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Concerns About Big Data

DZone's Guide to

Concerns About Big Data

The biggest are similar to IoT, except privacy is more important than security.

· Big Data Zone
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We asked 13 executives who work in Big Data what, if any, concerns they had about Big Data.

Respondents:

Dr. Greg Curtin, CEO and Founder, Civic Resource Group | Mikko Jarva, CTO Intelligent Data, Comptel Corporation |Matt Pfeil, CCO Co-Founder, DataStax |Dan Potter, CMO, Datawatch |Gena Rotstein, CEO and Founder, Dexterity Ventures, Inc. | Puneet Pandit, Founder and CEO, Glassbeam | Philip Rathle, VP Products, Neo Technology, Inc. | Guy Kol, Founder and V.P. R&D, NRGene | Hari Sankar, VP of Product Management, Oracle | Ray Kingman, CEO, Semcasting | Scott Sundvor, CTO, 6SensorLabs | Vikram Gaitonde, Vice President Products, Solix Technologies | Paul Kent, SVP Big Data, SAS | Margaret Roth, Co-Founder and CMO, Yet Analytics |

Several had no concerns and saw only upside. Others shared the privacy and security of concerns of the general public but feel these will be overcome as people’s lives are made easier and they realize the benefits of Big Data.

Here are their specific answers:

  1. People are concerned that their personal data will be used against them. Big Data will be used to penalize you for what you are doing rather than helping you become better and achieve your goals. We’ll see aggregated data trends over time that will ultimately help people. The challenge is to get people comfortable that big data will be used to help them, not hurt them.
  2. Citizens are concerned about what the government can find out about me. I believe we’ll have more transparency with findings and knowledge thereby enabling us to identify those doing bad things with the data. All of this will lead to better information and more control over abusers.
  3. Very promising. There’s concern about the pace of development and the infrastructure so many more entrepreneurs can get involved without having to dive into the details behind it. Everyone shouldn’t have to build their big data projects from scratch.
  4. Don’t let the pendulum swing too far. SarBox hurt cross-border charitable giving. Because we’re building an airplane while we’re flying, we don’t understand the upside and downside of what we’re doing. Instead of looking at Big Data from a risk management perspective, we can look at it from an opportunity perspective since it will provide data showcasing the value it provides.
  5. Privacy and security. If it falls into the wrong hands it can be used for negative things.
  6. No, as long as it is kept secure and used to make people’s lives simpler and easier versus for nefarious means.
  7. The opportunity is huge. The value to be created is mindboggling. How fast can we grow? How many smart people can we find?
  8. Monetization, security and privacy. Connecting data across different entities we need to ensure those connections are secure. We are using more and more open source; however, the maturity of their solutions is an issue.
  9. No concerns. Set realistic customer expectations. We need more expertise. Can’t use Hadoop for every problem. Need a vendor that understands that as well as all the other tools.
  10. The expectations of the results being yielded by data lakes may be unrealistic. It could turn into a waste of time and money for companies. Legacy collections are structured in some format - semi-structured but consistent. Data in lakes are in a variety of formats that don’t align or join. They will be hard to join and extract meaningful information from.
  11. Fine line with regards to privacy so people are not exploited. You can learn a lot about someone from four or five pieces of information. We must maintain security around information. The ease with which you can put someone into a bigger picture, people don’t even realize it. Don’t be evil. Most banks have models to predict lifetime events. Great at catching bad guys - identifying medical fraud, tax evasion and identity theft. Stretching the understanding of network computing. Telcos are looking for the influencer of a group to predict, and address, churn. They’re going down to two levels of an individual’s call tree and creating relationship graphs.
  12. We’re putting more power in the hands of machines. If we’re not careful we’ll need to worry about machines optimizing the world to a point where people are not needed. We’re in the early phases of processing data. Worry about parties with malicious intent or the lack of the quality in the machine. Security is always an issue.
  13. We have technology able to enter every aspect of people’s lives. Companies must have ethics and protection around data. Even well-intentioned companies are not protecting data and keeping it secure.

What are your concerns around Big Data? Do they differ from those shared above?

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Topics:
big data ,analytics ,hadoop ,algorithm

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