Business analytics refers to the methodical and iterative exploration of data in an organization. It emphasizes the use of statistical methods of analysis. The information obtained is used for decision making, usually referred to as data-driven decision making. Not only do these insights inform decisions, they can also be used to optimize as well as automate business processes. Data oriented companies consider their data a corporate asset and utilize it for competitive advantage.

For Business Analytics to be successful, the organization needs to employ skilled analysts with experience in the working of the technologies and business. The organization should also be committed to data-oriented decision making. Quality of data must also be very high. Business analysis techniques fall under two main categories:

· Business intelligence

· Statistical analysis

Business intelligence involves study and examination of historical data in order to get an idea on how the business has previously performed in a given department. This practice is rather straightforward and has been perfected by many organizations. Statistical analysis, on the other hand, involves performance of predictive analysis which involves use of statistical algorithms to analyze historical data and hence come up with a prediction on the possible performance of a service or product in the future. It could also involve application of other existing analytical techniques such as cluster analysis, to group consumers across certain data points based on similarity.

Business analytics for a hypothetical company. (Telecommunications)

The business in place is a telecommunications company that offers data, messaging as well as calling services. The telecommunication industry faces significant challenges such as ever increasing customer demands, competition and technological advancements which demand for constant upgrades in order to keep up with the changing times as well as competitors. In the current world, the telecommunication industry is less concerned about technology and more concerned about customer satisfaction. Massive competition in the industry creates a critical need for intelligent decision making.

In the Telecommunications industry, Descriptive Analytics is the most suitable method of analysis. It involves observation of Key Performance Indicators to determine the current state of the business and to determine how it can be made better. Key performance indicators in the field of telecommunication include:

· Subscribers: Total subscribers, Subscriber segmentation and subscriber per employee

· Usage: Minutes of usage, % airtime capacity utilization, Average call duration, Roaming minutes etc

· Revenue: Average Revenue per call, cell site, employee, Roaming revenue, wireless voice, data, internet

· Coverage and Spread: towns covered, area covered, globalization, population covered

· Market share: Revenue market share, Subs share, Minutes share.

Other Key Performance Indicators include incremental performance, operational efficiency, Marketing, Quality, Sustainability, Financial valuation, Spectrum efficiency, and Telecommunication towers utilization.

Benefits of business analytics

Business analytics help in quantifying the values of a company. All businesses and companies have a vision and mission. Every action performed such as hiring and training of employees and any others are directed towards the achievement of these goals. Business Analysis enables a company to attach a numerical value to these values and as a result it is possible to determine the processes that are actually valuable and those that are not (Harriot & Isson, 2012). A business is able to quantify both intangible returns like giving back to society as well the tangible returns like profits.

Analytics instill a culture of intelligent decision making. With abundant information accessible to a company, it is possible to empower employees into quick decision making. This is important for development of a business that seeks to be step ahead of competition. Since the data is obtained through scientific methods, it ensures relatively accurate decision making. the point is to make intelligent decisions in the shortest time possible.

Business analysis methods provide data visualization hence giving faster and greater insight to the issues being addressed. It therefore becomes easier to identify trends which quicken the process of decision making and as a result timely seizing of opportunities (Stubbs, 2016). Data visualization is usually achieved by use of visual aids such as graphs and charts.

Disadvantages of Business Analytics

Business Analytics methods may result in piling up of data. Business Intelligence systems depend on historical data to predict the future. This data may be dated back to several years amounting to a massive size. While this information is important in data analysis, most of it may not be useful for the day to day operation of the business. Since the market is also constantly changing, the stocked-up data could easily be rendered useless within a short period.

Business Analytics is also known for its complexity in data implementation. It can also be extremely intricate making the business techniques too rigid to deal with. In the long run, these intricacies can become the downfall of the business. Execution of systems can also be time consuming, which may cost the business valuable time.

Originally, Business intelligence was established specifically for affluent organizations which had the purchasing power. Even currently, Business intelligence still remains unaffordable to small and medium sized companies. However, traders have started creating versions that are suitable for smaller companies. The issue of complexity is still a barrier.

Limitations the organization may experience;

As a result of adopting Business analytics, the organization may be affected by the time-consuming nature of the process. To address this, the old systems of operation would be kept in place until full implementation is complete. The process of implementation can also be started as early as possible so that implementation may occur sooner.

The organization may also be affected by the fact that most Businesses Analytics models are designed for large companies. Recently though, it is possible to get a model that suits the size of the company as well as the needs. This is because traders have learnt to modify services to suit a wide variety of organizations. The issue of cost is also addressed by the variety of models at varying prices.

Types of business analytics

Prescriptive Analytics

This method examines past performance and comes up with recommendations on how to address similar occurrences in future. Prescriptive Analysis provides timely and detailed information on customer habits and trends hence accelerating sales. It also enables management of gross margin by providing insights on optimal product combinations, enhancing profitability and productivity. The drawback is that in order to utilize this method, large volumes of data are required, which are sometimes difficult to come by (Barga, Fontama & Tok, 2015). Time is also a factor that reduces the effectiveness of this method. Rapid changes in the market might not favor this method.

Descriptive Analytics

This method monitors the Key Performance Indicators (KPI), in an attempt to understand the current state of the business. While the values of certain variables are under the control of the analyst, some other variables are out of control since they are dependent on factors that fluctuate uncertainly (Hsu, 2014). This is a major drawback of this method. KPIs are also subject to interpretation, which can create discrepancies hence influencing the decisions.

Predictive Analysis

This method of analysis keeps track of trends occurring in the data and assesses the likelihood of an event occurring in future. It exists in multiple forms such as: transaction profiling, predictive modeling, predictive search and decision analysis (Cokins & Maisel, 2013). This gives the method the advantage of being applicable to a wide range of business models. It also offers techniques for decision making and manufacturing optimization. The drawback to this method is that this method also requires large amounts of data which are not easily found. Effect of time on consumer behavior is also a shortcoming.

IMPLEMENTATION PLAN

In order to come up with a Business Analytic plan, several factors have to be considered. It is important to understand well the business problem being addressed. This will inform your decision in choosing the appropriate method to solve the problem. The type of project is also a factor worth considering. The project being undertaken could be a critical or non-critical mission and this affects the process of implementation. Lastly, one should asses their level of experience with a technology to determine whether external help is required or not. Below is the implementation plan for this project:

Approach for BA work: the descriptive method of business analysis is used. Key performance indicators include: Usage, Revenue, Coverage and Spread, Market share, incremental performance, operational efficiency, Marketing, Quality, Sustainability, Financial valuation, Spectrum efficiency, and Telecommunication towers utilization.

Timing of BA: Monitoring of KPIs will occur iteratively throughout the project.

Formality of details: protocol will be observed always.

Prioritization approach: Key shareholders will actively be involved in determining prioritization of requirements

Tools required: data on customers, revenue, market, expenditure and coverage.

Project complexity: the process will require recruiting personnel skilled in this area and will also require integration of equipment to enable adoption of this system.

Approach to change in scope or management: changes in the scope or management of the project will be handled as stated in the organization’s constitution.

Approach to sign off: all the three signatories must append their signatures on any sign offs according to the constitution.

Approach to Communication: communication will occur observing hierarchy from top management to bottom, and vice versa.

Back up implementation plan

Changes in the backup plan include:

Approach to BA work: Prescriptive method of analysis will be employed which examines past performance and comes up with recommendations on how to address similar occurrences in future.

Timing of BA work: the assessment is going to be done after certain intervals of time distributed through the span of the project.

Project complexity: project will be relatively less complex due to the reduction of the iterative process used for analysis.

CONCLUSION

Business Analytics is a technology that all companies should consider adopting to keep abreast with changing times and stay ahead of their competitors. Even though it has shortcomings like any other technology, the pros far outweigh the cons. Since these methods exists in several forms, it is important for an organization to determine the model that suits it best and employ an experienced professional to assist with implementation. Business Analytics has provided a way to optimize and automate business processes, which is a great step in the field of business.

References

Barga, R. , Fontama, V. & Tok, W. (2015). Predictive Analytics with Microsoft Azure Machine Learning. Shanghai: China.

Cokins, G. & Maisel, L. (2013). Predictive Business Analytics: Forward –Looking Capabilities to Improve Business Performance. New York: USA

Harriot, J. & Isson, J. (2012). Win with Advanced Business Analytics: Creating Business Value From Your Data. Amsterdam: Netherlands.

Hsu, W. (2014). Emerging Methods in Predictive Analytics: Risk Management and Decision Making. Kansas: USA

Stubbs, E. (2013). Delivering Business Analytics: Practical Guidelines for Best Practice. Amsterdam: Netherlands