(b) What are the important metrics of business performance management?
(a) Show the various stages in data warehousing and business analytics and also explain it in brief.
Data warehouse is a repository of an organization's electronically stored data. Data warehouses are designed to facilitate reporting and analysis.
A data warehouse houses a standardized, consistent, clean and integrated form of data sourced from various operational systems in use in the organization, structured in a way to specifically address the reporting and analytic requirements.
This definition of the data warehouse focuses on data storage. However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata.
Data warehousing is commonly used by companies to analyze trends over time. In other words, companies may very well use data warehousing to view day-to-day operations, but its primary function is facilitating strategic planning resulting from long-term data overviews. From such overviews, business models, forecasts, and other reports and projections can be made. Routinely, because the data stored in data warehouses is intended to provide more overview-like reporting, the data is read-only. If you want to update the data stored via data warehousing, you'll need to build a new query when you're done.
Data warehousing is typically used by larger companies analyzing larger sets of data for enterprise purposes. Smaller companies wishing to analyze just one subject, for example, usually access data marts, which are much more specific and targeted in their storage and reporting. Data warehousing often includes smaller amounts of data grouped into data marts. In this way, a larger company might have at its disposal both data warehousing and data marts, allowing users to choose the source and functionality depending on current needs.
One possible simple conceptualization of a data warehouse architecture consists of the following interconnected layers:
- Operational database layer : The source data for the data warehouse - An organization's Enterprise Resource Planning systems fall into this layer.
- Data access layer : The interface between the operational and informational access layer - Tools to extract, transform, load data into the warehouse fall into this layer.
- Metadata layer : The data directory - This is usually more detailed than an operational system data directory. There are dictionaries for the entire warehouse and sometimes dictionaries for the data that can be accessed by a particular reporting and analysis tool.
- Informational access layer : The data accessed for reporting and analyzing and the tools for reporting and analyzing data - Business intelligence tools fall into this layer. And the Inmon-Kimball differences about design methodology, discussed later in this article, have to do with this layer.
There are also disadvantages to using a data warehouse. Some of them are:
- Data warehouses are not the optimal environment for unstructured data.
- Because data must be extracted, transformed and loaded into the warehouse, there is an element of latency in data warehouse data.
- Over their life, data warehouses can have high costs. Maintenance costs are high.
- Data warehouses can get outdated relatively quickly. There is a cost of delivering suboptimal information to the organization.
- There is often a fine line between data warehouses and operational systems. Duplicate, expensive functionality may be developed. Or, functionality may be developed in the data warehouse that, in retrospect, should have been developed in the operational systems and vice versa.
Business analytics can make extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling , and fact-based management to drive decision making. Analytics may be used as input for human decisions or may drive fully automated decisions. Business intelligence is querying, reporting, OLAP, and "alerts". In other words, querying, reporting, OLAP, and "alert" tools can answer the questions: what happened; how many, how often, where; where exactly is the problem; what actions are needed. Business analytics can answer the questions: why is this happening; what if these trends continue; what will happen next; what is the best that can happen.
Davenport argues that businesses can optimize a distinct business capability via analytics and thus better compete. He identifies these characteristics of an organization that are apt to compete on analytics:
- One or more senior executives who strongly advocate, in general, fact based decision making and, specifically, analytics
- Widespread use of not just descriptive statistics, but predictive modeling and complex optimization techniques
- Substantial use of analytics across multiple business functions or processes
- Movement toward an enterprise level approach to managing analytical tools, data, and organizational skills and capabilities.
- Extracting, cleansing, aggregating, transforming and validating the data to ensure accuracy and consistency
- Defining the correct level of summarization to support business decision making
- Establishing a refresh program that is consistent with business needs, timing and cycles
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(b) What are the important metrics of business performance management?
Business performance management consists of a set of processes that help organizations optimize their business performance. It provides a framework for organizing, automating and analyzing business methodologies, metrics, processes and systems that drive business performance.
Colin White describes a link between business intelligence and business performance management. "The biggest growth area in operational BI analysis is in the area of business performance management (BPM). Operational BPM applications not only analyze the performance ..., but also compare the measured performance against business goals and alert business users when actual performance is out of line with business goals."
BPM helps businesses make efficient use of their financial, human, material and other resources. The key differentiator between BI and BPM is that BPM contains the concept of a control or feedback loop that helps guide the business towards its goals. BI may provide the analytics to help the business set those goals and to monitor progress towards them.
In the past, owners have sought to drive strategy down and across their organizations, they have struggled to transform strategies into actionable metrics and they have grappled with meaningful analysis to expose the cause-and-effect relationships that, if understood, could give profitable insight to their operational decision-makers.
For business data analysis to become a useful tool, an enterprise must understand its goals and objectives – essentially, it must know the desired direction of progress To help with this analysis, someone prescribes key performance indicators (KPIs) to assess the present state of the business and to prescribe a course of action.
Metrics and KPIs are critical in prioritization what has to be measured. The methodology used helps in determining the metrics to be used by the organization. Managerial folk-wisdom says that one cannot manage what cannot be measured. Identifying the key metrics and determining how they are to be measured helps the organizations to monitor performance across the board without getting deluged by a surfeit of data; a scenario plaguing most companies.
Most of the time, BPM simply means use of several financial/non-financial metrics/key performance indicators to assess the present state of a business and to prescribe a course of action.
Some of the areas from which top management analysis could gain knowledge by using BPM may include:
- customer-related numbers:
- new customers acquired
- status of existing customers
- attrition of customers (including breakup by reason for attrition)
- turnover generated by segments of the customers - possibly using demographic filters
- outstanding balances held by segments of customers and terms of payment - possibly using demographic filters
- collection of bad debts within customer relationships
- demographic analysis of individuals (potential customers) applying to become customers, and the levels of approval, rejections and pending numbers
- delinquency analysis of customers behind on payments
- profitability of customers by demographic segments and segmentation of customers by profitability
- campaign management
- realtime dashboard on key operational metrics
- overall equipment effectiveness
- clickstream analysis on a website
- key product portfolio trackers
- marketing channel analysis
- sales data analysis by product segments
- callcenter metrics