From Big Data to Smart Data

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From Big Data to Smart Data

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From Big Data to Smart Data

  1. 1. May 2013From Big Data to Smart DataMarin Dimitrov - CTO
  2. 2. About Ontotext• Provides products and services for creating,managing and exploiting semantic data– Founded in 2000– Offices in Bulgaria, USA and UK• Major clients and industries– Media & Publishing (BBC, Press Association, EuroMoney,NDP Nieuwsmedia)– HCLS (AstraZeneca, UCB, NIBIO)– Cultural Heritage (The British Museum, The NationalArchives, Polish National Museum, Dutch Public Library)– Government (UK Parliament, United Nations FAO, LMI)#2May 2013From Big Data to Smart Data (Semantic Days 2013)
  3. 3. Contents• The Problem with Big Data for BI• From Big Data to Smart Data• Success Stories by Ontotext#3From Big Data to Smart Data (Semantic Days 2013) May 2013
  4. 4. BIG DATA FOR BUSINESSINTELLIGENCE#4From Big Data to Smart Data (Semantic Days 2013) May 2013
  5. 5. The Problem with Big Data for BI#5From Big Data to Smart Data (Semantic Days 2013) May 2013
  6. 6. The Problem with Big Data for BI• It’s not only about Volume, Velocity & Variety• Too much focus on processing speed & storagevolume• “Brute force” approaches increase the amount ofdata processed…– But not necessarily the Value & insight derived from data– May lead to even more data quality & inconsistencyproblems– Problems with data visualisation & exploration– Often do not lead to better decision making#6From Big Data to Smart Data (Semantic Days 2013) May 2013
  7. 7. The Problem with Big Data for BI• BI success is not measured by Volume, Velocity &Variety, but by more derived Value• Organisations should learn how to better utilise their“small data” before targeting Big Data– Quality over quantity– Better understanding of the data leads to better decisionmaking– Avoid “needle in a haystack” situations#7From Big Data to Smart Data (Semantic Days 2013) May 2013
  8. 8. The Problem with Big Data for BI#8From Big Data to Smart Data (Semantic Days 2013) May 2013
  9. 9. Smart Data for Better BI• Efficiently analyse unstructured data– Most of the enterprise data is still unstructured– Even within structured & transactional data sources thereis a lot of embedded unstructured data– … and this unstructured data is poorly analysed (if at all) =>lots of potential value still remains locked– (sometimes even within semantic / Linked Data withinsufficient granularity)#9From Big Data to Smart Data (Semantic Days 2013) May 2013
  10. 10. Smart Data for Better BI• Focus on metadata first, Big Data later– (As opposed to: Big Data first, metadata later)• Enrich data• Interlink data• Provide a common metadata layer– Break legacy silos– Align heterogeneous metadata if necessary• Better analysis of the data, better insight#10From Big Data to Smart Data (Semantic Days 2013) May 2013
  11. 11. SUCCESS STORIES#11From Big Data to Smart Data (Semantic Days 2013) May 2013
  12. 12. UK Job Market Intelligence• Comprehensive recruitment database for the UK– 4 million job ads / vacancies (dynamic)– 220,000 company websites & 700 job boards monitored• Questions we can answer– What skills are in demand at present?– Which are the top job boards in a region?– Which is the right Job board for your industry sector?– Which are the most active job advertisers / employers?– Which are the agencies and employers that do notadvertise on your job board?#12From Big Data to Smart Data (Semantic Days 2013) May 2013
  13. 13. UK Job Market Intelligence#13From Big Data to Smart Data (Semantic Days 2013) May 2013
  14. 14. UK Job Market Intelligence• Technology stack– Web mining & focussed crawling– KB construction from open & proprietary data sources– Skills taxonomy (based on DISCO)– Text mining & semantic enrichment– Reconciliation & interlinking– BI reporting & dashboards#14From Big Data to Smart Data (Semantic Days 2013) May 2013
  15. 15. UK Job Market Intelligence#15From Big Data to Smart Data (Semantic Days 2013) May 2013
  16. 16. UK Job Market Intelligence#16From Big Data to Smart Data (Semantic Days 2013) May 2013
  17. 17. UK Job Market Intelligence#17From Big Data to Smart Data (Semantic Days 2013) May 2013
  18. 18. Asset Recovery Intelligence System (ARIS)• Support Financial Intelligence Units with trackingstolen assets, fight corruption & money laundering• Questions we can answer– What are the reported activities related to a person?– What is the person’s personal/professional network?– What are corruptions cases reported in regional news?• Data sources– News feeds from major news agencies– Dow Jones data & news feeds– SARs to the FIU– Open data (people & companies, Wikipedia)#18From Big Data to Smart Data (Semantic Days 2013) May 2013
  19. 19. Asset Recovery Intelligence System (ARIS)#19From Big Data to Smart Data (Semantic Days 2013) May 2013
  20. 20. Asset Recovery Intelligence System (ARIS)• Technology stack– Web Mining– Text mining & semantic enrichment (KIM)– ARIS ontology• People, companies, assets, relations, financial transactions, …– Reconciliation & Interlinking– Triplestore (OWLIM)– Semantic search & exploration UX– BI reporting / factsheets / alerts#20From Big Data to Smart Data (Semantic Days 2013) May 2013
  21. 21. Semantic Information Integration & Enrichment#21From Big Data to Smart Data (Semantic Days 2013) May 2013
  22. 22. Q & AThank you!@ontotext#22From Big Data to Smart Data (Semantic Days 2013) May 2013

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