Joe’s Foods Company is a medium-sized organic food firm in New York, United States. The company produces, distributes and directly sells organic foods in four categories: meats and poultry, dairy products and eggs, legumes, and fruits and vegetables which include mushrooms. Joe’s Foods has been in operation for a decade and has grown to become a force to reckon in the organic foods industry. With annual revenue of over 34.5 billion US dollars, 109 employees and more than 16 outlets across four states, the company currently has modern technology in place, but the databases are not synchronized. Based on the size of this firm, the implementation of a robust business analytics plan is long overdue.

As Duan and Xiong (2015) explain, business analytics improves the decision-making process by exploring an organization’s data iteratively and methodically. Using business analytics, the firm can gain insights that information significant business decisions and automate the business process. The food company can apply business analytics like business intelligence and statistical analysis. In business intelligence, historical data of the firm is extracted and examined to understand the performance of employees, a business department over a specified period (Duan & Xiong, 2015). Business intelligence platforms can improve how the firm performs in the future using historical data. The firm could also use statistical analysis which involves descriptive analytics, predictive analytics, and prescriptive analytics.

Benefits and Disadvantages of Business Analytics

Using business analytics, as explored by Duan and Xiong (2015) benefits an organization concerning decision making and improving future performance. Firstly, through analytics, Joe’s Food company values can be quantified. With business analytics, the firm can measure how its benefits can be translated to quantities or figures. In using numbers which can be quantified, mission statements and significant value of the firm can also be translated also into numbers, instead of being open to interpretation. For instance, the firm can quantify how much of intangible returns it intends to achieve by specifying some expectations for staff.

Secondly, business analytics helps in making evidence-based, insightful and smart decisions which are reflected in performance (Seddon, Constantinidis, Tamm & Dod, 2017). Since more accurate information will be at the fingertips of Joe’s Foods’ management, teams will be easily empowered through smart and quick decisions. Fast development and movement are crucial for business especially in staying ahead of the competition. Using data, the process of making decisions is hastened and improved. Thirdly, through data visualization, analytics provide faster and greater insight (Cao, Duan & Li, 2015). Data visualization is like a picture and pictures speak volumes. Data visualization on dashboards, for instance, helps in establishing trends and making companies more efficient regarding agility.

Common disadvantages that may come along with analytics include the stress of piling of historical data, the cost of implementing the systems and time-consuming implementation. Historical evidence can pile over a long period limiting the ability to effectively use business analytics to understand some business trends (Seddon, Constantinidis, Tamm & Dod, 2017). For medium-sized companies like Joe’s Foods, implementing a seamless business analytics plan can be overwhelming regarding cost. The other issue is time-consuming implementation. Since the business environment is fast-paced, most firms do not want to wait for more than a year implementing a BA system. To proactively address these disadvantages, the firm can implement these changes in phases and small bits.

Challenges in using Business Analytics

Joe’s Foods is likely to face three significant problems in implementing business analytics: data quality and volume, model complexity and inadequate staffing. Data of poor quality often yields poor outcomes. The company is likely to face the challenge of spending countless hours looking for complete data. In so doing, time and resources are wasted. Data volumes and amounts can be overwhelming especially with significant data trends (Cao, Duan & Li, 2015). The challenge of quality data and significant data amounts can be solved by implementing high-quality data analytic tools and allowing self-monitoring of these solutions.

Secondly, BA tools can be too complicated for staff to understand especially when statistical tools like SPSS, STATA are used. A lot of IT time cannot be spent on managing projects since other basic operations would be stalled (Cao, Duan & Li, 2015). To solve model complexity, the company can use the top-down modeling approach to maximize outcomes while the time taken to reach them is minimized.

Lastly, medium-sized firms like Joe’s Foods have inadequately trained IT experts who can manage working on business analytics platforms (Cao, Duan & Li, 2015). There is a lack of in-house expertise in implementing business analytics. Additional staff also comes with extra costs in salaries, training, and other benefits. To overcome the staffing challenge, Joe’s Foods can outsource IT team who are experts in business analytics solutions at lower costs at the start (Cao, Duan & Li, 2015). In the meantime, the experts can provide training to the in-house staff so that the currently existing pool of employees understands how these solutions can be navigated.

Business Analytics Techniques

Three techniques include: descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive analytics is used to explain what happened. For instance, Joe’s Foods may want to learn the average weekly sales volume for the food products. To understand historical happenings, the business analyst uses raw data from several sources to understand what happened in the past (Sharda, Delen & Turban, 2016). Descriptive analytics is beneficial in two ways: provides the perspective of what happened in the past and signals whether something is wrong or right in the performance of the firm. However, it has disadvantages; it does not explain why something is right or wrong with the firm’s performance and fails to give details on ways of moving forward.

The second technique is predictive analytics. Here, the firm uses data to understand what is likely to happen. The findings of diagnostic and descriptive analytics are used in detecting tendencies, exceptions, and clusters. The technique is also used in predicting what is likely to happen in the future and hence an essential method for forecasting business performance. Predictive analytics have benefits like detecting hidden insights in consumer data and painting a picture of how the company will be in the future (Sharda, Delen & Turban, 2016). Disadvantages, however, include the challenge of handling large volumes of data which may not be available and quality of data which largely depends on the stability of conditions and how data is treated. As such, the organization must be continuously and carefully handled.

The third technique commonly used is prescriptive analytics. This technique takes advantage of promising trends and the actions that should be made to eliminate future problems (Appelbaum, et al., 2017). An example is exposing opportunities for repeat food purchases based on analytics of customers and sales information. In prescriptive analytics, both external information and historical data are required because the statistical algorithm requires both.

Prescriptive analytics comes with two main benefits: apart from painting a picture of how the firm is performing, it tells the executives the course of action that should be taken and can identify challenges that may be faced in the future (Sharda, Delen & Turban, 2016). However, the disadvantages of this technique are that it requires sophisticated technologies and tools which may be challenging to implement and manage effectively. Also, this technique involves a lot of historical data and external information about industry performance which may not be accurate going forward.

Implementation Plan

The plan is implemented in five phases: creating and designing a business analytics roadmap; assembling a team of IT personnel to form a business analytics team; having a stable data source; developing a warehouse; and putting up a system of performance monitoring and evaluation. The business analytics roadmap is created by understanding and organizing report and analytics needs, industry KPIs, custom KPIs, historical data and business analytics vendors (Appelbaum, et al., 2017). Secondly, the assembled analytics team must have the head of analytics, business analytics developer, data analysts, and strategists.

The third phase is organizing data sources. Data sources will include core data which comes from websites and online shopping, external data which is generated from purchased food products and external data which is gathered from sentiment analysis. The fourth phase is putting up a data warehouse. In designing a data warehouse, schema design, concurrency, scaling, DB size, and cloud or on-premise are factors that have to be determined (Appelbaum, et al., 2017). The last stage is monitoring and evaluating the implemented plan. Monitoring performance is done through ongoing training of Joe’s Foods staff to improve knowledge of these platforms.

Backup Proposal

To back up the plan, the following are essential: assembling required technology for implementation; creating business intelligence and analytics platforms which are coherent with a business model; and translating business processes into analytics in a collaborative work environment. Required technology includes analytics tools, business intelligence software, and computer hardware.

Creating the needed analytics platforms involve the process of outsourcing experts in the field and procuring the services of business analytics vendors based on the chosen tools (Appelbaum, et al., 2017). The third phase is to convert the standard business processes like customer service, the volume of sales, number of customer requests and purchases made online into quantifiable data which can be analyzed using analytics tools and synthesized for decision making.

Conclusion

Being a food producing, distributing and promoting company, Joe’s Foods can benefit from business analytics in decision making and forecasting its future performance using historical data and other relevant information obtained from external sources such as industry performance reports. The most significant tools and techniques the firm can use to gain insights on crucial business decisions and automate business process include business intelligence, predictive and prescriptive analytics. The most critical aspects of the plan are getting the right business analytics team and tools that can provide the required insights.

References

Appelbaum, et al. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29-44.

Cao, G., Duan, Y., & Li, G. (2015). Linking business analytics to decision making effectiveness: a path model analysis. IEEE Transactions on Engineering Management, 62(3), 384-395.

Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal of Management Analytics, 2(1), 1-21.

Seddon, P. B., Constantinidis, D., Tamm, T., & Dod, H. (2017). How does business analytics contribute to business value?. Information Systems Journal, 27(3), 237-269.

Sharda, R., Delen, D., & Turban, E. (2016). Business intelligence, analytics, and data science: a managerial perspective. Pearson.