Integrating and Scaling AI is Silver Lining for Analytics

Analytics is central to intelligent business where appropriate data plays a prominent role in driving operations and is a key factor of digital transformation. The game-changer in the last two years were, data analytics and artificial intelligence (AI) (If your company plans to scale AI, know which trend to go with), and the functionalities of business started shifting more towards data-driven culture and data governance. This provided digital capabilities to organizations with greater opportunities to create business values. 

Data analytics leaders must focus on technologies that bridge the gap between development and continuous value delivery system in 2022 and beyond. In the age of smart technologies like artificial intelligence (AI), the focus will shift more towards the continuous delivery of AI-based systems. 

According to Gartner’s prediction, by 2024, 70% of enterprises will use cloud and cloud-based AI infrastructure (Know which cloud infrastructure would best your company.) to operationalize AI, thereby significantly alleviating concerns about integration and upscaling. How organizations approach AI engineering has a cascading impact on the capabilities of analytics to deliver deeper and better insights beyond what human analysts can provide. Hence, companies must focus on building data analytics and AI trends for better decision-making. 

5 Data Analytics and AI Trends in 2022 Expediting Business Operations

The silver lining of AI engineering is that it is expected to have a significant or high impact on innovation especially in data analytics (Know which technological advancement in data analytics would take customer experience to next level) not only in 2022 but for decades to come as the capabilities of AI are still unutilized. With analytics and AI models integrated together will provide facile strategies and solutions, and is likely to enhance the analytics. Following trends will gain traction in 2022 and beyond:

1. Data-driven Customer Experience (CX): As experience evolution was seen in the past two years, the role of data analytics to tailor the strategies for better insights of customer experience (CX) (Transform customer experience with digital technologies and strategies in 2022) has become prominent. Before reading data, companies must focus on what data is collected, also, assessing the customer interaction journey from an experimental perspective. The key enablers of data-driven customer experience (CX) are:

• Consolidate and Clean Data: Collecting data in a central repository where it is clean, analyzed, and activated is paramount to getting a holistic understanding of your customer.
• Making Data Accessible: Data should be accessible to all applications unrestricted and in a timely manner.
• Data Governance: More focus should be given to formulating policies of how it is stored and accessed.

2. Data Analytics on Cloud: CIOs need to build data strategy on the cloud for greater collaboration, agility, and resiliency for which modernizing the cloud infrastructure is significant.  Developing a bespoke cloud strategy by fusing industry and process expertise with the power of AI engineering would work, as cloud migration alone won’t work for innovation. 

AI-powered digital business platform in the cloud provides visibility of end-to-end data on which action has to be taken. This helps in frequent forecasting and improves accuracy. Genpact, deployed full-stack analytics on Cora that functions the same and provides visibility of supply chain data. 

3. Model-centric to Data-centric AI: AI systems are composed of code and data, the former being the model, programmed using a different framework, and the model architecture is called a model-centric AI system. The one which is composed of data and architecture built is a data-centric AI system. In the model-centric approach, empirical tests are done around the model whereas in the data-centric approach dataset is cleaned and enhanced to improve the accuracy of AI systems. 

The data-centric approach demands enhancing dataset, which has different meanings for organizations i.e., maintaining the consistency of label, sampling the training data, choosing batches appropriately, etc. Since the flow of data is continuous, the collection of data and training on patterns can be automated through MLOps.

4. Scalable MLOps and AI Engineering: MLOps enables organizations to expedite the AI application life cycle and enables reliable scaling of AI across business domains. MLOps substantiates key practices across the life cycle that increase productivity, speed, reliability, and reduce risk. The key enablers of MLOps in enhancing AI engineering are:

• Technology Stack and Tooling: Optimizes workflow.
• Compliance, Security, and Risk: Models are designed to allow audits, bias checks, and risk assessments.
• Reusable Components: With MLOps teams assemble modules rather than reinventing unlike in the previous model without MLOps where developers used to build components from scratch.
  Hence, MLOps can automate processes, build reusable assets and components, managing quality and risks.

5. Composable Data and Infrastructure: Composable data and infrastructures impart the ability to store different resources to remote machines or devices. This means sets of information or software applications are only provided when requested by the end-user. Processing hardware is bogged down in infrastructure when its storage houses too much data. So, moving that data to another storage system and only providing it when needed,  greatly improves the speed of operation.
Composable data and infrastructures are all about managing data to maximize productivity within the business. With data growing exponentially, computation speed greatly decreases.
Its important to use case is seen in healthcare, where professionals can access medical software and information as needed, without bogging down their hardware. This allows for faster processing and reduced data costs, allowing medical institutions to focus more on improving patient care and digitalizing processes.
Companies understanding the potential of data analytics and AI collectively, and making these technologies operational will see innovation and a high rate of success.

Commoditization of AI to Enhance Analytical Capability

Given the transformational potential of AI, companies would do well when focusing on particular AI capabilities that can advance and differentiate the business in 2022 and beyond. Companies will need to put more emphasis on ethics in the early stages when harnessing data analytics and implementing the use of AI, than post-launch. Continued attention to ethics can ensure that AI solutions are unaffected by human biases, whether intentional or not, and strengthen trust between the company, its employees, and customers.

The data-centric AI model is gaining momentum as it lifts performance that is coming from a deep understanding of data. Data-Centric AI is an effort to make the sustainable way of putting machine learning to work on real use-cases by helping AI/ML practitioners understand, program, and iterate on data instead of endlessly tweaking models.

The excitement around data-centric AI should galvanize the researchers and companies to focus more attention on building data-centric tools and frameworks, enabling a systematic and principled approach towards data excellence by improving data quality throughout the lifecycle of a machine learning project.

Techment Technology has stepped into data analytics and already working in AI to act with agility and stay competitive. Also, we extend partnerships with vendors in this evolving AI ecosystem to quickly adapt to new technologies and demands. For business initiatives or free consultation, visit here.