Trends in Data and AI
Technology / February, 04 2022

Top Data & AI trends leaders should know in 2022

If one technology area has witnessed continuous disruptive innovation in the last decade and offered great promise for both business and technology stakeholders, it is Data and Analytics. As COVID-19 hit the world, the digital transformation project accelerated at an incredible speed, and it has further paved the way for every business to become a data business. So, as organizations rethink the data strategy, D&A leaders have to identify data monetization opportunities and improve data literacy across the business. Here are a few Trends in Data and AI we have identified which will be valuable to business leaders.

1. As we enter the intelligent, automated enterprise era, every company will become a data company.

Even before the COVID-19 outbreak, the most successful companies were data-driven and stayed innovative by using data. What was once a common misconception that data is a strategic asset for the cloud-born tech companies like Googles, Ubers, and Facebooks of the world, but not for the traditional businesses, is no longer true due to the availability of similar data infrastructure and tools at an affordable cost. These are established use cases that demonstrate how the value of data is being unlocked through cheap technology for most enterprises to access. The good news for enterprises that have not mined the data yet is that they are sitting on sizeable potential gold mines. The cloud-based data and analytics tools that are now widely available and affordable offer the key to liberating that trapped data and putting it to work. If the enterprises do not tap into this, they will leave behind a large amount of cash on the table.

2. Cloud data warehouse and lakehouse will potentially unlock enormous benefits for the organizations.

Cloud data warehouse and lake house solutions have revolutionized the traditional methods of data platform construction. It provides the capability to create and start using a data warehouse service within a few minutes without guidance from platform technical experts. Organizations are adopting this new modern data architecture to realize the combined value of the low cost, scalability, and flexibility of a cloud data lake with the performance and governance of a traditional data warehouse.

3. The future of the data location will be hybrid as the battle of Centralization vs. Data Mesh continues.

Data management strategies are constantly evolving, and enterprises must be ready for such change to stay competitive via timely and reliably delivered insights. From the 1980s, the paradigm has shifted from data warehouse to data lakes to data lakehouse and in recent times to data mesh. Traditional data architectures work very well in a world with fewer data sources and a narrow set of use cases. As the data sources grow and the need for business teams to tap into raw data sources increases, these centralized models can create a bottleneck for the users.

These four data design patterns are not mutually exclusive — they may co-exist in an enterprise, for instance, with a cross-functional domain team with its data lake. Hybrid data architecture represents the best path to engage with a rapidly changing infrastructure landscape. It offers the flexibility to manage legacy data-intensive processes while simultaneously embracing new “born-in-the-cloud” data frameworks and Trends in Data and AI.

4. 2022 will be a year of Data Ops

As we gain momentum behind modular enterprise architectures, data ops will be critical to making such transformation projects successful. DataOps addresses decentralized organizational structure/architecture challenges such as a data mesh. Data mesh and DataOps make an effective team to enable innovation through decentralization while harmonizing domain activities in a coherent end-to-end pipeline of workflows. DataOps can perfectly handle global orchestration, shared infrastructure, inter-domain dependencies, and enable policy enforcement. DataOps creates a superstructure by unifying all the infrastructure requirements demanded by various domains to own a self-service infrastructure-as-a-platform. DataOps is the perfect ally to data mesh.

5. Acceleration of Real-time analytics

The trend in recent years is a clear indication that data volumes and velocity at the organization level will continue to follow the upward trajectory, surging more than ever before. This, combined with the convergence of data lakes and warehouses and the need to make quick decisions, is expected to drive real-time analytics improvements in the response time. As the data computation and storage move closer to where data is generated, communication can occur at lightning speed between the edge devices. This becomes the prime mover for enterprises to leverage collaborative analytics for real-time decision-making.

6. ML Ops will make way to all-encompassing Model Ops.

Model Ops includes models based on machine learning (ML), knowledge graphs, rules, optimization, natural language techniques, and agents. In contrast to MLOps (which focuses only on using ML models) and AIOps (AI for IT operations), ModelOps focuses on operationalizing all AI and decision models. ModelOps acts as a bridge between data scientists, data engineers, application owners, and infrastructure owners. It fosters dynamic collaboration and improved productivity. ModelOps help enterprises to address challenges like regulatory compliance interoperability between silos and create a single view of all models in the enterprise. Further, they make it easier to integrate and adopt modern technologies.

7. AI-generated content will become more rampant.

Content is no longer simply an information blog about a product/service or social media post. It is the critical factor of any business as it shapes the narrative and builds the customer’s emotional connection with the business. AI-generated content can help businesses respond to customer queries, create and manage FAQs about products/services, create quick ad copies with less overhead. For any marketer or entrepreneur, it’s the most cost-effective way to reach their audience.

8. Data marketplace will gain momentum as data sharing becomes essential.

For any data management or analytics initiative, the high-level motivations for enterprises are to be able to get new business value, improve efficiency and respond to external forces. Data marketplaces (or exchanges) particularly seem to be building momentum with their fresh data democratization and monetization value propositions. When it comes to their adoption, we expect that in 2022, more organizations will embrace external use cases and ecosystem-focused approaches.

9. The rise of the modern data stack from ETL to ELT provides agility and scale.

The cloud data warehousing revolution means more companies are breaking away from an ETL approach and towards an ELT approach for managing analytical data. The advantage of using ELT is that it provides:

  • the agility comes from the fact that data can be stored in a data warehouse without transformation to be used as needed
  • simple transformation layer is written in easily understandable SQL statements
  • self-service analytics can be plugged into extracting intelligence from raw data.

10. Analytics engineer will become the hottest job role displacing the Data Scientist of its coveted position.

The analytics engineer sits at the cusp of data scientists, analysts, and data engineers, inheriting all three attributes. Analytics engineers are business-outcome driven and operate with an analytical mindset, thereby influencing the data engineering practice to deliver measurable results. They are primarily responsible for laying the foundation of tools and infrastructure to support the entire data and analytics team.

11. Supply chain data analytics will go from Planning and forecasting to forecasting to nowcasting.

COVID-19 has demanded that enterprises can swiftly respond and scale in real-time. In such challenging times, traditional forecasting techniques that rely on substantial historical data sets will fail to offer reliable predictions for any business. Enterprises will seek tools that can help them make better and more informed decisions in real-time.

12. People analytics rise to the top – great resignation will push it to be a priority.

The coronavirus pandemic became a significant disruptor, leading to the Great Resignation phenomenon. So, people analytics, which has been at the lower end of the analytics stack, is suddenly bubbling to be of high importance to business leaders. Companies will want to be well-prepared to tackle 2022 and beyond challenges in this environment. One should watch closely the Trends in Data and AI.

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