Sensika Intra.NET Reloaded Berlin 2013
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This document discusses the large amount of data available today and the issues with human processing of data. It proposes using algorithms and servers to analyze large amounts of data in order to maximize return on information. The document recommends shifting the awareness curve to the right through a single access point for data and shifting the action curve to the left through tools that are easy to use, relevant, personalized and flexible. This would inject collaboration into processes and provide benefits like increased reaction speed and decision efficiency while decreasing decision delays and barriers to use.
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Sensika Intra.NET Reloaded Berlin 2013
- 1. Actionable intelligence for the whole enterprise Lessons learned Konstantin Christoff
- 2. MY STORY BWL BANK @
- 3. RnD
- 4. HOW IT ALL STARTED?
- 5. OBVIOUS SUSPECTS 1. Manual data harvesting & processing 2. Periodical reports (.pdf via e-mail) 3. Isolated delivery INFORMATION INTELLIGENCE
- 6. THE ISSUE… 2.5 QBytes* *1 Quintillion = 1,000,000,000,000,000,000 bytes LOTS OF DATA
- 7. 2.5 QBytes* Some web-data facts: • 340 million Tweets a day. • 684,000 new shares on Facebook a day. • 72 hours of video on YouTube a minute. • $ 2.7 Billion spent on Web shopping a day. • 2 million search queries on Google a minute. • 27,000 new posts on Tumblr a minute. • 400 K comments on Wordpress posts a day • 3,600 new photos on Instagram a minute. • 571 new websites launched a minute. THE ISSUE… LOTS OF DATA WEB-ENABLED DATA Sources = IDG, McKinsey, The Economist, IBIS World Group, IBM
- 8. 2.5 QBytes* Some examples: • 2.5 Petabytes Wallmart transactions. • 10 Terabytes sensor data for 30 min flight. • 1 Terabyte NYSE data per day. • $ 6 Trillion Big data cost THE ISSUE… LOTS OF DATA INTERNAL DATA Sources = IDG, McKinsey, The Economist, IBIS World Group, IBM
- 9. 2.5 QBytes* THE ISSUE… LOTS OF DATA HUMAN PROCESSING
- 10. 2.5 QBytes* THE ISSUE… LOTS OF DATA HUMAN PROCESSING ACTION
- 11. 2.5 QBytes* THE ISSUE… ACTION ALGORITHMS & SERVERSLOTS OF DATA
- 12. STRATEGIC GOAL ROI on INFORMATION MAXIMIZE
- 13. QUALITY OF DATA ECONOMY
- 14. QUALITY OF DATA ECONOMY
- 15. QUALITY OF DATA ECONOMY
- 16. QUALITY OF DATA ECONOMY
- 17. Shifting the Violet Curve to the right
- 18. SHIFTING THE VIOLET CURVE TO THE RIGHT
- 19. SHIFTING THE VIOLET CURVE TO THE RIGHT AWARENESS SINGLE ACCESS
- 20. 1 EVENT PROCESS GAPS DISCOVERED 2 3 AWARENESS ACTION SHIFTING THE VIOLET CURVE TO THE RIGHT
- 21. 1 EVENT INTEGRATION OF APPROPRIATE TOOLS 2 3 AWARENESS ACTION SHIFTING THE VIOLET CURVE TO THE RIGHT
- 22. 1 EVENT PROCESS GAPS CLOSED 2 3 AWARENESS ACTION SHIFTING THE VIOLET CURVE TO THE RIGHT
- 23. Leaning the Red Curve to the left
- 24. LEANING THE RED CURVE
- 25. LEANING THE RED CURVE 1 2 3 4 5 6EASE-OF-USE ACTIONABILITY ENGAGEMENT INTERACTIVITY RELEVANCE ACCESSIBLE 7 8 9PERSONALIZATION AUTONOMITY FLEXIBILITY
- 26. LEANING THE RED CURVE AWARENESS SINGLE ACCESS COLLABORATION
- 27. AVAILABLE TOOLS Specialized, for isolated teams Collaborative, for the whole company
- 28. INJECT COLLABORATION IN PROCESSES DATA FEED MANAGEMENT AWARENESS RAISING KNOWLEDGE TRANSFER DECISION MAKING ANALYTICS TASK EXECUTION INSIGHTS DISTRIBUTION REPORTING
- 29. MARKETING
- 30. VERIFIED • REACTION SPEED • DECISION EFFICIENCY • EXECUTION PRECISION • PROCESSING CAPACITY • USAGE FREQUENCY • OPPORTUNITY UTILIZATION • KNOWLEDGE SHARING • CROSS-COMPANY INTERACTIONS • KNOWLEDGE TRANSFER • EMPLOYEE MASTERY • EMPLOYEE SATISFACTION INCREASE
- 31. BENEFITS • DECISION DELAYS • TOOLS IN USE • UNAWARENESS RISK • INFRASTRUCTURE COST • INFRASTRUCTURE RISK • PROCESSING COST • SUPPORT COST • PERSONNEL COST • BARRIERS TO USE • EMPLOYEE ISOLATION • TEAM SEGREGATION • CHURN RATE DECREASE
- 33. Konstantin Christoff e-mail: konstantin.christoff@sensika.com mobile: +359 896 863 500 THANK YOU!
Editor's Notes
- BWLciti fallen in love with the web technologies and 8 years back started my first company. 18 months back I’ve started my 4th project. And what we learned in this crazy intense time about the way corporate organizations function and would function event more efficiently in the future is something I want to share with you. I warn you up front: at times it may appear as pretty eclectic journey as we will stumbled upon all sorts of things like big data, quality of data economy, information intelligence, enterprise software consumerization, novel concepts of human resource management, even Leonardo da Vinci . But at the end – it made perfect sense, at least for us Everything for us started with a vaccine malfunction somewhere in Brazil. The pharmaceutical company that produced it has sold it to some health organization, this organization didn’t store it properly, the vaccine unfortunatelly caused a lot of serious damage once injected. For some reason, the producer learned about it whole 26 days later when it was really late. It could not intervene on time, spare a lot of suffering and the quite expensive litigation case afterwards. Because one of my partners in our current venture was a close professional witness to what actually happened, we’ve got a lot of specific insights to understand why it came to this. On prima vista it seemed the reporting system has let this pharma company down. Driven by inertia, it is neglecting the free web in general and because of processing
- During the RnD stage.
- Everything for us started with a vaccine malfunction somewhere in Brazil. The pharmaceutical company that produced it has sold it to some health organization, this organization didn’t store it properly, the vaccine unfortunatelly caused a lot of serious damage once injected. For some reason, the producer learned about it whole 26 days later when it was really late. It could not intervene on time, spare a lot of suffering and the quite expensive litigation case afterwards. We felt that we (me and my partners) have what it takes to change this for good:
- WE’VE TAKEN A DEEP LOOK INTO THE MATTER TO REALIZE THAT THE PHARMA COMPANY WAS LET DOWN BY ITS INFORMATION INTELLIGENCE SYSTEM.IT RELIED HEAVILY ON…..All these 3 points are so inappropriate for the times we live in. Why was it inaapropriate
- How you can process 2.5 QB of information produced everyday ?Sue Feldman: In today’s market, there are massive volumes and variety of data being generated at an alarming speed.
- It is physiologically impossible for the human brain to process all the available information. We all know why businesses need to process that data1.25 Terabytes: The amount of data the human brain can hold. It performs at roughly 100 teraflops. (Ray Kurzweil as cited by IBM’s Tony Pearson)
- …because they need to find relevant facts, patterns and relationships that help them act efficiently. Because gut feel is no longer acceptable.
- This is why the most competitive organizations globally turned to a combination of mathematical techniques and computing power to uncover vital intelligence that exists in the data. This empowers the businesses to tap into the wealth of available data to make decisions based on “what they know to be true” rather than “what they believe to be true”.In general every company out there tries to maximize ROI on Information
- This ROI on information is actually a sweet spot every enterprise is looking for, where at a affordable expense, the company gets data good enough to keep the losses due to low data quality in check.
- As we wanted to solidify and improve this situation, it was paramount to find ways to shift the violet curve to right and
- Lean the red curve towards left as much as possible
- And help companies end up with higher data quality at the same or even lower expense and as a result dramatically lower cost due to low data quality.
- During the RnD stage.
- It was relitively easy to realize that automation, unification and centralization would deliver on the first one.
- As for the second one we had our assumptions as all the partners involved were big business guys and they dealt with this for quite a long time. Our approach was a bit of empirical and a bit of scientific. We had our assumptions and we went to verify them with real businesses carefully chosing such from the pharma, energy, telecom, finance and fmcg. And within these companies we went to look for answers across the various departments. We’ve heard a lot of stories In the end the ingredients of the magic sauce that would help us lean the red curve shaped up
- During the RnD stage.
- As for the second one we had our assumptions as all the partners involved were big business guys and they dealt with this for quite a long time. Our approach was a bit of empirical and a bit of scientific. We had our assumptions and we went to verify them with real businesses carefully chosing such from the pharma, energy, telecom, finance and fmcg. And within these companies we went to look for answers across the various departments. We’ve heard a lot of stories In the end the ingredients of the magic sauce that would help us lean the red curve shaped up
- Quality of data industry chart
- COLLABORATION
- 19 Billion $ industry
- In today’s market, there are massive volumes and variety of data being generated at an alarming speed. It is physiologically impossible for the human brain to process all the available information to find patterns and relationships. Coupled with the diverse nature of the available data, the speed in which business is expected to react and make decisions, and the increasingly limited resources, “gut feel” decisions are no longer acceptable.Today, the most competitive organisations globally use a combination of mathematical techniques and computing power to uncover vital intelligence that exists in the data. This unique combination of technologies empowers business to tap into the wealth of available data to make decisions based on “what they know to be true” rather than “what they believe to be true”.