Discuss about the Portfolio of Research info Digital Analytics and Data.
Globalization and technological advent has dramatically changed business ambience (Nesbit, 2012). Competitive industries are making use of extensive digital analytical tools for arriving at data that can allow them to extend their core competencies. Digital industry makes use of Big Data then applies several tools on them such that strategic decisions can be made. The scope of the current report encompasses analysing tools that are used in the digital industry, which will help emerge as a successful professional in the future. In the second part of the study self-reflection regarding these tools that allows understanding regarding knowledge and specific skills set are undertaken.
There are various tools present in the digital industry, amongst which only some are evaluated for undertaking this reflection analysis. For the sake of self-reflection and analysis there are only 3 tools that have been considered SPSS, Excel and Tableau. Professional areas in which these tools can be applied, capability required in order to operate on these tools and their applications relative to business applications are evaluated.
Forecasting of sales figures is a necessity in modern business industry, where automated inventory control has created rapid development in the field of manufacturing. Regular and prompt forecasts of sales for various finished products reflect the responsive nature of the commodity market. Bigger inventory capacity and reduced computing time have created inevitability for future predictions.
Sales forecasting can be performed using the IBM SPSS (Statistical Package for the Social Sciences) software package (Coakes & Steed, 2009). The software is the most accepted statistical package in modern era, and it performs big data analysis with complex attributes in no time. The quality of reading any data from various platforms and concise handling of data makes it a standout performer. Starting from data examination, descriptive statistics, validity check, reliability test, regression analysis, MANOVA and many other hypothetical testing, SPSS handle big data with ease. Time series analysis with exponential smoothing of moving averages helps in predicting the future trend of the data (Field, 2013). The strings of monthly, quarterly or yearly inventory data for sales figures are analyzed by the help of SPSS package and market share of the stocks are identified.
The program also offers help mode and readymade statistical examples to understand the required operational skills. SPSS training courses are also available for learners.
Senior quality statistician, data analyst, product analyst are some major job roles which can be performed using the SPSS tool (Norusis, 2008). Some of the sectors are insurance domain, sales analyst position and many others.
This sophisticated package is helping in market analysis from 1970, assisting in market research by real estate, banking and commodity markets. Future demands in commodity market, sales figures in marketing industry, and profit margins in banking sectors are speculated with probabilistic modeling. It also facilitates market segmentation which eases the path of policy formulation.
Business runs on profit, profit margins depend on sales figures; sales figures are dependent on sum of total sales by the salespersons. In this entire chain of money, customer is the deciding factor. Behavior of customers, requirements, needs and change of preferences for articles are the root causes for sales figures. Therefore customer data has to be properly organized and profile of individual clients should be available to the sales team.
Microsoft Excel is the most popular platform for client profiling since decades (Jablonsky, 2014). The tool pack facility of MS Excel creates customer data base with ease. The main plan of sales teams is to woo customers based on various offers for different commodities. But they lack knowledge about customer characteristics and whereabouts, unless provided with handy data. MS Excel can segregate the big data to classify customer information and identify the major trend, helping in formation of business strategy (Few, 2009). Excel Add-in analyzer tool pack facilitates identification of the answers for strategically important questions. The obtained data is cleaned for obtaining recent values with required parameters. The cleaned datasheet is analyzed by the descriptive measures and built-in functions.
The MS Excel package can help in resource management analysis, digital marketing analysis and reporting, data reporting and analysis and also in various fields of investigation.
Starting from customization of finished products, exploring the capacity of the most popular products or analysis of the new customer list, MS Excel performs various complex data organization (Grapov & Newman, 2012). Graphical representation of year old data, cross tabulation of different fields enables in strategy formation for sales and production. Accounting plans, financial forecasting regarding interest rate movements and assessment of nonperforming assets are some breakthrough achievement performed using MS Excel. Banking industry uses this package for reporting sales data, analysing customer deposit variations and movement of lending or deposit interest rates. Environmental management, risk management, compliance
Business sustainability can be achieved through management of environmental hazards in organization. The financial benefits along with reputational promotion build the pillars of a successful business. Each and every business has its own environmental hazard, the challenge is to nullify or minimize the risk of adverse impact by preventive measures.
Tableau is one of the tools use in modern industry to minimize the risk factor of the environmental vulnerability (Kosara & Mackinlay, 2013). This data visualization tool is intelligent enough to help in understanding big data by fast analysis and attractive graphical representation. Tableau can build smart dashboards by merging different sectors of the data which can provide more accurate insight of the statistical information. The package can connect with Haadoop platform as well as with any cloud based storage (Murray, 2013). The exploration of business data becomes easy with Tableau. The dashboard created by Tableau can be shared, uploaded in any platform or website; this facilitates quick access to information.
Data analysts are more in demand than ever in modern age of big data. Tableau offers skills related to decision making, thus making the job of data scientists a lot easier. Data visualization, data analysis, Tableau business analysis are some of the roles in the industry, which are in high demand.
As the package offers better visuals of the industry for the team, the factors causing environment risk can be identified easily. The comparative analysis reflects the urgency of conservation of natural resources. Air, water and soil pollution due to waste dumping can be minimized by improving efficiency of industrial components. Geographical maps in Tableau provide the scenario of climate change across latitudes and longitudes favors the business rerouting for environmental causes.
Demands posed by the digital analytical industry are rising constantly with increasing complexity of Big Data and analytics. As a professional with clear focus to establish oneself in the digital industry, one has to learn skills regarding SPSS, Excel and Tableau (Tangney & Tracy, 2012). In the scope of self-reflection an analysis is undertaken and then a skills development with learning tool is encompassed for the same purpose. I have gained tremendous knowledge from this course and module unit. Along with this high transfer of learning I will need to accomplish some more learning and skill development in practical sphere that will help be become a capable senior manager. I have set goals of becoming a senior data analyst manager within the industry within short time span. Hence, I need to gain working knowledge of SPSS, Excel and Tableau along with various other tools and software. In order to gain working knowledge of these software I will adopt Kolb’s reflective cycle for the same. Kolb’s reflective cycle has been adopted for this purpose (Clow, 2012). In which learning has been stated can arise by various methods such as reflective, visual, conceptualization, experience based and actively and so on.
Figure 1: Kob’s Reflective Cycle
SPSS is a crucial and integral tool that has several capabilities. Amongst the entire digital analytical tool present I selected SPPS as being one of the most critical due to its several applications it has in the industry. The package has capability to handle big data in an effective and efficient manner hence it is considered to be integral software for the industry. Due to its vast applicability across so many industries, knowledge of this software is a must. I have been taught this software in the course and also learned ways to apply it. However, I am apprehensive regarding ways to deal with the software real world application. Learning of SPSS alone cannot suffice knowledge and its applications thereof. There needs to be basic understanding related to statistical concepts. I do not have in-depth understanding related to various statistical concepts and formula such that I can easily identify which formula to use and which ones to apply in any given problem. For example in case of consumer data analysis often regression analysis is deemed to be appropriate and at other instances ANOVA is considered to be applicable. Therefore, determining tests to be ascertained is a critical factor of success in the digital analytical industry. This attribute can be gained only with sound knowledge of relevant statistical ideas and concepts.
While applying in business projects and assignments we had opportunity to learn and make calculations pertaining to a few techniques only. But when in the industry we need to undertake vast data analysis, such as customer surveys, marketing data and other relevant fields then I will need capabilities pertaining to all domains of SPSS calculations. Before going on to the industry and applying for a job role and meeting my self-set targets I will need to accomplish learning of this software thoroughly. I should have capabilities such that I am able to teach others related to this software, this is only possible if I have gone through multiple case examples in the domain of this software.
While incorporating Kolb’s reflective cycle, I will make use of active experimentation for learning this software (Dunlap, Dobrovolny & Young, 2008). Active experimentation includes undertake continuous learning by means of books, journals, self-study books and internet sources that can help develop learning in the area. Going through multiple resolve case studies and other real world examples will allow me to develop competency in this software tool. According to this learning method it is said that learning does not alone accomplishes the entire goals rather incorporation of learning with experiment will allow testing of interpretation. Prior to establishing my skills and knowledge in the industry it is crucial that I undertake self-evaluation of skills and knowledge. This will help me apply my knowledge flawlessly in the industry, where there are very little chances to make mistakes and then learn from them. This method of learning does not allow theorization of learning rather reflection based learning by way of experiences.
MS Excel was devised by Microsoft and has several built-in applications and tools. MS Excel has capability to analyse various complex data and information. There are large numbers of applications of MS Excel that are present, which is not possible to know. Most individuals working on MS Excel are aware of quite a few functionalities however knowing all the functionalities requires gaining practical experience and regular working with MS Excel tool pack. I have learned quite a few tools and techniques that can be used in MS Excel and I feel that the package has immense capability to provide various analytical charts and information from it. It is an appropriate tool pack that is used across a number of industries to make basic calculations, charts, tables and various other projections. Therefore, it can be considered to be a useful tool that has multiple applications across a wide range of industries. I have capability in operating on it, but I am apprehensive regarding the various ways that digital analytical industry makes use of it. I need to gain practical insights into working of MS Excel such that I can call myself expert in the domain.
Presence of sufficient theoretical knowledge and absence of practical knowledge makes it relevant that I adopt Kolb’s concrete experience based learning to fill in the gap of knowledge existing (Kolb, 2014). I will take feedback from my professors and senior pass outs from the University regarding applications that one is supposed to know when operating on MS Excel. When I am working in the industry I will also collect relevant data from my peers and other colleagues who are employed in other companies regarding skills that I need in MS Excel. Once I have gained areas in which I need to develop my skills in MS Excel I will immediately make a full lists of experiences that will allow me to learn from. I will make use of the countless number of opportunities that are offered to me to jump-start into the learning processes and get into the learning cycle. I will maintain a self-journal where I will record my learning’s within a gap of every 6 months period. Every 6 month period, I am going to add the relevant skills that I have learnt in MS Excel and add them to the journal such that I am able to assimilate my learning and evaluate them. Then after certain period of time I will be easily able to monitor my progress.
In any digital analytical industry, it is often required to make several pictorial and graphical representations. Tableau is software that is integral for specialists in digital analytical industry to learn. In my course amongst various software that we have been taught Tableau is one of them. The software allows pictorial, graphical and several other formats to be displayed that are integral in the digital analytical industry. I had been introduced to this software in my course but have not gained detailed insights into the working of the software. Therefore there needs to be high transfer of learning of the several skills that are required to be developed in order to apply this software. In case I am able to learn this software in an effective manner and apply my knowledge pertaining to it, then I will easily be able to make decisions and help by client’s make strategic decisions. Making decisions in any business situation requires skills and knowledge, which this software is able to undertake in an easy manner. Hence, developing skills to operate on this software is good enough for this skill extension of mine. Through this skill and knowledge I will be able to gain great heights as I will be able to assists my seniors and client’s make decisions by assembling information at a click of a mouse.
I will integrate Kolb’s reflective cycle for the purpose of learning in this software through reflective observation. Though reflective observation is used in case of picking up general practices or skills, but I will adopt this practice for gaining knowledge and skills in this software. I am naturally good at learning and picking up ways to operate on any particular software. I can deliberately pick up from other’s working in the software and then learn operating from them. Once I practice operating the software on my own, I will review my performance to understand if at all I am capable of working on it or not. Moreover for evaluating my personal development and learning or skills development, I will maintain a self-reflective journal where I will maintain my skill development on a 6 month basis. Post every 6 months I will review my skills and knowledge gained and then monitor my progress accordingly. This self-evaluation process will allow me to extend my capabilities in the stated digital analytical tools and attain my professional skills faster. Frequently I will make evaluation of the several skills learned so as to understand my skills and knowledge gained in relevant areas that will allow me to become a leading professional in my domain.
Conclusion
Analysis of relevant learning and experiences reveal that there are various knowledge gaps that need to be filled. In absence of that knowledge in the digital industry, I will not be able to present myself as a capable individual. As my personal and professional goal is very high and I aim to attain senior management roles within a short time span, I have to strive hard and adopt as much as possible measures in order to achieve my goals. Accomplishing self-set goals by adopting Kolb’s reflective cycle will allow me to emerge as a capable professional within short time span.
Reference Lists
Clow, D., 2012, April. The learning analytics cycle: closing the loop effectively. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 134-138). ACM.
Coakes, S.J. and Steed, L., 2009. SPSS: Analysis without anguish using SPSS version 14.0 for Windows. John Wiley & Sons, Inc.
Dunlap, J., Dobrovolny, J. and Young, D., 2008. Preparing e-learning designers using Kolb’s model of experiential learning. Innovate: Journal of Online Education, 4(4), p.3.
Few, S., 2009. Now you see it: simple visualization techniques for quantitative analysis. Analytics Press.
Field, A., 2013. Discovering statistics using IBM SPSS statistics. sage.
Grapov, D. and Newman, J.W., 2012. imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel. Bioinformatics, 28(17), pp.2288-2290.
Jablonsky, J., 2014. MS Excel based software support tools for decision problems with multiple criteria. Procedia Economics and Finance, 12, pp.251-258.
Kolb, D.A., 2014. Experiential learning: Experience as the source of learning and development. FT press.
Kosara, R. and Mackinlay, J., 2013. Storytelling: The next step for visualization. Computer, 46(5), pp.44-50.
Murray, D.G., 2013. Tableau your data!: fast and easy visual analysis with tableau software. John Wiley & Sons.
Nesbit, P.L., 2012. The role of self-reflection, emotional management of feedback, and self-regulation processes in self-directed leadership development. Human Resource Development Review, 11(2), pp.203-226.
Norusis, M., 2008. SPSS 16.0 advanced statistical procedures companion. Prentice Hall Press.
Tangney, J.P.E. and Tracy, J.L., 2012. Self-conscious emotions.
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