Executive Summary 3 Managerial Problem 4 Situation Analysis 5 Company 5 Context 5 Customers 5 Competitors 5 Collaborators 5 Complementors 5 Marketing Strategy 6 Primary Strategy 6 Proposed Strategy 6 Growth Strategy 6 Segmentation, Targeting, and Positioning 7 Segmentation 7 Targeting 7 Positioning 7 Action Plan 8 Product 8 Price 8 Place 8 Promotion 8 Conclusion 9 Appendices 10 References 14 Back Cover 15 Objective 15 Target 15 Theory 15 Executive Summary Tableau software is making it easier for businesses to analyze data to better their business strategies and intelligence. This software also allows you to connect to specified information from many different sources and can then analyze the collected data in multiple ways. Tableau software ensures that companies are seeing the same picture across multiple departments and levels. Traditional business intelligence tools are being replaced by data discovery software. The data discovery software has numerous capabilities that are dominating purchase requirements for larger distribution. A challenge remaining is the ability to meet the dual demands of enterprise IT and business users. Be first and win. This is the ultimate goal in business, but most businesses are not up for the task. Tableau wants to partner with these businesses to propel them into the next generation of business modeling. Most businesses are not aware of our software, they struggle with our brand and mission. We offer many products to enable the business user to further execute their plan and be first in the market. Most businesses do not recognize their own potential and in their own data. Tableau wants to help extract the value add. In order to do this, we nee... ... middle of paper ... ....citeworld.com/consumerization/21918/excel-versus-tableau Rueter, M., & Fields, E. (2012). Tableau for the enterprise:an overview for it. In Retrieved from http://www.tableausoftware.com/learn/whitepapers Sallam, Rita L; Tapadinhas, Joao; Parenteau, Josh; Yuen, Daniel;Hostman, Bill (2014, February 20). Magic quadrant for business intelligence and analytics platforms. Retrieved from http://www.gartner.com/technology/reprints.do?id=1-1QLGACN&ct=140210&st=sb Smith, W., & Jewett, D. (2009). Tableau software and teradata database the visual approach to the active data warehouse. In Retrieved from http://www.tableausoftware.com/learn/whitepapers Tableau Software. (2013, May 5). Form S-1/A. Retrieved from http://tableau.q4cdn.com/c9ce5cc2-b942-4a1d-9726-51d493b35ba0.pdf?noexit=true Back Cover Objective Target Theory
With the correct metrics in place information can be gathered and reported on in order to form knowledge. Data is raw numbers, information is data with context, and knowledge is the information with understanding, which leads to decisions (Hunter Whitney, 2007). Basing decisions on every metric is a waste of resources and time. As a result, Key Performance Indicators (KPIs) distill the vast amount of data into information that is pertinent to the decision making. Some KPIs could be the items per hour, visitors per day, customer retention rate, conversion rate, etc. However, not all companies need to know all of the indicators, that is why KPIs are based on the business model and needs of the company.
Big Data is changing the arena for big businesses. Big Data is the technology trend that has made it possible for businesses to better understand their markets. Big Data is the new natural resource, the new “oil.”
From a business perspective, utilizing web analytics aids companies earning profits and increasing their return on investment (Matous, 2015). In spite of the importance of web analytics, it will not stand alone to help the company if
Companies have transformed technology from a supporting tool into a strategic weapon. ”(Davenport, 2006) In business research, technology has become an essential means that many organizations use in their daily operations. According to the article, Analytics is a major technological tool used. It is described as “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
Analytics can show more comprehensive overview of your business, reducing the risk of making decisions on personal bias and of utmost importance to plan a more efficient future.
There are many ways to handle the organizational performance data and visualize them for an effective decision making. For example, in the web analytics, it helps to answer the critical queries like "how the website is performing with respects to our marketing objectives?" From a corporate’s perspective, a new visualization method such as Dashboards offer a quick way to view data and information. The end results may include variance comparisons, single metrics, geographical maps and graphical trend analysis. These types of user interface will helps others to easily comprehend the complex data relationships and performance metrics in such a format that is easily understandable and digestible by time pressured managers.
First of all, business intelligence analysis requires the capturing of information and storing in a single location for effective data analysis. Currently, data analysis is supported by transactional systems, business specific data marts, and other ad-hoc processes. Information is distributed making it difficult and time-consuming to access. Business teams have adapted to this environment by creating user maintained databases and manual “work-arounds” to support new types of reporting and analysis. This has resulted in inconsistent data, redundant data storage, significant resource use for maintenance, and inefficient response to changing business needs.
OLAP is used for mainly for analysis. This means that the system copes with any business logic and statistical analysis that is relevant for the application and the user, and keep it easy enough for the target user. This analysis is done in the application’s own engine or in a linked external product such as a spreadsheet. All the required analysis functionality can be provided in an intuitive manner for the target users. This could include specific features like time series analysis, cost allocations, currency translation, goal seeking, ad hoc multidimensional structural changes, non-procedural modeling, exception alerting, data mining and other application dependent features.
Decision making refers to the process of finding and selecting options according to the priorities and values of the person making the decision. Since there are many choices involved, it is important to identify as many options as possible so as to pick the option that best fits a company’s target, goals, values and vision. Due to the integral role of decision making in company growth and financial progress, many firms such as Amazon.com and EBay are pumping in huge investments in business intelligence systems, which are made up of certain technological tools and technological applications that are created for the purpose of facilitating improved decision making process in business. In this paper, I take a critical look at Decision Support Systems and how they affect organizational Decision making.
Some faculties and departments are already using Oracle applications in their day-to-day operations. As time goes by, more and more information users will be working with an application based on Oracle database technology. If you get the opportunity to be a member of an application development team, you will become familiar with the workings of Oracle and relational databases. Other users may have to learn about this popular database management system through their own experience. This article is for our readers who, as of yet, have no access to Oracle databases but have a yearning for learning what they're all about.
It is obvious that there is no organization running without data. The data can be viewed as tangible assets of an organization just as any physical asset. So, they need to be stored and made available to those who need them when they need them. However, the data by themselves are useless. So, they must be put together to produce useful information. In turn, information becomes the basis for relational decision making. To facilitate the decision-making process, a new development of database systems was developed called "data warehouse".
Prior to the start of the Information Age in the late 20th century, businesses had to collect data from non-automated sources. Businesses then lacked the computing resources necessary to properly analyze the data, and as a result, companies often made business d...
Because revenue goals are a collective responsibility among front-end sales and company management, organizations are realizing the importance of sales analytics solutions to have collaborative decision-making environments for sales effectiveness. Tools like dash-boarding and reporting should be complemented with robust workflows and auditing capabilities in addition to automation and self-service that can reduce administration effort and time that aid in higher sales staff effectiveness. Thus, sales metrics can make your sales processes both efficient and
Welcome changing requirements, harnessing change for competitive advantage: It's the business analyst's tasks that help identify competitive advantage and the changes that will achieve that