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Companies are relying on data today in a much broader spectrum than they used to. Moreover, the methods of obtaining, storing, and applying data are changing rapidly. The advent of breakthrough technologies is the largest catalyst for the new forms of information usage. New connectivity methods, including mobile devices and IoT sensors, expand the available types of information types and spur continual evolution of the data market.
Growing demand for data has triggered the emergence and active development of data processing companies referred to as Data as a Service (DaaS) companies.
What do they do, and how does the Data as a Service concept address the data related challenges of modern organizations?
In a nutshell, data processing firms deliver expeditious, superior quality data that can match the needs of the most demanding clients. How does this data help optimize operations, which benefits or hidden dangers does it hold, and primarily—what is DaaS? This article digs deeper into the concept to find out this and more based on field experience and real-life case studies.
DaaS provides all the data surrounding services over a network connection. This is the essential goal of all specialized platforms—to grant quick and secure access to critical data to all stakeholders at any location.
This technology concept resembles Software as a Service (SaaS), which denotes software accessible online and through the cloud without a need to install a program. Every “as a service” organization relies on the cloud to operate business resources. While the SaaS term has already become a buzzword, Data as a Service is not so widespread yet. One of the reasons for this is the quick evolution of data storage and analytics, as well as cloud technologies.
Data as a Service can include all types of data:
This data management strategy removes the reliance on infrastructure by keeping data in a unified location. Such an approach streamlines operations for businesses that have multiple internal units located in diverse areas, share data with clients, or adopt data monetization strategies.
The DaaS market is skyrocketing. Experts forecast its size to approach $12 billion in 2023.
What exactly is the meaning of Data as a Service for users, and what are the key differences between conventional data organizations and DaaS?
To ease the implementation process for businesses, DaaS solutions should flawlessly integrate into existing workflows instead of demanding customers to alter their processes. It requires a great deal of customer knowledge, smooth integration opportunities, and data that provides instant business value. It is vital for specialized firms to focus on trouble-free integration and addressing specific customer issues.
The production costs of a typical data company are significant, especially if a company operates on a small scale. However, gross margins tend to elevate together with business growth. Business owners should evaluate how their expenses’ structure of obtaining, creating, and processing data changes with the growth of their client base. The economies of scale are attained if dozens of new clients make your gross margin considerably higher than low-scale operations. A rising bottom line is essential in developing an extensive and resilient niche organization.
Data quality and accuracy spur innovation enabled by machine learning and AI. At the same time, data errors can cause disasters. By making DaaS machine-readable, companies open a range of opportunities to ensure the correctness of the information. Numerous tools, including Crowdflower and Mighty AI, are aimed to fine-tune your solution, label your information, and prepare it for algorithmic use.
Data offers maximum value when it ensures two fundamental things:
When data transfer rates are high, the company’s data features a remarkable potential for value. The market data in the real estate or stock markets are illustrative examples of quick-pace value growth.
With the continually-elevating demand for AI-enabled products, DaaS will grow as well. But the success of the companies adopting this technology will be dictated by the data quality, speed, and margins.
The platform is an internal technology utilized within an organization. The essential role of this end-to-end solution is empowering the interconnection between various data sources and tools, such as reports, microservices, business intelligence, and applications. The platform grants end users an opportunity to access data using SQL over ODBC or REST.
Organizations can also employ external DaaS services to acquire data. Predominantly, companies deliver such services via simple APIs. Several examples of outstanding company data providers will be discussed further in the article.
When successfully implemented, the solution can benefit the entire enterprise and its customers. Below are the most significant benefits to date.
Obtaining sufficient data is not a problem anymore. The main challenge for corporations today is organizing and operationalizing the information. Although many business owners adopt data monetization initiatives, few of them manage to exploit their information to the fullest. Data as a Service can become the path towards reaching that goal.
DaaS solutions can help organizations minimize time and money spent on improper decisions. By what means?
Moreover, by employing predictive analytics, you get invaluable aid in:
When data is the core of a business, growth occurs rapidly. Why? Because data-driven strategies decrease risk and fuel innovation. When all the necessary teams and departments get timely and accurate data, they can generate successful ideas that would bring stellar results upon implementation. The new winning initiatives will follow and spur further growth.
With the help of DaaS, information becomes a vital business asset. The technology enables more strategic decision-making and successful data management. These solutions fuse internal and external data sources, such as clients, partners, and open data sources, to allow for obtaining a comprehensive view of a business. How else can companies employ the service to enhance decision-making?
Structuring data and providing every team with the information they need is a considerable challenge for modern businesses. With the growing spectrum of data sources, DaaS delivers integrated information, reinforcing a data-driven culture and simplifying the use of data in working processes. Besides, the technology helps organizations handle complex data via reusable data sets available for broad applications.
Personal biases and incomplete information can hamper decision-making and put organizations at risk. When companies cooperate with a Data as a Service provider, they dispose of all the data to make winning choices. They can also adopt technologies like data virtualization to access, integrate, alter, and transfer data through reusable services, facilitating query performance and retaining data security. Therefore, the service reduces the risks related to conflicting views and insufficient data quality.
As a cloud solution, the DaaS technology poses such major concerns as information security and privacy and requires high flexibility of integration solutions for extended functionality. Let’s discuss the main challenges in more detail.
While providing unobstructed access to data in the needed business environment, the service can potentially make critical information subject to unauthorized use. Data breaches are becoming more and more widespread today, and businesses should adopt appropriate cybersecurity measures. The approach to data security is one of the key parameters to consider when selecting a DaaS vendor. A reliable partner would exercise thorough security measures to eliminate the risks of losses in terms of finances and reputation.
Business data that becomes accessible with DaaS may contain confidential information. In a specialized environment, privacy issues gain vital meaning since the data shared is often related to critical aspects of business processes. Users should ensure that data providers adopt the necessary measures to guarantee the confidentiality of private information.
Your data service vendors may have their own data sets that need to be combined with yours. However, the rules for data preparation in your company and at the vendor may not match, leading to dirty data. Therefore, your vendor should recognize the need to integrate with your data set cleanly.
Massive data volumes from a variety of sources are hard to manage. A supreme level of data governance is attainable only if the data integrity in a DaaS environment is verified to guarantee its consistency with any other information. Verification can be complicated, yet it’s a vital constituent to ensure that your company complies with data quality requirements.
When a company implements a Data as a Service platform, it can operate only with tools compatible with this particular platform. This limits its choice of tools for developing its own data processing and analysis solutions. Therefore, your selected platform should provide maximum flexibility in the selection of tools.
Once we have identified the essential opportunities and threats of the solution, let’s consider how the platform is built.
The Data as a Service architecture can be viewed as a general template including major concepts and patterns from different disciplines to promote the implementation of the new environment.
The architecture primarily includes the components illustrated below:
The technologies that enable Data as a Service can be segmented into several categories:
This category includes tools that enable the selection, extraction, preparation, transformation, and transferring of information from various sources into a unified one. For instance:
This is a complete software system to identify, create, update, and operate databases.
Tools in this category help enterprises democratize information. They leverage analytics capabilities to examine complex data, enable scalability, and manage the end analytic outcomes.
What are the leading DaaS companies, and what exactly do they specialize in? Take a look at several remarkable Data as a Service examples.
One of the pioneering DaaS firms that offer data accumulation, data sharing, data lake, and data exchange opportunities. It aids businesses in handling both structured and semi-structured information and squeezing out valuable insights.
A high-performance database, the platform offers breakthrough analytics on multi-model data, whether in the cloud or not. It enables companies to develop data solutions with contemporary architectures and obtain workable real-time insights.
A marketing intelligence solution that utilizes Oracle-owned Datalogix and BlueKai. It includes two branches:
Being at the forefront of breakthrough technologies, these and other organizations help to make complicated data-related processes smoother and easier.
Data as a Service allows businesses to enhance their internal processes and attain go-to-market success. In which ways? There are numerous examples of successful case studies. Let’s consider several significant use cases.
Organizations that utilize physical address information can use DaaS to have precise location data. Third-party information can be combined with internal client records to embrace all types of addresses, including warehouses, branch offices, small storefronts, and satellite structures.
Defining new consumer segments becomes easier, especially if your products are directed to a specific market niche. Specialized tools can help connect detailed information about the company and contact data with internal consumer information. This comprehensive data can be leveraged to reveal new customer categories.
Deeper segmentation of the target audience is another vivid case study. Learning more about their clients and prioritizing accounts is critical for businesses. The number of default industry segmentations, such as technology or manufacturing, can be too vast. With the right tools, however, organizations can select a few ideal accounts and include their relevant keywords in a company semantics list. Thus, a company will expand its prospects’ base in new or similar industry categories.
This case study shows that organizations can elevate lead conversion through their website. The tools that provide real-time enrichment will filter leads to offer the ones with the most relevant business data to improve analytics and drive conversions.
Inbound lead processing also becomes more efficient. Data as a Service solutions automatically clean, enhance, and link your website traffic information to specific CRM fields. All departments obtain easy access to the needed information, while sales representatives receive trustworthy, marketing-qualified leads.
Verification
A new client or partner may pose certain risks for a company if not verified. DaaS offers advanced opportunities to convert unstructured business information into structured intelligence. The indicators include not only the company industry sector and its progress in the technology stack but also the firm’s technology competence rating and finance history. Therefore, businesses are empowered to check their potential partner’s reliability.
With all the impressive benefits mentioned above, getting started with DaaS may seem complicated. It’s no wonder though, since the solution is still new and insufficiently explored for many. However, this task is accomplishable and indispensable for getting sensational business results.
Data as a Service spares you from the big deal of setup and preparation work. It is easy to deploy, while plenty of specialized vendors are ready to provide you with technical support. Therefore, you will not have to hire additional personnel.
Below are the fundamental steps to start implementing the type of service in discussion:
Embracing cutting-edge technologies is vital for companies to advance in the swiftly evolving business landscape. Data as a Service aids in simplifying and streamlining internal operations, promoting customer experiences, optimizing target audience segmentation, driving conversions, and fueling innovation. Define your goals, outline your strategy to counteract challenges, and select the appropriate vendor to make your transformation path smooth and successful.
An AI Solutions Consultant with more than 10 years of experience in business consulting for the software development industry. He always follows tech trends and applies the most efficient ones in the software production process. Finding himself in the Data Science world, Evgeniy realized that this is exactly where the cutting-edge AI solutions are being adopted and optimized for business issues solving. In his work, he mostly focuses on the process of business automation and software products development, business analysis and consulting.
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