Cloud-based technology is increasingly being adopted by multiple companies. The number of organizations turning toward data as a service (DaaS), as a solution for data management, integration, analytics, and storage to update their infrastructure and manage workloads, has increased considerably. Companies can improve the reliability and integrity of their data, reduce time-to-insight, and enhance the agility of data workloads by opting for data as a service (DaaS).

What Is Data as a Service?

Data as a service can be defined as a data management strategy companies use, which leverages the cloud to deliver data integration, processing, storage, and analytics services through a network connection. It is an information provision and distribution model in which data files are made accessible to customers over the internet or any other network. It is a cloud-based technology that supports web services and service-oriented architecture (SOA). Data as a service (DaaS) information is generally stored on the cloud and can be accessed through different devices.

Alternatively, data as a service can also be described as open-source software solutions that provide critical capabilities through a unified set of data models and APIs for various data sources for analytical workloads. DaaS platforms address key requirements by accelerating analytical processing, curating datasets, securing and masking data, simplifying access, and providing a unified catalog of data.

Key Elements of Data as a Service

• Data Collection

Involves the identification of the most efficient methodology and timing to collect insights and gather data.

• Data Aggregation

It is the process in which data points are compiled for a specific purpose and then analyzed and summarized in the form of actionable insights.

• Data Correlation

Statistical analysis of the strength of a relationship between two points. Stronger relationships between two points demonstrate a strong correlation, thereby facilitating low-risk decision making.

• Statistical Significance

It is the process that measures risk tolerance and confidence levels associated with data sets, which enables executives to make intelligent, data-centric decisions.

• Data Visualization

With this process, companies can gain buy-in from teams and stakeholders by identifying patterns and displaying insights visually.

• Advanced Analytics

This process involves the development of complex models to simplify big data in order to deepen insights and avoid analysis paralysis.

Benefits of Data as a Service

There are plenty of benefits that companies can enjoy while working with a data as a service provider. Some of these advantages are as follows.

• Reduced Costs

People use smart devices for a variety of needs throughout the day. Google has come up with a term for these times that people use their phones: micro-moments. These micro-moments can present as highly beneficial opportunities for companies in today’s data-centric business environment.

Predictive analysis allows brands to personalize experiences by predicting what an individual wants from a particular brand. This, in turn, allows companies to move target audiences down the sales funnel all along their path-to-purchase. Algorithms can analyze data points and chart patterns to predict future behavior through the process of machine learning. This saves brands from spending money on marketing to customers that are not interested in the products or from marketing the wrong products to the wrong customers.

• True Insights

With businesses that run agile operations, a commonly noted mistake is the making of decisions that are based on bias. A large chunk of organizations does not make data-driven business decisions and, instead, base them on gut feeling or experience. While this may sometimes turn out to be profitable, it can almost always be problematic as humans are biased by nature. These biases end up costing businesses large amounts of money when they try determining market demands by simply guessing. Businesses are advised to steer clear from any bias and align their organizations with their customers’ desired experience.

• Faster Take On New Business Routes

Another essential benefit of data as a service is that it allows organizations to run intelligent operations that make use of data to facilitate their decisions. These decisions are insights-centric and help brands move forward at a faster pace than it would normally take. Data as a service rids business processes of the guesswork in understanding what their customers want and enables them to mitigate risk and comprehend and innovate at a never seen before pace.

Challenges of Data as a Service

While there are many benefits that come with it, data as a service also has certain shortcomings that could later turn into serious problems. Some of the challenges of data as a service are as follows.

• Data Complexity

This is one of the biggest challenges to data as a service. DaaS has seen a relatively slow growth as most employees and DaaS vendors do not possess enough knowledge of navigating the different types of datasets. Another reason for this slow growth could be the hiring of data scientists who are relatively young in the field and looked only at certain sets of information, whereas experienced professionals have a better understanding of insights that can be impactful by comparing and contrasting data. Data as a service (DaaS) requires strategic and methodical thinking as organization data must fulfill several company strategies and must be nuanced to work toward a certain company objective.

• Data Security

In cases where organizations make use of a DaaS provider, the transmission of data brings with it the threat of being hacked and leaked. Cyber hacks resulting in the leak of such data are detrimental to the company e due to the sensitive nature of content within big data you and the legislations, such as GDPR, that are associated with it. Most commonly, the data that is collected and mined, to turn into actionable insights is transactional data. This data includes customer financials and private information that organizations cannot afford to lose to leaks.

Which Businesses Outsource the DaaS Process?

Regardless of the size of business, data and analytics are required by all. The only difference is how this data is collected, mined, and used. Anyway, here’s a breakdown for how each business, depending on size, can make use of a DaaS provider.

• Large Corporations

Over the years, these corporations have implemented powerful data platforms to gain insights. These platforms are a mixture of platforms that small and mid-tier companies make use of along with an additional component. Despite companies having the team of data scientists sift through and visualize the incoming data, DaaS providers are still required to structure the overload of unstructured data, democratize datasets for company-wide usage, breakdown silo walls, tab into data lakes and utilize that information to develop actionable insights. Large corporations focus on a top-down leadership approach rather than Talking across departments. DaaS providers can break down such silos and optimize team members to keep each team working cohesively.

• Mid-Tier Organizations

During the growth of a company, full-time staff is hired, and new platforms are implemented to manage the data that they are collecting. These companies require more and better quality of data if they are to be poised as market disruptors. If these businesses do not innovate fast enough to propel their brand’s growth, they will reach a stagnating point due to the lack of the right kind of data. Mid-tier companies require qualitative data to comprehend human behavior taking place throughout the customer journey. By outsourcing, they can gain qualitative insights, deploy strategic methodology to collect information, and set up the infrastructure they need to mitigate the overload of incoming data.

• Small Businesses

Small businesses generally collect data from a single platform, with the data collection team taking the data to their founder or CFO to analyze and make future decisions. For small businesses, DaaS providers can provide deeper insights than what they gain from their single platform, help minimize the risk of entering analysis paralysis for small businesses, and give them a boost to the next growth level. At their stage, small businesses must steer clear from common traps such as the deciphering of data, and a DaaS provider provides the most actionable insights and places them in data visualization tools for easy understanding by the company. This allows small businesses to see exactly where their greatest opportunities lie.

Conclusion

The idea behind the data as a service (DaaS) model is the minimization of risks and burdens of data management. It is true that companies have traditionally collected and managed their own data themselves, however, the problem here lies in the fact that data becomes increasingly complex with time, difficult to interpret, and expensive to maintain. Data as a service is a new way in which organizations can access critical business data within an existing datacenter.