13 2792 big-data_keynote_presentation_finalpass_05_d_v02
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13 2792 big-data_keynote_presentation_finalpass_05_d_v02
- 1. 1 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 2. Big Data at Work Subtitle 2 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 3. 3 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 4. 4 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 5. 5 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 6. Thoughts 6 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Things Processes
- 7. Thoughts Thoughts 7 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Things Things Processes Processes
- 8. 1.3 Billion Today 12.5 Billion 22 2011-2016 2020 Smart Device Growth 8 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Data Production Increase
- 9. Produce Data 12% Executives who feel they understand the impact data will have on their organizations Use Data 9 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 10. Improve What Exists 10 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Create New Possibilities
- 11. 11 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 12. Run the Business 12 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 13. 13 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 14. Change the Business 14 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 15. Run the Business Organize data to do something specific Change the Business Take data as-is to figure out what it can do 15 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 16. Continuous Innovation Big Data at Work 16 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 17. Evolve to a Unified Information Architecture Acquisition Management Analysis Relational Transaction Processing Data Warehousing Reporting Dashboarding Forecasting Streaming On-Device Capture Caching Event Processing Machine-Learning Predictive Modeling Interaction Processing Low-Cost Storage Processing Discovery of Both Non-Relational 17 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 18. Big Data at Work 18 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 19. Get Fast Answers to New Questions 19 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Predict More, More Accurately Create a Data Reservoir Accelerate Data-Driven Action
- 20. Get Fast Answers to New Questions 20 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 21. Delphi Electronics and Safety Warranty Strategies Run the Business 340K Active data points in millions of vehicles spread across multiple databases Warranty Analysis Change the Business 21 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 22. Get Fast Answers to New Questions Exadata Relational Data Flexible Data Model Hadoop Non-Relational Data 22 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 23. Predict More, More Accurately 23 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 24. Dell Computers Inc. Social Media Website Machine Learning Algorithms Offline Customer Data $132M Incremental Revenue in 2012 Big Data Farm 24 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 30% 30% Increase in Conversions Increase in Satisfaction
- 25. Dell Computers Inc. Real-Time Decisions Machine Learning Algorithms Potential New Data Technical Services and Sales 25 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Email Correspondence Social Media
- 26. Create a Data Reservoir 26 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 27. Large, Full-Service Bank 16 % 15 Necessary data that could be pulled from their source systems 27 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Different nightly extracts, resulting in multiple data marts
- 28. Large, Full-Service Bank One Reservoir to be accessed at any time % 85 Gained from Big Data at Work 28 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 29. Large, Full-Service Bank Data Reservoir Big Data Appliance + Hadoop 29 Exadata + Oracle Database Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 30. Accelerate Data-Driven Action 30 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 31. Major European Bank Internal/ External Non-Relational Data variables tracked for each customer Wanted to deliver location-based service 31 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 32. Major European Bank Relational Data Filter Processed Data BUY NOW Non-Relational 32 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 33. Get Fast Answers to New Questions 33 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Predict More, More Accurately Create a Data Reservoir Accelerate Data-Driven Action
- 34. Tightest Integration of Big Data into Business Analytics Big Data Discovery in Weeks Not Months Predictive Analytics For All Big Data Environments Best Price/Performance Big Data Platform Fastest Connection Between Big Data Analytical Environments Big Data at Work 34 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 35. Electric Car Smart Grid Electricity at Work 35 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Big Data at Work
- 36. 36 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
- 37. 37 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
Notes de l'éditeur
- Hello, welcome, etc.An apology for human heart, truth, beauty? Those days are gone for all the reasons that McAfee said.Industry going to go through the Moneyball experience. Movie scene: Great face, pretty girlfriend means confidence. But it’s not true.Data helps you overcome biases.What’s moneyball like for you? What aspects of your business transformed by data? And how do you get there first?Can’t think of industry that won’t be affected by big data. Big data is electricity of 21st C
- In 1752, Benjamin Franklin conducted the now famous experiment of attaching a key to his kite and flying it during a thunderstormThe sparks that jumped from the key to his hand confirmed that lighting was, indeed, electric in nature.Knuckles close to key and sparks jump. Captures in leden jar – proved lightning same as what he could do in his labProblem he was trying to solve was lightning strikes – instaled first rods that summer
- His discovery, and the work of many other inventors in the field of electricity (i.e. Nikola Tesla, Thomas Edison, Alessandro Volta), went on to change the world in ways previously unimaginableThe power provided by electricity opened the floodgates of innovation in business, government, and private lifeTook a while to make electric useful – change factories, lights in houses, etc. Huge
- Today, we are on the cusp of the same magnitude of transformation thanks to dataficationDatafication is the capture and use of information in more daily activities, and we are seeing this happen everywhereBig data is at the same stage. Huge wave of innovation coming that come from capturing dat.Datafication is just as big a deal.
- The datafication of things through sensors collecting information from cars, medical devices, stop lights, and factory equipmentOur daily activities and processes are experiencing datafication and have been for years. You no longer have to go to the bank to deposit a check; you can use your phone. Running on the treadmill at the gym? Chances are good you might be wearing a heart-rate monitor or a Nike Fuel Band to track your physical activityEven the natural world is being data-fied through the use of satellite imagery and climate sensing devicesThe sheer amount of data captured in our day-to-day activities is astounding.In just one minute, Facebook users share over half a million pieces of content (Source: http://www.visualnews.com/2012/06/19/how-much-data-created-every-minute/)And it’s not just people generating data: “a single jet engine can generate 10TB of data in 30 minutes. With more than 25,000 airline flights per day, the daily volume of just this single data source runs into the Petabytes.” http://www.oracle.com/us/products/database/big-data-for-enterprise-519135.pdfEngines aren’t owned. Airlines buy the thrust. So need good data on what that engine delivers.
- All this new data holds tremendous power and potential to change the way our organizations do businessWhether it be capturing the thoughts and opinions of our customers to create better marketing campaignsUsing sensors to manage buildings, capital equipment, and improve maintenance and service costsOr streamlining processes based on new data insights, the possibilities are endlessOracle bought Proteus. Pills swallowed. Stomach acid supplied power. Reduces readmissions.Huge datafication of all.
- While we are in the early stages of datafication, all signs point to continued growthSmart Devices are predicted to grow from 1.3B in 2013 to 12.5B in 2020And data generated from “things” is growing at a rate of 22 times over 5 years, from 2011-2016 (Source: IDC 2011, Cisco,, Cloudera, and Machina Research http://blog.iobridge.com/2012/02/cisco-reports-mobile-internet-of-things-traffic-to-grow/)Don’t run into things that grow that big that quick.What’s not to like
- However, while we are creating and collecting mountains of data, our ability to produce it has outstripped our ability to use itAccording to a study we conducted with The Economist Intelligence Unit, only 12% of executives feel they understand the impact data will have on their organizations over the next three years.” (Source: http://www.oracle.com/webapps/dialogue/ns/dlgwelcome.jsp?p_ext=Y&p_dlg_id=13367869&src=7634271&Act=143 )A great example of this is airlines Airlines were one of the first data innovators, going all the way back to the 1960s when they began using SABRE: the first online ticketing system and one of the first big enterprise applications.Today, data is still very important to airlines that publish an average of half a million fares every day, and update them four times per day.Most have large analytics teams, with dozens of operations research analysts.But even with a large team dedicated to analytics, they throw away their fleet operational data every day because it’s so big there’s nowhere to put it and analyze itAs a result, they don’t have access to the potential insights this data holds (Source: a presentation by Jim Diamond, Managing Director of Operations & Research at American Airlines. Given at the Evanta CIO event in Dallas, TX 6/7/13)The same is true for many businesses: the information they need to improve products and services already exists, they’re just not quite sure how to use it.
- Electricity followed a similar two path trajectory.Benjamin Franklin conducted his experiment in 1752, but it took a century for his discovery to move from a scientific phenomenon to something with practical implicationsWhen we think of how electricity changed the world, we often think of the major innovations that replaced previous methods of generating energyThe incandescent light bulb replaced dangerous kerosene and gas lampsThe electric locomotive replaced the steam engineElectrical power transformed factories and production facilities, ushering in the Second Industrial RevolutionBut electricity also changed the world by providing a platform for the creation of products that never existed prior to its discoveryMicrowaves, toaster ovens, and dishwashers are just a few examplesToday, it’s difficult to imagine a life without these modern conveniences.PS:Don’t worry. This has happened before. Electricity to a similar path. Benjamin Franklin conducted his experiment in 1752, but it took a century for his discovery to move from a scientific phenomenon to something with practical implications. The same is happening with big data. And a lot faster.But this raises a new question: How do you make big data useful? Every company is asking this question right now. But for large organizations like yours, the question is slightly different. How do you bring big data into the enterprise to make it useful? You already have hundreds of millions, if not billions, of dollars invested in analytical technologies. How do you bring big data into the enterprise analytical environment to make it useful?To understand this, we need to look at big data’s effect on the enterprise analytical environment.Don’t have 150 years with big dataNot a greenfield. So how do you bring big data into the existing environment with $MMM already invested. What effedt does big data have in what exists
- Until recently, most companies have been extracting value from data by carefully selecting and standardizing the data collected based on pre-determined relationshipsWe call this the “Run the Business” approach to big data because it is primarily about keeping existing systems and processes functioning properly.PS:You already have analytics you use to run the business – data warehouses, reports and dashboards. You carefully select and standardize the data you need to solve a specific problems, like running a marketing campaign or billing customers. Relational, analytical envs run the business
- This enables companies to solve very specific problems, like automating customer bill pay, running supply chain systems efficiently, or confidently closing fiscal periods with accurate numbers—and is a powerful way to reduce the time, cost and effort of standardizing and controlling processes This is the world of the relational database that modern companies and economies run on todayPS:This is a powerful way to reduce the time, cost and effort of standardizing and controlling processes to run the business. The data coursing through this enviroment will increase in volume and velocity – it will get bigger and arrive faster than today. You’ll need more processing power to handle this, but that’s pretty straightforward.
- Now there’s something new. New streams all different. Not easy to represente relationally. Oportunity to learn from data before org into model is where you can change the business.Learn things about your business, supplietsetcHowever, with the magnitude of data now available (proprietary and third-party), it is not always clear which information might be useful By examining the data in a non-relational environment and letting it tell YOU what you can learn from it, companies are able to form and test more hypotheses more quickly, resulting in new insights they would have missed otherwisePS:But that’s not all. The increase in variety, volume, and velocity from the datafication of everything opens a new possibility – the possibility to learn from the data in new ways. Now there’s a huge amount of available data, most of it captured by other organizations. But it’s not always clear which data might be useful to you or what you might learn from it. By examining the data in a non-relational environment and letting it tell YOU what you can learn from it, companies are able to form and test more hypotheses more quickly, resulting in new insights they would have missed otherwise
- We call this the “Change the Business” approach because new ideas uncovered through learning from the data often leads companies to make changes, or pivot processes and systems to achieve better results
- The critical difference between the run-the-business and change-the-business environments boils down to one thing:To run the business, you organize data to make it do something specific; to change the business, you take data as-is to figure out what it can do for you.Relational technologies excel at the first, non-relational technologies at the second.
- These two approaches are more powerful together than either alone. Real problem is to bring the two together.Rivals will be running the experiemtes to get their first. You need to get their first.Like the electrification of the 21st century, big data is about powering your business AND providing a platform for innovation.But to bring a non-relational environment into the corporate fold, it has to have the same basic capabilities as the relational environment the company already counts onIt has to acquire, manage,and analyze, whatever data happens to be in it, just like the traditional relational environment
- To bring big data into the enterprise analytical environment you’re going to need all the standard enterprise capabilties for data acquisition, management, and analysis for relational, non-relational, and streaming environments (which can be both).For acquisition, this means you’ll need relational databases as well as NoSQL databases, plus super-small-footprint Java embedded in devices for real-time capture.For management, you’ll still need your relational data warehouses, and they’ll be complemented by Hadoop clusters plus real-time caching and event processing.For analysis, you’ll still use BI reports and dashboards, and they’ll be complemented by non-relational discovery plus real-time recommendations, alerts, and predictive analyticsThese technologies will be deployed on-premise, as well as in private and public clouds, depending on your needs.But the real magic of big data at work is having these relational, non-relational, and streaming environments seamlessly integrated together.What if you could do that? What if you could have acquisition, management and analysis for any kind of data for any purpose you could imagine? What would it mean to you?Not all data speeds up to real time, but some of both will.Go through the table and talk about how they will all interact. This is big data at work from a capability standpoint. Lofot work
- Only Oracle creates products for every aspect of this unified architecture. Rather than relying on custom solutions So we can help with all that work. We have one of everything. Link the various products back to previous page. Can all be deplyed in cloud as well.But what would it mean to you to buy this stuff?
- An integrated big data solution will enable you to…Get fast answers to new questions Predict more, and more accuratelyCreate a reservoir of data for potential reuseAccelerate data-driven actionLet’s look at a couple companies realizing the benefits of big data at workHere’s what it would mean (cover each of these categories and give a sentence or two on each of 4So let’s look at some stories.
- Whether you’re analyzing data in a run, or change the business environment, it’s common for new insights to lead to new questions which, in turn, requires a deeper dive in to the data for answersThis process may sound never-ending, but has the potential to present new marketing opportunities, and help you discover information for solving lurking product issuesThis is the heart anssould of learning about data before it’s organized (eg cholera in haiti)
- ProblemDelphi Electronics and Safety , a division of leading global auto parts supplier Delphi Automotive, had a data analysis challenge: they needed to determine if the performance of certain parts were meeting contractual levels and if improvements were neededSeems straightforward, right? Not so much. Delphi receives huge amounts of warranty data generated by its customer. Every month, the automotive manufacturers (OEMs) delivers performance data including verbatim text descriptions of issues related to its 340,000 active parts in service in millions of vehicles worldwide That’s data from over a dozen different OEM systems, each with its own distinct format, as well as data from Delphi’s own parts databases, manufacturing systems, and industry data. Additionally, Delphi had to adhere to strict time guidelines to provide responses to performance issues with parts—including a complete analysis to support their response—or be financially penalized. The real challenge was the diversity of the issues. Delphi’s warranty engineers needed to quickly combine and explore a variety of customer data sets based on the issue under investigation. Warranty Engineers were spending more time manipulating data than getting answers from it. Contractually required to pay unless prove it’s not there. All investigations different.CLICKSolutionIn the first month alone, engineers discovered the root cause of three field performance issues that could have cost them lots of money. Since then, Endeca has paid for itself many times overBut more importantly, their warranty analysts could now spend more time investigating issues and less time manipulating data. The shift in this work was so great that they had a new idea. They realized they could have a warranty strategy for each of 20,000 individual parts they manufacture and ship at a rate of 7 million pieces per month– an unprecedented innovation.This is the power of Big Data At Work – creating discoveries in a change-the-business environment which are then injected into run-the-business processes and applications to perform at a higher level. Too much data to meet 30 day window.EID pour data together without modeling. Why matters? Paid for itself in 30 days by proving not responsible for a claimBut the bigger prize was new warranty plan for 20000 parts. Much ore granular. Feed also info into design to improve products.Fast answers to new questions means more problems solves, more granular faster.
- Using Endeca Information Discovery, Delphi combined these sources without having to build a model first. By indexing all the necessary data – with its diverse structures, the text with no structure at all – Delphi combined the data without having to predetermine a model to hold it. This is extremely important when you can’t know ahead of time what questions you’ll want to ask.By combining diverse sources of data types together, warranty engineers were able to quickly explore the data using Endeca’s easy to use visual analysisBringing data together. Fastest onramp to big dataBI platform can be indexed into EID. So all that investment you’ve already made comes into discovery env. Also now have native connector to HIVE and HDFS data.
- Big Data has the potential to enable organizations to better predict customer action and forecast for the futureAnd, the more precise your organization can be at predicting the future (or potential future), the more you are able to adjust, reallocate resources, and reduce uncertaintyOne of those ways is real-time pricing to meet the customer demand—taking into account the unique individual, their needs, and their specific price sensitivity It can also help you prevent negative outcomes, like fraudCapturing additional details about customers can require you to upgrade your current data warehouse capabilitiesBut like adding insulation to an old house to improve energy efficiency, the investment pays offNate Silver book “why most predictions fail and some don’t” use data appropriately
- Dell is a great example of a company using predictive analytics to improve their customer experience with targeted cross-sell and upsell offers.Dell brings together data from its website, social media channels, as well as offline customer data into a big data farm. This big variety of data then drives predictive analytics for promotions at the website, in the call center, in email campaigns, and even on-demand print materials.Since deploying the system, Dell has realized $132M in incremental revenue for FY12They have also seen a 10% increase in revenue, and a 20% increase in profit margin per call at their call centersThis also is the power of Big Data At Work – predictive analytics driven by machine-learning algorithms chewing on masses of diverse and changing data. This new non-relational technology is now integral to Dell’s cross-channel customer experience. Here, change-the-business analytics have become the way Dell runs its business. Making many small decisions correctly adds up over time,All this data made avail to RTD, recommends offers, etc, Dell has seen the aboveFeed all this data into algorithms, tracks what happens, iproves thingsUse it for on-demand print to determine what offers printed. Big data and physical world
- They are currently using Oracle’s Real-Time Decisions in 15 countries and 30 languages in their call centers for technical service and sales, email correspondence, and social media to offer personalized, targeted product and service recommendations across multiple channels.Each of these channels is able to perform self-learning decisions to optimize the next best action.Real-Time Decisions is the non-relational predictive analytics technology making all these predictions. It learns from the responses and adjusts its algorithms to continuously improve. It also adjusts its algorithms to use new data sources Dell believes will be valuable.
- Information is unlike almost anything else in that it is not used up when you use it. You can reuse it and even repurpose it infinitely.The reason this matters is that all of you are sitting on data assets with huge potential value. According to McKinsey, the vast majority of American companies store more data than the US Library of Congress. But most of it is locked away in separate buckets. What if you could pour it all together into a great big reservoir, ready to be tapped at any moment?That would be great, but how can you do this in a cost effective way when you don’t know what value the reservoir will produce?What is the option value of data? Unlike electricty you can use and reuse data.EIU (Kukier, Mahershomberger) Value created by secondary uses of data. (cfPassur)
- We worked with a large, full-service bank faced with this exact problem. The bank had to comply with regulations requiring more data to support stress testing. But there was a problem. The bank could only pull 10-15% of the necessary data from their source systems, which took 16 different nightly extracts, resulting in multiple data marts. Plus, managers also suspected there would be new requirements to the stress tests which would start this whole process over again.So they needed to evolve their information architecture to support their run the business relational warehouse with a change the business data reservoir. The bank is reaping the benefits of lower costs thanks to the reduction in the number of data marts, duplicate data stores, and fewer extracts. They can now also work with all their data; not just the 10-15% they could access beforeTheir big data solution added the missing 85%. And because they are not Hadoop experts, they appreciated the speed, time to value, and overall TCO of an appliance So created a reservoir. All into Hadoop on BDANow got access to all the data.
- The bank is reaping the benefits of lower costs thanks to the reduction in the number of data marts, duplicate data stores, and fewer extracts. They can now also work with all their data; not just the 10-15% they could access beforeTheir big data solution added the missing 85%. And because they are not Hadoop experts, they appreciated the speed, time to value, and overall TCO of an appliance
- They used a combination of Oracle’s Big Data Appliance with Cloudera’s distribution of Hadoop and Exadata running Oracle Database, and they seamlessly integrated those two together using Oracle’s Big Data Connectors and extreme network performance provided by Infiniband.For those of you not familiar with Hadoop, it’s a non-relational method of storing data and processing it. This bank filled the Big Data Appliance with data from legacy mainframes, operational databases, enterprise applications, and more. They created a great, big reservoir of diverse data that’s ready to be tapped and siphoned into the enterprise warehouse at a moment’s notice.Not only is this bank prepared for future changes to the stress tests, it’s now also prepared to do customer, product, and process analyses that would have been cost-prohibitive before.They are prepared for the inevitable changes in stress test and also new kinds of analysis. Data is accessible for repurposing.
- One of the most critical day-to-day necessities of big data is being able to operate at a high-speedIn a constantly changing business environment, the value of real-time analytics can reduce fraud, and help your workforce create fast and accurate reporting based on the most up to date information Some data arrives quickly and want to take action really quickly
- La Caixa is another example of a bank that needed a big data at work solutionThis bank was looking to monetize the relationship they have with their existing customers by delivering a location-based serviceThey do this with customers who have joined their shopping club looking for potential dealsWhen a customer uses an ATM, the bank knows where they are. They can use this information to deliver a relevant, targeted advertisement or message.But in order to make the message relevant and targeted they have to know the customer well. So they build a model that incorporates internal (to the bank and customers) and external social media, purchase records and location information (where has the customer purchased items, interacted with the bank, used ATMs)
- From this model of both relational and non-relational data, they can build a model of potential interests.Once the customer uses an ATM they take the location, what the model tells them, and a list of partner merchants with offers and discounts. They automatically send the offer that seems to be the best fit to the customer’s mobile phone.They monitor the success and failure of the offers to improve their knowledge of the customer and increase future success rates
- An integrated big data solution will enable you to…Get fast answers to new questions Predict more, and more accuratelyCreate a reservoir of data for potential reuseAccelerate data-driven actionLet’s look at a couple companies realizing the benefits of big data at workThis is what we are talking about – run and change business togetherHere’s why we think we can work with you
- With products created for every place in the big data picture, Oracle’s portfolio provides customers with…The highest performance real-time data collectionMarket-leading data management solutionsOnly enterprise self-service Information DiscoveryBroadest range of analytics for every needBest performance with Engineered SystemsNo-Compromise deployment options: cloud, on premise, =hybrid.Unparalleled security and privacy featuresLowest total cost of ownership to procure, deploy and maintainTight integ into what you haveDiscovery in weeks not months, faster and change requirementsPredictive analytics for all envs. RTD complemented by OAA (Turkcell/fraud example)Best price/perf – see ESG paper. Exadata perfFast connection between the envs (eg Hadoop connector, fast IB etc) And ODI/HadoopSo fastest path because we make products for all these tieres.
- Today, more than 250 years after electricity was discovered, people continue to use it as a platform for innovationThe electric car and the smart grid are just a couple examplesWe are in the early days of Big Data and already, we are experiencing the tremendous value and power it has to change our worldReturn to the startTook a long time for elect1893, Worlds Fair, 200,000 bulbs. Lamps were gas inhouses. This was still “magic”. Outside fair, world dark.Same with data. World is waiting for your ideas and what you can do with your data. Look forward to working with you on this.
- It’s time to think about building a big data strategy that will give you a competitive advantage for years to comeThank you!
- So what is the next step for your organization?Maybe you’re in need of a change the business big data environment Or perhaps you are looking to add processing power to your current run the business big data warehouseRegardless of where you find yourself, let me give you three ideas to consider as you build your big data strategy
- Look beyond the value of data captured from the daily activities of your organization to the extended value chainEverything every player in your industry does produces dataWhat share of the data available does your company have access to?You might need to consider buying external dataOr extending your capabilities to capture more proprietary dataThink of this as an investment in a powerful, revenue generating source of energy for your organization
- Even an investment in buying or capturing data available to your competition can give you a competitive advantageBy mixing publicly available data with your proprietary data, you make the entire collection proprietary
- And using proprietary data can give you greater data market shareThis is a staple internet strategy:Google uses data from search result click-throughs to refine results for the next person who searches for the same term; Amazon uses data about customers’ past purchases to create product bundles and recommendations for other shoppersUsing data to make data opens up a first-mover advantage, and is effective in even the most traditional industriesFor example, a shipping company can use package-level sensor data to cut the cost of handling perishable goods, opening up the service to a new marketBy capturing the data exhaust from new customers using the service, they are able to refine the service to better meet customer needsThe cycle of data capture and use creates a competitive advantage that is very difficult, if not impossible for rivals to catch