In this overall report, a brief overview of business analytics and the implementation of AI processes are discussed. Further, some of the techniques being discussed are responsible for business processes and developing various strategies in the company. Implementation of business analytics and AI systems increases the overall efficiency of the company and develops new strategies to operate their operations. Further, the topic discussed the various kinds of challenges faced by companies in implementing business analytics and AI systems in their organization. Proper figures and charts are developed to understand the working principle of business analytics in an agile business environment. Furthermore, the characteristics of business intelligence and business analytics are discussed in this report.
1.Description of Purpose, Importance, and Role of Business Analytics in Creating Strategic Value and Competitive Advantage
Business analytics in the case of the organizations has been a competitive advantage, as well as, has been essential for the application of Business Analytics especially variant of business analytics which is predictive in nature. When initiatives of business analytics have been adopted in a correct manner, success has been guaranteed in the case of business (Ashrafi et al., 2019). Competitive advantage has been driven by business analytics with the help of generation of economies possessed by scale, scope, and improvement in quality. There has been an achievement of comparative efficiency of cost by the companies by taking benefit of economies possessed by scale which refers to the first method.
With the help of this, competitive advantage has been driven by the companies in opposition to the peers. The second method with the help of there has been a benefit in the case of organizations is economies possessed by scope. These are the purpose of Business Analytics in generating strategic value, as well as, competitive advantage. It is important as capability of operations of business has been improved by it. This has been done by them by bestowing worthy information to business owners regarding business performance. The capabilities that have accumulated at the time of embracement of technology of Big Data by the firms which have deliberated the ripple impact of expanded production, and alleviated business costs.
The role that has been played by the Business Analytics is that an insight has been provided by them to the companies into their behavior of customers, as well as, requirements. In addition to that, it is not impossible in the case of the companies to acknowledge opinions of public of the brand (Kristoffersen et al., 2021). This has been done to follow several marketing campaigns, as well as, strategize the process with the help of which betterment of marketing strategy can be made. This has been done for nurturing long, as well as fruitful relationships with the consumers. Immense help has been acquired by Business Analytics to acknowledge the position of them within the industry.
2. Definition of Analytics Ecosystem and Illustration of the Process of their Adoption by Various Industry
Business Analytics refers to the approach, skills or proficiencies that have been applied by which past performance can be explored. This past performance is of business consistently to forecast and form strategies of business. It includes an analytical ecosystem which is descriptive, prescriptive, predictive and exploratory analytics. The first one generally looks into past data, as well as, raw data has been summarized with the help of the measures of the tendency that is central, as well as, dispersion. The distinct measures that central tendency possesses are mean, median, mode. In addition to that, measures that dispersion have are kurtosis, standard deviation, as well as, skewness (Lnenicka, and Komarkova,2019). Features of data have been described by these measures and bestow insight regarding the pattern. Calculation of Descriptive Analytics has been calculated by excel and SPSS. Instances of this analytics are the reports by which insights have been acquired with data with regards to the production. Inventory status, as well as, distribution within distinct financial years that company possesses. Predictive Analysis is regarding prediction of the thing that will occur with the help of this analytics, prediction of outcomes has been there with specific confidence. With the increase in confidence, there has been an increase in the degree of prediction (Sun, 2021). There has been a beginning of the process for data analysis in the Exploratory Data Analytics. It refers to the method of the visualization of raw data with the help of scatter plots, stem, as well as, leaf plots, and box plots. Along with this, there has been a presence of histograms and these are utilized for making sense regarding the features of the information. Big Analytics have been adopted by the companies through the installation of software of Big Data within their systems. In addition to that, systems have been updated for supporting packages of Big Data.
3. Challenges in implementing business analytics and artificial intelligence in an agile business environment
Challenges in implementing business analytics and artificial intelligence
Deep learning, artificial intelligence, machine learning, and other innovations in the field of technology have become the most important device that drives force for the recent business studies. AI is related to technology that can make revolutionary changes in the business field. Further, implementing AI and business analytics in the company comprises different aspects that are mentioned below;
Determining the right data set
It becomes quite important to have the most eminent set of data that provides effective help to the company in developing its business (Sharma, 2021). Therefore, it has become quite important for businesses to imply the right combination of business analytics and AI intelligence in businesses to increase their productivity and manufacturing process. As in an agile business environment, the company is mainly designed to work flexibly in the atmosphere to increase the growth of the company.
Data security and storage
In an agile business environment, the availability of huge data and information are being required by the company to gain successive advantages over other companies (Grabowska and Saniuk, 2022). Therefore, sometimes it creates drawbacks that utilize the large volume of data to store in businesses. Further, this kind of data-driven automation creates business operations that result in issues in data security to maintain the job role.
One of the most eminent challenges that businesses face is related to the infrastructure requirements to maintain the data and information collected from various sources (Shams et al. 2021). Replacing the old outdated infrastructure with traditional legacy systems is recently a major challenge for most organizations due to various impacts and infrastructure.
AI integration into an existing system
As the AI implementation process is related to huge complications and high cost is required to enable it into the company, therefore, the most organization faces challenges to implement AI and business analytics in their company (Elbegzaya, 2020). Lack of expertise and lack of proper integration is another challenge faced by the company.
Complex algorithms and training of AI models
Business analytics and AI systems both are quite high-level technologies that require some expertise in a proper understanding of the models and algorithms (Rainbird, 2019). Therefore, the most organization faces the complexity and huge data-driven degree to evaluate and implement AI and business analytics in their company.
4. Role of Big Tech in leveraging business analytics and Artificial Intelligence to generate organizational value for internal and external stakeholders
Organizations like “Facebook, Apple, Microsoft, Google and Amazon” use business analytics and AI implementation in their organization to understand consumer requirements and study the market changes continuously. Further, the implementation of business analytics and AI implementation allow companies to collect adequate data-driven decisions that can improve business-related outcomes.
Facebook uses business analytics and AI implementation in their businesses to deliver satisfaction to consumers. According to Desouza (2020), they implement the AI system in their company to identify the market trends and access their business globally and provide value to their internal stakeholders.
Apple uses big data analytics in their company to identify the harness required to protect their data and use it to identify new opportunities related to the market and provide help to take decisions by their external stakeholders (Battisti et al. 2022). The AI implementation process highlights technological advancement and provides help to the company in identifying the present trend of the consumers.
Microsoft makes use of AI in developing its technologies to provide better services and increase its brand value. The business analytics process provides effective help to the company by decreasing the overall risk and cost evaluation of the company and thus increases the value of their internal stakeholders. As mentioned by Gupta et al. (2020), implementing AI and business analytics in a company comprise better operations, higher profits, and satisfactory consumers.
Google and Amazon
Companies like Amazon and Google make use of AI and business analytics in gathering data and information, analyzing, and gaining conclusions over their selected decisions by stakeholders (Ranjan and Foropon, 2021). Further, implementing automation processes provides effective help to these companies in increasing their efficiency, and revenue.
(Source: Developed by researcher)
Figure 2: Box Plot
b)The key descriptive properties of “mean, median, mode, range, quartile, IQR, skewness, standard deviation, and variances” are described below;
The mean value that is derived from the given calculation is 1172.295349. The mean value can be obtained through adding up the collection of numbers that is further divided by the total number of counts in the collecting of numbers.
The median value that is being obtained from the given sources is measured as 977. The median can be calculated from separating the overall higher value from the lower one. This highlights the basic average of the numbers that are being generated in this calculation.
The mode is the value that is obtained from the given set of calculations and appears most often in a set of data values. In this overall report, the outcome mode is recorded with 861. Mode is being calculated in this report to identify the common data that is being used repeatedly (Buijsman et al. 2020). Mode is one of the important factors in calculating data, as it measures the central tendencies of acquired sets of data.
Range refers to the set of data that highlights the difference between the smallest and largest values that are obtained in the calculation. Here, in this report the ranges is calculationa dn set the value as 5977. Range is obtained through subtracting the overall sample’s maximum value from minimum value.
Quartile is a statistical term that mainly provides help in describing the overall division of observation into four defined intervals. This is quite important for collecting the data and information based on the division of year into four parts and identifying the values of it. This provides help in comparing the entire set of observations through several intervals or breakdown in parts. In this section, the quartile obtained is.
The interquartile range helps the study to identify the descriptive statistics and talks about the spread of the middle half for the selected calculation. Further, the main difference between the first and third quartiles are identified in the calculation that offers variability and outliers the data sets. The obtained interquartile range in this data set is.
Standard deviation is mainly used in statistics to identify or measure the amount of variation or dispersion from the selected calculation. The low standard deviation indicates that the overall value generated tends towards the mean value of the data. Whereas, on the other hand, when the set of data shows high standard deviation then it can be said that the values are widespread among the very ranges. Here, in this report the standard deviation is recorded with 678.3105499.
Variance is related to the expectation of the squared deviation that is being randomly selected from variables. Here, the variance is recorded as 460105.2022.
Skewness is the statistical measure of asymmetry that highlights the probability of the distribution of real-valued that is chosen randomly. In this report, the skewness is recorded with 2.350859313.
c) The price distribution of Eastern Metropolitan is recorded with 379350, whereas on the other hand, the price distribution of the Western Metropolitan is recorded with 470092. Outlier analysis is mainly used in wide varieties that provides effective help to the organization or firms in identifying the financial upliftment. Mainly, outliners are related to the ANOVA and it is too sensitive to have an impact over ANOVA. For example, if the size of the sample is larger then there is a higher chance of occurring type-1 error.
d) From the given values it can be said that the Highest house price is recorded with 6311 and the suburb is associated with Malvern.
While on the other hand, the Lowest price house is recorded with 334 and the related house is Melton South.
a)Figure 1: Simple Linear Regression
b)The developed linear regression model has been refined and improved. In this case, there has been the development of this linear regression model and hence, there has been a change in the values of the dependent, as well as, independent variables. In the section of Anova, there has been a change in the value of SS which is 1216.431 and the same value has been obtained in the case of MS. This change has been observed in the case of the Regression portion. In addition to that, there has been a change in the values of SS and MS in the Residual section.
There has been an increase in SS and MS which is 5333.284 and 53.87155. Hence, with the change of these values, there has been change in the total of the Anova. In the case of Regression Statistics, there has been a change in the value of Standard Error. Besides this, there has been a change in the values of Intercept too. There has been an increase in p-value under the section of Intercept. It has been observed that p-value is less than 0.05. Hence, success has been gained in understanding the factor that null hypothesis can be rejected.
In addition to that, there has been an acceptance of alternative hypotheses in this context. There has been an enhancement of the model as the previous data has not been considered as sufficient in the demonstration of the application of the techniques of descriptive analytics. It will be proved as difficult in analyzing the factors that are associated with the analytics of descriptive data in the case of Property Experts. The acquisition of a huge dataset by the firm will be of no use if the developed linear regression model has not been refined.
Moreover, there has been the requirement of the increase in the p-value with the help of which there will be the acceptance of alternative hypotheses. There has been a requirement of the enhancement of the linear regression model as the firm has desire to expand its business. If entrance can be made by Property Experts, into Melbourne property market, it can be able to increase the productivity of its firm. In addition to that, there will be an enhancement in the organizational performance of the firm with the enhanced linear regression model. With the help of this model, there has been an acquisition of proper p-value and the firm can be able to enter into the property market in Melbourne easily.
This is going to be a report in which there will be a detailed analysis of the identification of vital descriptive statistics of property price. In addition to that, there will be an analysis of the linear regression. Along with these, there will be an involvement of a few external research. This will be done so that the explanation and analysis of both the statistics and regression model.
Explanation of Descriptive Statistics of Property Price
The performance of analysis of initial distribution on Price has been done with the help of histogram. There has been a calculation of mean, median, as well as, mode in this case. These have been considered as major parts for calculation. In addition to that, there has been a presence of several other factors with the help of which there has been a determination of exact descriptive statistics. There has been a calculation of mean, median, mode, quartile, IQR, range, Standard Deviation, Variances, as well as, Skewness. With the help of the calculations of the above factors, there has been a clear understanding of the descriptive statistics in the case of Property Price.
There has been an analysis of the price distribution in the middle of Eastern Metropolitan and that of Western Metropolitan. With the analysis of the price distribution in the middle of both, it has been understood that the distribution of price is more in the case of the Western Metropolitan as compared to that of the Eastern Metropolitan. There has been the utilization of outlier analysis mainly within broad varieties. The outlier analysis has been understood with the help of box plot. Immense help has been provided with the help of those broad varieties to the firms for the identification of the economic upliftment. The outlier analysis has been an association with ANOVA.
There has been an analysis of suburbs with the help of the histogram and the given data. Besides this, there has been an analysis of the highest and lowest house prices in the case of the suburbs. With the help of the above description and analysis, Property Experts can be able to understand their profit. In this case, it can be said that they can be able to determine the place where they will acquire immense profit by establishing their business. There has been an identification of the place where extra profit can be generated. With the help of this, fruitful business can be done by this real estate advocacy of the buyer.
The analysis of the above factors will help the business to expand and there can be an easy entrance of it in the property market of Melbourne. As a business analyst, it is very essential to analyze the factors that are responsible in the case of the management of business. In the case of management of business, it is necessary to detect the records of the previous years. With the help of the analysis of the records of previous years, there has been an identification of the issues and scarcities for which the business has not progressed in the previous years.
Explanation of the Requirement of Analysis of Linear Correlation
In the case of analysis of the linear correlation, it has been understood that it has been required in the case of the analysis of the real estate of Property Experts
This has been done as there has been the utilization of correlation coefficients for the measurement of the relationship in the middle of the two variables. In the case of the analysis, there has been the determination of the strength of the relationship. In this case, Price is the dependent one and the Distance is the independent one. With the help of the analysis of the regression, it has been understood that there has been a requirement of this. The requirement of this analysis has been done with the help of excel in this case. The root cause of the requirement of this analysis is to understand how effective the business of Property Experts is. In this case, it has been understood that the p-value is not greater than 0.05. This means that there will be an expectation of progress in the case of business. This indicates its performance of the business and there has been an understanding of the matter of the progress in the coming years. However, there has been an understanding of the refinement of linear regression in this case. This has been understood that the above linear regression is not effective in the case of the acknowledgement of the performance of business of the real estate. Hence, there has been a requirement of new and latest linear regression analysis. With the help of this analysis, it has been understood that there has been an increase in the profit in the coming years in the business. In addition to that, there has been an analysis of the business of real estate and the process with the help of which it can make progress in the business. There has been the analysis of the factors that are responsible for the matter of the profit in the case of this real estate firm. The firm will be able to calculate the profit of the coming years. Moreover, there has been the analysis of the factors that are not effective enough for which the previous linear regression has been refined.
The above report has made a conclusion that there has been a requirement of the analysis of the descriptive statistics. In addition to that, the understanding of the calculations that has been done with the help of the given data and the histogram, as well as, box plot. Moreover, there is the requirement of linear regression in the case of the establishment of the business. There has been the analysis of the different statistical methods with the help of which the descriptive statistical analysis has been understood. Moreover, there has been the analysis of the relationship that has been understood best with the refined regression analysis. With the help of the above analysis of linear regression, it has been understood that there has been a strong relationship in the middle of both variables.
Conclusively, it can be said that this report consists of data analytics and various statistical methods that provide effective help to companies or organizations in developing their business and increasing their brand value. Further, it can be stated from this report that the agile business environment provides an overall impact on the business and develops a clean and healthy environment that encourages individuals to develop their skills. There are different forms of graphs and images that are used in developing this report to highlight the impact of business analytics on companies. Further, various kinds of challenges are being faced by companies in implementing AI systems and business analytics in their organization. In most cases, it was seen that due to the storage issue of huge amounts of data and information, a proper infrastructure is needed. There is a lack of infrastructure and an increased level of complexity in understanding business analytics and AI systems creates most of the challenges for the companies.
Ashrafi, A., Ravasan, A.Z., Trkman, P. and Afshari, S., 2019. The role of business analytics capabilities in bolstering firms’ agility and performance. International Journal of Information Management, 47, pp.1-15.
Battisti, S., Agarwal, N. and Brem, A., 2022. Creating new tech entrepreneurs with digital platforms: Meta-organizations for shared value in data-driven retail ecosystems. Technological Forecasting and Social Change, 175, p.121392.
Buijsman, W., Cheianov, V. and Gritsev, V., 2020. Sensitivity of the spectral form factor to short-range level statistics. Physical Review E, 102(4), p.042216.
Desouza, K.C., Dawson, G.S. and Chenok, D., 2020. Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector. Business Horizons, 63(2), pp.205-213.
Elbegzaya, T., 2020. Application AI in Traditional Supply Chain Management Decision-Making.
Grabowska, S. and Saniuk, S., 2022. Business Models in the Industry 4.0 Environment—Results of Web of Science Bibliometric Analysis. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), p.19.
Gupta, A. and Singhal, D., 2019. Global median filtering forensic method based on Pearson parameter statistics. IET Image Processing, 13(12), pp.2045-2057.
Gupta, S., Drave, V.A., Dwivedi, Y.K., Baabdullah, A.M. and Ismagilova, E., 2020. Achieving superior organizational performance via big data predictive analytics: A dynamic capability view. Industrial Marketing Management, 90, pp.581-592.
Kristoffersen, E., Mikalef, P., Blomsma, F. and Li, J., 2021. The effects of business analytics capability on circular economy implementation, resource orchestration capability, and firm performance. International Journal of Production Economics, 239, p.108205.
Lnenicka, M. and Komarkova, J., 2019. Big and open linked data analytics ecosystem: Theoretical background and essential elements. Government Information Quarterly, 36(1), pp.129-144.
Rainbird, S., 2019. Aligning business strategy with business analytics to create value for the firm.
Ranjan, J. and Foropon, C., 2021. Big data analytics in building the competitive intelligence of organizations. International Journal of Information Management, 56, p.102231.
Shams, A., Sharif, H. and Helfert, M., 2021. A novel model for cloud computing analytics and measurement.
Sharma, A., 2021. The Study on Impact of Artificial Intelligence on Innovation.
Sun, Z., 2021. An introduction to intelligent analytics ecosystems. PNG UoT BAIS, 6(3), pp.1-11.
Yang, Y. and Barnes, C., 2018, May. Approximate program smoothing using mean‐variance statistics, with application to procedural shader bandlimiting. In Computer Graphics Forum (Vol. 37, No. 2, pp. 443-454).