As companies become more overwhelmed with data and eager to leverage it for better business performance, data science implementation steadily gains popularity. After all, who wouldn’t want to have access to real-time analytics, accurate sales forecasts, and other opportunities that this technology delivers?
However, before undertaking development, it’s important for business leaders to understand that there are several data science challenges that can arise along the way. Being armed with information on the difficulties you may face and how to solve them is crucial for the success of your project.
So, in today’s post, we will rely on the years we have spent delivering data science services to share some potential problems you ought to be prepared for. Let’s get started.
6 Common Challenges of Data Science Projects
No matter how many software development initiatives you’ve already worked on, every new one can throw curveballs at you and cause project delays. Naturally, leaders want to minimize the impact of these surprising difficulties, and for that, preparation is key.
Specialist Search
Whether you already know what kind of a data science project you want to pursue or are just looking for a consultation on this subject, finding skilled specialists will be the first challenge you face.
You may come to realize that you don’t have the needed in-house resources for a successful completion of the initiative. So, you’ll start the hunt for data science experts but quickly recognize that they are either unavailable or lack domain-specific knowledge you require.
Depending on the project goal, it’s important for your IT team to not only have data scientists, developers, and business analysts but potentially Big Data architects and machine learning engineers as well. After all, if you’re building a large-scale solution or are revamping the entire IT infrastructure, you’ll need people who can deliver an excellent result.
Since skilled tech talent with domain-specific expertise is hard to come by these days, it might be a good idea to consider outsourcing development. That way, you’ll be able to skip the time-consuming efforts of recruitment and partner with a vendor that can advise you on the best course of action in your unique business case.
Of course, everything depends on the scale of your project and the capabilities of your existing team. However, if you run into the challenge of finding the right data science professionals, don’t exclude the possibility of outsourcing.
Expanding a Team
Watch our webinar to unveil the tricks of onboarding a tech partner and incorporating it into the process to foster your product delivery.
Legacy Technology
Another one of challenges in projects has to do with having outdated systems within your IT infrastructure. You see, in order to fully benefit from implementation of data science solutions, you’ve got to ensure that they’ll be able to integrate through APIs with your existing software.
Find out how we performed a VoIP System Integration With Salesforce
Additionally, intelligent data models thrive on quality data, and if outdated platforms don’t grant access to useful digital information, the success of the final solution might be limited. So, it’s a good idea to modernize legacy systems before you embark on data science implementation.
Whether you’re looking to transition to the cloud, migrate to new servers, or replace a dated CRM platform, all this should be done prior to the start of your data science project. That way, you can be sure that the algorithms your specialists implement will harness the full potential of your data.
Read up on Legacy System Migration for a Plants Growing Company
Poor Data Quality
Even if you don’t have legacy systems to worry about, another challenge you might still be susceptible to is poor quality data. You see, companies in the modern world are using a multitude of software. From ERP systems and CRM platforms to contact center solutions and mobile apps that complement them — there’s no shortage of IT tools organizations rely on.
Naturally, most of these disparate sources generate data in non-uniform formats, leaving you with unstructured or semi-structured information that needs to be consolidated.
As you may suspect, this is one of the major data science problems. Without aggregated, cleansed, and well-prepared data, it’s difficult to perform an analysis and acquire accurate insights.
Inconsistencies, errors, and duplicates in data will all lead to poor results and slow down the work of your data scientists. So, your IT team might advise you to first implement a centralized platform for integrating data from all of the sources you currently work with.
Additionally, consider creating a thorough data strategy and laying out quality management practices for all business departments to follow. By doing this, you’ll ensure that any future software projects you undertake can be started swiftly as the digital information you possess is in the best possible format.
Overfitting
One of the relatively common problems your specialists may run into during the practice of data science is overfitting. It is a statistical error that occurs when a model fits precisely against its training data.
This happens when a model is too complex or is trained on a sample data for too long. Thus, incorporating the irrelevant information within the dataset into its memory instead of disregarding it. As a result, an “overfitted” model can’t generalize properly to new data, making the predictions it delivers inaccurate.
Generally speaking, high variance and low error rates indicate overfitting. So, how can you avoid this data science challenge?
Well, there are several recommendations. First, you can consider stopping training earlier so that the model doesn’t learn the “noise” within the dataset. Of course, this creates the risk of not conducting enough training, but experienced data scientists should be able to find the most opportune moment to halt the process.
Another option is to add more training data, if possible, so that dominant relationships between variables can be parsed out more thoroughly.
Finally, it’s important to carry out cross-validation. During this process, the data that was used for the initial training is broken down into folds or partitions. Then, the model can run on each one individually, and the team can assess how well it performs.
Data Security
The natural byproduct of working with a lot of data is having to ensure its security. Cyberattacks are on the rise, and hackers are always on the hunt to get confidential information you may possess. Not to mention, there are more and more regulations that companies have to adhere to as governments aim to protect personal consumer data.
So, one of the main challenges your team might face when incorporating data science and business intelligence into your infrastructure is ensuring secure operations. How can that be done?
For starters, it’s a good idea to conduct regular checks of your systems and keep them updated with the latest safety standards. Additionally, implementing things like two-factor authentication, encryption, and pseudonymization can also help your security strategy.
If you want to take things to the next level, look into blockchain-based solutions that rely on cryptography and decentralization to keep organizations safe. This innovative technology is steadily gaining traction in the world of business and is definitely worthy of your consideration.
Take a look at a Blockchain-Based Intrusion Detection System
Unclear Success Criteria
Lastly, a data science challenge that’s bound to cast a shadow over the finalization of your project is having undefined success criteria. Sometimes, it’s easy to forget setting KPIs and accurate metrics to determine whether your software initiative has accomplished what was initially intended.
However, it is a crucial aspect of any project that should be thought about before development begins and reviewed if the set out goals change. Hence, consider keeping track of the following elements after your solution has been implemented:
- Accuracy of analytics the data science model generates
- KPIs to track impact of data analysis on business performance
- Return on investment
Make note of the success criteria that’s important to you at the start of the project. That way, you’ll always be able to refer back and ensure that the end product is being built to cater to the goals you’ve set out.
Get Help Avoiding Data Science Challenges
The corporate world is well on its way to completely embracing data science and business analytics, and the above-outlined roadblocks won’t stand in the way of adoption. Nonetheless, it’s always good to be prepared for any difficulties you may face within your IT project.
Velvetech’s team has ample expertise in delivering data science services and accounting for the challenges that a project within a specific industry may present. So, if you’re looking for help with your initiative, don’t hesitate to reach out to our team.
Our specialists will quickly get on board and recommend optimal ways of achieving your unique business goals. Together, we’ll swiftly deal with any issues that may arise during development or after deployment.
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