Data Analytics : different areas

• Agile BI
• Big Data Analytics
• Business Analytics
• Business Intelligence
• Data Analysis and Design
• Data Management
• Data Warehousing
• Performance Management
• Program Management
• Master Data Management

Agile BI

Agile business intelligence addresses a broad need to enable flexibility by accelerating the time it takes to deliver value with BI projects. It can include technology deployment options such as self-service BI, cloud-based BI, and data discovery dashboards that allow users to begin working with data more rapidly and adjust to changing needs.

To transform traditional BI project development to fit dynamic user requirements, many organizations implement formal methodologies that utilize agile software development techniques and tools to accelerate development, testing, and deployment. Ongoing scoping, rapid iterations that deliver working components, evolving requirements, scrum sessions, frequent and thorough testing, and business/development communication are important facets of a formal agile approach.

Big Data Analytics

Big data analytics is the application of advanced analytic techniques to very large, diverse data sets that often include varied data types and streaming data.

Big data analytics explores the granular details of business operations and customer interactions that seldom find their way into a data warehouse or standard report, including unstructured data coming from sensors, devices, third parties, Web applications, and social media - much of it sourced in real time on a large scale. Using advanced analytics techniques such as predictive analytics, data mining, statistics, and natural language processing, businesses can study big data to understand the current state of the business and track evolving aspects such as customer behavior. New methods of working with big data, such as Hadoop and MapReduce, also offer alternatives to traditional data warehousing.

Business Analytics

Business analytics allows users to examine and manipulate data to drive positive business actions. Armed with advanced analytics insights, business users can make well-informed, fact-based decisions to support their organizations’ tactical and strategic goals.

Business analytics includes advanced techniques such as spatial analytics, customer analytics, and enterprise decision management. Analytic applications bundle tools for data access, dashboard reporting, scorecards, and analytics into packages. Predictive analytics identify relationships and patterns in large volumes of data to create predictive models. Text mining parses unstructured data and merges it with structured data to support user queries, reports, and analyses. “Big data” analytics implement MapReduce, Hadoop, and specialized, non-SQL programming methods to speed insight from huge volumes of data drawn typically from online sources.

Business Intelligence

Business intelligence (BI) unites data, technology, analytics, and human knowledge to optimize business decisions and ultimately drive an enterprise’s success. BI programs usually combine an enterprise data warehouse and a BI platform or tool set to transform data into usable, actionable business information.

Agile BI utilizes agile software techniques and tools to accelerate development and deployment. (See Agile BI.) In-memory BI exploits the reduced cost and increasing power of computer memory and processing.  Self-service BI enables users to access and analyze data with less dependence on IT resources.  Real-time BI focuses on delivering information to users or systems as events are occurring.  Search integrates access to unstructured content and structured data in reports or dashboards.  Open source and software-as-a-service BI provide alternative licensing and service options.

Data Analysis and Design

Data analysis and design provides the foundation for delivering BI applications. Analysis concentrates on understanding business needs for data and information. Design translates business information needs into data structures that are adaptable, extensible, and sustainable. Core skills include needs analysis, metrics definition, and data modeling.
This BI discipline includes gathering accurate business requirements; designing business rules; analyzing and modeling data; dimensional modeling; key performance indicators (KPIs) and metrics; and testing and documentation.

Data Management

Data management (aka enterprise information management) encompasses techniques for data quality, integration, and governance. It includes all the practices necessary to manage data as a critical enterprise asset.

Data quality includes techniques for name-and-address cleansing, data standardization, verification, profiling, monitoring, matching, householding, and enrichment. Data integration (DI) acquires data from sources and transforms and cleanses it. ETL (extract, transform, and load) is the most common form; others include ELT, customer data integration (CDI), data federation, database replication, and data synchronization. Data integration may be analytic or operational. Data governance boards or com­mittees create and enforce policies and procedures for data usage and manage­ment. (See Master Data Management.)

Data Warehousing

Data warehousing incorporates data stores and conceptual, logical, and physical models to support business goals and end-user information needs. A data warehouse (DW) is the foundation for a successful BI program.
Creating a DW requires mapping data between sources and targets, then capturing the details of the transformation in a metadata repository. The data warehouse provides a single, comprehensive source of current and historical information.
Data warehousing techniques and tools include DW appliances, platforms, architectures, data stores, and spreadmarts; database architectures, structures, scalability, security, and services; and DW as a service.

Performance Management

Performance management (PM) is a powerful tool of organizational change. Companies that define objectives, establish goals, measure progress, reward achievement, and display the results for all to see can turbo-charge productivity and move an organization in a new direction.
Performance management typically involves performance dashboards, scorecards, and other visualization solutions; key performance indicators (KPIs) and metrics; and BI applications that address financial management, compliance, and profitability and cost management.

Program Management

Program management includes a strong focus on effective leadership for integrating people, processes, and technology to deliver business value. It requires knowledge of development methodology and program and project management, as well as organizational and team-building skills.
Program management includes project planning and scoping; staffing and structuring the BI team; developing a road map; training and support; vendor negotiations; and sponsoring BI competency centers to ensure governance and IT/business alignment. Related disciplines include business performance management (BPM), customer relationship management (CRM), and supply chain management (SCM).

Master Data Management

Master data management (MDM) is the practice of acquiring, improving, and sharing master data. MDM involves creating consistent defini­tions of business entities via integration techniques across multiple internal IT systems and often to partners or customers. MDM is enabled by integration tools and techniques for ETL, EAI, EII, and replication. MDM is related to data governance, which aims to improve data’s quality, share it broadly, leverage it for competitive advantage, manage change, and comply with regulations and standards.

Source : http://tdwi.org