Business Intelligence and Analytics and Performance Management

Question: Discuss about the Business Intelligence and Analytics and Performance Management. Answer: Introduction There is a significant relationship between the performance management and business intelligence. This relationship will be established through different aspect of performance management role in a business functions. The next part of the essay will analyse the different aspect of performance analytics. This next hypothesis will take into account, different relevant factor of performance measure in an organisation that leads to necessity of measuring them. The key performance indicators are to create the portfolio of the businesss strategical improvement (Bronzo et al. 2013). The objectives, mission and values are the focus point of creating business intelligence, which leads to business analytics. For the betterment and increasing, the effectiveness of the decision-making business intelligence and analytics is needed. Both of this aspect is complement of each other. Integrating business analytics with performance calculations have become necessary to build the necessary outcome of th e process. It also enables top-level management to verify the data that supports the decision (Hazen et al. 2014). Discussion: The relationship between business intelligent and business performance management Performance management may lead to consistency in efficient and effective management. The process is to align the enterprise strategically align the objectives and priorities. A better decision making can be backed by the knowledge of their business. Performance can be managed by different metrics known as key performance indicators. Thus, the improvements can be measurable adding the values in the indicators. The cycle is to measure, the performance is planning, progress review and evaluation (George et al. 2014). Business intelligence is deriving the concepts with the business decision making and fact based support system. Converting the raw data got from different sources to information that can be used in future are the basic steps of business intelligence. Thus, the information is used in intelligence and used in the reporting department. Whereas business analytics is evidently based in building mathematical models through statistical analysis and predicting the future based on the historic data. Business intelligence helps in reporting the data based on the predictive model based on interrelationships of the variables identified (Hazen et al. 2014). In this case, the identified variables are the different metrics of performance indicators. The evidence based decision-making and reporting on the outcome of the performance is known as the performance analysis. Business analysis is used in every kind of business. The function where it is necessary are customer acquisition and customer loyalty, cross and up selling, insurance rate setting and others, risk in fraudulent. Performance management and business intelligence complements each other. The correlations are shown on the strategic dashboards with periodic intervals. The predictive models of performance management, after feeding data gives output on the scales and measure the outliers in their performance. The periodical data can also be compared with respect to improvement in the performances regard. Performance management is a departmental function of human resource management. The key are of performance indicators are performance appraisals, compensation and their implementati ons and evaluation. Relevant performance information is necessary for better decision making (Waller and Fawcett 2013). The integrated software solutions for the support to decision making are CPM (corporate Performance Indicators) or EPM (Enterprise Performance Management). Oracle Big data are being used by the Pepsico, IBM, PG, Ford, Dell. The most important part where it is being used are in enhancing the customer relationship management (George et al. 2014). The Business Performance management area where BI can be included are balance score cards, quality management, and comparison between previous and current data. The measurable aspects of a balance scorecard are measure quality of execution on periodic basis and dashboards includes visual display of BI implementation through graphs and lights (Waller and Fawcett 2013). This may be system generated and can reflect the result on real time information at a glance. The top down approach in management balance scorecard combines the vision of ultimate goal by monitoring key metrics of the financial information and growth, customer interaction and business process and operational effectiveness The variables of that are calculated to gauge the customer satisfaction are Customers experience Loyalty Customer satisfaction Trend in visits and buying rate On a financial perspective, the balance scorecard contains the following balancing units for measure Revenue growth - Shareholder value which are divide in two that are building franchise and customers value Productivity growth strategy that can be build to improve cost and asset structure The benefits of BSC are, it drives to enhance strategy using all the components measured in BSC dashboards. The building blocks of the framework are given emphasis in terms getting the result. This also takes into consideration about different level of scores and integrates them for quick and visual prediction on data (Walle and Fawcett 2013). Managing the quality of the operations and continuously improving the performance is done through six-sigma scorecard. Main criteria of measuring the performance are to driving for excellence through effectiveness of operations. Thus, the performances are managed on financial perspectives. Increasing the market share and sales volume is also part of business intelligence process. Business analytics comes after business intelligence, as data needs to be gathered on the above-mentioned aspects are and then information will be processed. The performance indicators metrics are decided based on the functions that they belong to (Rausch et al. 2013). Relevance of business analytics in business performance management as well as performance analytics Managers face problems in categorising the competitive advantage. Affectively using the business analytics can help in decision-making. The visualisation of the performance metrics generalises the outcome and facilitate the decision-making capability of the managers. Considering the internal and external output of the environment can lead to betterment of the outcome. A strategic business decision making helps in drawing conclusion, identify, and implement the alignment of the strategic goals. The direction of the goals is directed towards the desired performance standards. Business analytics identifies the efficient way of decision-making variables. The forecasting of sales and demand as well as analysing the trends of the marketing, consumer behaviour gives valid insights in the problem-solving path (Kasemsap 2015). Forecasting and predicting based on the variable on performance level are implemented in the organisation. However, the down turn of the predicting based on the histori cal data have some drawbacks but at the same time, it gives approximately close value of the variables. Transforming the data by using the business intelligence tools like data warehousing tools, reporting and dashboards and analytics tools are is initial stage and output are measured by the business performance management models and dimension. Aligning the business objectives of the company with the business process and creating BI portfolio map are used in approaching the objective (George et al. 2014). The prospect of using analytics in planning, forecasting, budgeting to assess the soundness of the performance is very important. To check trends, identify relationships analytics is needed. The prescriptive data regarding any performance indicators like key performance drivers are identified in the first essay are balance scorecard, total quality management and customer relationship management. These ones use the business analysis of to achieve the ultimate goals. The performance analytics is used in finding the meaning in the data that can help in forming decisions. Creating a novel insight from statistical analysis of the dataset helps in embracing the possibilities that could not have been identified in a manual analysis. Executive level of business analysis of optimises the business process which serves as a asset to companies which are data driven (Kasemsap 2015). Performance analytics uses the indicators, targets and threshold decided by the administrators. The power user here uses the system configuration data on collection of jobs as well as in data modification layer. Performance analytics target administrator is in position of formulating dashboards and scorecards. The analytics viewer is the one that views the dashboards based on assigned rights. The access control breaks-down the elements of security from the source. The relevancy of the Performance Analytics lies in the six-divided part of the performance overview. The dashboards, scorecards, widgets, indicators, data collector, automation, system. The dashboard of performance contains the property and to view points. The scorecards are to break down performance type of the indicators. The indicators are manual or automated which gauge the relevant updates of the score sheets (Laursen and Thorlund 2016). Targets and threshold are the elements reporting and alerting about the score of t he performance after breaking it down to view and analyse it. The most essential part of performance analytics are the data collector phase. Creating and running schedule jobs to collect scores periodically. To view information the job logs are searched for different information (Stefanovic 2014). The reason behind the failure of strategic improvement is the lack of getting meaningful clue of the data gathered. Thus, business analytics helps enabling the manager to draw proper conclusion of the inputs of the business. The performance management helps in targeting specific part of the organisation to implement analytics with holistic approach. The factors discussed above are foundation of the variables that are required to assess the performance. As companies is complex part of societies. The interdependency factor that works in them is also complex in nature (Phillips-Wren et al. 2015). Therefore, creating a link between the organisational strategy and performance has become necessary for the organisations. Thus, the data driven outcomes becomes most reliable part for the managers to make decision. Overload of data leads to limit the processing the data capabilities. The performance investigation is includes more variables that indicates the success factor of the individuals. Therefore, it can be said that the affective decision-making is enhanced by several level of business analytics and business intelligence (Hazen et al. 2014). Conclusion Therefore, it can be concluded that Business Intelligence and Business Analytics has become essential for organisations who are looking for While taking into consideration the internal and external factors of environment in organisation integration of business analytics with the performance drivers also helps in management action and control system. Due to individualisation and globalisation of business environment, the dire need of business analytics has increased. Considering the factors of performance drivers also equips the managers with implementing the best outcome of taken actions in the business process. Benefits of business analytics can be reaped if chosen strategically. The necessity of the proper internal and external environment can be used in terms of implementation of analytics in business environment. 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