Data Analytics in Consulting Processes
Topic: Improving Consulting Process With Data Analytics.
Data analytics has been evolving as a unit in a drastic rate. Data Analytics takes into consideration the qualitative and quantitative research technique. The process that is induced in the commencing of the data analytics includes better enhancement of the productivity. This aspect also helps in improving the business gain. Data that is collected is initially categorized. This helps in better identification and categorizing of the data that are present as per the behavioral data. Processing of consulting process with the help of data analytics includes identification of the major issues that are present in the commencing of the project. Marketing evaluation of the data is also performed in the consulting process. Systemized and Cohesive nature of the marketing evaluation is also checked. Strategic innovation is also introduced in the commencement of the project. Growth, branding, sales and marketing aspect of the entire business system is also taken into consideration. After completing these phases, analysis of the data that are collected are performed. This incurs the fact that the management of the entire business requires proper monitoring of the project. This is where the role of data analytics enter in the scope of bettering the consulting process. Reporting is also done with the help of the better management of the project. After having a brief ideology of the entire project, strategizing the collection of the data must be performed. Implementation of the strategized data management helps in better prosecution of the project. This report also helps in managing of the data analytics with the help of the better management of Grounded Theory. The importance and advantages of Grounded data analytics theory is stated in the project management terminology. Leading consultancies have been performing in a very high rate and this leads to the fact that the implementation of the data analytics has been very efficient in functioning of the better processing of the edge competition. Digital disruption has been acting as a reason of increase in usage of data analytics in the completion of the project. This report will provide a literature review of the currents state of data analytics. This report will also help in identification of the issues and challenges that are present in the data analytics methodology.
Current state of the art
According to Brandon-Jones, Lewis and Verma (2016), data analytics is a technology that has been evolving at a great pace; it helps in harnessing Artificial intelligence, statistics along with the advanced insights of market that is utilized in identifying various meaningful patterns in a huge amount of data sets. If analytics are developed with utter smartness, they provide various insights of premium quality in the performance metrics of an organization, these results in complex and frequently bewildering changes that happens around them. Presently global organizations spending on the consulting of analytics had risen to $43 billion by 2016. The investment that was made was equally split among various developing capabilities of in-house and external consultants. Both of these were expected to grow along with around 91% of executives getting convinced regarding the fact that data analysis has generated a lump sum of value for the utilization of their firm.
Advantages and Challenges of Data Analysis in Consulting
Again according to Chaker, Baumgartner and Den Elzen 2015), the leading consultancies realize that interpretation of Big Data in industry does not provide them an edge in the running competition. Whereas in the era of the digital disruption, it is very important for their survival. This happens because analytics consulting does not help in generating a unique value of revenue. This consists of various impacts which are cross functional in nature and provides indispensable to the strategy, HR, IT consultants and operations.
In a consecutive manner for the consultancies as well as the clients belonging to them, data science is considered as a specific commodity. Once a specific impenetrable field containing of coders who are not much experienced is created, consultancies make their way through it. People are able to leverage as well as interpret to the consultancies regarding who must be considered as a real estate for clients, this job cannot be performed by data only. Chaker, Baumgartner and Den Elzen (2016), stated that qualified scientists who have detailed knowledge regarding data are rarely available. Consultancies that are capable of combining their credentials of data science along with strategic acumen and the ability to explain the analytics in the terms of Layman are very rare as well. According to Philip Rowland who had been a partner at the global management consultancy, consultants must be advised for enhancing a specific standard strategic plan for the project that would include more muscular analytics. This would involve the learning of data analysis at the advanced level. In order to keep the consultancies relevant, the techniques as well as advices are usually more sophisticated compared to the ones that are already operating with the clients (Crowder 2017). This requires a smart overhaul regarding how the budding consultants are educated in early stages as well as in universities of the professional career of people.
According to Gonzalez, Campbell and Holekamp (2016), data analysis is used in consultancies for various purposes, some include the understanding of the necessities of the clients, orchestrating the complete life cycle of the provided project, developing various solutions that are innovative in nature and then presenting the resultant solution to the end users. Tapping the existing knowledge base of the organization along with the capabilities of identifying the best solution for fulfilling the needs of clients based on their timelines, constraints and budgets is also one of the factors. Recognition of various business opportunities for allowing the organization to secure various new projects from the existing or new clients is considered to be a major responsibility that has to be followed in order to implement data analytics for consultancies.
Qualities and Skills of Analytics Consultant
According to Manap and Voulvoulis (2016), along with various responsibilities there are numerous qualities that a professional must include in order to carry out the combination of data analytics in consultancies for achieving best results. Some responsibilities include analytics and consulting sills, domain expertise, programming, spreadsheet modelling, advanced mathematical and statistical skills, problem framing, problem solving, project management, communication skills, presentation skills, supply chain, developers and research, forecasting and many more.
Project management, collaboration and facilitation skills are some of the most important skills that are usually made use of in order to make the entire process more productive and effective as well. The analytics consultant while implementing data analytics in consultancies might consider various factors. According to the process must be unbiased in nature. In order to improve performance of the entire organization, employees and clients are provided with their respective responsibilities according to their expertise and specialization.
According to Obeidat, Al-Suradi and Masa’deh (2016), In case this is not done, the employees who are allocated with tasks regarding which they have no knowledge, the employees are to be provided with training so that they attain enough knowledge regarding the subject. This is avoided with the implementation of data analysis, the time consumed in extra training provided to the employees is saved. The problems ae communicated in details with the help of data analysis along with brainstorming the solutions relevant to it. This helps in building a good sense of coordination and multitasking skills among employees. This helps the consultants in managing more than one projects at the same time and regardless of the fact that the projects might be situated across different verticals.
As per Eijnatten, Ark and Holloway (2015), implementation of data analytics in consultancies provides an organization a client-facing role. After a specific project is completed and tested, a structured report is prepared which represents the way the entire process has performed (Ruff, Giugliano and Braunwald 2015). In the traditional method, the report has to be created manually considering the performance of the employees as well as the organization as a whole, but the implementation of data analysis have reduced the effort that is put in making the structure report. After the report is prepared, various recommendations are to be figured out in such a way that the management as well clients are able to understand it and get convinced. These planning of recommendation is also done with the help of data analysis and employees do not need to work more hard for that, this allows them to concentrate on the factors that affect the performance of the organization and improve them in order to increase the revenue of the organization.
Implementation of Data Analytics in Consultancy
According to Swerdlow, Preiss and Kuchenbaecker (2015), data analysis in improving consulting process is one of the most coveted as well as financially rewarding profile for every organization. It usually has a huge demand by numerous global organizations like HP, Accenture, Dell, BNY Mellon and many more. Various ways by which data analysis can be implementing in a business of consultancy in order to improve its processes include exceptional communication and very rich experience.
According to Rabinowitz, Werbeloff and Mandel (2016), there are numerous issues and challenges that are faced due to the implementation of data analysis in the process of consulting. These issues might create major impacts on the clients relate to the organization, the organization itself as well as the effectiveness and performance of the organization. These issues are as follows: –
- Hadoop not compatible: Hadoop is a specific tool that is widely used for the purpose of implementing data analysis in consultancies. This sometimes might not be able to handle the huge amount of data saved in it. This might require the system to utilize various other tools, this utilization of other tools increase the expectation that in future, Hadoop would add functionality for providing real time approach (Yandell 2017). The utilization of new systems might create problems for an organization, because clients might need time in coping up with the new system. This would require more time and slow productivity.
- New approach: sometimes various organizations receive insights every week, however there is a constant flow of data, due to this an entirely different approach is needed. This might prove to be a serious challenge for various organizations. This might also lead to the remodeling of various plans and decisions. The new decisions and plans change the way and process using which the entire process works, a small change in process results in problems in adjustments among employees and clients. Hence the new plans and procedures must be chosen in such a way that they are not entirely different from the previous ones and the clients do not face much problem in adapting them.
- Possible failure: some organizations utilize the data analysis as a feature which would be utilized by them for their entire future and they change their working environment according to the utilization of data analysis in the processes. The advantages provided to various other companies attract the other organizations and they tend to implement it as soon as possible. In case, the data analysis is not implemented properly, this might result in a multitude of problems (Manap and Voulvoulis 2016). In case an organization is not used to handling a huge amount of data, at such a rapid rate, it might result in analysis that is incorrect in nature. This as a result might cause huge problems for the organization causing impacts in its performance and effectiveness.
Selection of theory
The theory selected for improving the consulting process with data analytics is Grounded theory. Grounded theory is being implemented since the year 1967 and no such changes have taken place since the time of initiation. The process of grounded theory includes development of well integrated concepts which provides a better understanding of the theoretical explanation of the social and economic scenario of the society. The theory that is present in the Grounded theory includes underpinning of the data from Pragmatism and Interactionism that is symbolic in nature. Despite the interlinking if the theory with social and economic aspect of data analytics, philosophical and sociological interactions are not taken into consideration (Eisenhardt, Graebner and Sonenshein 2016). The first principle that acts as the main source of determining path of the project that uses the grounded theory imbibes that the result is subject to change as long as the data analysis project is being conducted. The second principle on which the entire project is dependent on includes the fact that determination of the output of the project is not possible (Abbasi, Sarker and Chiang 2016). The interlink age of first principle and the second principle is very high. This is the main reason that the projection of the management of the entire data analysis project gets performed in an efficient manner. With implementation of this data analytics theory, the choices that are made as per the proposition of the project requirement are better and more accurate. In case the data that were estimated gets validated changes in perceptions are made. With the change in perception that are made accuracy of the project often increases. Pragmatism and Symbolic Interaction stances are used in order to get a better and accurate output. The data collection method that is used in the commencing of the process induces the method of taking interviews. Gaining data from government sources are also accepted n this theory. Questionnaire set that are prepared includes better management of the project (Charmaz and Henwood 2017). The sole concern that is present includes the fact that the data that are used in the project completion method must be related to the project commencement of data analytics. The investigator will use the process of data management and this includes the fact that the management of the project will be performed with the basis of credibility of the data that are collected. Data collection methodologies are stated in the initial stages of data analytics. Understanding of the concepts are treated as a vital prospect of the management of the data collection. In case the data that are collected are not well understood, collection of data might lose its accuracy. Categorizing of the data also acts as on of the main steps that is needed to be performed for better understanding of the project of data analytics. Sampling of the data that are collected as per the requirements for the data analytics is also taken into consideration (Wang and Hajli 2017). With the methodology of sampling the main instance that is taken into consideration includes the fact that the projection of the data analysis is performed in a better and efficient manner. This is the reason that implementation of the Grounded theory helps in improving the consulting process with the help of data analytics
Conclusion
The reason of implementing the Grounded theory in the context of improving consulting process with data analytics are as follows: –
- Implementation of the Grounded theory provides Intuitive appeal to the entire system.
Grounded theory is considered to be beneficial as the projection of the management of the project will get performed with the help of the data analysis of the data that are collected by the specific methods as the data that are collected might vary as the data that are collected are subject to change. With the implementation of the grounded theory the immersion is also permitted (Walsh 2015). This leads to the fact that the data that are consulting aspect of the data analytics gets benefitted. With the help of the Grounded theory the intuition that is implemented will help in better discussion of the data analytics. This immersion process helps in translating practical data with the help of constant comparison with the previously collected data. Grounded theory helps in coding and provides memoing approach to data analytics system (Birks and Mills 2015). Grounded theory also provides the new researchers with the heuristic device and this helps the new employee to converse in a better manner. The heuristic tool helps the new researchers to stay connected along with the project. This aspect also helps in betterment of the consulting process regarding data analytics.
- Creativity fostering
Creativity fostering is also one of the main reason that implementation of Grounded theory will help in better management of the data analytics. With the help of the Grounded theory, investigation before processing of the data analytics is encouraged. This process will help in reducing the pre conceived data management. This aspect leads to the fact that the management of the project will get commenced with higher integrity. This is the reason that emergence of the Grounded theory have been very high (Stern and Porr 2017). With the help of the investigated data, it can be stated that the management of the data analytics will be performed in a better manner. In case proper investigation is made it can be stated that the project management will get performed with higher creativity. In essence grounded theory, the meaning of the data analytics get diversifies from performing of the activity as per the recorded data to data that are established after proper investigation of data.
- Imbibes Conceptualizing:
Concept development is essential in better development of the project. Building concept can be done in a better manner only if the data that is collected during the commencement of the project. As the Grounded theory emphasizes on proper research before commencing of the project. Due to the fact that grounded theory supports creativity continuous interplay in between the data that are collected and the data that are already present. This instills the fact that the transferability of the data that are collected are done in a better manner.
As per the critical appraisal following statements can be provided: –
- According to critics in case of implementation of Grounded theory, the researchers who are present includes new and novice researchers trying to get their research done for completion of a project. In case the researchers that are novice in nature and gets involved in coding of the project. As the experience of the researchers are less they face difficulty in completion of the project (Corbin 2016). This aspect reduces the insight of the project. Innovation of the project also decreases. This might reduce the efficiency of the project.
- Methodological lines are blurred by new and novice employees. The methodology lines are replaced with the purposeful. Theoretical sampling of data is often missed in the commencing of the project. Absence of theoretical sampling affects the management of the project as commencement of emerging theory is required in proposing the project. Hence it is required to perform purposeful sampling. In case of failing to perform theoretical sampling, conceptual depth is also lost. Another issue that a new comer might suffer from is the fact that they have only one source of data collection. Taking interviews will be the sole methodology that will be implemented. Grounded theory has been criticized for this issue of not being capable enough to provide the new researchers ample research projections. In case the research methodologies are not efficient, the data that will be present in the commencing of the project will provide inaccurate dataset as output of the project.
- Generalizability of projects helps in faster completion of the project. Despite the fact the data that are gained exclusively helps in better completion of the project. Many projects can be completed with the help of data that are previously collected and are available previously. The approaches that are considered in Grounded theory incudes better management of the data as the data that are collected are done with the help of unique approaches. Despite the fact that the data that will be collected can perform better projection of data accuracy, the time that is consumed in the management of the project is very high.
Exploratory data analysis insists the fact that the management of the data collection will be performed with the help of initial analysis that is performed. Data sets that are already present insists the fact that the research can be performed by having a peek at the already present. Whereas in case of the Grounded theory, the main disadvantage that is present is that usage of already present data sets are prohibited (Lewis 2015). Due to this aspect the data sets that are used in the commencing of the project gets performed in a better manner. This also helps in gaining distinct and precise output of the data as the data that are collected is unique in nature.
Confirmatory data analytics gets performed with the help of the data acquisition and estimation of data. Estimation of data is performed with the help of the prior model analysis of the entire project. Estimation of the testing model analysis can also be performed with better commencing of the project. Whereas in case of grounded theory no model analysis is done and the data set that is collected is done with the help of exclusive research.
The reasons of implementing the Grounded theory over the other data analytics theory are as follows: –
- Choosing of Grounded theory insists the fact that the management of the project gets performed with the help of immediate and firm decision making. Rigidity of the data analysis is very low and the flexibility of the entire data analysis projection gets performed with better learning curve. Conceptualizing the entire project before completion of the entire project helps in improving the consulting process with the help of the data analytics. This is the main reason that the implementation of this process has been very high. Despite the fact that the data analytics will affect the autonomy of the project in negative manner. QDA procedural description is also followed in the projection of management of the data analytics.
- Novice researchers are well affected by the management of the Grounded theory as they will be enjoying the scope of openness and diversity of the project (Glaser and Strauss 2017). This is the main reason that the commencement of the project will include innovation in the project completion. In case researchers see better scope for their future, the main advantage that will be present includes improvement in the consulting process with regards to data analytics.
Conclusion
From the above discussion it can be stated that the management of the data analytics can be processed with the help of the digital progression in the management of the project. In case the management. With the help of artificial intelligence the main advantage that is enjoyed in the project management is that the implementation of the data analytics will get performed in a better manner. This is one of the major reason that the high end consultancy service organizations are spending on the analytics. Data analytics is used in business organization for different aspects, namely understanding the demands ad requirement of the users. This process also helps in better estimating the life cycle of the project. Project management gets facilitates with the help of the data analytics. Advantage along with disadvantages are present in the commencement of the project completion with the help of the data analytics. New approach is introduced in a very frequent rate. This issue might be problematic in the commencement of the project. Selection of Grounded theory is important in the projection of better management of the data analytics. Pragmatism and Interactionism is introduced in the management of the project. With the help of this project management methodology the first part that is introduced is determining the path that is to be taken during the commencing of the path. Estimating the output of the project is not viable for the Grounded theory. Another aspect that must be taken into consideration includes understanding the concepts of the project as per the requirement of the project. In case the completion of the project is to be performed with the help of the Grounded theory the main advantage is that innovation can be included in the commencing of the project. This report also helps in understanding the implementing the Grounded theory in improving the consulting process with respect to data analytics.
References
Abbasi, A., Sarker, S. and Chiang, R.H., 2016. Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2).
Birks, M. and Mills, J., 2015. Grounded theory: A practical guide. Sage.
Boone, C.A., Skipper, J.B. and Hazen, B.T., 2017. A framework for investigating the role of big data in service parts management. Journal of cleaner production, 153, pp.687-691.
Brandon-Jones, A., Lewis, M., Verma, R. and Walsman, M.C., 2016. Examining the characteristics and managerial challenges of professional services: An empirical study of management consultancy in the travel, tourism, and hospitality sector. Journal of Operations Management, 42, pp.9-24.
Chaker, L., Baumgartner, C., Den Elzen, W.P., Collet, T.H., Ikram, M.A., Blum, M.R., Dehghan, A., Drechsler, C., Luben, R.N., Portegies, M.L. and Iervasi, G., 2016. Thyroid function within the reference range and the risk of stroke: an individual participant data analysis. The Journal of Clinical Endocrinology & Metabolism, 101(11), pp.4270-4282.
Chaker, L., Baumgartner, C., Den Elzen, W.P., Ikram, M.A., Blum, M.R., Collet, T.H., Bakker, S.J., Dehghan, A., Drechsler, C., Luben, R.N. and Hofman, A., 2015. Subclinical hypothyroidism and the risk of stroke events and fatal stroke: an individual participant data analysis. The Journal of Clinical Endocrinology & Metabolism, 100(6), pp.2181-2191.
Charmaz, K. and Henwood, K., 2017. Grounded theory methods for qualitative psychology. The SAGE handbook of qualitative research in psychology, pp.238-260.
Corbin, J., 2016. Taking an analytic journey. In Developing grounded theory (pp. 35-54). Routledge.
Crowder, M., 2017. Analysis of repeated measures. Routledge.
Eijnatten, F.M., van der Ark, L.A. and Holloway, S.S., 2015. Ipsative measurement and the analysis of organizational values: an alternative approach for data analysis. Quality & Quantity, 49(2), pp.559-579.
Eisenhardt, K.M., Graebner, M.E. and Sonenshein, S., 2016. Grand challenges and inductive methods: Rigor without rigor mortis.
Flick, U., 2018. An introduction to qualitative research. Sage Publications Limited
Glaser, B.G. and Strauss, A.L., 2017. Discovery of grounded theory: Strategies for qualitative research. Routledge.
Gonzalez, V.H., Campbell, J., Holekamp, N.M., Kiss, S., Loewenstein, A., Augustin, A.J., Ma, J., Ho, A.C., Patel, V., Whitcup, S.M. and Dugel, P.U., 2016. Early and Long-Term Responses to Anti–Vascular Endothelial Growth Factor Therapy in Diabetic Macular Edema: Analysis of Protocol I Data. American journal of ophthalmology, 172, pp.72-79.
Holton, J.A. and Walsh, I., 2016. Classic grounded theory: Applications with qualitative and quantitative data. Sage Publications.
Lewis, S., 2015. Qualitative inquiry and research design: Choosing among five approaches. Health promotion practice, 16(4), pp.473-475.
Manap, N. and Voulvoulis, N., 2016. Data analysis for environmental impact of dredging. Journal of cleaner production, 137, pp.394-404.
McEneny-King, A., Foster, G., Iorio, A. and Edginton, A.N., 2016. Data Analysis Protocol for the Development and Evaluation of Population Pharmacokinetic Models for Incorporation Into the Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo). JMIR research protocols, 5(4).
Obeidat, B.Y., Al-Suradi, M.M., Masa’deh, R.E. and Tarhini, A., 2016. The impact of knowledge management on innovation: An empirical study on Jordanian consultancy firms. Management Research Review, 39(10), pp.1214-1238.
Rabinowitz, J., Werbeloff, N., Mandel, F.S., Menard, F., Marangell, L. and Kapur, S., 2016. Initial depression severity and response to antidepressants v. placebo: patient-level data analysis from 34 randomised controlled trials. The British Journal of Psychiatry, 209(5), pp.427-428.
Ruff, C.T., Giugliano, R.P., Braunwald, E., Morrow, D.A., Murphy, S.A., Kuder, J.F., Deenadayalu, N., Jarolim, P., Betcher, J., Shi, M. and Brown, K., 2015. Association between edoxaban dose, concentration, anti-Factor Xa activity, and outcomes: an analysis of data from the randomised, double-blind ENGAGE AF-TIMI 48 trial. The Lancet, 385(9984), pp.2288-2295.
Stern, P.N. and Porr, C.J., 2017. Essentials of accessible grounded theory. Routledge.
Swerdlow, D.I., Preiss, D., Kuchenbaecker, K.B., Holmes, M.V., Engmann, J.E., Shah, T., Sofat, R., Stender, S., Johnson, P.C., Scott, R.A. and Leusink, M., 2015. HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials. The Lancet, 385(9965), pp.351-361.
Walsh, I., 2015. Using quantitative data in mixed-design grounded theory studies: an enhanced path to formal grounded theory in information systems. European Journal of Information Systems, 24(5), pp.531-557.
Wang, Y. and Hajli, N., 2017. Exploring the path to big data analytics success in healthcare. Journal of Business Research, 70, pp.287-299.
Yandell, B., 2017. Practical data analysis for designed experiments. Routledge.
Order an Essay Now & Get These Features For Free:
Turnitin Report
Formatting
Title Page
Citation
Outline
