At the centre of your plan must be clarity

The concepts of Data Science, AI and Cloud have gone from buzz words to becoming fundamental components of how businesses now operate. Data now sits at the core of operations and the Covid pandemic has significantly sped this up.

Transforming data into insight has revolutionised business. The people involved in creating the team need a foundational understanding of the key components and ideas behind data science and big data analytics.

The urgency to apply Data, AI and Machine Learning principles to improve, for example, efficiency of remote working means that many companies may not fully understand what they are trying to achieve. It’s one thing to say, “We need to implement AI, because it will make us more efficient and our competitors are doing it” but what are the specific objectives and how do you get that result?

The speed at which the data is required to pass through the system differs from project to project but largely large data projects require faster processing to make sure the data is sorted in a timely manner. The data will also likely be entering the process from different directions and sources and must be cleaned, sorted and ordered in order for it to make sense compared to the data already in the system and for insights to be found.

For a big data process to run smoothly, these days it is common for it to utilise a streaming system which will work in a close to real time capacity. Data pipelines must be solid and strong to cope with so much data passing through.

Big data is really no different than any other kind of dataset apart from the size and speed of the data being processed, plus the fact that the data will likely require new solutions to challenges it presents. Forming insights from huge amounts of data is the end goal and the key is usually finding innovative ways of making this possible.

The storage and processing of largescale amounts of data is what will define a big data system. This is, more often than not, happening on more than one server, which is where Cloud computing comes in, which brings with it other challenges such as security and allocation. Tasks must be broke up into smaller parts in a variety of ways in order to make the most of the resources needed.

What is the data and where is it coming from? Text, images, logs? APIs, Servers, sensors, social media? There are so many directions that your data can come from and it must be configured somehow, eventually, into one process. The perfect scenario is for the data to be transformed so that it is organised and formatted at the point of entry but that is not always possible and the work must be done at the backend by talent data scientists.

Quality is the watchword when it comes to data, and the system must be able to sort and separate good data from bad, making sure the processing power of the servers, be they physically on the premises or Cloud-based, is best used and not wasted on fool’s errands. Using resources to clean the data first can save time and money in the long run. At all stages it must be ascertained whether the data is providing real value.

When you are clear about what you are looking to get out of data, it becomes apparent which type of individual you need to hire to achieve those goals. Companies will have specific needs relating to the type of data specialist they need and for which job.

The major problem at this stage is that, if the goal is not clear, candidates will try and probably fail at interview as they attempt to mould their skill set to the generalist job requirements set out on the job spec. Dedicated recruitment partners will guide you through this minefield.

Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.