Voice of the DBA

Tag: data analysis

  • Fuzzy Processing

    This is really interesting. A computer chip that doesn’t exactly perform mathematical calculations correctly. That sounds crazy, and in some cases it is. Most of us want math to be exact, and work perfectly every time when we run calculations. Can you imagine if Excel didn’t always add up the totals in your expense report according to the rules of addition?

    However in more complex operations, such as pattern matching for images, we might not want exact calculations. If two radar images (one problem in the article) don’t match exactly at the binary level, would we want a search for similar images to discard one? After all, exactness and perfect matching are great in some areas, such as financial accounting, but in other areas, such as imaging, there is plenty of noise that isn’t important to the content of the image.

    Do we care? Perhaps. As data professionals, I suspect that we will get more and more data of disparate types, including images, that will become part of our databases. There will be clients the need to search and query this data, which means that we may need fuzzy search tools that work well.

    New search tools will likely mean that we will need to learn more about how to tune queries, or even indexes, that might deal with data in a way that isn’t exact. I suspect that there will be opportunities for those that learn how to deal with these types of problems in an effective manner. There might even be really good paychecks that come along with these jobs.

    It’s a bit disconcerting to think that we might want computer applications that don’t work exactly as we expect with regards to calculations. However, I think the science of uncertainty will become more relevant and valuable to us data professionals in the future as we deal with lots of data that is more complex than a simple addition problem.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 2.6MB) podcast or subscribe to the feed at iTunes and LibSyn.

  • Democratic Data Science

    I know quite a few people that are jumping on the data science bandwagon, brushing up on statistics, learning R, and hoping to land one of those lucrative data scientist positions. I wish you good luck, and some of you will succeed, but I wouldn’t count on there being lots and lots of data science positions in the future. The work is hard, and it will take a lot of learning and practice to become good. Plus, perhaps we won’t need as many data scientists as some are predicting.

    There seems to be a lot of work being done with intelligent agents and “bots” that can perform some lower level analysis of data and interact with humans. However, there is also work being done with more capable software that might analyze data and find patterns by applying different algorithms and analysis to make it easier for business analysts to comb through the mountains of data. After all, the growth of IoT, of more metrics and measurements being made in all kinds of applications mean that we can’t just throw bodies at problems. We need better ways to work with large amounts of data.

    This work isn’t really aimed at eliminating data scientists or data analysts. Rather, this is aimed at trying to free up their time to do the creative, thoughtful work of examining data. The grunt work of playing with algorithms and testing them according to some measure of success is handled by software. I think that this is a mantra that will be repeated over and over in the next couple decades in computing. Grunt work is automated, thoughtful work is done by (well-paid) humans. If you’re not doing the latter, you might not have a job with the former.

    I do think there is still plenty of opportunity for data professionals, after all, we need to manage all that data, find ways to ensure it’s available and can be queried, data is somewhat clean, and certainly that we have some idea of what the data means to our business. I’m not sure software agents will easily be able to set those things up, though certainly they’ll do the work of applying our rules to large sets of data.

    There will still be plenty of data science roles, and certainly humans need to be watching the software to be sure it’s working well. There will always be tuning efforts, and perhaps always reports that users need help in building. However, as software becomes more sophisticated, there will certainly be less grunt work in our industry, just like many other industries have seen lower, or at least lower mid-level positions reduced in number. We should be prepared to prove our value and ensure our skills are useful to our organizations.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 3.0MB) podcast or subscribe to the feed at iTunes and LibSyn.

  • We Manage Algorithms

    “Every business is an algorithmic business.”

    That was a phrase that Microsoft’s Joseph Sirosh used in a keynote at SQL Nexus, talking about the future of software and data. Rather than managing data, many of us will move to manage algorithms, which will determine how data is interpreted, used, processed, and potentially returned to users as information. There are starting to be too many sources of data, too much data itself, being generated at too quick a rate, to the point where algorithms become more important than the actual data in examining, grading, interpreting, filtering, and more.

    This is exciting on one hand, with new opportunities for those that can develop, choose, write, tune, or enhance algorithms. I can easily see greater influence from both developers and DBAs as we work to better manage the floods of data. Especially with 50 billion sensors, IoT devices, and more that are predicted to be online in the 5 years. That’s potentially a tremendous amount of data being generated.

    On the other hand, this is a bit scary as separating good data from bad in the ocean of bits, and choosing helpful rather than hurtful algorithms might create lots of stress, and perhaps even fewer opportunities if few algortihms are reused. This also means we will need algorithms that can help us determine if data is actually good enough to use. After all, in the deluge, there will be bad data, that potentially needs to be excluded from queries. Will software developers become more important than DBAs as we end up with more unstructured data stores, data lakes, or other constructs that might require less administration?

    I’m not sure how things will change, but it will be an interesting world the next few years as we work with larger and larger, more diverse sets of data in our organizations.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 2.3MB) podcast or subscribe to the feed at iTunes and LibSyn.

  • Grabbing Fitbit Data

    Recently the T-SQL Tuesday topic was Power BI. A cool topic, and an interesting application.

    I saw Rob Farley build a dashboard based on his Fitbit data. That was intriguing, so I went to get my data. I’ve worn a Fitbit for a couple years, recently having lost it. However, when I went to export my data from Fitbit, I found this.

    2016-02-17 10_46_02-Fitbit Data Raw Export

    Notice two things. One is the top announcement that my data belongs to me. Then notice the bottom left note that I can get 31 days of data.

    Jerks, but feel free to substitute another word yourself.

    Then I found this Fitbit Data Export site. I connected and ran this, which grabbed my data. I appreciate there’s a Github link with the source code, though I have no idea of what’s in Github corresponds to what’s running in Azure. After reading The God’s Eye View, it’s a little nerve wracking to use a service to connect somewhere that I have data stored, but Fitbit exposes some of this publically anyways, and besides, it’s not critical data for me.

    In any case, I had a CSV of my data, which I am now going to upload into Power BI and play with it. Adding a CSV is straightforward.