It’s cliche these days to talk about “data driven growth” and “data led decision making”. These topics have (or at least should have) been implemented with most businesses, once the first mile of integrating data points into their various systems is complete. Even if it is as simple as integrating Google Analytics to their site to get better insight into how their site performs and channels perform.
Generally we now come across organisations that have at least the following on hand:
- Google Analytics,
- Meta Pixel,
- A CRM of some sort,
- Email or Marketing automation platform.
This is, in general, great to see BUT when asked how they are using them, the water gets a little muddy. It is like someone somewhere told them they needed these items but didn’t bother to explain why or how to use them together to drive real growth in their organisation. I suggest this is actually a fair outcome as many suppliers are only tasked with providing the platform and few likely have an integrated data strategy team to help a client build robust data processes to derive high value from said platforms. Again, fair as this is a different speciality.
If we consider the world of data specialists you would likely come across some of the following data people/professionals:
The Data Architect:
This individual is responsible for designing the systems, integrations and processes from which an organisation can collect and process data turning raw unstructured data into structured usable data. This will be a highly technical person capable of solving complex system requirements and developing documented processes. This person would likely be responsible for:
- Data modelling and design
- Database management
- Data integration
- Data Governance and security
- Tech evaluation and implementation
- Documentation and standards
The Data Engineer
Similar to the data architect this individual is also a highly technical individual who would have similar responsibilities to the data architect but, although there is some overlap there are also differences. In general the architect has a focus on high-level design and strategy of the data systems whereas the engineer would have a focus on implementation and maintenance of these systems. An engineer would focus on activities like:
- Building and maintaining data pipelines and ETL (Extract, Process, Load) processes
- Implement the architecture the architect designed
- Ensure the smooth operation of the data systems
- Write, deploy and manage code for data processing and storage
The Data Analyst
In terms of the business function (where the rubber hits the road) the analyst is the person responsible for making the data useful to the business. So, this is the person the average team within an organisation would deal with. Your analysts are the ones likely to deal with the Architects and Engineers when specific data needs are required. This person is both the compiler, executor and interpreter of the data. Although not as technically capable this will be a practical, strategic thinker (usually with a sprinkle of creativity) able to turn the data into something useful to various departments. This person would tackle actions such as:
- Data collection from various sources and the use of tools to interrogate that data such as SQL and Python
- Data cleaning which means making sure the data is a true (or close as possible) representation of the item under investigation
- Data Analysis (Would never have guessed right!). This means using statistical methods and tools to identify trends, patterns and correlations in the data as well as identifying things like outliers and potential golden nuggets
- Reporting and presentation of the data. I.e making the data useful to the business
- Problem solving and strategic thinking and interpretation. Most departments may be given the data and have it reported on but fall short on actioning something from it. The analyst should be able to use this data to solve problems and assist in developing strategies
The Data Scientist
If you haven’t heard of this position in the last few years I can only assume you live under a rock in a cave under the ocean. (I say this with love and desire to educate on such an important role). Probably one of the most hyped up positions in the data workforce is the Data Scientist, and for good reason. This person is an amalgamation of the best parts of the other data specialties and capable of solving extremely complex data problems and turning these into useful data sets as well as potentially assisting in strategic direction. This individual can build systems, write algorithms, use machine learning and predictive modelling and then be able to report on all of this. These are very smart individuals. Just some of the activities they are responsible for can include:
- Exploratory data analysis
- Feature engineering – creating new features or variables from existing data to improve outcomes and the performance of machine learning algorithms
- Perform statistical analysis
- Build their own machine learning and predictive modelling systems
- Visualise data
- Communicate and report on the data
- Collaborate with other business teams to derive meaningful insights
Now there are a number of other specialties such as Business Intelligence Developers, Quantitative analysts and similar but the ones described here would likely be those you have heard of. So the next question I am sure you are asking is, “So why not just hire a data scientist? They seem to do it all”.
Well, yes, they do seem to do it all but this individual is much broader in scope so there may be limitations that the specialists would be more suited for. Additionally they could come at a premium and so could be an unnecessary expense on a business when a similar system or tool could fulfil enough of the role that you really only need an analyst (as an example). Also your organisation’s data complexity is important here. Not every business has extremely complex data sets that bring actual value. So keeping it simple can often be the best option.
Additionally an organisation doesn’t need to hire an expert in one of these fields when they could outsource to a specialist service provider to support them. These providers usually will come with the expertise and systems needed to provide what 90% of all businesses need at a lower overall cost to the business over the long term (need and project dependent of course)
Ok great, now you know about these data specialists and some options for managing data. In the future that really helps but right now you want some practical thinking on using data. Cool, in our next article we really get into how this would work for a business with some scenarios and outcomes.
In the next two parts of this blog series we are going to look at how you can make data available to a business with some examples of what scenarios a business may encounter when working with Marketing data.
First up we need you to get a sense of how we actually access data, enter Extract, Transform, Load (ETL) tools and systems.
See you later
