1 # BIG DATA
The term big data is referred from billion to trillion of records from various sources” structured and unstructured data sets beyond DBMS to capture, store and analyse . Laudon.C.K.,Laudon.J.P,.(20012&2013
As the world is increasingly interconnected, instrumented and intelligent and in this new world the growth in volume, variety, and velocity of data has created new challenges and opportunities. Big Data technologies can be defined as a new generation of technologies and architectures designed to extract value economically from very large volumes of a wide variety of data by enabling high-velocity capture, discovery, and/or analysis.
Big Data encompasses hardware, software, and services that integrate, organize, manage, analyze, and present data characterized by ” three Vs” volume, variety, velocity, and value that is the benefits from Big Data projects such as: Capital cost reduction, Operational efficiency, Business process.
Volume is the primary attribute of big data, that is seen as the huge flood of data that is generated every minute and every second. The quantity of data to be captured continues to grow exponentially. Yes businesses are big into big data, because of availability of data mining tools that reveal interesting patterns trends with the potential to provide new insights into customer behavior, financial market activities, even water behavior.
This unprecedented quantity and quality Data is generated by mobile communications, social networks, machines and sensors devices, which continuously generate data streams without human intervention and increase the velocity of data aggregation and processing. Ralf Konrad(2012) & Russomo Phillip (2011) agreed that variety is just as big as volume, Variety and volume tend to fuel each other .
Variety in big data is a critical attribute .With big data, the data streams are uncontrolled and often unstructured, come from diverse channels and are in different formats which is a key criteria in determining whether the application is considered to be big data.85% of generated data is unstructured. Agreed McAfee.A ., Brynjolfsson.E,. (2012 that Big data takes the form of messages, updates, and images posted to social networks; readings from sensors; GPS signals from cell phones, and more”. Then the challenge is to process this data in such a way that valuable information can
Variety in big data is a critical attribute With big data, the data streams are uncontrolled and often unstructured, come from diverse channels and are in different formats which is a key criteria in determining whether the application is considered to be big data.85% of generated data is unstructured. Agreed McAfee.A ., Brynjolfsson.E,. (2012 that Big data takes the form of messages, updates, and images posted to social networks; readings from sensors; GPS signals from cell phones, and more”. Then the challenge is to process this data in such a way that valuable information can be derived from it.
Big data is described by its velocity/speed. Velocity refers to the speed at which the data is captured ,processed and produced not just in burst but in a continuous flow. As big data refers to the fact that the data is being produced not just in bursts, but in a continuous flow, resulting in many companies facing with the challenge of having to process huge amounts of data faster and faster ideally in real time”. Laudon.C.K.,Laudon.J.P,.(20012&2013). “Real-time or nearly real-time information makes it possible for a company to be much more agile than its competitors”. McAfee .A., Brynjolfsson .E,.(2011) . According to Ralf Konrad.C,.(2015), 85% to 90% of all bits and bytes are unstructured stem from various sources and must be up to date and have to be processed at high speed .So extracting relevant information is a key factor to turning the data into competitive advantage.http://www.t-systems.com. Velocity data moving through firm’s systems varies from batch integration and loading of data at predetermined intervals to real-time streaming of data that is processed by hadoop that moment
VALUE “ value comes from knowing more than the rest”
Value is becoming more recognizable as the fourth characteristic of big data , the fact that data stream are uncontrolled, unstructured coming from different channels in different formats, valuable information is extracted from the three VS. Mining for data is used to find patterns and trends, Process data in real time with the right tool such as hadoop.. allows to efficiently and timely produce valuable information from unstructured data. Transporting data to warehousing and Data mart for accessible, Store data securely where data needed will be guaranteed available ,then Refining the data to recognize patterns, trends, meaning, and correlation by using analytical tools such as Chi squared.. Value means guaranteed added value and new opportunities thanks to intelligent analysis. As new data is transformed into information, which in turn is combined with business know-how to secure a valid basis for decision-making, and deliver Value to customers .Implementing is the mean to and end that best practice is reached to become a responsive entity operating smartly and achieve the six operational objectives, Which mean VALUE=COMPETITIVE ADVANTAGE over competitors.
Analyzing big data in motion
As with certain kinds of data( generated by sensor, fraud detection data), there is no time to store it before acting on it. Because it is constantly changing. The key to evaluating the velocity requirements of Big Data is to understand the business/organizational processes and requirements of end users. Also high-velocity, high-volume data calls for in-motion analytic.
2 # MASTER DATA MANAGEMENT (MDM)
MDM is synchronized enterprise-wide business data that provides definitions and identifiers of internal and external objects involved in business transactions (e.g., customer, product, reporting unit, market share). http://www.information-management.com/channels/master-data-management.html
MDM is defined as a transformative effort requiring firms to restructure their human resources, business policies internal processes and its management mind sets. Laudon (2012,2013) It is also “a strategic business driver as it enables organizations to unify and consolidate data about their customers, products and organizations; data that is often fragmented across different systems. http://www.evancarmichael.com/Small-Business-Consulting/1650/The-Importance-of-Master-Data-Management-MDM.html.
Is data that is shared across systems and used to classify transactional data. Without data integrity, transaction data cannot be analyzed or reported in a meaningful way. John Kopcke http://www.oracle.com/us/products/middleware/bus-int/064333.pdf(2008)
MDM Enhance BIG DATA
MDM creates context for big data by providing trusted information about how incoming unstructured data fits into the business environment. big data creates context for MDM by providing new insights from social media and other sources, which helps companies build richer customer. http://public.dhe.ibm.com/common/ssi/ecm/im/en/imm14124usen/IMM14124USEN.PDF
MDM relates to data governance (DG)which is a formal set of practices or a set of processes that ensures the importance of data assets to be formally managed throughout the enterprise, that this data can be trusted and that people can be made accountable for any adverse event that happens because of low data quality. DG needs an clear & buy-in “top down” structure and a significant bottom up to take on duties. http://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/sas-data-governance-framework–
Data governance guiding Principles
Business users need accurate, clean, timely data about their prospects, customers, competition, etc. to meet business objectives and goals. http://www.b-eye-network.com/blogs/oneal/assets_c/2012/11/DG-530.php
Once the data governance role is part of a people’s jobs, they are more likely to make better decisions about the role of data – and how it applies to the corporate mission.
Data Stewardship ,Data Security Roles & Tasks
Data stewards are the keepers of the data throughout the organization. They serves as the points of contact for data definitions, usability, questions, and access requests. Creating clear and unambiguous definitions of data. Defining a range of acceptable values, such as data type and length. Enforcing the policies set by a data governance council or any other oversight board. Monitoring data quality and starting root cause investigations when problems arise. Participating in the definition and revision of data policy. Understanding the usage of data in the business units. Reporting metrics and issues to the data governance council
Source: https://www.google.ie/search?q=information+quality+dimension&newwindow Data quality is data that is trusted, “fit for the purpose”, data that fall within information quality dimension.
3 # Information System (IS)
An a system is composed of people and computers that collect, processes, store and distribute information to support decision making and control organization with a view to achieve businesses’ short and long-term goals.
Importance of information system
“What the business would like to do in five years often depends on what IS will be able to do”Laudon.C.K.,Laudon.J.P,.(2013,2013). Businesses responsiveness in a fast moving business environment heavily depends on the quality of information system. Also IS enables business firms to achieve strategic objectives. see below.
Check Laudon.C.K.,Laudon.J.P,.(2013:42-44) to read more Further, when IS at a heart of a business firm, it enables to tighten linkage with suppliers and develop customers intimacy as well as Identifying and provide Solutions to challenges from business environment. See below how IS engage with firm internally and externally.
INFORMATION SYSTEMS AND CAREERS
4 # BUSINESS INTELLIGENCE (BI)
Business Intelligence and decision making
Business Intelligence is a contemporary term for data and software tools to organize, analyse and provide access to data to support all levels of Managers & other users make more informed decision. Jane P. Laudon.C., Laudon.J.P.,(2012;2013) MIS,DSS,ESS deliver information and knowledge to the organisational citizens to better perform their duties.
“ High quality decision require high quality information”
High – velocity Automated decision making
The class of decision that are highly structured and automated is growing at a fast pace, as decision are not necessary made by humans/managers but machines . ex: solutions to querying in Google search Engine where Google decide which URLs to display in a matter of less than a second. Computer algorithms enable automated high-speed decision making.
5 # GOOGLE FUSION TABLE
A heat-map depicts a census results of Irish population in 2011
1# Fusion table and heat map
Google Fusion Tables is a cloud-based service for data management and integration . It enables users to upload tabular data files (spreadsheets, CSV, KML) Gonzalez,H., levy,A., Jensen,S.C,Langen,A.,Warren, S,., And that Fusion Tables supports the rendering of heat maps. http://homes.cs.washington.edu
2 # Heat map creation
Heat map in different colours, display Irish population density from 26 counties in 2011. Data was compiled in excel table format save in as CSV form, securing geocode data recognition by fusion table and allowed import in fusion table. KML Google map that contains geographic data was uploaded from Google drive, then emerged with Excel table. To learn more about how to compile data table in excel, create and emerge with fusion table click here https://www.youtube.com/watch?v=0SLyS4-zGeo & https://www.youtube.com/watch?v=p0xnk9zFQpY
3 # Heat Map heat map information :Visually display population density of each county , Dublin is in red colour and depicts the highest number of population over 1million and 11 counties have <100,00 population, more 11 counties have <200,000 population , Cork and Galway have over 200,000 population. From this data many purposes can be served to make strategic decisions . For example: HSE decisions makers could use the heat map visual display using legends of information from the pie chart that hold proportion of population of each county, then they can decide on the closure of hospitals where counties are less populated, the same as disease control in case of outbreak. Politicians can get information from the heat map for their campaign targeting the most populated counties to convert vast majority of voters.Department of education can use the heat map to decide on school building. Super markets business development managers can use the heat map to analyse the feasibility of opening super market in towns based on population density.
4 # Importance of Google Fusion table
Data visualization is one of the most powerful features of Fusion Tables is that users can visualize their data immediately after uploading, and when it comes to geographic features , rendering a massive geographic data set is attributed to fusion table component . Also fusion table t enables users to upload data and visualize it in many different ways. As heat map creation is a method of point data visualization that shows the density of points in a given area. http://www.sco.wisc.edu. As Fusion Tables supports the production of heat maps in line with the density of features in space , users can easily extract information needed for decision makers .Fusion table allow the integration of data from many sources by executing joins. Briefly, Fusion tables provide a tool to data owners to safely upload data to fusion tables , where all users can visually share same data in interactive way without emails traffic. Google fusion tables facilitate users to imbed many visualisations, reveal trends, interpret information and stories. Users can easily create and publish compelling visualizations on the website. By using Google fusion tables, it allows to analyse the geographical situation and tailor solutions to any given scenario.