Business intelligence (BI) is crucial for the effective management and employment of data. It emerged in the latter part of the 20th century and has become an integral aspect of the decision making processes for prudent companies looking to make use of a host of data relating to customer service, inventory, pricing, and so much more.
BI is a collection of applications and software that analyzes various aspects of data and presents it in forms that enhance decision making. It has evolved from generating rudimentary reports and tools used for historical query, to include a host of components such as forecasting, online analytical processing, predictive modeling, data management, data mining, and optimization. Armed with these essential tools, companies can accurately assess what is or is not working at present, discern what historical factors occurred to make it so, and readily identify future trends to maximize their potential.
One of the most tangible benefits of BI is its ability to offer predictive analysis of data that relates to future occurrences. Analytics such as data mining, forecasting, and online analytical processing (OLAP) discern relationships between data that make it possible to determine future trends. Data mining tools analyze specific components of data through parameters such as path analysis and association, which in turn determine useful relationships between data events. Clustering parameters deliver a visual representation of information not previously discerned, while forecasting provides a solid basis for the anticipation of trend fluctuation.
Data integration is a vital feature of any BI solution and can account for factors that actually affect specific market concerns, such as customer demographics, economic conditions, and marketplace environments. These predictive analytics allow enterprises to refine business processes in preparation for the future. One can argue that the individual tailoring of business processes is an essential distinction between enterprises in the same industry. They often utilize similar technologies and products, but customize them in vastly different ways.
Another feature of most competitive BI products, particularly those involved in various aspects of design, is predictive modeling. This facet of BI works in conjunction with forecasting and analytics to help provide visual representations of hypothetical scenarios, permitting for companies to literally “see” which option works best. Furthermore, its integration with other BI tools allows it to access the same data that is relevant to a company, therefore influencing the effectiveness of alternative business scenario models.
BI offers predictive modeling for both quantitative and qualitative factors. Users simply input their company’s data into a variety of channels that analyze and produce different outputs, all of which utilize the same predefined rules that address the particular concern of a hypothetical situation. This presentation of multiple scenarios enables users to efficiently and cheaply examine their options and predicted results before selecting the most desirable. Additionally, this aspect of BI encourages a culture of innovation and creativity, since employees can readily attempt different situations with minimal cost and waste of company expenses.
A further boon of BI is the degree of flexibility in the types of data it can analyze and incorporate into the decision making process. It takes into account a host of logistical and marketing concerns that could affect a company’s processes and, with predictive modeling, can provide an impact analysis that offers end-to-end results of varying scenarios. This particular feature is ideal for potential actions that have high implementation costs, since the design aspect is readily calculable via BI. Predictive modeling can also yield representations that are accessible to non-technical users who can easily manipulate them via assorted input factors. The total integration of all of the individual components of BI – forecasting, reporting, OLAP, data management and predictive modeling – allows for real-time analysis of the most viable courses of actions for businesses based on their own data.
BI facilitates the centralization of data so that it is accessible to a variety of departments and end users. This is particularly useful for enterprises consisting of multiple silos, but still requiring a comprehensive overview of processes between departments and the entire enterprise. BI products can be specific to a particular division of a company, provide data that pertains to a specific project, and to the company’s objectives as a whole. They include data integration technology that allows for the storing of all data relevant to a particular function of a company such as sales, orders, shipping, and pricing. Competitive BI solutions can also account for outside factors relating to a specific industry, including nationwide factors such as GDP, interest rates, or competitor data.
BI permits users to access the information they need in a single view that can be stratified as necessary. OLAP provides extremely specific representations of data – frequently stored in a multidimensional database – so that users can examine it from a variety of viewpoints with attributes that may include time, pricing, and other quantifiable information. Users can choose to extract whatever data is most useful at the time and analyze it in relation to others. Mobile applications allow for access anywhere there is an Internet connection.
BI presents a number of distinct advantages for businesses that deal directly with consumers. It offers valuable data that can assist with opportunities for up-selling and cross-selling products, giving users the ability to readily identify new markets. Several BI products utilize decision support systems that present data relevant for comparisons and a particular demographic. Users can access previous customer habits as well as determine products and services which directly correlate with such habits; there are abundant cross promotional opportunities. Sales and marketing efforts become increasingly streamlined as a result, increasing the likelihood for customer satisfaction. The usage of analytics and predictive models enables enterprises to identify what services will affect their customers the most, as well as to record the information gained from customer contact. By keeping this information in a single database that representatives from various company divisions can access, BI products enable a more fulfilling customer experience, increase customer retention, and allow sales and marketing personnel to make the most of their resources; overall company efficiency is increased, reducing costs and increasing revenues.
BI facilitates proper enterprise structure by providing an assortment of data that details its needs. It can identify areas that have a dearth or surplus of attention, expediting the streamlining of company resources that can assist in productivity. Competitive BI solutions allow for immediate updating, granting users the most current information about company processes and their effects. These effects are critical for inventory applications, utilizing real-time and predictive analytics to eliminate overstocking and to allow customers to receive their products and services in a timely fashion. Prudent BI users can also accumulate data regarding the company processes of competitors in an effort to analyze which approaches are working or not, and to elucidate points of distinction between them.