Lately, it's all about data, and it makes sense. With around 2.5 quintillion bytes of data created every day and an estimated 163 zettabytes of digital data to be generated by 2025, it's no wonder companies are rushing to make the most out of the information their users generate daily. But, leveraging this massive amount of data calls for very specialized help. To do so, companies may look to IT outsourcing companies or other providers to help them fill the gap, but a lack of clarity on the type of professional or area of specialization needed may hinder their efforts.
For companies looking to make the most out of their data, data scientists, data analysts, and data managers are usually the best fit to help identify, collect and evaluate data. However, each role has varying characteristics and depending on the goals of the organization, it's important to select a person with the right skills to face these data challenges. Consequently, it's not uncommon for companies and recruiters to feel confused when it comes to hiring a data-oriented specialist. It's even more important to hire the right person for the job not only to save time and money but also to ensure that a person is able to grow and feel challenged in the right role.So, data scientist, data analyst, or data manager: what's the difference?
[READ MORE: How Much Is Your Business Getting Out of Big Data?]
Science, Management, or Analytics
When we talk about data, we're talking about information. So, the difference between these disciplines is how deep a specialist goes into the analysis of that information. That's the short version, anyway. The longer version requires we consider a couple more factors than just depth of study.
For instance, data science looks at the bigger picture, covering various disciplines and understanding where information comes from and how to capture the right info. A data scientist can predict trends, explore data sets—structured or unstructured—and use statistics and mathematical models to inform decisions.
Data analytics, on the other hand, also deals with data analysis, but is generally more interested in gaining insights to specific questions or objectives. A data analyst works with databases and manipulates data to either support or challenge a given strategic decision but won't necessarily consider information that does not pertain to it.
Lastly, data management focuses on making sure data usage and operation is efficient and effective. A data manager oversees the integration of data management processes with other processes to meet service level requirements, and they act as an interface to other groups, like business systems and data center.
Think of it this way: a data analyst is scavenging a trail, finding tracks, and following a map to find a pot of gold. A data scientist is looking at the land, reading the natural signs and making associations between different elements to find ways of hitting a gold-bearing vein. A data manager is making sure the gold extraction operation is running smoothly.
Hiring your data specialist
An offshore software development provider can be a great help selecting a specialist with the right skills for your project. However, it's vital you know exactly what they should be looking for. Therefore, when it comes time to hire either a data scientist, a data manager, or a data analyst, make sure to keep an eye out for these skills and qualities:
- Should know all about database systems (MySQL, Hive, etc.)
- Should also be familiar with Java, Python, MapReduce job developments
- Should understand analytical functions like median, rank, and others, and how to use them on data sets
- Should be a mathematical marvel, great at statistics, correlation, data mining and predictive analysis. (Better analysis = better business decisions)
- Should know 'R' like the back of his hand
- Should be able to glean deep statistical insights and utilize machine learning – Mahout, Bayesian, Clustering, etc.
- Should be familiar with data warehousing and business intelligence concepts
- Should have in-depth exposure to SQL and analytics
- Should know all about Hadoop-based analytics (HBase, Hive, MapReduce jobs, Impala, Cascading, et al)
- Should be a master of data storing and retrieving
- Should handle tools and data architecture components expertly
- Should know several ETL tools to transform different sources into analytics data stores
- Should be able to decide on critical business features in real time
- Should be an effective and confident decision maker
- Should have a thorough understanding of data management and data administration principles
- Should be familiar with modern databases and IT systems (Oracle, MySQL, MongoDB or others)
- Should be awesome at processing and evaluating large amounts of data
- Should work well under pressure—data managers need to be able to handle problems that arise efficiently
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Your specialist should also come with a set of transversal skills that'll make it easy for her or him to work with a team and achieve objectives with ease. Keep an eye out for the following:
- Communication – clearly express themselves, report problems, and interface with coworkers
- Teamwork – share the workload, support colleagues and receive help
- Adaptability – find alternate solutions and remain flexible when plans fall apart
- Problem-solving – focus on acting to remedy a situation rather than complain or wait for someone else's intervention
- Critical observation – spot patterns, notice signs and interpret them
- Conflict resolution – address issues with coworkers directly and delicately to arrive at a compromise that allows them to work harmoniously with the team
- Leadership – rally coworkers and get them on board with new ideas
Need help finding the right data expert for your needs? PSL's got you covered.