Right now, we are seeing more and more companies look to use data analytics solutions to streamline their everyday processes. However, the steps towards becoming a data-driven organization includes a lot of challenges. From technical hurdles to recruiting and training issues, organizations find themselves running into common obstacles. In this post, we will explore the common obstacles companies face when adopting data analytics and how Altair Engineering’s solutions can help overcome these challenges.
Common Obstacles in Adopting Data Analytics
- Data Silos/Disconnected Data
One of the first challenges many organizations encounter is the presence of data silos. This is basically data stored in isolated systems or data coming from multiple sources, like vendors and partners, which makes it difficult to have a complete, high-level view of product information. In a Forrester article titled “Predictions 2024: Data And Analytics Set the Stage for Generative AI”, Zeid Khater mentions that the amount of unstructured data needing to be managed by organizations will double in 2024.
Additionally, integrating this disparate data can be technically challenging and time-consuming. Some organizations employ team members who spend much of their time manually entering data from an unstructured data source just to make it usable. This process is also error prone as it relies on human input.
Fig 1: Example of separate data sources
- Skills Gap
The last few years in the data science arena have been a roller coaster. We’ve seen the impact that AI has had on the available positions for data analysts and scientists. With that volatility, many of these skilled workers have looked to other, more stable roles. As these workforce trends continue to evolve, one thing that will remain steady is the growing need for skilled professionals who understand both the technical and business aspects of data.
Finding candidates that are proficient in coding languages like Python, R and even database languages like SQL, who also possess the ability to translate data results into business insights, can be tough. These candidates are extremely valuable and can lead some organizations to believe they don’t have the means to invest in this kind of resource.
- Data Quality and Management
Poor data quality is a significant barrier to effective analytics. Data that is outdated, incomplete, or inaccurate can lead to misguided insights and poor business decisions. One of the biggest challenges we see at TrueInsight is that there are many organizations today that receive so many different formats of data, that combining this data and getting usable business insights is nearly impossible. In other scenarios, many companies are receiving the same format of data, but missing or incomplete datasets still creates a lot of manual entry for their team members.
One trend that has been a topic of conversation lately is the idea of Generative AI, which is basically the deep-learning models that can generate text and images. At the Gartner Data and Analytics Summit in early 2024, Arun Chandraskaran estimated that by 2025, 30% of Generative AI projects will be abandoned after proof of concept or due to poor data quality. So, while Generative AI is accelerating at a high rate, there are also risks with relying solely on this technology for data quality management.
Fig 2: Attributes of high data quality
How Altair Solutions Can Help Ease the Pain
Sometimes, it’s hard to look at all the solutions on the market right now and know if there is one that can meet your needs. Altair offers a comprehensive suite of data analytics solutions that are designed to address these challenges effectively.
Breaking Down Data Silos and Data Quality
Altair RapidMiner’s data analytics platform facilitates the integration of data from multiple sources, including traditional databases, cloud environments, and even unstructured data and data from IoT devices. This integration capability helps break down data silos, allowing for a complete view of their data, which is crucial for effective analysis. This also helps organizations leverage their current technology investments while adopting new technologies without the fear of compatibility. Additionally, Altair’s tools are built to handle large volumes of data, addressing the challenges of big data analytics.
On the data preparation side, Altair Monarch is a very powerful, easy to use platform that excels at bringing in multiple sources of data, including PDFs and Text Files, and combining them into a single source of truth for your company. There are also thousands of pre-built Monarch Models designed to work with a variety of ERP systems.
No matter which tool you work with, users can create frameworks to ensure high data quality and governance. These frameworks help companies clean, process, and maintain their data efficiently, ensuring that the data remains accurate and relevant for analytics purposes.
Fig 3: Join Options in Altair Monarch
Empowering Teams through Education and Tools
One of the biggest strengths of the Altair Data Analytics portfolio is that none of the tools require coding experience. Their drag and drop interfaces (Fig 4), library of algorithms and built-in visualization templates means users can get valuable results without scripting knowledge. If your users do have scripting experience, that’s even better, because all the Altair tools can use scripts for more custom results.
Fig 4: Altair RapidMiner Drag and Drop Interface
Understanding the importance of skills development or “Upskilling”, Altair offers extensive training and support to help teams develop the necessary competencies to leverage data analytics effectively. From online tutorials, Self-Paced courses or Virtual or In-Person Instructor-Led Courses, Altair ensures that your team is well-equipped to handle the tools and technologies provided.
Fig 5: Altair RapidMiner Academy Certification Badges
Adopting data analytics is something many companies want and need to do but aren’t sure how to get started. With the right partner, the process can be significantly smoother. Altair Engineering provides the tools, training, and technologies necessary to overcome the common obstacles to data analytics adoption. Contact us to discuss further or get more information.
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