Data engineers know the secret that makes or breaks any analytics and AI initiative. They know, from experience, that even the most evolved analytics and AI algorithms or models can fall short, unless they are fed a continuous stream of real-time, high-quality data.
Why AI and Analytics Projects Fail Without Effective Data Management
The biggest challenge with AI and analytics is not building an advanced algorithm or model. In fact, writing the AI and ML code or designing the analytics model is just a fraction of the work that goes into analytics and AI projects.
The real challenge concerns the multiple data-fueled processes surrounding the development of an AI or advanced analytics model. Analytics projects need clean, high-fidelity data; and AI projects demand a continuous flow of accurate data. Not having this can lead to many output deficiencies including incorrect, outdated responses, biases, and even hallucinations. Gartner predicts that through 2025, at least 30% of GenAI projects will be abandoned after proof of concept due to poor data quality, inadequate risk controls, escalating costs or unclear business value.1
Recurring Data Bottlenecks in AI and Analytics Projects
As data engineers, you work behind the scenes to ensure data scientists get a continuous flow of the best quality, real-time data to feed their advanced AI and ML models. However, your time is often consumed by operational inefficiencies and bottlenecks that slow things down and threaten the integrity of the data. In that context, some or all of these questions may sound familiar:
- How do I organize and streamline the many elements (people/processes) around data management?
- How do I get a repeatable data pipeline and efficiently deliver multiple projects?
- How do I continuously update my data in formats that the ML needs?
- How do I automate the data to stream continuously with the latest information?
- How do I make that data available in the right place, at the right time, to the right people who need it?
- How can I ensure access to data without compromising on security and compliance?
- How can I scale up models and prototypes without loss in data fidelity or project delays and cost overruns?
Judging by the questions plaguing data engineers supporting multiple advanced AI or analytics projects, it’s clear that most obstructions in seamless data management occur during data integration.
How DataOps Helps Solve AI and Analytics Challenges
With the world set to generate over 181 zettabytes of data by 2025,2 the process of combining structured, semi-structured, and unstructured data from different sources into a unified source of truth is only set to get increasingly challenging.
Despite the many tools and platforms that promise to make data integration a breeze, data engineers still struggle because their data integration foundation is not cohesive. From ingesting to transformation and delivery, data integration is a process that can quickly become complex and inefficient if not managed well, and this can derail project deliveries, costs and security.
DataOps has emerged as a response to address data management complexity, by optimizing, automating and improving processes at each stage of the data journey. It ensures a continuous:
- Inflow of high-quality input data for data scientists designing the analytics and ML models.
- Outflow of business-ready insights for data consumers for smarter decision-making.
Recognizing that a continuous, accessible flow of high-quality data is the key to analytics and AI success, DataOps specifically addresses the challenges around data collaboration, integration and automation, and helps build end-to-end intelligent data pipelines for advanced analytics and AI/ML projects.
By making the right interventions at each stage of the data management journey, DataOps can dissolve bottlenecks, ensure they don’t recur and build safeguards to enable efficiency and compliance in workflows, at any scale.
Five Essential DataOps Interventions for Success
Considering the crucial importance of data integration for successful data management, let’s take a look at the interventions DataOps can make at each stage of the data integration workflow to achieve these goals.
- Data Ingestion and Integration: Collect data from various hybrid source
- Data Preparation and Transformation: Build and operationalize data pipelines for advanced analytics and AI
- Data Movement and Streaming: Continuous and reliable flow of real-time data for AI projects
- Data Governance and Observability: Easily discover and track trusted data across the ecosystem
- Data Security and Compliance: Ensure data security and ensure compliance with industry standards
1. Data Ingestion and Integration
Business functions collect and store data in varied formats — structured, unstructured and semi-structured. This data is spread across numerous systems such as edge computing, IoT devices and SaaS platforms; both on-premises and in the cloud.
DataOps breaks through these data silos and automates the ingestion of multi-format data from hybrid sources. Data ingestion tools help consolidate the diverse data into a data warehouse, data lake or lake house, where it can be transformed into a unified data set and made ready for use by AI/ML and analytics models.
Key Takeaway: Tools with pre-built connectors help capture data from anywhere so data engineers can focus on building analytics and ML models rather than pipeline mechanics.
2. Data Preparation and Transformation
DataOps teams often spend nearly 80% of their time trying to locate, validate and prep data. Processes such as cleaning, enriching, and transforming help DataOps streamline and prepare the ingested data and make it ready for advanced analytics and ML uses. At this stage, the data must be accurate, complete and in the correct format for use by the models.
AI projects not only need accurate but also streaming data. DataOps accelerates the setup of high-performance data pipelines by automating versioning, collaboration and CI/CD aspects for smooth data flow.
Key Takeaway: Intelligent data integration tools enable no-code, templatized, reusable, scalable and flexible data pipelines for any data architecture, while also optimizing pipelines for operational efficiency.
3. Data Movement and Streaming
AI and advanced analytics projects need real-time streaming data. In use cases such as fraud detection or recommendation and personalization engines, the AI engine has to analyze and respond in real-time to events or situations.
DataOps facilitates the real-time movement of operational data between transactional systems and eliminates the need for complex ETL processes. This simplifies data storage, whether for a data warehouse, lake or lakehouse, so that data is always available to the models for analysis. DataOps also puts in place mechanisms for instant responses to situational changes which enables faster and more accurate decision-making for the model.
Key Takeaway: Advanced tools enable continuous integration of new/updated data with CI/CD pipelines so analytics and ML model results and predictions stay relevant and current.
4. Data Governance and Observability
AI models and advanced analytics rely heavily on accurate, trusted and verified input data. Data observability and governance are essential for improving explainability and building trust in the results. DataOps ensures clarity on end-to-end data lineage, helping engineers easily find the data they need along with all the details on where the data came from, how it moved through the data stack, the ways it was transformed and where it was delivered.
Key Takeaway: Intelligent data management tools automate data catalog and metadata management, clarify data relationships, define upstream or downstream change impact and establish a standard business glossary.
5. Data Security and Compliance
Sensitive data used for LLM training has to adhere to regulatory compliance standards. DataOps ensures data pipelines are built around data security and compliance frameworks, as the ethical use of data is crucial to building trust in AI technologies.
Key Takeaway: Advanced tools follow a quality-by-design approach where observability, governance, security and compliance are baked into the platform at each stage of the data lifecycle.
Choosing the Right DataOps Platform
With AI/ML and advanced analytics models, you want to avoid GiGo, or garbage in, garbage out. Ensuring high-quality input data depends on collaboration between data producers and consumers; integration and automation of data flows; and built-in data quality and governance.
DataOps addresses data integration bottlenecks and improves the long-term efficiency, effectiveness, speed and compliance of data-led projects. However, simply implementing DataOps will not guarantee success for analytics and AI projects. Operationalizing data integration workflows still requires robust data integration tools to prepare data for advanced analytics and ML models.
Using point or custom solutions for each DataOps intervention for better data integration will add complexity and cost. Instead, a cohesive approach to data engineering and data preparation lets DataOps teams pivot to true data collaboration, speed data preparation at enterprise scale and democratize the use of actionable data and insights.
Informatica Intelligent Data Management Cloud™ (IDMC) is a comprehensive end-to-end data management platform with all DataOps capabilities built into the workflows. For example:
- A no-code, visual development environment that increases developer productivity by up to 5X compared to hand-coding
- Dynamic mapping and templates for faster development of mass data ingestions
- Hundreds of prebuilt transformations, connectors and parsers
- Future-proofing against inevitable changes in open-source technologies
Implementing DataOps in Your Organization
Learn more about empowering DataOps with IDMC to reduce data integration costs and complexity, reduce errors, elevate data quality and improve analytics and AI outcomes.
