Data Science: Insights and Opportunities in an AI world

6 minute read
Updated On | May 11, 2024 1:44:04 PM
Publish On | Feb 28, 2022 9:37:40 AM

There has never been more data around us: according to Deloitte, global data volumes will reach as much as 175 zettabytes by 2025. This means there has never been a greater opportunity for data science insights. Within these insights lie the chance for businesses to gain a competitive edge, improve their customer experiences, and drive new efficiencies in their operations.

This blog highlights the current state of play in data science and analytics, and how you can take full advantage. We’ll explore why data science and big data can’t be treated the same, why many data scientists are under pressure to deliver on expectations, and how technology is helping Ciklum connect clients to the data science insights they’re looking for.

Global Data Volume_Stats

Data Science and Big Data - are they the same?

Not quite - there are some subtle differences between data science and Big Data, and it’s important to make the distinction between them because the two are often conflated and confused.

Data science is the practice of analyzing data to gain valuable business insights. It brings together math, statistics, computing power, engineering and even AI to find answers to questions and patterns in information. Big Data, on the other hand, refers to the large, complex data collections that are normally so big that normal data processing software is unable to reasonably handle them.

Both approaches will bring together data from many different sources, and will combine data that is fully structured, partially structured or completely unstructured. Big Data, however, generally involves raw data that has not been aggregated, and involves analytical approaches that are mainly focused on volume, quantity and ‘getting through it all’. Data science, on the other hand, goes into the content of that data in much more detail.

Data Science and Big Data Comparison

What is a data scientist?

A data scientist is a person who collects, analyzes and derives findings from data, so that their organization can make informed, data-driven decisions. These people will normally be experienced in analytics, and able to deploy the very latest and most advanced in analytics techniques, including machine learning, predictive analytics and more.

The Data Science and Big Data Experts at Ciklum are an excellent example of the kind of work that a good data science team does, uncovering new business opportunities by identifying hidden patterns in data. They can rapidly set up data analytics tools that are aligned with clients’ business goals, so that they can micro-segment their markets, customize products, and transform raw technical data into clear information for making key decisions.

What are the issues facing data scientists today?

Like many areas of technology related to analytics and AI, data science is an area that’s moving and evolving all the time. This means data scientists are constantly having to update their knowledge in order to keep up with the latest trends, as well as deal with these current challenges:

  • Data preparation:

    For data science insights to be worthwhile, data has to be properly prepared in advance, and that means collecting, cleaning and organizing that data. This is one of the most time-consuming and repetitive parts of a data scientist’s job, and so can be prone to human error

  • Multiple-source data generation:

    Some experts believe that up to 90% of data science work is data cleaning. This means that generating and collating data from a wide range of different sources, and in a variety of different formats, can be laborious for a data scientist

  • Identifying business issues:

    Data science is largely pointless if it isn’t executed in the context of a specific business need or problem. Data scientists must discover and evaluate what those demands are, and design data science to address them - not the other way around
  • Communicating results:

    Data science is an advanced concept, and many key decision-makers in senior management may not be up to speed with how it works. A key part of a data scientist’s work is to communicate results of analysis in forms that everyone can understand

  • Security: 

    By the end of 2023, the cost to the global economy of cyber attacks is expected to pass $10.5 trillion. The threat of cybercrime is rising all the time, as is the scope of regulations around cybersecurity and data protection. This has complicated matters for data scientists, who are now expected to take measures to keep data safe throughout their processes, including encryption and machine learning-based protections

  • Collaboration: 

    Data scientists rarely work autonomously - good processes will also incorporate data engineers to ensure the technology in place runs as it should. Close collaboration between both parties is key to ensure that workflows remain aligned throughout

What are the issues facing data scientists today_

Closing the gap to meet the demand for data science & AI

The good news is that many of the challenges above are being addressed by automation and AI. Indeed, the relationship between Artificial Intelligence and data science is relieving much of the administrative pressure and workload on data scientists that are already in high demand.

Many of those repetitive, time-consuming tasks in the data science and analytics process can be taken care of by automation and Artificial Intelligence: collecting and collating data from diverse sources, and preparing them for analysis. Combining data science and Artificial Intelligence will result in a process that is faster, more efficient, more cost-effective, more insightful and less vulnerable to human error.

In summary: how Ciklum is paving the way for data scientists

Over the years, Ciklum has worked tirelessly to advance the concept of efficient data science; to expand the use of automation and AI for this purpose; and to help meet global demand for skilled data scientists. 

For example, we have run the DataminDS programme that helps develop new data science talent, through online and in-person educational courses and platforms, as well as internships inside Ciklum that have helped people progress. We have also provided scholarships at Masters level for a specialization in Data Science at the Ukrainian Catholic University, which has opened more doors for aspiring data engineers to become part of the Ciklum team.

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It’s by promoting talent like this that we stand out as one of the leading enablers of data science insights for businesses just like yours. Get in touch with our team today to find out more on how we can help you, and help you address your biggest data science challenges.

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