Big and Smart Data for efficient decisions: How to share with decision makers the practices of Big Data Analytics?

Transcription

1 Big and Smart Data for efficient decisions: How to share with decision makers the practices of Big Data Analytics? Ali FOULADKAR PhD candidate, Grenoble University (UPMF), France Academic membership, CERAG laboratory, France Academic membership, InnovDoc, Grenoble, France PhD student, membership, Organizational Design Community, Aarhus, Denmark 1 st Big Data and Analytics Education Conference, Las Vegas, Nov. 2-3, 2013 Sunday, November 3 th,

2 EMERGENCE OF A NEW «BIG DATA ANALYTICS» CONCEPT Decision-Making in 2

3 BIG DATA CHALLENGES RELATED TO DECISION MAKING 3

4 Is Big Data, Big Noise only? Huge collections (volume) of heterogeneous data (variety), ingested at increasing speed (velocity) and integrated (veracity) The availability of huge amount of data is analyzed in new ways New forms of data analysis go beyond the traditional relational data model, by analyzing data of varying structure (e.g., flat records, hierarchies, graphs) and modality (e.g., relational, text, audio, video). Decision-Making in 4

5 Does volume equate to value? Organizations capture billions of bytes of information about their customers, suppliers and operations, but their ability to collect, manage and interpret these information can be an obstacle to their use Many events can be recorded within organizations generating a never-ending sea of data Decision-Making in 5

6 Does Big Data requires Big Decisions? It is time to deal with learning and treating Big Data in order to improve organizational performance facing competitors through Smarter Decisions Organizations that succeed with Big Data Analytics will be those that understand the possibilities and choose the right deployment model Decision-Making in 6

7 In recent years, research works largely caught up with the industrial progress related to emerging of Big Data. However, this emergence is not yet touched by the education sector We must assist decision-makers and educate the next decisionmaker s generation about new Big Data Technologies, by developing new approaches to education mainly based on new analytical tools Decision-Making in 7

8 Most major universities offer some form of Big Data course, however curriculum is not consistent across institutions The fast growth of data incite many universities to develop courses and programs in the era related to Big Data spectrum, mostly: data mining, distributed systems and most recently data science Example of advanced study programs in Data Science: Data Science at New York University, Information and Data Science at UC Berkley, Data Science at Syracuse University... Decision-Making in 8

9 Decision-Making in Big Data Analytics describes data and analytics in large and complex applications that they require advanced and unique data analysis (La Valle R. 2011) Analytical methods Methods for qualitative analysis (presence/absence of deadlocks, correctness and soundness, ) Methods for quantitative analysis (compute the average completion times of cases, average waiting time, resources utilization, compute the border suitable to split ing of simple and complicated cases and similar aspects, ) 9

10 The definition of Big Data Analytics is easy to understand, but do decision-makers actually use the term? Decision-Making in Big Data Volume Size Velocity Freshness Variability Type Veracity Quality Analytics Selection & Grouping Relational Operators Join/Correlation Extraction & Integration Data Mining Predictive Models 10

11 There is an irreversible trend toward the criticality of Big Data Analytics, capability and exercise, which is specifically related to the improvement of traditional Data-Driven Decision Making approaches We are dealing with decision making related to Analytical Methods in order to explore the challenges that decision-makers face in Decision-Making in 11

12 It is time to exploit this emergence of Big Data to develop a new era of Smart Data. How to transform our Big Data to Smart Data Once the structural meaning of «Big data» is understood, the most important aspect of Big Data Management is the actual extraction of knowledge through massive Processing and Data Analysis Decision-Making in Smart Data is the lethal weapon for modern enterprises, and businesses, who want to survive and improve their performance in this digital market 12

13 How is Smart Data really different from Big Data? Smart Data means information that actually makes sense. It is data from which signals and patterns have been extracted by intelligent algorithms. What makes Smart Data? Decision-Making in Scalable action matters 13

14 Simon (1945) is one of the first researchers who have treated decision-making, which was considered «satisfaction» rather than «optimization» «The initial effort toward designing management information systems started with available data rather than with decisions to be made» (Simon H. 1960) Decision-Making in 14

15 Before the emergence of «Big Data Analytics» concept, some decisions were ed in a standard way without complexity, but after the new use of large amounts of data, decision-making has changed radically specifically for strategic decisions This new requirement is not based on the experience of the makers, but it based on techniques and data management systems currently being developed and proposed Decision-Making in 15

16 Available training was built around technical and engineering problems and it does not offer proper theoretical basis to support decision makers in pertinent exploration of the Big Data fields Define coherent and stable learning objectives in a highly dynamic field Balance between theory and practice for various educational and training needs and availability of high quality teaching materials Decision-Making in 16

17 We aim to provide guidance and recommendations to develop and build new approaches to education mostly based on new analytical tools regarding: (1) the implementation of Big Data technologies in the development of Big Data Decision Makers skills (2) data collection procedures for the implementation of learning analytics (3) educate students (future decision makers) from university to be prepared to explore these new analytical techniques in the era of Big Data Decision-Making in 17

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