There is no sector exempt from the insight that data can bring. Whatever industry we consider, the acquisition and study of data are critical to understanding consumer behaviour or how our businesses can work smarter and harder to improve their service.
It helps us to understand and predict our customer behaviours. It allows us to analyse what has worked and what hasn’t; we can make educated business decisions as a result. This is a science that is complex and requires deep levels of data analysis.
This emerging world of incredible insight doesn’t happen by itself. It requires a team of highly educated and talented individuals all working together, intricately combining their area of expertise to build a detailed picture of the business.
Building a data analytics team consists of finding a range of data professionals, bringing them together to work as one unit.
Who are these people, and what roles do they perform? This post is to help you if you are looking to build or grow your data analytics team. We will look at the roles you ought to be hiring for, and what each of these incredibly smart individuals will bring to the table.
Building a data analytics team: The roles.
Data Engineer.
One of the main functions that a Data Engineer performs is to compile, store, and analyse data on a large scale. Through various programs, they collect vast amounts of raw data and turn this into data that can be used by Data Scientists. Trying to work without a data engineer will be tricky. Without them, you will find it difficult to transform the collected data into usable information for the rest of the team.
Data Scientist.
The role of the Data Scientist is to use advanced mathematics and statistics to make viable predictions based on the collected data. The work of the Data Scientist allows actionable plans to be made by the company execs. Among other things, these predictions may be geared toward ways to increase sales, keep your current customer base, and secure new ones.
Data Analyst.
This specialist plays a critical role. They translate the findings into language that can be understood by non-technical teams. Their reports combine what the scientist finds and their interpretation of it. The Data Analyst cleans and interprets the data so that questions can be answered, and problems can be solved.
Business Analyst.
The role of Business Analyst goes one step further. Combining the knowledge gleaned from the data and their own acumen, they bridge the gap between the IT specialists and the business team comprised of the owners and executives. Business Analysts can understand the terminology and help to interpret anything that is not easily understood. Their insight offers affords them input on how the organisation can approach next step planning and any change of direction the organisation may need to take.
Management roles.
In addition to the ones above, many data analytics teams in larger companies include a leadership or management role. These positions include Data Manager, Data Director, or even Chief Data Officer.
3 Considerations when building the perfect data analytics team.
How big does my data team need to be?
When considering how large the data team needs to be, you must think about several factors. Generally, large organisations have larger data sets to master; the more data-driven the business, the larger the team needs to be.
Questions to think about are:
- How much data is being generated?
- How many projects will the team work on simultaneously?
- Who will the data team serve? Will they report to a single stakeholder or department, or will they assist teams across the organisation?
How centralised will the team be?
In some models, the data team is centralised. A single team serves the entire organisation. In others, the data team is not so highly centralised. Each department has access to its own resources, processes, and employees. Others use a bit of both, choosing something in-between.
There is no ideal; each organisation will require its own unique structure based on its data dependence. Despite this it is an important consideration; it can significantly affect the data governance processes required.
What is the organisation’s overall data strategy?
There needs to be a clear roadmap for how data-dependent the organisation will be. If, for example, there is a need/requirement for every business action to be driven by data, then there needs to be significant data infrastructure in place. The processes, tools, and professionals required to conduct significant analysis must be in place. On the other hand, if the plan is to back significant business decisions in data but to make smaller, less important daily decisions without data analysis, you may not require such a large team.
In sum.
Companies that pursue data-driven decision making are always one step ahead. Embracing data means that decision-makers can make impactful and educated steps toward business excellence. Predictions, trends, challenges, and opportunities can be identified based on tangible evidence.
Not only this, but it also gives leaders a yardstick from which to measure progress; they are able to assess whether their strategy is working and if they should pivot.
The roles needed will be dependent upon the sector and size of the organisation in question. In a start-up, or if you have a small team, it is likely that everyone will have a role to play in gathering, analysing, and understanding any data. As the organisation grows or your needs become more complex, it will become more and more important to have a team solely focussed on the collection and interpretation of the data.
Are you looking to expand your data department? Is your business growing, and you feel that now is the time to start making more data driven decisions? We have sourced and placed talented data professionals who are thriving in all these data roles today. Let us help build your team. Reach out now.
