Artificial intelligence-based tools can be used to derive advanced analytical insights that enable smart manufacturing in various industries. Here, we’ll discuss the data engineering gaps manufacturers face when implementing AI technologies for business intelligence.
The manufacturing industry has been embracing digital transformation and Industry 4.0 by adopting various advanced tools and technologies, including business intelligence, analytics, artificial intelligence, and digital twins. For instance, the AI market in the manufacturing sector is projected to reach $68.36 billion by 2032, growing at a compound annual growth rate (CAGR) of 33.5%. BI is just as popular, and the combination of AI with business intelligence to derive powerful insights in real-time is changing how manufacturers look at data and decision-making.
Statistics show that the global business intelligence market is expected to be $54.9 billion by 2026. Over 78% of businesses worldwide have adopted at least one analytics or BI platform in 2025. Furthermore, AI-driven analytics take a 40% of the total BI investment in 2025. These numbers clearly indicate the accelerated adoption of analytics and business intelligence solutions across industries and regions.
However, the challenge persists in the form of a data engineering gap, which affects efficiency, ROI, and overall performance. That’s because data is the core of the BI system, and mishandling of datasets can lead to various complications and inefficiencies. Implementing BI tools is just one part of the process. The actual work starts with streamlining data and building a robust data architecture, training employees to use the new systems, and optimizing the infrastructure.
In this blog, we’ll read more about data engineering gaps plant managers face and how to overcome these to derive meaningful and accurate insights for smart decision-making.
BI vs. AI Analytics
Manufacturers generate a lot of data. However, what you do with this data matters more. That’s why many enterprises have been investing in manufacturing BI solutions to convert raw data into insights that can be shared through customized dashboards and help managers make swift decisions.
Business intelligence is a suite of processes, tools, and technologies for collecting, transforming, analyzing, and visualizing data. Earlier, many of these processes were performed manually, which led to expensive systems, time-consuming recurring tasks, and outdated insights. If it takes a month to analyze a small dataset, imagine how much data would have been added during that time. Moreover, manual operations have a high risk of human error.
The popularity of artificial intelligence resulted in the adoption of AI in manufacturing analytics to automate such recurring tasks, streamline data flow, and accelerate analysis. Instead of waiting for weeks or days, or even hours, plant managers can derive insights in a few minutes. Furthermore, with cloud-based AI solutions and edge computing, manufacturers can access analytical insights in real-time. In fact, you can also derive advanced analytics, like predictive analysis, to predict future outcomes.
It is also used for predictive maintenance, which ensures that the equipment is in good health and contributes to a higher OEE (overall equipment effectiveness) score and less unplanned downtime. Upgrading BI to AI-powered analytics systems requires domain expertise, skills, and knowledge provided by artificial intelligence consulting companies. That’s why many manufacturers partner with AI third-party offshore service providers to revamp their existing setup and embrace digital transformation.
8 Data Engineering Gaps Plant Managers Face and How to Bridge Them
Correctly identifying the data engineering gaps is a crucial part of an AI readiness assessment. This step ensures that C-level executives and plant managers are clearly aware of the strengths and weaknesses of the existing system and how the weaknesses can be overcome to build a comprehensive AI-powered data architecture to generate business intelligence reports in real-time.
Here are some common data engineering gaps experienced by many enterprises, and ways to overcome them:
Poor Data Quality and Fragmented Architecture
Data architecture and data quality are top concerns as they determine the accuracy of the insights. Poor quality data leads to incorrect insights, which result in wrong decisions. For a manufacturing enterprise, it can be fatal in terms of money, brand name, and legal stance. Poor data quality can be because of fragmented data stored in silos rather than in a central repository.
Plant managers can overcome the challenges and implement smart factory analytics by automating data collection and cleaning using AI and ML technologies. Data engineers can build automated data pipelines and set up a data warehouse or a data lake to ensure seamless workflow and meaningful insights.
Complex Data Transfers and Storage
Industrial data analytics is complex as you deal with massive datasets. Collecting, storing, and transferring such huge volumes of data can be complicated and lead to errors if the architecture does not support scalability. Delays, broken data, missing tags, duplicates, etc., become a concern, widening the data engineering gap between your existing systems and your objectives.
This gap has to be carefully bridged, building a comprehensive cloud-based data architecture for manufacturing analytics that is flexible, scalable, and agile. It requires AI and ML-powered tools, data lakes and lakehouses, and other advanced solutions to streamline the workflow across the factory and throughout the enterprise. From plant managers to top management, all decision makers should have access to these insights.
Data Pipeline Development
Data pipelines automate data flow from multiple sources to the destination systems so that the collected data can be cleaned, transformed, and analyzed. This involves various processes, such as data ingestion, transformation, storage, etc., which are necessary when implementing a modern manufacturing data platform. Traditional data pipelines cannot handle the complexity of large data movement.
AI-powered data pipeline automation is vital to share analytical insights in real-time to facilitate data-driven decision-making in the manufacturing units. By hiring Data Pipeline Consultants from experienced and certified companies, you can build automated data pipelines and seamlessly integrate them into your IT infrastructure.
Data Security and Access Control
The strategic plan for data engineering in manufacturing should also involve data security and access control management. This is where many enterprises struggle to find a balance. How do you ensure system and data security while providing easy access to data? How can employees make smart decisions if they cannot access the data or insights from their dashboards?
Misalignment between security plans and access control can make it more difficult to protect sensitive information. This data engineering gap can be bridged by migrating the data architecture to a cloud platform like Azure, AWS, etc. You can use a hybrid cloud solution to ensure your business data is secure while setting up access control through two-factor authentication, role-based access, data encryption, and so on.
Data Governance and Compliance
Data governance is a framework that deals with how enterprise data is managed, secured, and made available to employees. The policies and processes should align with both industry-wide standards and regional data regulations. For example, if the region has data localization laws, the governance framework has to comply with these. The data architecture has to be built to adhere to the same regulations to prevent legal complications. By working with certified Power BI consultants, plant managers can be assured of developing and implementing compliant systems in the manufacturing enterprise.
Technical and Skill Gap
Another common data engineering gap is between the existing in-house talent and the skills required to use AI-powered advanced tools to derive real-time insights. Modern manufacturing data analytics are automated to save time and resources for the enterprise. The dashboards are customized for each manager to align with their roles and responsibilities.
However, even with self-service BI, you will need a tech team to build, deploy, optimize, and maintain the data architecture. You either have to hire the right talent permanently or augment the internal teams with experts from outside. Another cost-effective method to bridge the technical and skill gap is by partnering with a service provider for end-to-end support and maintenance.
Lack of Organization-wide Strategy
Adopting AI solutions for advanced business intelligence requires a detailed blueprint or strategy that considers how the new systems will impact the enterprise at different levels. The focus should not only be on the technical aspects or the cost, but also on the human element.
That’s because your employees will be the ones to use the new tools, and they should be aware of the developments. Considering their input, providing the necessary training, and maintaining transparency will make the transformation easier. When you partner with a reliable Power BI company, you can ask them to support employee training as well.
Monitoring and Optimization
A common data engineering gap in moving BI to AI systems is the ability to monitor the workflows for continuous development and to optimize the data architecture for cost-efficiency, speed, scalability, and overall effectiveness. Optimization is a continuous process required to maintain good health of the systems and achieve the desired objectives without incurring too many expenses. This can be taken care of by data engineers and AI experts from service providers.
Conclusion
In 2026, it is time for manufacturers to adopt AI as a part of their operations and use automation effectively to achieve the desired objectives. Advanced manufacturing analytics and intelligence can give you a competitive edge in the global market and prepare you to make proactive decisions quickly.
By hiring artificial intelligence consulting services from reputable companies, plant managers and top-level executives can bridge the data engineering gap while ensuring cost-efficiency in the long-term, as well as higher ROI and greater customer experience. Make the most of AI technologies to redefine your enterprise and stand out as an industry leader.
More in Data Engineering Services Providers
Data engineering services offer end-to-end solutions to design, build, and maintain the data architecture for collecting, cleaning, storing, and analyzing large datasets. It also includes data pipelines, data security and governance, data analysis, and data visualization to share the insights in readable, user-friendly reports. AI and ML technologies are a part of the data engineering solutions and make the architecture more flexible, scalable, agile, and robust.
Check out the links below for more information about data engineering and its role in modern manufacturing.
- Data Strategy for 2026: What Data-Driven Leaders Must Get Right
- Inside the Cross Hybrid Teams Tech Stack: What Actually Works in 2026?
- Data Lakehouses vs. Data Warehouses vs. Data Lakes: A Decision Framework for C-suites
- 14 AI Consulting Companies Driving Workflow Automation in 60 Days(or Less)
FAQs
Why do my BI dashboards fail when we try to move toward AI use cases?
BI dashboards fail when you move them to AI use cases due to various reasons. A few of them are as follows:
- Lack of clarity
- Data overload
- Data quality
- Technical gap
- Dashboard complexity
- Technology mismatch, etc.
With DataToBiz as your partner, you can seamlessly overcome these and many other data engineering gaps to integrate business intelligence with AI capabilities.
What data engineering gaps typically block AI adoption in manufacturing plants?
Typically, the following data engineering gaps block AI adoption in manufacturing plants, causing trouble for plant managers:
- Misalignment of vision, objectives, and strategy
- Data fragmentation and quality
- Skill and talent gap
- Lack of trust in AI, etc.
Partner with a reputable BI company like DataToBiz to overcome the challenges and embrace AI technologies throughout the manufacturing enterprise.
Do I need real-time data pipelines to support AI on the shop floor?
Yes, real-time data pipelines are necessary to support AI capabilities on the shop floor. They are critical to ensure streamlined and automated data flow between systems to convert raw data into actionable insights and real-time reports. Data pipelines are a vital part of data engineering in manufacturing. Schedule a meeting with our experts at DataToBiz for more information about data pipeline implementation.
How clean and structured does my plant data need to be for AI models?
To use AI models for analytics in manufacturing plants, the data has to be clean and structured. It should be accurate and of high-quality to provide reliable insights. Low-quality data is a major data engineering gap in many enterprises, making it difficult to implement AI-powered analytics in your existing business intelligence systems. At DataToBiz, our certified experts work with MSMEs and large enterprises to empower them in making smart decisions using robust BI solutions.
How long does it realistically take to upgrade data foundations for AI readiness?
Many factors determine the actual time required to upgrade data foundations for AI readiness. Typically, it could be between a few months (for small enterprises) and a couple of years (for large manufacturing enterprises). Our team at DataToBiz will audit your existing systems to identify the data engineering gap and create a strategy to update the data foundations for AI readiness.
Fact checked by –
Akansha Rani ~ Content Management Executive
