The 10 biggest ML and data science challenges in 2022
Big data is accelerating at such a rapid pace that it’s leading to massive amounts of innovation in emerging tech, particularly in applications that involve AI and machine learning (ML). While to say this is great would be an understatement, we cannot ignore the fact that the pace of change can be very difficult for organizations to keep up with.
Indeed, the situation we now find ourselves in is very much a catch-22: While data has become the lifeblood of businesses and is an integral part of all decision-making processes, collecting, making sense of, and using it is challenging.
The key ML and data science challenges facing firms today
As the adoption of big data, analytics, and emerging technologies like AI and ML has increased on a scale that nobody — not even Bill Gates himself — could honestly claim to have foreseen, so have the challenges that face ML engineers and data scientists.
In this post, we are going to give you the rundown of what we believe to be the biggest challenges facing the industry in 2022, and how organizations can look to address them.
1. Data collection
The first step of any ML or data science project is finding and collecting necessary data assets. However, the availability of suitable data is still one of the most common challenges that organizations and data scientists face, and this directly impacts their ability to build robust ML models. But what makes data so difficult to find in a world where lots of it is readily available?
The first problem is that organizations collect huge amounts of data without doing anything to determine whether it’s useful or not. This has been driven by a general fear of missing out on key insights that could be gained from it, and the widespread availability of cheap data storage. Unfortunately, all this does is clog up organizations with lots of useless data that causes more harm than good.
The second problem is the sheer abundance of data sources, which makes it difficult to find the right data...
2. The abundance of data sources
Companies now collect data about their customers, sales, employees, and more as a matter of course. They do this using lots of different tools, software, and CRMs, and the sheer volume of data being fired at companies by the many sources can start to cause problems when it comes to data consolidation and management.
As organizations continue to collect all the data that they can by using the many available apps and tools, there will always be more data sources that data scientists need to consolidate and assess to produce meaningful decisions. This is where problems can begin to arise because consolidating data from lots of disparate and semi-structured sources is a complex process.
To stay above the water and avoid being drowned by growing mounds of data, organizations need a centralized platform that can integrate with all their data sources so that they can instantly access structured, organized, and meaningful information — this can potentially save huge amounts of time and money.
3. Data security and privacy
When the right datasets have been found, the next challenge is accessing them. But growing privacy concerns and compliance requirements are making it harder for data scientists to access datasets.
Not only that, but the widespread transition to cloud environments also means that cyberattacks have become a lot more common in recent years. These have naturally led to tightened security and regulatory requirements. As a result of these two factors, it’s now a lot harder for data scientists and ML teams to access the datasets that they need.
In situations when organizations do provide interested parties with access to their datasets, there’s the added challenge of ensuring continued security and adherence to data protection regulations like GDPR. A failure to do either of these things could lead to severe financial penalties and stressful, expensive audits by regulatory bodies.
While many organizations are tightening their grip over their datasets because of these factors, they alone shouldn’t preclude interested parties from having access. With the right access management tools, organizations can exercise more control over who can access data, when they can access it, and what they can access.
4. Data preparation
The challenges don’t end with finding the right datasets and gaining access, though. Real-life data is very messy, and this means that data scientists and ML teams must spend a lot of time processing and preparing data so that it’s consistent and structured enough to be analyzed. This is time that would otherwise be spent on more importnt tasks such as building meaningful models.
While data preparation is a laborious task that is considered by many to be the worst part of any ML project, it is a crucial process that ensures ML models are built on high-quality data. This ultimately leads to a more powerful model that’s more accurate at making predictions. Fortunately, there are now many tools available on the market that help ML teams pre-process their data by automating certain aspects of the data cleansing process. This saves a huge amount of time that ML teams can use to develop their models.
5. Managing large data volumes
As we’ve already mentioned, the volume of available data is growing at a rapid pace each day. According to the IDC Digital Universe report, the amount of data that’s stored in the world’s IT systems is doubling every two years.
It should therefore come as no surprise that handling these huge amounts of data is a big challenge for organizations. This is particularly true given that, as we have also mentioned, most of this data is unstructured and is not organized into a traditional database.
At the same time, critical business decisions need to be made efficiently and effectively, and this necessitates putting a strong infrastructure in place that is capable of processing data more quickly and delivering real-time insights.
To deal with the challenge of managing burgeoning data volumes, organizations are more frequently turning to big data platforms to process for storage, management, cleansing, and analytics so that they can extract the insights that their organizations need, when they need them.
6. Data discovery
You would have thought that by this point, data and ML teams would be well on their way to building powerful ML models… right!?
Well, this isn’t always the case. There’s still more work to be done, and ML teams will often have questions like:
- Why are there so many values missing
- What does X or Y column name mean?
- Who can I ask about?
While these questions might sound straightforward, getting an answer isn’t always the easiest thing to do. This is because organizations often fail to take full ownership of their datasets, so finding the right person who has the answer to your questions isn’t always a fruitful endeavor.
The solution to this problem is to thoroughly document datasets and other data assets. It’s as simple as that. Thorough documentation prevents basic questions from arising over and over again, which are a drain on resources and do nothing but waste time.
7. Extracting the right insights
There’s not much point in processing, storing, and cleaning data if it’s just going to sit there gathering dust. Organizations want to use their data to achieve their goals, and the only way to do this is by extracting relevant insights from it so that leaders can use it to make their decisions.
When it comes to extracting insights, however, organizations are increasingly pushing for faster delivery and self-service reporting. And to get this, they are turning to a new generation of analytics tools and platforms that have the capacity to rapidly reduce the time it takes to generate insights and deliver real-time, high-quality insights.
8. Finding the right talent
There’s a huge skills gap and talent shortage not only in data science but also in the general tech sector. Organizations often struggle to find the right people with the right level of knowledge and domain expertise to put together their ML teams.
In addition to finding talent with the right domain expertise, organizations also struggle to find people who have the right business perspective on data science. This is just as important as domain expertise because a machine learning project can only be successful if ML teams are able to solve key business problems and tell the right story through data.
When organizations do manage to put an ML team together, they often experience problems in helping the team to function correctly. This is because data scientists are often seen as the go-to people for… well, everything to do with data. They’re asked to find it, clean it, organize it, analyze it, and build models, among other things.
Instead of asking all team members to take care of all these tasks, however, distribute them among individual team members instead. This helps to ensure efficiency and allows the team to function effectively.
9. Identifying data lineage
Data lineage is the process of understanding, recording, and visualizing data as it flows from data sources to consumption, including all transformations it went along the way, what changed, and why.
Merely knowing the source of a dataset isn’t always enough to fully understand it. Identifying data lineage can have a big impact in areas including data migrations, data governance, and strategic reliance on data, and enable organizations to:
- Track errors in data processes
- Implement changes with lower risk
- See how datasets are used
- Solve problems in existing applications faster
- Create new applications more quickly
10. High entry barriers
It’s no secret that putting together your own internal ML teams, managing your own projects, and building and deploying your own ML tools is an expensive undertaking. The sheer expense of it all can mean that even the bigger enterprise-level firms can struggle to stomach the costs, especially when their projects aren’t delivering the results they were hoping for.
While many smaller and mid-level organizations may feel as if taking advantage of data and ML for the benefit of their business is out of reach due to this cost, this isn’t exactly true. Although smaller firms will face significant barriers if they want to put together their own ML teams — logistics, cost, expertise, etcetera — there are plenty of tools and solutions on the market that allow organizations to fully outsource their ML projects without risking the overall quality of ML models.
In most cases, the challenges that we have covered in this article mean that outsourcing to a dedicated big data/ML engineering platform is often a better idea that delivers better results.
We are living in the age of digitalization and big data. This has made it necessary for companies to adapt themselves to the rapidly changing market and develop data science-led solutions and strategies that align with their goals and business needs.
Adopting a data-led approach and deploying problem-busting ML models is easier said than done, however. It is a highly involved task that requires a lot of planning and careful exaction to do right, and this involves facing and overcoming key challenges.
While some organizations overcome these challenges alone, the majority turn to full-service data and ML engineering platforms like Qwak that have the expert knowledge, infrastructure, and capacity in place to help organizations unlock the true power of their data without having to invest huge amounts of time or money.
Want to find out more about how Qwak could help you deploy your ML models effectively and efficiently? Get in touch for your free demo!
Also, there are many great machine learning podcasts that explore ML topics.