I am looking to hire a data specialist and want to get the right person. I want this person to own all of the data dashboards and be able to build them out, do all his/her own data pulling for the db, while also analyzing what comes back and exposing opportunity. I want this person to be a product person and think about what we can build. Is this asking too much?
First off, I have several people I could introduce. I'd also like to know the industry you're operating in, what the data looks like, where it comes from, and how much it needs to be cleaned up if putting into a relational database, or if the better solution would be a distributed file system like MongoDB where you don't necessarily need to normalize the data. Also, if you're a startup, or if the company is well established with many existing customers and if this is for a new initiative.
It makes sense to have this person act as initial product and to derive the insights out of the data. They're pretty much the only person who can do this anyways, because they're the ones on the data. If you're in an early stage startup, I would recommend the strongest business owner (usually the early stage startup's CEO) be directly involved with this person on communicating what value your solution brings to clients, and what they pay you for, and in brainstorming on potential features and reports. Once the solution becomes established, and many customers start using it directly, there should be a different product person interfacing to those customers over time, gathering feature requests from customers and bringing it back to the Data Scientist/Analyst who spends their time working on the data.
Depending on whether the solutions are SQL or NoSQL or hybrid, there are different types of Data Science professionals you should consider:
1. Data Scientist
2. Data Engineer
3. Data Modeler/Analyst
1. The Data Scientist handles experimenting with the data, and is able to prove statistically significant events and statistically valid arguments. Normally, this person would have modeling skills with Matlab, R, or perhaps SAS, and they should also have some programming/scripting skills with C++ or Python. It really depends on your whole environment and the flow of data. In my experience, Data Scientists that exclusively use SAS are sometimes extremely skilled PhD level statisticians and focused exclusively on the accuracy of the models (which is okay), but often not sufficiently skilled to fit within an early startup's big data environment in today's world and handle all of the responsibilities you'd like them to handle described in your question. I'm am not bad mouthing SAS people as they are often the MOST talented mathematicians and I have a great deal of respect for their minds, but if they do not have the programming skills, they become isolated within a group without a Data Engineer helping them along. Often a SAS user trying to fit into this environment will force you to use a stack of technologies that a skilled Data Architect would not recommend using. It takes programming in some object oriented language to fit into today's big data environments, and the better Data Scientists are using hybrid functional and OOP programming languages like Scala. Extremely hard to find Data Scientist can also work with graph databases like Neo4j, Titan, or Apache Giraph.
2. The Data Engineer, if you're dealing with a firehose of data like Twitter and capturing it into a NoSQL architecture, this is the person who would prepare the data for the Data Scientist to analyze. They often are capable of using machine learning libraries like WEKA to transform data, or techniques like MapReduce on Hadoop.
3. The Data Modeler/Analyst is someone who can use a tool like SAS, SPSS, Matlab, or even R, probably a very strong advanced Excel user, but likely won't be a strong programmer, although perhaps they will have a computer science degree and have some academic programming experience.
The most important thing to watch out for is someone who is too academic, and has not been proven to deliver a solution in the real world. This will really screw you up if you're a startup, and could be the reason you fail. Often, the startup will run out of money due to the time it takes to deliver a complete solution or in the startup's case, a minimally viable product. Ask for examples of their work, and specifically dig into what it is that they did for that solution.
I've tried to cover a pretty broad range of possibilities here, but it's best to talk in specifics. I'd be happy to discuss this with you in detail. To answer your question, is it perfectly reasonable for someone to handle all of the responsibilities described in your question, if you find the right type of person with the appropriate skills, and a history of success.
Having a lot of experience as a leader of several IT teams I would say that the key traits are responsibility and a strong will to discover how things work.
Why do I put responsibility on top? Because such person will be working with your data, may be with critical or confidential data, and you should be really sure that you can trust in him/her. Moreover, you must rely on him/her that his/her output is right, without any mistakes. Often you do not have any ways how to check such outputs.
Secondly, it is a will to discover. According to me you need a person that widens his/her eyes whenever you come with a problem. Such people are happy to dive into the problem and come up with an analysis and a solution. You need a motivated person that will be thinking about the data trying to get much as possible from it.
Hope I clearly understood your need. :-)
Not necessarily, but the more you ask for the harder it is to find (and the more expensive once you do).
Any time I've been in the role of data specialist I've had insight in to product development and could see potential new opportunities as a result, but the main focus was always on creating analytics reports and tools that others on the team could use to measure performance, mine for new opportunities, etc.
You may want to consider a solution that gives more than one person the ability to perform analytics in a controlled way - without giving everyone full access to the database. There are a number of tools available that you can use to access and gather the data in a way that allows meaningful analysis by a wider range of personnel. Then the power of your analyst becomes their ability to come up with new data questions that feed the creativity of the whole team.
When you consider this approach, you might not even need a data specialist permanently/full time. If you outsource the development of a quality Business Intelligence solution the design and implementation should include a reasonable degree of flexibility so you and your team can ask more detailed data questions as they arise. This, and a little training could turn several members of your team in to data analysts.
Call me if you'd like to discuss more examples specific to your industry.