The cycle of data
- 1. The cycle of data: Introduction
- 2. WELCOME TO BIG DATA Introduction 2017 has been a great year for big data, the silent companion in our lives, it seems that we are losing the fear of something starting with BIG already scares. The big data is going to be used not only by the big players, to be present and to be a fundamental axis in the strategies of any company. Starting, at last, to democratize the use of data as we say. We have seen the birth of many companies that for sure are going to change the way we see and do things, giving us the transparency and visibility the market needs. GRPD is a hot topic nowadays, does not come to change things comes but to regulate them and that each of us know in what position we are in each moment and take the necessary responsibilities so that the user is more informed and therefore more protected.
- 3. R E P O R T A U T U M N / W I N T E R 1 6 O N L I N E S T O R E . C O M A U T U M N / W I N T E R 1 6 I N D E X Introduction What is big data? The expanding digital universe The future is now Where do we get the data? Method of collection Data structure Where is all that data stored? Data Warehouse VS Data Lake How does the data arrive to the DMP? Data management platform The 7 VS of Big Data Lack of profesional data profiles New Profesional Profiles Data transparency GDPR
- 4. What is big data? Big data is a term that describes the large volume of data that inundates a business. But it’s not the amount of data that’s important, It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves. Source: Verve systems Source: sas.com
- 5. THEEXPANDINGDIGITA UNIVERSE The digital universe has been rising exponentially since 2013. It is estimated that, by 2020 the size of digital and data universe, will reach a long size unimaginable a few years ago. Source: @Tiffani Bova
- 6. THE FUTURE IS NOW Big Data in Europe 6 million people in Europe worked in data-related jobs in 2015 and 6.16 million in 2016. As far as medium-term developments are concerned, it is estimated that under a high-growth scenario, the number of data workers in Europe will increase up to 10.43 million, with a compound average growth rate of 14.1% by 2020.
- 7. THE FUTURE IS NOW Source: IDC, Big Data Market Forecast, Big Data in Spain Big data market in Spain has grown exponentially over the past few years, and 2019 is expected to reach $313.7 M Big Data market forecast in Spain
- 8. Using big data, organizations can generate actionable insights that enable them to drive their business forward. Rapid integration of the ever-expanding pool of data sources and types opening a whole new world of possibilities. Wheredoweget thedata? Source: nextgov.com Source: columnfivemedia.com
- 9. #01 #02 S E C O N D P A R T Y D A T A F I R S T P A R T Y D A T A Browser and serverside cookies set and recorded on visitors to web and app properties Cross-device Ids Device model, operating system, connection type, mobile network Personally Identifiable Information (PII) like name, email, phone, postal address Behavioural information such as who bought what and how often It is first party data made available for use by another organisation, shared with transparency. The organisation using the data knows where it came from, how it was collected, and what it signifies. The data sources are identical to those given above. Data ingestion from multiple on and off-line sources Data storage Data mapping Customer profiling Cross-device identity graphing Activation in media buying platforms Marketplaces for sharing and monetising data sets Analytics #03 T H I R D P A R T Y D A T A Method of collection DATA COLLECTED DIRECTLY BY THE ORGANIZATION DATA SHARED BY A TRUSTED SOURCE AGGREGATED DATA FROM OTHER SOURCES Source: :xelsionmedia.com
- 10. B I J O U M E D I A M A R K E T I N G P R O P O S A L D a t a s t r u c t u r e Unstructured: Data that does not reside in fixed locations generally refers to free-form text, which is ubiquitous. Semi-structured : Between the two forms where “tags” or “structure” are associated or embedded within unstructured data. Structured: Data that resides in fixed fields within a record or file. Source: medium.com THE CHALLENGE IS TO STRUCTURE ALL THAT DATA Source: :sherpasoftware.com
- 11. DATA WAREHOUSE (DWH) Where is all that data stored? A data warehouse is a large store of data accumulated from a varied range of sources within an organization. It is used to guide management decisions. ETL is normally a continuous, ongoing process with a well-defined workflow. It extracts data from homogeneous or heterogeneous data sources. Then, data is cleansed, enriched, transformed, and stored either back in the lake or in a data warehouse. Source: Xplenty
- 12. DATA LAKE A data lake is a storage repository or a storage bank that holds a huge amount of raw data in its original format until it’s needed. ELT (Extract, Load, Transform) is a variant of ETL wherein the extracted data is first loaded into the target system. Transformations are performed after the data is loaded into the data warehouse. ELT typically works well when the target system is powerful enough to handle transformations. Source: Xplenty Where is all that data stored?
- 13. Data Warehouse Data LakeVS DATA PROCESSING STORAGE AGILITY SECURITY USERS Structured, processed Structured, semi-structured, unstructured, raw Schema-on-write Schema-on-write Expensive for large data volumes Designed for low-cost storage Less agile fixed configuration Highly agile, configure & reconfigure as needed Mature Maturing Business professionals Data scientists Source: datamation.com
- 14. H o w d o e s t h e d a t a a r r i v e s t o t h e D M P ? DATA LAKE DATA WAREHOUSE Capture Curate Aggregate Data Build Customer profiles Engage customers Activate DMP DSP Source: prnewswire.com
- 16. THE 7 Vs OF BIG DATA 14% 14% 14% 14% 14% 14% 14% Velocity Variety Veracity Visualisation Viability Volume Value
- 17. 1 . V O L U M E Amount of data that is generated in our environment THE 7 Vs OF BIG DATA Source: Medium
- 18. 2 . V E L O C I T Y The speed in which data is accessible THE 7 Vs OF BIG DATA
- 19. 3 . V A R I E T Y Forms, types and sources from which data are recorded THE 7 Vs OF BIG DATA Source:Medium
- 20. 4 . V E R A C I T Y Is all about making sure the data is accurate THE 7 Vs OF BIG DATA Source:Medium
- 21. 5 . V I A B I L I T Y The capacity of companies to generate an effective use of the large volume of data that handle. THE 7 Vs OF BIG DATA Source:Medium
- 22. 6 . V I S U A L I Z A T I O N Importance of the visual representation, understandable of data in a pictorial or graphical format. THE 7 Vs OF BIG DATA Source: Mtkander
- 23. 7 . V A L U E Be sure that your organization is getting value from the data THE 7 Vs OF BIG DATA Getting Business Value from Big Data #01 #02 #03 #04 Estimate Analyze Integrate Discover Estimate expediture & hardware investment Analyze streaming Big Data Integrate Big Data with older enterprise sources Discover new business opportunities Big Data is the ability to achieve greater value through insights from superior analytics Source: Medium
- 24. CHALLENGES
- 25. New Professional Profiles DATA SCIENTIST DATA ANALYSTDATA ARCHITECT Cleans, massages and organizes (big) data Collects, processes and performs statistical data analysis Creates blueprints for data management systems to integrate, centralyze, protect and maintain data sources DATA ENGINEER Develop, constructs, tests and maintains architectures (such databases and large- scale processing systems) STATISCIAN DATABASE ADMINISTRATOR Collects, analyzes and interprets-qualitative as well as quantitative data with statistical theories and methods Ensures that the database is available to all relevant users, is performing properly and is being kept safe Source: Pinterest.com
- 26. DATA TRANSPARENCY AND VISIBILITY Source: businessinsider.com Transparencyisakeyelementwhenpurchasingdata.Advertisersneed toknowthesourcewherethedataiscomingfromanddatasuppliers shouldalwaysprovidethisinformation. The85%oftheexpertsinmarketingandbusinessthinkthatitisnecessary toincreasethevisibilityofthedatausedtodefineaudiences,tobeableto takereallyadvantageofallthatdata.
- 27. Source: powerdata.es DATA QUALITY It refers to the quality of a set of information collected in a database, an information system or a data warehouse with attributes like: accuracy, integrity, updating, coherence, relevance, accessibility and reliability necessary to be useful to the processing, analysis, and any other purpose that a user you want to give.
- 28. The GDPR has been born of a need to regulate the flow of data and protect it, developing clear policies and procedures to protect personal data, and adopt appropriate technical and organisational measures
- 29. THE NEW GDPR Source: powerdata.es
- 30. W. Edwards Deming Data Scientist " " source:@reymondin
- 31. spain@datmean.com EMAIL www.datmean.com WEBSITE +34 91 052 83 84 PHONE NUMBER EASTWAY UNIVERSITY OF SOCIAL SCIENCES @DatmeanOfficialDatmean Datmean