How to Assemble the Perfect Big Data Team
Take a look at these stats from a special report published by Raconteur, a globally-renowned content marketing firm.
The infographic breaks down the current production of data into sections based on the source. It also predicts that by the year 2025, we will be creating over 400 exabytes (4 x 1020 bytes) every single day! Also worth noting is the 28 petabytes of data that are expected to be generated from wearable devices alone by 2020. That is possibly just a small fraction of the data that is expected from the millions of other IoT devices.
Given these statistics, there is no doubt that the future of corporations will be even more heavily data-driven than it is right now. If you feel that your business is becoming increasingly dependent on data, then it is time for you to look into big data solutions. This does not mean “Just buy a few fancy data analytics tools and your job will be done!” You need to bring in some people who specialize in this particular field and know how to use these special tools in the right way in order to turn your haphazard data into useful information.
What Is Big Data?
The term big data is quite self-explanatory and simple to comprehend. It means exactly what you are thinking right now! Big data is a very specific type of data that possesses the five below-mentioned qualities (also known as the five Vs):
-Volume
-Velocity
-Value
-Variety
-Veracity
Let’s examine these terms in a little bit more detail.
1. Volume: One of the qualities of big data is that it is collected in very large quantities or volumes. Hence, it is termed “high-volume”.
2. Velocity: This means the data is generated at a very high speed. In other words, if the data is being produced at very high speeds, it could qualify as big data.
3. Value: The data must also be valuable to its users in order to qualify as big data. The data should be useful or have the potential to be turned into something valuable.
4. Variety: A very important feature of big data is that there is no fixed data type for it. It is just a huge chunk of many different types of data all mixed up together.
5. Veracity: Some call it the most important V of big data. It refers to the usefulness and reliability of the data. One needs to make sure that the data is coming from an accurate, unbiased source, and is free from errors and noise.
The main use of big data is seen in the process of making critical business decisions. There are many industries that make use of big data, such as the finance, telecommunication, and healthcare industries.
Regardless of which industry your business is a part of, if you are generating data that falls under the ‘big data’ category, you need a big data team to help you make better decisions for your business.
How Do You Structure Your Big Data Team?
No matter when you decide to put together a big data team for your business, you need to keep one thing in mind: don’t hire too many people right away. You need to start with a small, manageable team of dedicated individuals who know how to do their job. Once they sort out the initial problems and fully understand the data needs of your business, you can ask for their advice and have an in-depth discussion and consultation with them on how to expand the team.
If you have finally made the decision to dive into the world of big data, then you have a few options on how to go about it.
There are three possible ways that you can structure a big data team for your business.
Use the in-house IT team
If your business is large enough or operates in the IT industry, then your organisation might already have a proper team of IT professionals. Instead of hiring new people, you can conduct extensive training sessions for your in-house IT team and teach them how to fulfil your big data needs.
They can be trained on how to use the necessary tools for data collection, analysis, and interpretation. This might help you to easily kickstart your data department. However, it may prove to be a less-than-ideal solution for your business’s data needs in the long-run. If your big data needs become far more complex over time, you might have to look toward other data solutions.
Build a brand new data science department
If you do not currently have a dedicated IT department, then the best option for you would be to hire new people who are big data experts. It is time for you to lay the foundation for a proper data science department for your business.
You will, of course, need to be aware of the various roles that you will have to fill in order to set up a proper big data team. We will discuss these shortly. Also, when you decide to hire new people, you will have to ensure that they are not only technically skilled but also understand business operations well enough.
Merge the existing IT team with a specialized data team
Do you feel like it will be too much of a hassle to train your existing IT team about big data from scratch? Will it also be too expensive to conduct fresh hiring for an entirely separate data science team?
If you answered yes to both of those questions, then you’ll be relieved to know that you can also choose the middle ground. Another option is to hire some data professionals and have them coordinate with your in-house IT team. They can then work together to achieve your data goals and business objectives.
Whom Do You Need to Hire for Your Big Data Team?
If you have decided to go with the second or third approach that we discussed for the structure of the big data team, you need to make sure that whomever you hire for your team possesses a combination of a variety of very different skills.
People who are part of a big data team are essentially responsible for bridging the gap between a) the raw data collected by the business and b) the decisions that are to be taken for greater business growth. Therefore, you should look for people who are not only proficient in programming, development, or using the latest data science tools, but also have some knowledge of how business processes work. Analytical and problem-solving skills, along with the art of critical thinking, are also a basic requirement.
Here is a list of roles that you may have to fill in order to assemble a complete big data team.
Data Scientists
The role of a data scientist is perhaps one of the most important roles to fill in a big data team. Data scientists are expected to be good at multiple aspects of data science. Many big data teams treat the role of data scientist as that of a team leader.
A good data scientist has knowledge of the various popular technical tools that are used in the field of data science, such as MySQL, Python, Apache Spark, and Hadoop. Along with that, they should also be familiar with the concepts of machine learning and artificial intelligence.
A data scientist must also be good at grasping business concepts so that they can implement their technical knowledge into data analysis and produce results that are in line with the business’s goals.
Software Engineers
They form a basic component of any big data or data science team. They are the ones who are responsible for writing the code for applications that the end-users (any other employees in the organisation) will use to collect and/or process the data. Hence, they must be exceptionally good at programming and other software development processes.
Instead of developing applications from scratch, they can also be given the task of configuring or revamping current systems to fit the changing big data needs of the business. They should also be able to advise the company on which development technologies should be chosen.
Statisticians
Statistics is an essential part of analysing big data. When data is collected in huge amounts, it becomes impossible to process, analyse, and interpret it manually. Therefore, you will need expert statisticians who are also familiar with the latest analysis tools in the market. Some of the commonly-used technologies are Stata and Perl.
Data mining, which is very closely related to data science and its processes, requires a thorough knowledge of statistical concepts and methodologies. Therefore, it is important to hire a skilled statistician for your team who can work on the quantification and analysis of big data.
Data Hygienists
Ensuring that the data is clean and error-free requires human intervention. Someone needs to oversee the data and make sure it is “clean”. That is what the job of a data hygienist is. A 2018 article published by Datanami states that it is impossible to have a data hygienist ensure bias-free data due to the fact that there exist over 180 known forms of cognitive bias. However, most people still feel that the role of a data hygienist is an important one.
Data Architects
As their title suggests, a data architect is responsible for coming up with the architecture that will be used to collect and store the data. They are also in charge of the maintenance and management of that data once it is in storage. Data architects are also expected to come up with appropriate data models designed according to the business’s needs.
They should be proficient in database design and management and also possess skills such as:
- The ability to handle new data technologies such as MapReduce, NoSQL, NewSQL, and data testing and visualisation tools
- The ability to come up with certain standards of data quality
- Knowledge of the software development life cycle including programming and design
Sometimes, data architects are also given the additional responsibility of that of a data engineer. This means they are also asked to work on implementation, testing, and maintenance of their designed data models and architecture.
Data Analysts
Once the data has been collected, someone needs to work with it and process it in order to make sense of it. That can only be done if the data is relevant to the needs of the business. A data analyst is responsible for these tasks. They make sure that the collected data is useful and then they make it undergo various processes in order to prepare it for analysis. Once the analysis process is conducted, the data analyst should also be able to identify patterns or trends in the data. The interpretation of these results is critical in that they would then be taken into consideration when making important decisions for the business.
Visualisers
As the name suggests, the role of a data or app visualizer revolves around the presentation of data. Large amounts of data, in the original form, are impossible to interpret. It is not easy to make sense of a large amount of data in raw form. Therefore, you need to hire someone who will be able to employ the latest visualization tools or technologies to make it easier for the other members of the organisation to extract useful patterns or trends from the collected data.
One thing to note is that this role is often filled by developers or other professionals from the IT department. Hence, you don’t need to exclusively hire a visualization engineer unless it is a very specialized role.
Business Analysts
The role of a business analyst is more focused on the business part than the data part. In other words, their job is to thoroughly investigate and explore the business goals and objectives and then communicate with the data science team to make sure that they are clear about the same.
For this role, you should look for someone who has an educational background and perhaps a few years of experience in the business department. They should be able to make sense of the latest data technologies, give advice related to them, and make sure they are in line with business needs. These should then be communicated to the business’s top management.
Chief Data Officers
The role of Chief Data Officers (CDOs) is not very refined. Usually, they are initially expected to act as the chainlink between the data and business sections, but later, their role may evolve to become similar to that of a data scientist.
Wrapping Up
No matter what structure you choose for your big data team, you will have to be careful with assigning the appropriate role to each individual member. In order to form a big data team which actively contributes to the growth of the business, you will have to train them properly about each and every business process of yours and make sure that the team then invests into only those data and analyses which provide some real value to the business.
