[MCS-052] What is the role of OLAP in decision-making? What does the term drill mean down in an executive information system?

DECISION MAKING USING OLAP

A Decision Support System (DSS) is any tool used to improve the process of decision making in complex systems. A DSS can range from a system that answer simple queries and allows a subsequent decision to be made, to a system that employ artificial intelligence and provides detailed querying across a spectrum of related datasets. Amongst the most important application areas of DSS are those complicated systems that directly “answer” questions, in particular high level “what-if” scenario modeling. Over the last decade there was a transition to decision support using data warehouses (Inmon 2002). The data warehouse environment is more controlled and therefore more reliable for decision support than the previous methods.

Dive in deep

OLAPThe term, of course, stands for ‘On-Line Analytical Processing’. But that is not only a definition; it’s not even a clear description of what OLAP means. It certainly gives no indication of why you would want to use an OLAP tool, or even what an OLAP tool actually does. And it gives you no help in deciding if a product is an OLAP tool or not.

We hit this problem as soon as we started researching The OLAP Report in late 1994 as we needed to decide which products fell into the category. Deciding what is an OLAP has not got any easier since then, as more and more vendors claim to have ‘OLAP compliant’ products, whatever that may mean (often they don’t even know). It is not possible to rely on the vendors’ own descriptions and membership of the long-defunct OLAP Council was not a reliable indicator of whether or not a company produces OLAP products. For example, several significant OLAP vendors were never members or resigned, and several members were not OLAP vendors. Membership of the instantly moribund replacement Analytical Solutions Forum was even less of a guide, as it was intended to include non-OLAP vendors.

The Codd rules also turned out to be an unsuitable way of detecting ‘OLAP compliance’, so we were forced to create our own definition. It had to be simple, memorable and product-independent, and the resulting definition is the ‘FASMI’ test. The key thing that all OLAP products have in common is multidimensionality, but that is not the only requirement for an OLAP product.

Online Analytical Processing, or OLAP is an approach to quickly provide answers to analytical queries that are multidimensional in nature. OLAP is part of the broader category business intelligence, which also encompasses relational reporting and data mining. The typical applications of OLAP are in business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas. The term OLAP was created as a slight modification of the traditional database term OLTP (Online Transaction Processing).

Databases configured for OLAP employ a multidimensional data model, allowing for complex analytical and ad-hoc queries with a rapid execution time. They borrow aspects of navigational databases and hierarchical databases that are speedier than their relational kin. Drill down in EIS

An Executive Information System (EIS) is a type of management information system intended to facilitate and support the information and decision making needs of senior executives by providing easy access to both internal and external information relevant to meeting the strategic goals of the organization. It is commonly considered as a specialized form of a Decision Support System (DSS).

The emphasis of EIS is on graphical displays and easy-to-use user interfaces. They offer strong reporting and drill-down capabilities. In general, EIS are enterprise-wide DSS that help top-level executives analyze, compare, and highlight trends in important variables so that they can monitor performance and identify opportunities and problems. EIS and data warehousing technologies are converging in the marketplace.

In recent years, the term EIS has lost popularity in favour of Business Intelligence (with the sub areas of reporting, analytics, and digital dashboards).

EIS enables executives to find those data according to user-defined criteria and promote information- based insight and understanding. Unlike a traditional management information system presentation, EIS can distinguish between vital and seldom-used data, and track different key critical activities for executives, both which are helpful in evaluate if the company is meeting its corporate objectives. After realizing its advantages, people have applied EIS in many areas, especially, in manufacturing, marketing,and finance areas.

Basically, manufacturing is the transformation of raw materials into finished goods for sale, or intermediate processes involving the production or finishing of semi-manufactures. It is a large branch of industry and of secondary production. Manufacturing operational control focuses on day-to-day operations, and the central idea of this process is effectiveness and efficiency. To produce meaningful

managerial and operational information for controlling manufacturing operations, the executive has to make changes in the decision processes. EIS provides the evaluation of vendors and buyers, the evaluation of purchased materials and parts, and analysis of critical purchasing areas. Therefore, the executive can oversee and review purchasing operations effectively with EIS. In addition, because production planning and control depends heavily on the plant’s data base and its communications with all manufacturing work centers, EIS also provides an approach to improve production planning and control.

The future of executive info systems will not be bound by mainframe computer systems. This trend allows executives escaping from learning different computer operating systems and substantially decreases the implementation costs for companies. Because utilizing existing software applications lies in this trend, executives will also eliminate the need to learn a new or special language for the EIS package. Future executive information systems will not only provide a system that supports senior executives, but also contain the information needs for middle managers. The future executive information systems will become diverse because of integrating potential new applications and technology into the systems, such as incorporating artificial intelligence (AI) and integrating multimedia characteristics and ISDN technology into an EIS.

In tandem with the growth of the Internet and e-business, the number of digital data sources has increased immensely. These data sources contain important transactional data and are generally interconnected via a network. This has created a pressing need for a suitable executive information system (EIS) that is capable of extracting data from internal and external data sources and providing data analysis on demand for business executives. On-demand data analysis requires an information integration approach that can manage rapid changes in data sources. Existing EISs commonly adopt data warehousing technology to consolidate data from multiple sources in a tailor-made fashion, and support predefined multidimensional data analysis. However, this architecture is neither adaptable to changes in local sources nor flexible enough for ad hoc analyses. This paper develops methods and algorithms for a new EIS architecture that takes advantage of a meta-database to achieve adaptability and flexibility. A PC-based prototype is built to prove the concept.