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Business Intelligence Master of Science (BIMS) Program Mastery Reflection

Looking back on the entirety of this degree program, I can say with confidence that it exceeded my expectations in regard to the breadth of skills and knowledge covered and the level of rigor involved. From more granular components of ETL design to higher-level insights surrounding acquisition of executive buy-in, the BIMS program proved highly comprehensive in establishing the required skills to move forward as a BI professional. A key requirement for success was undoubtedly proper time management, as the accelerated nature of the program did not equate to any lessening of the workload one would expect from graduate-level courses.

Knowledge attained through this program spanned areas such as database management, data mining, patterns and recognition, process modeling, analytics, visualization, and more. Numerous tools and technologies were vetted and compared, with a high degree of independent research conducted to analyze how certain tools may prove more or less beneficial based on the particular organizations and business problems analyzed. One constant, however, was a distinct focus on business objectives. Whether executing as a database architect, business intelligence analyst, or assuming a leadership role, each individual’s efforts and outcomes must be set upon and guided by a solid foundation of understanding the business models, industry positions, and overarching objectives that have been established to drive the company forward.

As I take this knowledge and step forward into the next stage of my career, I feel motivated and empowered to act upon the catalysts that drove me to pursue this program from the very beginning. Reaching nearly 12 years working in the field of marketing, I played witness to how efficiency and performance can be hindered by a disconnect between the teams executing advertising initiatives for an organization and the teams upon which they rely for timely, accurate data to help measure and propel those projects to new heights. Immediate impact has been gained throughout my studies by developing a stronger sense of common language, increasing the effectiveness by which our teams can collaborate to reduce project timelines. The next step is to take the culmination of these learnings to identify the gaps currently present in our marketing capabilities, the future states needed to meet our organizational goals, and the most valuable BI projects that may be implemented to begin bridging those gaps toward long-term success.

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Business Intelligence Case Studies Mastery Reflection

The BI Case Studies course brought together concepts from all prior courses to synthesize learnings into actionable, comprehensive solution plans. The textbook for this course, Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results, served as a reference point to review how the details of a solution come together to ultimately deliver results (Marr, 2016). Additionally, this book served as a source for selecting a company for which to develop an initial full-scale solution plan. Choosing Electronic Arts as the basis of my first full project, two weeks were spent conducting a comprehensive PESTEL, SWOT, and Gap Analysis, identifying and defining a business opportunity, and crafting an actionable BI solution plan with cost and benefit projections.

This led to refining the skills necessary to shift focus toward my capstone company, Activision Blizzard, and repeat the full solution plan creation process with a better understanding of how the pieces come together. While I had not expected to be given the opportunity to develop two full solution plans within this course, the initial practice provided an immensely valuable opportunity to seek feedback and address deficits before moving toward the final capstone project. Learnings from the first solution plan led to the development of more defined structure in areas such as crafting a succinct problem or opportunity statement, discovering and clarifying tangible and intangible benefits, and understanding operational costs incurred beyond tools and direct staffing requirements.

Taking the learnings from this course forward into my professional career, a core lesson I’ve internalized involves the critical need for a thorough and comprehensive business understanding prior to planning any solution. Foundational insights into how a business generates revenues, the industry in which they operate, where they stand within that industry, and what opportunities or threats they face are essential factors to consider prior to seeking to address a perceived need. Additionally, when planning to address an opportunity or threat after the initial understanding has been developed, I intend to apply these course learnings to maintain a holistic and strategic view on how the breadth of operational areas may be impacted by any new or modified implementations. The projects and exercises in this course required pulling together an expansive set of business intelligence skills, culminating in an understanding of not only how BI capabilities may be addressed to enable operational success but also how a higher-level strategic view of an organization must be developed to ensure solution alignment and viability.

References

Marr, B. (2016). Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. Hoboken, NJ: Wiley.

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BI Leadership & Communication Skills Mastery Reflection

In the Business Intelligence Leadership & Communication Skills course, I both learned the core tenets of effective leadership and reflected upon who I am and wish to become as a leader. The textbook for this course, The Leadership Challenge: How to Make Extraordinary Things Happen in Organizations, homed in one five key principles to effective leadership- “Model the Way,” “Inspire a Shared Vision,” “Challenge the Process,” Enable Others to Act,” and “Encourage the Heart” (Kouzes & Posner, 2012). Reading through these lessons, paired with assignments that forced me to reflect deeply on each component, let to a deeper internalization of the concepts than I’d expected to gain through the course.

Pairing this with reflective assignments, such as scoring my current leadership and communication skills, helped me define a path for growth and improvement to carry forward into future efforts. As discussed in the Mastery course at the beginning of the BIMS program, grit and ambition are key virtues that will be needed to achieve success in these growth efforts. Developing into an effective leader is not a short journey, nor is it one with a solid endpoint. Leadership and communication are skills that must be continuously and constantly refined, and ones that will need to evolve with each new challenge and environment with which I engage. My eagerness to confront these challenges is fueled by my ambition, but the willingness to stick with it and see it through will require grit.

While my initial expectations for this course may have been light, the content proved it a rigorous and valuable learning experience. Carrying these concepts forward into my professional career, I’ve begun immediately taking steps to improve my communication effectiveness through a detailed and measurable growth plan developed within the course. I have also begun seeking out opportunities to apply the five core principles of James Kouzes & Barry Posner’s leadership framework. These will be continuous and intentional acts as I continue to grow throughout my career and seek ways to better communicate with, inspire, and move teams toward greater achievements.

References

Kouzes, J. M., & Posner, B. Z. (2012). The leadership challenge: how to make extraordinary things happen in organizations. 5th ed. San Francisco, CA: Jossey-Bass.

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Process Modeling and Analysis Mastery Reflection

Entering into the Process Modeling and Analysis (PMA) course, I expected to cover a broad array of applications where process modeling could impact various operational units within an organization. This was indeed covered, as assignments explored applications across divisions including but not limited to manufacturing, pricing, and Human Resources. Discussions took on new form, as roleplaying scenarios were introduced to simulate real-world business meetings.

A particularly fascinating discussion involved leveraging PMA techniques to evaluate the viability of opening new production facilities in a high-crime area. From the standpoint of a BI professional, risk assessment was performed from which a decision model could be developed to support viability analysis based on stakeholders’ levels of risk aversion. In responding to a classmate’s post, the role was flipped to take on the position of either CEO or CTO to whom the initial analysis was presented. Expanding beyond standard responses, the role play required introducing new concepts for the BI professional to consider. In this particular scenario, the development of Decision Support Systems (DSS) was to be introduced with context regarding how it may support the ability of the BI team and executive stakeholders to more efficiently evaluate current and future initiatives.

The added dimension provided in this course facilitated deeper comprehension of lessons covered. Translating process models through formats such as decision trees, stock and flow models, causal loop diagrams, and more helped distill complex concepts into digestible visual formats. Leveraging the lessons learned through PMA, both in future courses and beyond graduation, one of the primary learnings I intend to carry forward is taking a step back to analyze the larger scope of variables that must be accounted for in developing an effective process model.

Even when focusing in on a single event, it’s important to be cognizant of the interconnected nature of variables across the processes you seek to optimize. The project must always be driven by a well-defined purpose or goal, and in adjusting any process it’s critical to evaluate what risks may be encountered both internally and externally that could impact a proposed solution. The additional insight gained from the roleplaying scenarios specifically will be leveraged to continually evaluate my work through the perspective of the parties to whom I intend to present so to proactively identify and solve for stakeholder priorities and concerns.

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Visualization & Creative Reporting Mastery Reflection

For the Data Visualization and Creative Reporting course, the main takeaway could be summed up with the phrase “a picture is worth a thousand words.” With each assignment came the task of taking an otherwise complex or cumbersome data set and distilling it into a quickly digestible, succinct visualization. This went beyond standard bar charts and line graphs, exploring use of color and placement to convey meaning in a wide variety of formats. Building upon prior mediums encountered, infographics were explored as a means to telling more diverse stories with varied data type requirements.

While the variety of visualizations explored was within my initial expectations, one fascinating and beneficial requirement to the assignments involved experimenting with multiple tools within the scope of an individual deliverable. Charts and graphics were produced from a wide array of tools, including but not limited to Excel, Google Sheets, Google Data Studio, Tableau, and Canva. Exploring the capabilities and limitations of each tool enhanced my understanding of how to select the right tool based on the data and the story my visuals would seek to tell.

One particularly fascinating concept explored was through the development of dashboards. These may be leveraged to present a wide variety of data, customizable to suit the needs at any level of an organization. While one dashboard may focus on account-level data to support marketing professionals, another may aggregate that data into one of several modules providing executives with a holistic view of company performance. Learning the core components to developing an impactful, digestible dashboard helped me to build one immediately that I am now leveraging within my current organization.

As I move through the remaining courses in this degree program, the lessons absorbed in this course will help me bring better focus to data visualization. Whether that involves optimizing a simple bar chart to provide the cleanest and most accurate view or composing a high-impact infographic that distills complex topics into a succinct and shareable story, delivering coherent visuals will be key to engaging the audience each visual means to serve. Beyond graduation, continuing to hone these skills will allow me to ensure that critical business concepts may be delivered in a way that resonates with and can be acted upon by the key stakeholders they intend to support.

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Patterns and Recognition Mastery Reflection

The Patterns and Recognition course shed light on the breadth of data types from which meaningful patterns may be extracted and analyzed. From social media sentiment analysis to determining athletic attributes most strongly correlated with success, the variety of applications studied provided valuable opportunities to visualize diverse data sets and uncover hidden patterns of significance. While my initial expectations for this course were to learn such concepts from a higher level, the hands-on exercises allowed me to move more directly through the processes of data collection, data preparation, visualization, and analysis.

Three of the most exciting exercises involved analyzing data from Ironman competitions to propose a training plan, uncovering growth patterns in quick service restaurants to recommend a franchise opportunity, and conducting stock market analysis to inform shareholder activities. For the first of these three assignments, analyzing past Ironman competitions, five years worth of past competition data was collected to determine which performance areas correlated most strongly with a competitor achieving top rank in their respective division. Focusing only on the men’s USA division within Panama City Beach events, for competitors aged 40-44, it was possible to uncover patterns to inform where a colleague of mine may wish to focus his training for highest impact on his future rankings.

When leveraging data to recommend a fast food franchise investment, fascinating data was uncovered that defied my initial hypotheses. While McDonald’s and Subway consistently rank in the top three for QSR Magazine’s Top 50 report, data revealed little to no growth in earnings per unit or total units per year; in fact, Subway showed a reduction in both earnings per unit and total franchise units over the past three years (QSR, 2017). By reviewing trends among several variables, both by company and by category of cuisine, a viable recommendation could be composed as to which particular chain may show the best return on investment over the next few years.

Perhaps my favorite assignment from this course, an analysis was conducted regarding Activision Blizzard (ATVI) stock fluctuation. While ten years worth of data was collected, some analyses required focusing in on only the last 12 months. The two most fascinating subjects covered in this project involved Whale Tracks and Bollinger Bands. While the overall volume of shares purchased showed little fluctuation during most time periods, short bursts of high-volume purchases emerged, referred to as Whale Tracks. These patterns of purchases occurred when the price of the stock hit significant low points; at each of these dips, millions of shares were purchased. As the price of the stock rose immediately following each large-scale stock purchase, it could be argued that those purchases themselves contributed to such increases.

Bollinger bands were a highly beneficial concept to visualize. When charting out the closing price of this stock over time, the mean was charted based on a running average of the prior 20 days. The upper and lower Bollinger Bands were then calculated as two standard deviations above and below the mean; as standard price fluctuations have a tendency to return to the mean, hitting the lower band suggested a strong time to purchase stock while hitting the upper band indicated a strong time to sell. While these patterns were discernible on a 10-year visualization, focusing in on only the past 12 months provided a richer opportunity to recognize short-term patterns and ultimately decide what action may be most beneficial to present-day shareholders.

Moving into future courses, these enhanced skills in data collection and preparation, visualization, statistical calculation, and pattern analysis will facilitate the ability to conduct richer and more complex analyses. A broadened view of the types of data that may be collected, as well as experience in selecting the right data set for the problem at hand, will serve to improve my ability to address such projects in a timely and efficient manner. Moving beyond graduation, I am excited to apply such skills to issues facing marketing teams. As my ultimate career goal is to attain a role as a data scientist with an emphasis on supporting marketing departments, it will be fascinating and fulfilling to dive deeper into how pattern recognition can help such teams become more efficient and focused in their efforts.

References

The QSR 50 (2017). In QSR. Retrieved July 10, 2018, from https://www.qsrmagazine.com/content/qsr50-2017-top-50-chart

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Data Mining Mastery Reflection

When developing my initial Mastery Journey Timeline, there were three goals established for the Data Mining course. These included learning to use Tableau, learning how to leverage Excel for basic data mining tasks, and increasing my skill level in SQL. Interestingly, the third week assignment for the Data Mining course was a full Tableau suite analysis, which provided an unexpected level of insight into the company and its products. Inspired by these learnings and various resources uncovered in the research process, I began experimenting with Tableau to visualize data sets that had been extracted from my daily professional tasks. The primary focus was on mapping various engagement metrics for regional and national campaigns, through which I was able to accomplish the first goal of developing basic skills in Tableau.

My initial expectation of learning Excel functions for data mining was not accomplished directly, as new insight into the purpose and applications of data mining led me to explore more comprehensive and targeted tools. Instead, Excel was primarily leveraged for composing initial data sets that could be fed into tools such as Tableau and RapidMiner. Progress toward learning SQL was temporarily delayed as the heavy emphasis on R in data mining refocused my research efforts during this course. This should not be perceived as a disruption or setback in the Mastery Journey, but instead a valuable opportunity in which the insights gleaned from the studies uncovered richer areas to explore. I intend to resume studying SQL in the weeks to come, but with greater context as to how the different database languages may vary in strength and application across varied BI processes.

Overall, the Data Mining course provided a wealth of knowledge regarding the processes and potential in uncovering hidden patterns within large-scale data sets. Developing an understanding of the Cross-Industry Standard Process for Data Mining (CRISP-DM), and the alternate process SEMMA, illustrated the importance of applying a comprehensive structure to a data mining project. While project goals may vary widely, applying such a process ensures that critical tasks are not missed throughout execution. CRISP-DM in particular provides the most robust model, expanding beyond SEMMA to place focus on developing the business understanding and data understanding prior to data mining execution. The importance of these phases should not be understated, as the foundation of a project can dramatically alter the course and output of such efforts.

Learning various data mining methods shed light onto the breadth of business applications data mining may serve. Leveraging methods such as association to uncover patterns of co-occurrence, classification to categorize data into discrete groups, and regression to predict numerical outcomes can serve to inform a variety of business needs. A common example of applying the association rule involves uncovering retail products commonly purchased in conjunction, which may help inform marketing or physical placement of materials to influence higher sales volumes. Classification may be used in areas such as auto sales, where the analysis of an individual’s risk in regards to “high,” “moderate,” or “low” categories may impact financing options extended to a consumer. Regression may be applied to past financial data to predict future revenues given the introduction of new or changing conditions. These business applications help illustrate how data mining may prove valuable to organizations as volumes of data continue to expand beyond what may be viably analyzed through human effort alone.

As I continue through the remainder of the MS in Business Intelligence program, knowledge of these foundational processes and methods will facilitate enhanced comprehension of successive techniques. Moving into the courses of Patterns and Recognition, Process Modeling and Analysis, and Data Visualization and Creative Reporting, my initial expectation is that the foundational concepts of data mining will permeate each area. This is a key benefit of the structure of the program, in that learnings are continuously compounding. Beyond graduation, my intention will be to continue to explore and develop skills in predictive modeling and analytics. This is an area I believe will only continue to grow in importance in the coming years, and the ability to hone such skills will likely provide a strong career advantage.

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Business Intelligence Analytics Mastery Reflection

While my initial expectations for the Business Intelligence Analytics (BIA) course were geared more toward practical use of BIA tools and software, actual course content took concepts back a few steps. Focusing on foundational statistical formulas, the course was abundant with opportunities to gather or build operational data sets and apply various calculations to help guide potential business decisions. In a sense it looked toward the core statistical formulas that may be facilitated through the tools I expected to encounter while also framing subjects from an operational perspective.

Key learnings from this course included leveraging data to evaluate opportunities for market expansion, promotional activities, growth phase adjustments, forecasting, and quality control. Between discussions and a rigorously constructed tech blog, analyses were abundant. Focusing efforts for these analyses on Activision Blizzard (ATVI) proved to be a strong decision, with vast amounts of data publicly available on many core operational areas. This proved to be a very intensive course in terms of research skills, as assignments in many cases required skimming SEC filings and global population data sets.

A recurring theme that has emerged from the last few courses has been viewing concepts from an executive perspective, keeping focus on the big picture while retaining awareness of the full spectrum of variables affecting an organization and its overall market. Moving through the next courses and beyond, this continued emphasis on organizational purpose and value will be instrumental in ensuring that decisions are always well-grounded and fully vetted to maximize on potential gains while minimizing factors of risk. While there is no reward to be gained without some element of risk, the statistical knowledge gained through BIA will serve as a powerful tool to maximizing the potential of success.

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Business Intelligence Technologies Mastery Reflection

Three goals established for the Business Intelligence Technologies (BIT) course included developing basic coding skills in SQL, developing real-world connections to course topics, and acquiring a mentor in my field. The work initiated toward accomplishing the first goal actually began in month three, as the Enterprise Data Management learning materials included SQL tutorials. While topics covered in BIT did not directly tie into this area, I continued to progress through independent tutorials throughout the duration of this course in order to stay on track with the overall Mastery Timeline. 

Lessons and topics covered within BIT did, however, play a strong role in accomplishing the remaining goals. Throughout the webinars and assignments, Professor Michael Taylor provided concise examples that went beyond academic theory and into real-world practice. When considering the purpose behind a BI initiative and the rationale that goes into selecting the right tools for a particular solution, I turned to my BI department to learn how these processes were applied within my own company. Having recently conducted a SWOT analysis on BI vendors, I was equipped to discuss and develop a deeper understanding of how the selection process occurred that ultimately resulted in the implementation of Microsoft Power BI.

As a result of these conversations, in addition to strengthening real-world connections to course topics, I was able to develop stronger rapport with members of the BI team. In doing so, one individual stood out as highly interested in continuing discussions and helping me develop a better understanding of the expectations and role of a BI professional. This natural evolution brought about by genuine curiosity and interest has led to the successful formation of a mentor-mentee relationship, thereby accomplishing the third goal laid out for this course.

While my expectations for the course were primarily to learn about the various tools and technologies leveraged in BI, the learnings that resulted both met and exceeded such expectations. While developing a stronger understanding of the difference between a BI tool and a BI solution, I conducted an analysis of four major vendors highlighted in Gartner’s 2018 Magic Quadrant for Analytics and Business Intelligence Platforms (Howson, Sallam, Richardson, Tapadinhas, Idoine, & Woodward, 2018). This allowed for a deeper understanding of how comparable products across vendors must be evaluated, as no single tool may prove ideal in all circumstances. 

Expanding upon this idea, an assignment analyzing BI Adoption led to additional research into key features of a strong BI solution supported by case studies examining real-world implementations. Performing analyses on actual implementations provided insight into the organizational thought process driving BI projects. Learning to view a BI project from a higher level, focusing first and foremost on the primary business objectives and how BI may factor into an organization’s ability to resolve key issues or capitalize on a major opportunity, would become a skill that would carry through the remainder of the course.

Developing a solution to resolve an issue without properly identifying and fully describing why that issue is important and what impact it will have on an organization may lead to ineffective projects that fail to meet the organization’s core objectives. This became the focus of a final project in which I was to identify an observable problem within an existing organization and compose a plan for how an enhanced BI solution could serve the business need. While this included components such as database and ETL design, everything needed to tie into a precisely described analysis of the problem, how it was affecting the organization, and what benefits were to be realized through the expansion of BI technologies.

As I proceed through the remainder of the MSBI program and beyond graduation, one of the most fundamental lessons I will carry with me is maintaining perspective on the issue at hand. If you develop a strong understanding of the business need and the benefits you seek to achieve, those concepts will fuel the remainder of the solution development. By assessing the primary business goals, identifying the highest impact areas that may be addressed, and consulting with key stakeholders to develop a thorough understanding of how such issues impact overall organizational success, I can ensure that a BI project is properly aligned at the most foundational level. Only with that context can the true value of the knowledge I’ve attained on individual technologies be leveraged to their fullest potential.

References

Howson, C., Sallam, R. L., Richardson, J. L., Tapadinhas, J., Idoine, C. J., & Woodward, A. (2018, February 26). Magic Quadrant for Analytics and Business Intelligence Platforms. In Gartner. Retrieved March 13, 2018, from https://www.gartner.com/doc/reprints?id=1-4RUIGZP&ct=180226&st=sb

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Enterprise Data Management Mastery Reflection

This fast-paced and highly intensive course once again exceeded my expectations in terms of the breadth and complexity of knowledge attained. The most difficult of my courses so far, it was consistently challenging; it felt as if there was an expectation of entering into these assignments and supplements with at least a foundational knowledge of each concept. Coming into it with a business background, digging into the very granular technical concepts required a high level of diligence and focus. Even then, dedicating twice as much time as in previous courses to study and research the topics, I always seemed to fall just short of meeting all of the requirements. Despite this sense of overload, however, there’s one thing I can say for sure- I’ve learned far more in these last four weeks than I could have ever imagined.

Diving into concepts of data warehousing, dimensional modeling techniques, the ETL process, and the Kimball BI/DW Lifecycle helped distill an understanding of the complexity of planning required in launching an enterprise data management (EDM) initiative. From collecting business requirements and evaluating existing systems to developing a schema conducive to optimally serving the business need (Simon, 2014), the exercises helped instill a highly structured mindset in how to approach solving for business data challenges. Entering the course with some familiarity of relational database modeling by means of simple MS Access databases, it was fascinating to research the advanced reporting and processing capabilities enabled by moving to a dimensional model. When working with Big Data, leveraging star schemas and OLAP cubes appears to open the doors to much more robust solutions in terms of flexibility and speed of querying data in an EDW (Kimball & Ross, 2013).

The primary assignment revolved around developing an actionable and feasible EDM plan to address a business need within an existing company. For this, I chose my desired capstone company and developed a solution to manage the user gameplay data for Activision Blizzard (Activision Blizzard, n.d.). Working in increments over the weeks, I began by evaluating the current state of Activision Blizzard’s EDM systems with a particular focus on how gameplay data is collected and stored across business divisions. As a primary source from which Activision Blizzard can derive insights to optimize existing player experiences and plan future product development, the user gameplay data serves as a high-value source to drive business revenues (Hernandez, 2017).

Moving into key business imperatives, research was conducted into considerations such as strategic sourcing, mega processes, and risk mitigation and management as it related to the current systems facilitating the collection and management of this user data. Pairing the current state evaluation with a detailed review of these business processes facilitated the development of a new EDM system plan that would centralize gameplay data between the relevant business divisions. This consisted of elements such as proposing and detailing an optimal EDM schema, demonstrating the ETL (extract, transform, load) processes that would be implemented to migrate data to the new system, and proposing specific vendor tools best suited for the project. Cost analyses and product comparisons were conducted to ensure that the benefits of the new system in terms of performance and reporting capabilities would not be outweighed by the cost or effort required for implementation.

The plan concluded with a roadmap outlining prerequisites and urgent issues to be addressed prior to initiating development, as well as an overview of the post-implementation future state and accompanying benefits Activision Blizzard could expect to achieve through the new systems. Processes and timelines to implementation, including post-deployment support and training requirements, were outlined through the EDM roadmap to provide clear expectations for the lift and support requirements involved in this plan. Vendor management and adaptability to evolving business needs were addressed to ensure that the system could not only properly address the current business needs, but also encompass requirements for scalability, sustainability, and flexibility to grow with the evolution of Activision Blizzard over time.

Real-world applications of topics covered throughout this course by means of the EDM plan development helped distill a deeper understanding and retention of concepts explored. While I still have more to learn, I intend to continue study of these topics over the coming months by continuously reviewing course materials provided and expanding independent research efforts to supplement those materials. Moving through the coming months and beyond, expanding my knowledge of foundational EDM topics will provide a stronger level of context to how each of the other areas within BI depend upon and are fueled by the underlying data models. Leveraging this knowledge into my career, I’ll be equipped to develop solutions that care for the particular needs of a business from both a data and internal organizational requirements perspective. With this, I’m excited to see what challenges are next in line on my Mastery journey.

References

Hernandez, T. (2017, August 3). Activision Blizzard Q2 2017 Financial Conference Transcript. In BlizzPlanet. Retrieved March 21, 2018, from http://warcraft.blizzplanet.com/blog/comments/activision-blizzard-q2-2017-financial-conference-transcript

Kimball, R., & Ross, M. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling (3rd ed.). Hoboken, NJ: John Wiley & Sons.

Our Company. (n.d.). In Activision Blizzard. Retrieved March 1, 2018, from https://www.activisionblizzard.com/about-us

Simon, A. (2014). Modern enterprise business intelligence and data management. Waltham, MA: Morgan Kaufmann.

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Foundations of Business Intelligence Mastery Reflection

Entering into the Foundations of Business Intelligence (FBI) course, I expected to gain a broad understanding of the field of business intelligence with little emphasis on any topic in particular. What resulted from the class not only fulfilled but exceeded those expectations. In terms of the Mastery Journal Timeline goals that were established prior to entering FBI, exciting opportunities arose that helped me accomplish each as we progressed from week to week.

My first goal had been to network with at least three new individuals; this goal was achievable, but just barely. The fact of the matter is that I had only two active classmates in the course working alongside me. What made it achievable was being able to connect also with my professor, enhancing the relationship through brief discussions before each weekly webinar. Seeing the progress of my classmates while honing my own skills helped develop those relationships, setting the foundation for future collaboration.

The second goal involved setting up a meeting with my BI team to better connect course learnings to real-world applications. Ultimately, no formal meetings were scheduled- the discussions simply occurred naturally. During breaks, in passing through the halls, and when lingering behind for a few extra minutes following other meetings, I had the chance to speak with several colleagues from different teams across the BI department. From natural curiosity springs some of the most compelling discussions, and my colleagues were quite happy to share insights into the many tools and processes discussed in this course that they might leverage.

These discussions, paired with insights gleaned from the Lynda.com training “Data Science and Analytics Career Paths and Certifications: First Steps” (Ryoo, 2016), helped me better understand the best entry point into BI to set me on the path toward my ultimate goal of becoming a Data Scientist. Understanding the starting point is key to focusing in on key skills and competencies that will be required. With such a vast array of tools and technologies intertwined in the composition of a full-scale business intelligence solution, that focus is key to a successful transition.

The FBI course confirmed this for me, beginning with a foundational knowledge of the technologies that facilitate organizational memory capability and culminating a in full review of case studies of industries leveraging BI. In the case of organization memory, concepts of structured and unstructured data housed across disparate systems were examined. Serving as the foundational element of the four BI capabilities, we explored how these disparate systems could be tied together into a comprehensive Enterprise Resource Planning (ERP) system.

As organizational memory serves as the foundational BI capability, future weeks dove deeper into the other three core capabilities- information integration, insight creation, and presentation. Key readings from Business Intelligence: Practices, Technologies, and Management facilitated an understanding of the depth of planning and varied requirements needed for each (Sabherwal & Becerra-Fernandez, 2011). Conducting a comparison of BI mega-vendors, I was able to connect how each tool across IBM and Microsoft’s product suites supported those four BI capabilities. Contrasting each vendor’s equivalent tools provided insight into how different solutions posed different challenges and benefits depending on the organization and BI problem at hand.

As we turned toward the final week, all of these learnings came together to solidify a foundational understanding of the BI landscape. Each week, a Visio diagram was produced to demonstrate the corresponding lessons, supplemented with trainings on effective use of Visio itself through David Rivers’ “Visio 2016 Essential Training” (2016). After conducting a PESTLE analysis of the education industry, I dove into IBM case studies to review complex BI solutions developed to address problems across three different industries. Reviewing a variety of industries helped gain perspective on which industry I wanted to direct my capstone project toward. While I’ve been working in the field of education, choosing an unfamiliar avenue may prove more beneficial in the long run as my career evolves and the requirements demand an ability to develop solutions in a new context.

Moving forward through future courses and beyond, this foundational knowledge of the intricacies of BI will help me retain perspective on the big picture. While you explore solutions for the presentation capability for example, vetting out tools to create powerful visual representations for data insights, it’s important to understand how those tools must be able to tie into the overall BI solution. Regardless of short term gains, a long-term perspective should be applied to ensure that the elements of flexibility, portability, and scalability across the integrated technologies are accommodated for future success. This is a mindset I intend to carry with me each day as I excitedly enter into the next step on my BI Mastery Journey.

References:

Rivers, D. (2016, January 7). Visio 2016 Essential Training. In Lynda.com. Retrieved February 23, 2018, from https://www.lynda.com/Visio-tutorials/Visio-2016-Essential-Training/445423-2.html?org=fullsail.edu

Ryoo, J. (2016, August 15). Data Science and Analytics Career Paths and Certifications: First Steps. In Lynda.com. Retrieved January 28, 2018, from https://www.lynda.com/Data-Science-tutorials/Data-Science-Analytics-Career-Paths-Certifications/475941-2.html?org=fullsail.edu

Sabherwal, R., & Becerra-Fernandez, I. (2011). Business intelligence: practices, technologies, and management. Hoboken, NJ: John Wiley & Sons, Inc.

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Goals and strategies for developing my BI Mastery throughout my graduate studies at Full Sail University.

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Visual representation of my Mastery Journey Timeline.

References

Full Sail, LLC. (2018). Course Schedule. In Full Sail University. Retrieved January 21, 2018, from https://www.fullsail.edu/degrees/business-intelligence-master/courses
?fs=online

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In addition to fueling my love of nature and travel, this image serves as a metaphor for my Mastery journey. The continuously flowing streams evoke feelings of how I must constantly keep moving and learning, never settling or becoming complacent in my path. As the waters slowly carve and transform the rocks below, as will my learnings help shape the evolution of my career. Yet despite the constant activity, this imagery brings about a feeling of calm and peacefulness. This relates to me the deep focus I must strive to achieve, getting lost in exploration as I pursue my passions.

Source:

Mendez, J. (Photographer). (2012). Chittenango Falls, New York State. [Image of photograph]. Retrieved from https://jorgemphotography.deviantart.com/art/Chittenango-Falls-New-York-State-338834878

[jmcarrier-mastery]

Above is my personal logo created as part of my Mastery journey. As I began to establish an online presence years back, I found that most variations of my name were already registered across many websites and services. This variation, however, was available across 90% of the platforms I pursued. Additionally, omitting feminine names, such as through the use of initials, has been shown to reduce the likelihood of gender bias (Watts, 2014). 

The overall design is simplistic, clean, and readable. This fits with my brand, conveying a direct and professional tone as I seek to create intuitive and practical business solutions.

Watts, A. W. (2014, June 2). Why does John get the STEM job rather than Jennifer?. In The Clayman Institute for Gender Research. Retrieved January 19, 2018, from http://gender.stanford.edu/news/2014/why-does-john-get-stem-job-rather-jennifer

[jmcarrier-mastery]

No journey worth taking is going to be easy, but it’s often when things reach their darkest that you find yourself on the brink of a breakthrough. Appreciate the good times and be relentless through the bad; those challenges will be critical to your growth.

Image Source:

Good.Co Team+. (2013, September 17). 10 Career Inspiration Quotes That Will Make You Feel Better About Yourself (PHOTOS). In Good & Co. Retrieved January 19, 2018, from https://good.co/blog/30-tweetable-workplace-happiness-and-career-inspiration-quotes/

[jmcarrier-mastery]

https://www.linkedin.com/in/jessicacarrier/

My LinkedIn account. Established in 2010, this profile has been slowly refined over the years to begin emphasizing the more technical aspects each role. Continuing to optimize the account will help highlight skills and competencies conducive to a transition into business intelligence.