Business Intelligence and Big Data in Planning and Budgeting
Planning and budgeting are activities that take place in the context of very complex networks of interconnectedness. For example, on the one hand, we are dealing with management board guidelines for a multiyear development strategy, and on the other, with the need to prepare operational plans for a given year. To ensure maximum efficiency of a planning process, effective communication between all collaborating people is necessary. In practice, due to the lack of appropriate ICT support, the processes of goal distribution and the consolidation of expectations are very often implemented in mutual isolation, and their results are agreed only after the works are completed. BI&BD tools can support modeling and integration of all aspects of this reality. They are primarily used to link the day-to-day and operational activities of all departments, as well as employees with strategic goals. This facilitates the process of translating organization’s vision and strategy into measurable goals. OLAP mechanisms efficiently automate plan consolidation processes. Thanks to the use of allocation keys, it becomes possible to quickly map strategic plans onto the operational level and project budgets to subprojects or organizational units. These features combined with the possibilities of working in a group mode enable the firm to get the optimal version of the budget in a short time. In this way, owners and shareholders quickly obtain information on the directions of development and expected profits from the invested capital, and the management has guidelines for optimal refinement of operational plans based on the available resources.
OLAP tools also enable the cost analysis in great detail. Budgeting becomes possible based on the demand and performance indicators as well as effectiveness indicators for the use of individual resources. Models based on data mining tools can also be used to measure the level of exposure of the organization to various risk factors such as change in the structure of shares, instability of stock markets, and so on. Tlie designed models help predict the portfolio effectiveness in various adopted economic scenarios and future demand for current assets.
Business Intelligence and Big Data in Sales and Distribution
Increasingly, sales organizations do not limit their activities to one point-of-sale, one city, or even one region. However, having many sales locations requires knowledge about the allocation of individual goods. Experience shows that good financial results depend to a large extent on whether a given product is in the right place at the right time. Moving goods between stores (e.g., within one network) must often be done very quickly. Products that do not arouse interest in customers in one region may be of interest elsewhere. The possibility of sustainable movement of goods between stores allows the organization to significantly reduce storage costs. In addition, appropriate analyses enable prompt detection of such products, in a whole lot of transactions, on which the firm has, for example, the lowest margins. In other words, BI&BD tools allow the data exploration flowing from various distribution channels and provide information useful for their effective management, namely:
■ Distribution of sales departments: By using geographical analysis of the customer base, firms can optimally distribute sales departments in individual locations. This analysis should include information about potential customers and products offered by the firm.
■ Sales network development and contact management: BI&BD tools can be used to track the sales results of individual sellers. Such an analysis enables, for example, to identify the best sellers who can later be paid, trained, and so on, according to the merit.
■ Distribution channel analysis: Using BI&BD tools, organizations can compare the results of their activities within individual channels. In the process of assessing the effectiveness of distribution channels, the measurement of the profitability of distribution channels in relation to products sold through them as well as customers acquired and served by those channels deserves special attention.
In the retail industry, many organizations are already using Big Data analytics to improve the accuracy of forecasts, anticipate changes in demand, and then react accordingly. For example, Brooks Brothers, one of the oldest retailers in the United States, introduced business analytics developed by SAS, the US-based business analytics software group, to help improve the stock control. Using analytics to forecast its global stock enabled the store managers to make better decisions around stock levels and pricing.