Understanding Data Warehouse as a Service: 5 Critical Aspects

Organizations are under more pressure to save costs as they transition to data-driven value-based service models. Migrating their infrastructure to the cloud provides them with an elastic, secure, strong, and 24×7 available architecture that eases cash flow and keeps up with their growth. Data Warehouse as a Service [DWaaS] provide an method for this cloud transition for the business organisations.

In this article, you will be introduced to Data Warehouse as a Service [DWaaS]. You will learn about its applications in different industries and Advancements being carried out in the field.

Introduction to Data Warehouse as a Service

Data Warehouse as a Service or DWaaS for short is an outsourcing model where a service provider has delegated the responsibility of creating, managing, and upgrading a data warehouse. The DWaaS provider takes care of all the associated hardware and software stacks. 

More and more data and analytics are being used to assist decision-making, forecasting, strategic planning, and overall enterprise management. Data is becoming more and more complex and varied, its size is monumental in most cases, and it needs to be updated and pruned continuously. As managing this juggernaut becomes difficult, companies and individuals are shifting towards DWaaS. 

Data Warehouse as a Service [DWaaS] also offers advantages like low latency, higher availability, scalability, and enterprise-grade security.  DWaaS companies manage many of the associated complexities,  syndication from disparate sources, ensure timely upgrades and regulatory compliance. 

Cloud-based data warehouses can instantly scale up or scale down, based on the storage and computing needs of the hour, making them highly cost-effective. This data can be analyzed to provide valuable business insights, which lead to better decision making, fuel growth and profitability, increase operational efficiency, increase competitive advantage, and customer retention.  Moreover, choosing DWaaS for this data frees up the enterprise IT resources to perform more valuable tasks, which are directly linked to profits and smooth functioning of business processes. 

Understanding the Progress of Data Warehouse as a Service in the Market

The DWaaS market was valued at USD 1.44 billion last year and is expected to reach USD 4.3 billion by 2026, This means a combined annual growth rate of 20% in the time period 2021 – 2026. With constant improvement in cloud technology, and as acceptance of IoT and big data technologies grows across several industries, DWaaS market can only exponentially increase.  Moreover, it’s expected to reach USD 9 billion by the end of 2028! 

Growth Drivers:

• Rising need for data warehouses for disparate data storage
• Growing demand for data mining for BI and data analytics
• Increasing use of historical data for enhancing customer experience
• The proliferation of cloud technology in data warehousing

Industry Pitfalls & Challenges:

• High deployment costs and IT complexity

Industry Segments using Data Warehouse as a Service

Some of the industry segments using taking advantage of Data Warehouse as a Service are described below:

• Data Warehouse as a Service: BFSI [Banking, Financial Services and Insurance]
• Data Warehouse as a Service: Telecom & IT
• Data Warehouse as a Service: Healthcare

Data Warehouse as a Service: BFSI [Banking, Financial Services and Insurance]

Banking, Financial Services, and Insurance are one of the major consumers of DWaaS. Other major sectors include Telecom & IT, Healthcare, Retail, Manufacturing, and Governments, etc. 

Sectors that continuously generate and consume large amounts of data, and have the need to coalesce this disparate data from multiple sources to arrive at forecasts, key trends, and analytics; are major consumers of DWaaS. In BFSI sector companies, there is an ever-increasing need to cut down losses due to false/faulty claims, cyber-attacks, and phishing. 

DWaaS based solutions are being deployed to perform predictive fraud analysis, detect false or erroneous claims, and enhance security. Quantifying and assigning numerical values to factors like prospective profits, credit/investment risks, and the likelihood of a policy ending up in a claim is another usage that DWaaS assists in. 

Data Warehouse as a Service: Telecom & IT

The Telecom & IT sector requires an in-depth analysis of usage patterns/network uptimes/call drops/user behavior, etc., with respect to geographies/user demography/hours of the day.

Data Warehouse as a Service: Healthcare

The Healthcare sector can benefit from DWaaS via analysis of disease patterns/occurrence of pandemics/drug efficacy/side effects and fallouts of treatment. 

When the above knowledge is coupled with or viewed with respect to age groups/lifestyle/demographics etc., actionable insights into the effectiveness of treatment and profitability can be gained. The above are just a few use cases where DWaaS coupled with advanced analytics is being extensively used.

Challenges in using Data Warehouse as a Service 

Some of the threats currently facing the DWaaS deployements are described below:

• Threat of Data Breaches and Cyber Attacks
• Data Rigidity and Inefficient Architecture

Threat of Data Breaches and Cyber Attacks

Since the data that DWaaS holds is sensitive and mission-critical, security is of paramount importance. Cyber attackers keep devising new ways to penetrate cloud security which demands continuous up-gradation and innovation in security practices. 

Moreover, more than 90% of security breaches happen due to customer negligence, hence DWaaS providers need to keep devising methods to address this issue too. Hence, a comprehensive governance and security policy, adhering to regulatory compliances, is the need at the moment.

Data Rigidity and Inefficient Architecture

Historically, database and warehousing solutions were devised focussing on the structure and inherent properties of the incoming data.
Example: Relational DBMSs and flat-file DBMSs.

Today, the incoming data in a warehouse can be of multiple disparate types, reach the warehouse at varying speeds, and could have a multitude of pre-processing needs. A good data warehouse should assume nothing about the structure of incoming data and make its policies and implementation, as generalized as possible. This way, accommodating future data types/data flows would be easy and fast. 

Key Geographical Trends in Data Warehouse as a Service

Talking of geographies, North America, primarily fuelled by US is the largest consumer of DWaaS, US corporations are early adopters of new technology, and their executives are one of the most well informed about technological advancements. 

Widespread consumerism, leading to high usage of mobile devices and software applications, which in turn result in huge volumes of data being continuously generated, is another reason for high demand. 

If we consider absolute numbers, The Asia Pacific region is expected to grow at the fastest rate(percentage), with countries such as India as fastest growing data warehouse as a service (DWaaS) consumer. 

Countries such as Japan, China, and South Korea are experiencing high technological advancement and hence are anticipated to be at the forefront in the adoption of DWaaS. In APAC, big corporations were at the forefront of adopting DWaaS, but now Small and medium enterprises (SMEs) have started to follow in tandem in order to increase their competitive edge.  

Advancements in the Field of Data Warehouse as a Service 

We are seeing a tectonic shift towards the use of Artificial Intelligence (AI) and Machine Learning (ML) in computing solutions. This has primarily been fuelled by advancements in computing power and storage. 

Above is an approximate market share of AI usage in different segments in 2020 As businesses invest heavily in AI, mostly to study consumer behavior and predict their future preferences, adoption of DWaaS will only increase. Healthcare companies are depending heavily on AI and ML to analyze prospective lines of treatment, and predict treatment outcomes. Supply chain management is another application domain that will grow phenomenally as it benefits from AI/ML and enhances its scheduling, distribution, operational efficiency, and optimization. 

As ML allows machines to learn from the incoming data and environmental changes, it will increase the efficacy of AI systems. For all this to happen, massive data warehousing will be needed. Gartner – By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures. The corona pandemic has had major effects on how we work and how we function. Work from home and Online buying has necessitated the need for more training, more monitoring, and more vigilance. This in turn has increased the volumes of data to be processed and analyzed, thereby contributing to the growth of DWaaS. 

Data collection will occur more and more in real-time, and organizations will store disparate data to hybrid and multi-cloud environments, and access them frequently in different capacities to generate real-time advanced analytics. The move will be towards apps and applications being connected to the cloud and showing statistics and reports that use the most concurrent data. Data security, data protection, and standards compliance will gain paramount importance. Role-based concurrent access will be the norm of the day. 

The above two factors will lead to hyper-personalization and contextual communications. Example: In data warehousing and analytics itself, dashboards will be replaced by more fluent and automated stories. Users will be served relevant insights as streaming content augmented with related visuals; real-time streaming data will be used. So, the use of augmented reality, augmented analytics, natural language processing (NLP), and streaming collaboration, will increase manifold. The above factors will require more and more DWaaS services. The move would be towards the integration of data with analytics and vice versa, the boundaries between them will be blurred. 

Organizations with a more traditional approach will also need to offer their services/products in a more agile and lean fashion. To stay in the competition, their offerings will also need to be customized in a cost-effective manner. Consumers will be given customized products/services, at the precise time they want them, and in the packaging that they desire. 

Sooner than later, many of these old school companies will also endorse DWaaS in a limited or full-fledged manner. Another advantage DWaaS offers is that Analysts will get to spend more time on data analysis rather than spending time preparing the data for analysis. To conclude, Data is more than ever, an invaluable resource and a key ally for businesses. This data should be kept pruned and processed, in a secure and accessible location. Viewing data as a resource imperative for survival will increase the importance of DWaaS manifolds in near future. 

Conclusion

In this article, you learned about Data Warehouse as a Service [DWaaS], its applications in different industries, and advancements being carried out in the field. You also learned about the challenges in the DWaaS deployment and the key geographical trends in the market.

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