Voice of the DBA

Tag: data science

  • Is This Data Science?

    In looking at the preliminary results of his salary survey, Brent Ozar noticed that female salaries seemed to be lower than males. The post focused on a simple analysis of data, the kind that many of us have done in our organizations. We will look at some data, notice some anomalies, and produce some report that allows others to look deeper or make a decision.

    The examination of the salary data was taken a bit further by Eugene Meidinger, who wrote a post that started the title with “Practicing Statistics“. His analysis was interesting, not trying to determine the reasons or causes, but just decide if there were some significant patterns in the data. Whether you think there are or aren’t issues with salary, I thought the statistical analysis was done well, and really a basis for what I would call data science.

    Data Science is a hot topic right now in many organizations. In fact, for the last year, quite a few organizations are trying to incorporate more data science into their applications, and the hiring of “data scientists” is rising, with higher salaries being paid. There are various definitions of the practice, with none being standardized. There are many curriculums out there from colleges, and even one from Microsoft. In fact, quite a few SQL Server people have completed that coursework.

    Data Science seems to mean many things, which is both good and bad. Like being a DBA, there is a lot of room for interpretation and quite a bit of variance in what we may do as a job. I’ve often made a good living as a DBA, being slightly out of the normal reporting structure, having autonomy at work, and usually able to make a difference to a variety of groups. However, I’ve also found that many places don’t want to hire a DBA or don’t think they need one, preferring to let some Windows admin or developer perform those duties. Microsoft doesn’t have DBAs as a job title inside the company, usually using IT Ops staff for those duties. That means it can be hard to find a job at times if many companies don’t think they need that position, which might be the case for data scientists as well.

    I prefer to think positively, that data science and data scientist positions are going to grow and be profitable for some of us. We’ll need to learn to have some statistical basis for our analysis, and certainly regularly improve our knowledge of the tools for things like machine learning, but we will find ways to perform data analysis that’s beyond what most business people would complete in Excel. I think we’ll find that those of us that work with data analysis will have lots of opportunities in the future, no matter what we’re called.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 4.3MB) podcast or subscribe to the feed at iTunes and Libsyn.

  • Can Data Save the World?

    Years ago I read one of the Freakonomics books. It was an interesting look at how we might examine our world in unexpected ways, using lots of data. While I didn’t always agree with the conclusions of the authors in different areas, I did find the idea of using data to probe and examine for patterns in our world to be fascinating.

    Recently I caught a podcast with the authors, and the opening question caught my eye. It was “can data save the world?”, and I was hooked, needing to stop and listen to the episode. That’s because I think data is the most important asset in the world today, and data is what really powers all the software we have. We need data, and we need to ensure our data has a high level of quality and integrity in order to glean information from all those bits and bytes to get value from software. Certainly software matters, but data, to me, is more important.

    The authors’ position is interesting in that they see data as an important part of analyzing data. The type of data, industry, organization, etc. doesn’t matter, but the data also isn’t necessarily enough to change someone’s mind or convince them of an argument. There needs to be a story as well, which requires a different skill from that needed for analyzing data. I do think that often the way in which we present an analysis can be just as important as the data. Perhaps even more so.

    What I do find interesting in the podcast is that younger companies (and people) are willing to embrace more data driven approaches. I see that often. There have been no shortage of clients in my career that were sure they knew the answer to some question about their business job without referring to any data analysis. They trusted their experience. Even if there was data that might show their conclusions were slightly erroneous, they often didn’t want to change their decision or conclusion.

    Humans are creatures of habit. Even those of us that embrace some change will find that we like change in some parts of our lives, but not others. If someone has had success in their career without using data to support or alter their opinions, it can be hard to get them to change. I wish I had a good method for convincing people to get started using more data, but really I think that the best idea is to find a different person to convince. If you can do so, and produce real results, then you may be able to go back to the first person and show them some evidence.

    The one part of the podcast that I wish turned out different is at the end, where the authors note there isn’t a good way to teach someone how to analyze data and tell a story. They thought about creating their own curriculum, but gave up. It’s too much work, and apparently they weren’t sure they could do a good job.

    They imply this is harder than teaching someone to program, which is disappointing as we have lots of programmers that need help as well. If we can’t teach programmers at any scale, what does that mean for data scientists? The one part I would agree with them on is that data science is a great area to move towards your career if you have the talent.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 4.3MB) podcast or subscribe to the feed at iTunes and Libsyn.

  • Citizen Scientists

    The last few years have seen quite a bit of demand for people that can analyze data. Certainly the Microsoft platform has embraced more ways to import and visualize data with tools like Power BI, more intelligent analysis with SQL Server Machine Learning and Azure Machine Learning. I think this is because there’s been quite a demand for data scientists, and with all the media attention, plenty of IT executives are searching for these people.

    It’s a good time to change to that type of career, if it’s what you want. You certainly should investigate what’s involved if you have any interest. Buck Woody has a Data Science series that can help you. Microsoft has a Professional Program in Data Science, and there are not shortage of R and Python resources that can help you experiment with data analysis in a new way. SQL queries work well, but you might find that having other tools in your toolbox is helpful.

    As much as there is a demand for professionals, Microsoft, Amazon, Google, and others are trying to find ways to reduce the cumbersome nature of the tools so that anyone that understands the science part can do the work. Will this mean the average business analyst be able to leverage tools and platforms to perform complex data analysis? Maybe, but I don’t believe these citizen scientists will remove the need for dedicated professionals. There’s an argument in this piece that they might, so you’ll have to decide what you think.

    The danger is that it becomes so easy to perform some analysis and create a visualization that we will likely have lots of people building reports and drawing conclusions without really understanding how they’ve aggregated or filtered data, perhaps without even understanding the implication of making these changes. I could see all sorts of poor decisions being made because a manager thinks anyone can use a tool to extract information from data, so they let just anyone do so.

    Maybe this is where the data scientist steps in. Help users to refine their analysis, understand the problems when data is put together or taken apart with these tools. Certainly they should ensure that users have good, clean data sets. The last thing we want is another IT bottleneck, but perhaps using highly technical people to review other analysis and ensure no fundamental mistakes are being made by the analysts is a good use of IT skills.

    This is another place where we need to ensure that different groups inside of a company can work together to be more effective for the organization. That’s the DevOps mentality. We get things done, regardless of who does the work or whose responsibility it is on paper.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 4.4MB) podcast or subscribe to the feed at iTunes and Libsyn.

  • Data Science en Masse

    GE is an amazing company. They seem to have so many divisions and produce a wide variety of products. Their financial investment success amazed the world and made Jack Welch an icon to many businesspeople. I remember reading about revolutionary management techniques at one of their jet engine plants that dramatically increased efficiency and performance of that location, similar to the ways that DevOps can produce better software. They have transformed the company from light bulbs to televisions to nuclear power and more across the decades.

    And they’re not done yet. GE is building a workforce for the 21st century, teaching many of their employees about data science and machine learning. The company is working to retrain scientists and help them explore new ways of using AI techniques to build better software. There are machine learning and data analytics courses available to employees, with the aim of creating hybrid employees that add digital skills to the knowledge they already have in other areas.

    Why is GE looking to transform it’s workforce with data science skills? They are creating AI software for their products and hoping to expand this further into more areas. With the competition from many other vendors, the ability to generate better results for clients, even just slightly better, might be enough of a differentiator to allow them to continue to grow as a leading industrial company.

    Would this work for your company? Your import organization, service company, retail business? Perhaps. Machine Learning isn’t perfect and doesn’t produce the best decisions, but if it can slightly improve the performance of your organization, perhaps it’s worthwhile. Microsoft is certainly making some of practical elements cheaper with the easy to use with new products, such as Azure Machine Learning.

    The challenge is that this is just the final element. Your company still needs someone that has built some knowledge of the deep mathematical concepts behind machine learning and has spent quite a bit of time experimenting with your data, building models and determining the relevant features needed. Preparing and loading data is also a challenge, which is why I think one of the core skills future data professionals need is the ability to quickly and effectively build ETL pipelines. If you can get those things done, a tool like Azure ML might be just the thing to add a few efficiency (or profitability) points to your bottom line.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 3.8MB) podcast or subscribe to the feed at iTunes and Libsyn.