Abstract
This paper examines big data and the opportunities it presents for improved business intelligence and decision making. Big data comes in multiple forms. It can be structured, semi-structured, or unstructured. The opportunity it presents is that there is so much of it and it is readily available to organizations. Organizations use big data for business intelligence (BI). They can apply analytics in BI activities to assess big data in order to gain new insights and opportunities for decision making. The problem is that oftentimes the data is of poor quality and it contains personal information. This paper explores these issues and examines the importance of effective data management in facilitating sound business intelligence. The Master Data Management methodology is reviewed and the importance of management support in its deployment is emphasized. With the advent of new sources of big data from IoT devices, the need for even more management involvement is stressed to ensure that organizational BI yield sound decisions and that use of data are in compliance with new regulations.
TopBackground
The amount of data in our world has exploded exponentially such that data, especially unstructured data, is now referred to as “big data”. Where measures of data were once gradually evolving from megabytes to terabytes, the sudden phenomena of big data accelerated these measures to volumes expressed in petabytes (1,024 terabytes) or exabytes (1,024 petabytes). The new influx of data is derived from billions to trillions of records of millions of people—all from different sources (e.g. Web, sales, customer contact center, social media, mobile data and so on). The data is typically loosely structured and often incomplete.
Key Terms in this Chapter
Business Intelligence (BI): Is a business management term which refers to applications and technologies which are used to gather, provide access to, and analyze data and information about their company operations.
Artificial Intelligence (AI): Is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that traditionally required human intelligence.
Internet of Things (IoT): Refers to the billions of physical devices around the world thatare connected to the internet, collecting, and sharing data.
Data Governance: Is a combination of people, processes and technology that drives high-quality, high-value information. The technology portion of data governance combines data quality, data integration and master data management to ensure that data, processes, and people can be trusted and accountable, and that accurate information flows through the enterprise driving business efficiency.
Unstructured Data: Unstructured data (or unstructured information) refers to information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, facts, or video content as well.
Master Data Management (MDM): Is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise’s official shared master data assets. Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies, and chart of accounts.
Big Data: Is a popular term used to describe the exponential growth and availability of data, both structured and unstructured.
General Data Protection Regulation (GDPR): Is legislation designed to give European Union citizens more control over their personal data. It dictates laws that organizations must abide by in their acquisition and management of personal information.
Data Steward: A data steward is a job role that involves planning, implementing and managing the sourcing, use and maintenance of data assets in an organization. Data stewards enable an organization to take control and govern all the types and forms of data and their associated libraries or repositories.