The National Customer Service organization has enhanced its customer service tools over the past three years. The customer service tools have been consolidated from 10 separate platforms into 1 tool that is consistent company-wide. This consolidation lent itself to the National Customer Service Business Intelligence department having the ability to analyze agent performance from a “One Comcast” perspective. Specifically, the evolution of business intelligence and performance dashboard capabilities from vendors provided a unique opportunity for the company to expand insights from headquarters directly to the users in the field. The continued evolution of dashboard functionality over the past few years now provides additional …show more content…
Although the advanced analytics professionals could pull the data together in one consolidated view via Tableau for the executive team, the underlying data did not lend itself to a larger deployment of Tableau. Business users that were outside of the IT and analytics profession were not prepared for the level of complexity involved in self-service analysis.
One of the trends in performance dashboards was that “Visual-data-discovery tools have become synonymous with self-service BI and are growing at three times the pace of the overall BI market” . Fortunate for the customer service team, the Tableau tool that had been implemented was reviewed as one of the top “Leaders” in the Gartner Magic Quadrant in 2013 and continued to expand its functionality. After the consolidation of the customer service tools under one system, the Tableau dashboard tool provided the infrastructure to expand insights across the organization in a more meaningful way. The business intelligence team, therefore, deployed Tableau to the field given that self-service BI was not only the trend in the industry, but because it was also the most effective and efficient way to relay insights and results. Within the field, the HQ business intelligence team first deployed the dashboard tool to the BI teams to provide them with the flexibility to slice the data in a multitude of ways to provide insights directly to their
Three years later, it is still starting to connect the “client journey” where client interactions over the phone, web, and mail can be analyzed. As part of the enterprise-wide analytics, there are efforts to centralize a data mart where analysts across the firm can access to understand the full client experience.
Currently, the dashboards I’ve created have not been successful in their goal because instead of providing actionable insight, they have simply been regurgitation and filtered views of the data. Due to my lack of a full understanding of the data and the business they have not provided the actionable insight, have left the interpretation to the Executives and are siloed to only
The uses of executive dashboards have become widespread in most organization [9]. Not because of its visual representation but they assist the organization communicate, strategize, monitor and correct the execution of the strategy to deliver actionable insights to all [1]. A dashboard is an essential tool for monitoring or summarizing the high level information about the daily health status of an organization [1][4]. From a single interface, the top executives can retrieve key performance indicators (KPIs), actionable insight that can be used to actively guide business performance [4].
The dashboard should be viewed as a platform that helps drive decisions. Here is where the trinity mindset discussed in Kaushik’s blog ‘Occam’s Razor’ can help us design better dashboards. “Actionable Insights & Metrics are the uber-goal simply because they drive strategic differentiation and a sustainable competive advantage.” (Kaushik, 2006)
My organization Penske Truck Leasing has been experiencing a sharp decline in the revenue generated from its full service leasing. Results of initial sales analysis concluded that the inflow of the new customers for full service leasing has been growing steadily; thus to increase revenue, the focus needs to be on the renewals for the existing customers. The company realizes that a lack of validated customer loyalty drivers prevents effective focus on customer retention and thus directly affects profitability. Challenges with data availability and business metrics have made attempts to measure and diagnose our service delivery and customer experience, both, difficult and inconclusive. In order to address this challenge, the organization has launched an initiative to build a customer analytics system that can achieve a single view of customer experience and make decisions about how best to acquire and retain customers, recognize high-value customers and proactively interact with them. I have been identified as the lead architect for the BI and analytics team that is going to implement this project.
The smarter business intelligence provides market behaviour in new ways of analyzing customer’s behaviour much quicker than ever before.
The following report will detail the experience and findings with the Tableau Public. This was created by a fourth-year Capilano University business student who is relatively inexperienced with interactive data visualizations.
The executive dashboard is a snapshot view to the strength of key business strategies and is presented in a way that might trigger a response. In order for an executive dashboard to be effective, there are characteristics and best practices that should be considered. First, dashboard designs should begin with a specific audience in mind as well as contain meaningful information. For example, some executives specialize in operations that would benefit from metrics related to hardware and software functionality; while other executives focus on financial strategies that deal with cost, revenue and sales (Ravikumar, 2016). Additionally, the data being aggregated and presented should be updated as quickly as possible, in order to prevent any lag in mission critical actions (Kaushik, 2007). Ultimately, an effective dashboard should provide information that connects to executive management in a way that moves them to action. This is the basis behind the “Trinity Mindset” model; which can help refine information into meaningful intelligence (Kaushik, 2007, p.
Tableau, a Seattle based company founded in January 2003, is the undisputed leader of the data visualization industry. It is a public company with a market cap of 3.35B as of December 2016 (Yahoo Finance, 2016). The founders, Pat Hanrahan, Christian Chabot and Chris Stolte, were researchers at Stanford, who developed a querying and visualization language called VizQL which forms the core engine of Tableau. VizQL is capable of querying many different types of data sources and combining them into one. This one data view can be used to create customized and sophisticated dashboards. Tableau is such a smart tool that it can figure out the best way to visualize any given type of data, thus allowing even non-technical people to analyze and explore complex multi-dimensional data sets.
to 4 on the Pillars of Analytics scale (Davenport & Harris 36), Hero has some room for improvement based on acceptance by the entire resource group. At this point, 20% of the Hero team can dynamically use the analytics we currently offer. For example, 2015 was the first year that we, as an operations and sales team, had a dynamic forecasting plan. We took the top customers making up 80% of our annual revenue and projected out their volume by SKU, by month and kept a rolling 12 months of inventory planning based on each customer’s growth objectives. Putting this plan together resulted in an out of stock liability improvement of 15% and actual sales were within 1% of budget projections. This project, a huge jump in our planning procedures, was completely facilitated by three people in our organization that employs over fifty people. The issues above directly tie in to the second pillar of Enterprise Wide Analytics. Although all departments have access to dynamic analytical systems, most have zero interest in utilizing it. There have been several missed opportunities for growth on analytical competition from the Operations side of the business. However, with the success of the 2015 and 2016 forecasting models, the senior managers are working to align efforts and optimize standard operating procedures that rely heavily on analytical tools. This proven performance is slowly raising the bar for Senior Management commitment. Although there is work to be done in respect to each of
We are creating data in enormous quantities primarily because of improvements in data capture technologies. But much of this data are underused or never being used. A detailed analysis of this underused data is often impractical due to time, personnel, and other resource constraints. Data visualization techniques offer a good means of taking an immediate look at this data for exploring the underlying relationships then analyzing relationships and finally understanding the knowledge embedded in the data. While there is an increased interest in Business and Data Analytics and related areas, it appears that efforts to evaluate their contributions are lacking. The need for developing an unified framework for evaluating the data visualizations is of paramount importance. A special issues just devoted to this topic of evaluating visualizations exploring its complexities (Bertini, E., Lam, H., & Perer A., 2011) highlights its importance further.
In the 2012 IBM CEO study, 73 percent of CEOs indicated that they were making significant investments in their organizations’ ability to draw meaningful customer insights from available data (IBM 2012). As a mission-critical system, the CEO now expects analytics to provide information at the fingertips of the teams that run the business. It shows the importance about translating business strategy into actionable plans of achievement, and this is also today’s topic.
Organizations rely on data analysts to model customer engagement, streamline operations, improve production, inform business decisions, and combat fraud. Numerous analysis and visualization advanced tools available for analysts to work, but there are little research goes on how analysis happens in companies. To better understand the enterprise analysts’ ecosystem, we conducted semi structured interviews with 35 data analysts from 25 organizations. Based on our interview data, we characterize the process of industrial data analysis and document how organizational features of an enterprise impact it. We describe recurring pain points, outstanding challenges, and barriers to adoption for visual analytic tools.
We live in a world that is overflowing with data. Our company has to face competition from emerging new rivals constantly. Under these circumstances, dashboards provide a rapid understanding of business performance by monitoring the critical business data in an easy-to-understand manner. But what executives want is more than Dashboard, they want something is more visible and more effective to increase their decision. That why we build Executive Dashboard.
Power BI provides powerful functions with moderate level of visualization, however, the learning curve to reach to intermediate level can be largely steeper compared to Tableau. Many times, to even solve a very simple problem, a Data Analysis Expressions (DAX) script was needed during the test. Same problem was solved by 3 times of clicking at Tableau. This DAX was more difficult to utilize compared to the Tableau’s scripting language and the absent of staunch Power BI user communities exacerbated the difficulty when writing DAX scripts. The user communities for Power BI are currently in a nascent stage, and many times, I had to go over the references which were mainly written for experienced programmers. Unlike Power BI, Tableau have strong user communities and they provide a quick fix, allowing a novice to solve intermediate level of questions. The absent of mature user community of Power BI should not be overlooked. The availability and utilisation of user communities are very important when choosing IT products, considering that web searching is the most common and effective way when to learn new technology