc99ffe/bain_brief_big_data_the_organizational_challenge.pdf
Yulia, Leo Willyanto Santoso. “Data Warehouse with Big Data Technology for Higher Education.” Procedia-Computer Science, vol.124, no.1, 2017, 93-99.
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