The chatbot market is predicted to reach a whopping 1.25 billion US dollars by 2025. These figures are not just mind-boggling, but they are proof that shows the relevance of chatbots today. Also, 45% of end users have already got comfortable with chatbots, and they prefer chatbots over other media for communication.

So, we can expect that by 2019 chatbots will reach another level of accuracy and efficiency. Here are the trends to watch out for chatbots in 2019:

1. Intelligent systems:

Chatbots are the future of impeccable customer service. But to make it at par with human customer support it needs to be super interactive and smart. Companies deploying chatbots are continually working on optimizing their bots so they could be an equal replacement for a human counterpart. It is predicted that in the next few years conversational AI-first” user experience, or CUX, will become mainstream in most organizations. CUX is an advanced version of UI which is designed to help and improve the user experience so that they can reach their end goals faster.

2. The rise of website chatbots:

By the next year, one can expect that several small, as well as medium-sized businesses who have not yet implemented chatbots, will look forward to adopting it. The emergence of third-party companies that help the organization to develop and build the industry-specific chatbots at affordable prices has made the adoption of bot technology easier. Chatbots are a great source to enhance the user experience and provides a 24*7 customer support as such all businesses need it.

3. Stricter guidelines:

The crux of chatbots is DATA and more data. The introduction of GDPR guidelines this year has made the usage of data restricted. Even with stricter guidelines, the importance of data will not plummet, and it will only increase. The companies will need to find responsible methods of data processing so that they adhere to the new guidelines and develop GDPR-compliant solutions.

4. Chatbots beyond customer support:

The coming year will witness the use of chatbots for several more processes such as the B2B and B2E business workflows. Some existing examples include the chatbots for CRM, Intranet and IT help desks. As cited by a leading company Juniper Networks that chatbots can reduce the business costs by $8bn by 2022 and 2019 will witness some fantastic advancements in that direction.

5. Mobile app saturation:

The mobile app market is saturating, and in the coming years, it is said that 50% of companies will focus their resources and finances on chatbots rather than mobile apps.

Reasons why chatbots are more preferred than mobile apps:

• Chatbots are more efficient and intelligent and are a better way to reach customers.

• Smartphone users do not want to exhaust their limited memory space with unlimited applications.

• 75% of Smartphone users use some messaging app.

• The UI elements of an application or a website is a collection of information. If all of this can be packaged together and provided in a messaging app, it will be much easier for the users. Wouldn’t it be great if you can manage everything from booking a movie ticket to buying groceries through a single interface?

• Also, chatbots do not need a download you need to message them through a messaging app and ask them to perform a function.

We cannot ignore the immense benefits that chatbots have to offer. The exploration around chatbots is unlimited, and we will see them unravelling one at a time. The experience that chatbots provide whether it is finding a travelling location or getting information is super smooth and comfortable. The personal touch that chatbots offer is another plus point that makes chatbots a perfect fit for businesses.

Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

By : Sampada

Published on Dec 19, 2024

In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, E-commerce, Multi-Industry

Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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