Artificial Intelligence is the major trend in the industry today. Recent studies revealed that AI is estimated to add $13 trillion to the global economy in the upcoming decade. However, enterprises are still striving to scale up their AI framework. The need here is to recognize and understand the cultural and organizational hindrances that your AI efforts face and finding ways to evolve them.
To become successful in digital transformation, businesses must establish a strong AI foundation that conveys its benefits through innovation, efficiency, and high performance.
In this post, we are going to look at the core factors of AI foundation that eventually lead to successful digital transformation of businesses:
What is an AI framework?
Before we jump into the significant AI framework stages, let’s explore what exactly an AI framework is.
Artificial Intelligence framework allows businesses to seamlessly and quickly build AI applications. These applications include machine learning (and deep learning) operations research (optimization) and simulation models. AI frameworks can be considered as a template for an organization’s entire AI system. This makes the application development, deployment, and governance faster and easier, as everything is well-integrated and built to scale. Furthermore, applications with a strong AI framework are less likely to face security or quality control problems.
Five core stages of a solid AI Foundation:
1. Identify the right AI/ML initiatives to pursue
- How do you prioritize AI initiatives?
- Continuous tactical level process to help prioritize which AI initiatives to pursue for next month/quarter/half year/year.
- In this stage, the leads will identify all potential blockers and work with leadership to remove them.
- How do you discover AI initiatives?
- The key to success is the ability to find valuable initiatives. The business plays a critical role here but they must be educated and empowered to do this successfully.
- AI Initiatives should be anchored in a business process with a clearly defined decision point or business target that is a bottleneck (inefficiency).
- AI Initiatives should have a well defined ROI based on current business process KPIs.
- How do you evaluate business value-add for an AI initiative?
- This is where data scientists start playing an important role. They take an AI initiative, validate data availability and quality, and evaluate how an ML model can improve business process KPIs named in AI initiative by the business owners.
- It is important to make this step as fast as possible to spend org time and money on the initiatives that bring real value. This, is where most organizations fail – spending time and money pursuing AI initiatives that are not valuable pursuits in the first place.
- How do you implement an AI initiative in your org? (Hint: this is where transformation comes into play)
- Successfully implementing AI initiatives often involve material changes in org structure, people, processes and technology.
- All these aforementioned layers need to be synchronized to successfully implement ML models that have high business value and impact, at scale.
Building ML models is not the most difficult part. What is difficult, is the transformation required for an organization to build many models that deliver business value. For this you need to implement a set of processes as mentioned above and start using them.
2. Data Quality Assessment
Data Quality is often defined as a function of various dimensions including currency, accuracy, completeness, consistency, lack of bias, timeliness, and relevance of data related to the insights businesses want to extract. How does one measure, quantify, and maintain data quality?
For starters, organizations need to adopt effective data-quality frameworks and processes that involve precise storage, management, and secure transfer of data. Respective data owners need to take custody of data and ensure good data quality in each department.
Companies can leverage data accuracy solutions to evaluate the data veracity score and measure data quality. Such pre-built frameworks and tools can be used to determine the relevance, risk, and quality of data and monitor improvements over a while.
The primary assessment of data quality will create a baseline. New data cleaning, integration, transformation, and aggregation stages result in improved data quality, which is again measured against the initial baseline. These steps help businesses achieve good data quality and establish trust amongst users.
Quality data is critical when AI/ML is considered. There is a simple rule: garbage in – garbage out. No algorithm can create miracles if the data has no information that can be used to support the decision.
3. Data Governance
Data Governance is a set of practices and principles that retains high data quality throughout the entire lifecycle of data. Data governance helps businesses streamline data management by ensuring usability, high availability, security, and integrity of data. By leveraging the right tools and technologies, data governance can reinforce the AI framework and drive business ROI by ensuring there is a common understanding and trust of the data used for ML.
Data governance mainly involves a process-oriented framework to adopt and perform data quality initiatives such as planning, cleansing, profiling, assessment, troubleshooting, and consistent monitoring.
Here are a few outcomes that data governance can help achieve:
- Make strategic, consistent decisions based upon complete data that is well-aligned with different objectives for using data assets.
- Meet standard regulations and avoid heavy fines by adequately implementing data lineage and the access controls for the data.
- Take solid security measures by adopting data ownership and related roles/responsibilities.
- Determine and verify data distribution policies such as accountabilities and roles of all the entities involved
- Use data to boost monetary profit. Monetization from data is possible with precise storage, maintenance, usability, and classification.
- Appoint responsibilities to measure and keep track of data quality KPIs and compare them against general performance KPIs across an enterprise.
- Reduce repetitive tasks by owning data assets that are standardized, complete, trusted, and capable of fulfilling various purposes.
- Eliminate re-work by not performing data cleaning and data structuring during every planning stage
- Boost staff productivity by having data assets that meet the optimal data quality standards
- Increase data governance maturity levels step by step to enhance the data pipeline
These are just a few benefits that data governance can reap for your business. To summarize, data governance can significantly help you comply with regulatory standards and also scale your AI framework with time.
A typical data governance team involves a data governance strategist, architects, specialists, and analysts.
4. Data Engineering
Data Engineering goes hand in hand with data warehousing. Databases are a collection of accessible and consistent data. A large-scale enterprise generally consists of hi-tech operations management solutions such as production systems, CRM, ERP, and more, thereby gathering various databases as well. As more and more data sources get added, data is likely to scatter in different formats, and organizations might find it challenging to get a proper view into their databases.
To avoid this, enterprises need to integrate data systematically in a single unified storage system to accurately gather, reformat and use the data. This system is known as a data warehouse or a data lake. Transferring data from one system to another and building a systematic data infrastructure is referred to as data engineering.
Data engineering process involves designing, building, and integrating big data from different sources and managing it seamlessly. Further, data engineers make sure the data is usable, accessible, and works seamlessly round-the-clock. Their main objective is to enhance the performance of the big data ecosystem of an enterprise.
Data engineers will often build big data warehouses to be used by data scientists for analysis and reporting.
Data engineers play a crucial role in AI/ML as they are creating technology that delivers the right data to the right place at the right time. Scallable data pipelines are as important as a well maintained production line in a factory.
5. Machine Learning Operations (MLOps)
MLOps can be viewed as a similar concept to DevOps, but for the data scientist rather than the developer. It is a set of practices and technologies that facilitate smooth collaboration between operations teams and data scientists within an enterprise, and are critical to “scaling” ML in an organization. Again, this is part people, part process and part technology. Adopting MLOps practices simplifies the data management process, enhances data quality, and automates ML models deployment across large production environments. Moreover, MLOps makes it easier to align models with regulatory standards and business requirements.
MLOps is more like an individual means to ML lifecycle management, involving data collection, data analysis, data preparation, model training and building (software development lifecycle, CI/CD), orchestration, validation, deployment, model monitoring, and model re-training.
- MLOps helps to make a smooth transition from R&D world of data scientist and robust and stable world of IT. That is why this is important that foundations for MLOps are in sync with best IT practices but also tuned to fit data science needs.
To ensure the AI directives meet the business expectation and achieve the desired outcomes, enterprises must put in place a strong AI foundation framework to set the goals, objectives, make the right assessment, and then allow them to create a crisp and clear strategic plan to implement Machine Learning projects successfully.
Companies that embrace the AI foundation and implement the core principles have higher chances to achieve success in implementing AI projects with transformation outcomes for the business, as well as getting the ML “flywheel” turning and becoming able to successfully scale AI across an enterprise.