Data-as-A-Service (DaaS): The Data Innovation That's Here to Stay
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Data-as-A-Service (DaaS): The Data Innovation That’s Here to Stay in 2022


Karan Tulsani - January 12, 2022 - 0 comments

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Cloud-based technology is steadily being adopted by various companies. The total number of businesses shifting to Data-as-a-Service (DaaS), as a solution for data management, integration, analytics, and storage to revise their infrastructure and handle workloads, has risen significantly. Organizations can enhance the reliability and integrity of data, decrease time-to-insight, and improve data workloads by choosing Data-as-a-Service (DaaS). 

Data-as-a-service (DAAS) is a big data and analytics offering that enables companies to tap into the largest potential for meaningful analytics, by providing on-demand access to the largest platforms in the industry. DaaS makes it easy for any company with data needs to get customized solutions from its different providers. With a wide range of accessible services from simple acquisition to complex configuration, DaaS allows companies to hone in on specific services for acquiring and cleansing their data, building predictive models around it, and analyzing the results. This also means shorter timeframes for companies in every stage of their analytics process to come up with valuable insights.

In this blog, we will go through the basics of Data-as-a-Service, key elements of DaaS and the advantages and challenges of DaaS implementation for an organization.

What is Data as a Service?

DaaS

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“Data-as-a-Service” is a relatively new concept. DaaS is an all-inclusive, cost-effective and highly flexible way to get the data you need to make your business more efficient, agile and profitable. The technology industry has been turning businesses on their heads for years, and it’s no different in the data sector. If you’re looking at ways to improve your own business’ efficiency, agility, or profitability, DaaS could be the answer for you. With DaaS, you can create a customized solution tailored to your needs. 

What Is Data-as-a-Service (DaaS)? DaaS defines how an organization will store and access its data. Instead of paying the traditional upfront cost of purchasing hardware and software while also dealing with expensive maintenance fees, businesses can now “rent” the hardware and software they need as well as the employees who know how to use it. DaaS allows businesses to more easily integrate disparate systems into a seamless whole where data can move freely without being trapped by systems that were not created with each other in mind.

It can also be known as a data management strategy businesses use, which utilizes the cloud to provide data integration, processing, storage, and analytics services using a network connection. It is an information provision and distribution model in which data files are available to users over the internet or any other network. It is a cloud-based service that supports web services and service-oriented architecture (SOA). DaaS is typically stored on the cloud and can be accessible through multiple devices.

It can also be defined as open-source software solutions that offer important abilities using a unified set of data models and APIs for analytical workloads. DaaS platforms address critical requirements by accelerating analytical processing, curating datasets, securing and masking data, simplifying access, and offering a unified catalog of data.

Important Components of Data-as-a-Service

Data Collection

Includes the locating of the most effective method and timing to gather insights and data.

Data Aggregation

It is the method in which data points are collated for a particular reason and then examined and outlined in the form of useful data.

Data Correlation

Statistical data of the strength of a relationship between two points. Stronger relationships between two items show a significant correlation and provide low-risk decision making.

Statistical Importance

It is the method that measures risk tolerance and confidence levels linked with data sets, which allows data-focused decisions.

Data Visualization

In this method, businesses can get buy-in from teams and stakeholders by recognizing patterns and displaying insights visually.

Innovative Analytics

This method includes the development of complex models to simplify big data to deepen insights and get rid of analysis paralysis.

Advantages of Data-as-a-Service

There are lots of advantages that businesses can reap while working with a Data-as-a-Service provider. Some of the benefits includes:

Shrunken Costs

People use smart devices for a number of reasons throughout their day. Google has coined a term for these times that users use their smartphones: micro-moments. The micro-moments can offer highly advantageous possibilities for businesses in a data-focused business environment.

Predictive analysis enables brands to customize experiences by forecasting what a user wants from a brand. This enables brands to shift target audiences down the sales funnel along their path to purchase. Algorithms can examine data points and charts to anticipate data future behavior using a method of machine learning. This saves businesses to save money on marketing to customers who are not interested in the products or from marketing the wrong products to the wrong audience.

Correct Analysis

With organizations that run agile operations, a usually noted mistake is the decision making that occurs on bias. A large number of businesses do not make data-driven business decisions and make them based on experience. This may sometimes be profitable, it can always be an issue as humans are biased. The said biases can cost organizations large chunks of money when they try to identify market demands by pure guess. Organizations are expected to stay away from bias and work on offering better customer experience.

New Business Avenues

Another important advantage of data as a service enables businesses to run smart operations that make use of data to boost their decisions. The decisions are based on analysis and help businesses move ahead at a faster rate than normal conditions. Data as a Service relieves methods of guesswork in identifying what the target audiences want. It further allows organizations to avoid risk and grasp and innovate at a faster pace.

Limitations of Data-as-a-Service

Though there are various advantages that come with it, Data as a Service also has peculiar challenges that could later turn into major issues. Some of the shortcomings of Data as a Service are:

Data Complexity

It is one of the major issues to Data as a Service. DaaS has seen a definite slow growth as most users and DaaS vendors do not have significant knowledge to navigate multiple types of datasets. Another cause for slow growth could be the hiring of data scientists who are young in the field and look only at particular sets of information. On the other hand, expert professionals have an improved understanding of data that can be disruptive by comparing data. Data as a Service needs strategic and methodical thinking as organization data must serve business strategies and must be balanced to work toward a particular business objective.

Data Security

In instances where businesses use a DaaS provider, the exchange of data comes with a threat of being hacked or leaked. Cyber hacks constituting the leak of such data is damaging to the organization because of content security and legislations including GDPR. More often than not, the data once collected and mined, to make it into actionable insights is transactional data. This data consists of user financials and private data that businesses can ill afford to lose to cyber security threats.

Which Organizations Should Outsource the DaaS Process?

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Irrespective of the business size and nature of the business, data and analytics are necessary for all businesses. The major difference is how the data is collected, mined, and used. Though, here’s a summary of how each organization, based on their size, can use a DaaS provider.

Enterprises

In the last few years, these enterprises have implemented compelling data platforms to get insights. These platforms are a mix of platforms that small and medium sized businesses make use of, with an extra component. Although businesses have the team of data scientists to analyze the incoming data, DaaS vendors are still important to structure the data overload, democratize assets for business-wide use, break down silo walls, get into data lakes and leverage that data to build actionable insights. Large enterprises work on a top-down leadership approach rather than dialog across departments. DaaS providers can disintegrate such silos and optimize team members to keep each team working efficiently.

Medium Sized Organizations

In the growth phase of a business, full-time teams are required, and new platforms are introduced for data collection and management. These organizations need more and improved quality of data if they are to be hailed as market disruptors. If these organizations do not innovate fast enough to boost their forecasted growth, they will reach a point of stagnation due to the lack of appropriate type of data. Mid-level organizations need qualitative data to embrace human behavior during the customer journey. By using the services of a DaaS provider, they can gain valuable insights, use strategic methods to collect data, and build infrastructure they require to reduce the overload of incoming data.

Small Businesses

Small companies typically collect data from a single platform, with the data collection team organizing the data for the founder or CFO to analyze and use for future insights. For small companies, DaaS providers can offer in-depth analysis than what they actually get from a single platform. They also help reduce the risk of entering analysis paralysis for small businesses, and provide them a boost to the next level. At a lower level, small companies must stay away from business traps like data deciphering, and a DaaS provider offers the most usable insights and organizes them in data visualization tools for ease of understanding. This enables small companies to see where their opportunities lie.

Conclusion

The primary reason behind the Data as a Service (DaaS) model is the mitigation of risks and burden of data management. It is correct that businesses have conventionally collected and managed their own data. But, the issue lies in the fact that data becomes complex with time, difficult to interpret, and costly to maintain. Data as a Service is a novel way in which businesses can access crucial business data within an existing data center.

In other words, DaaS is set to transform the data landscape into one that’s more flexible, cost-effective, and efficient for businesses at every stage of their growth. Taking into consideration the high costs associated with maintaining a data lake on-premises, coupled with unpredictable and diminishing resources for IT departments in modern times, DaaS simply provides an all-in-one solution that’s set to take over in both the medium and long term.

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Karan Tulsani

Karan works as the Delivery Head at Conneqtion Group, a Oracle iPaaS and Process Automation company. He has an extensive experience with various Banking and financial services, FMCG, Supply chain management & public sector clients. He has also led/been part of teams in multitude of consulting engagements. He was part of Evosys and Oracle's consulting team previously and worked for clients in NA, EMEA & APAC region.

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