What is Data as a Service?Data as a service (DaaS) is a data management strategy that uses the cloud to provide storage, integration, processing, and/or analytics services over a network connection. DaaS can be compared to SaaS (Software as a Service). Just as SaaS eliminates the necessity of software installation on devices and gives users access to digital solutions over the network, DaaS transfers most of the storage, integration, and processing operations to the Cloud.
DaaS Key Attributes
Why use Data as a Service?It is no secret that in any industry, a business has two fundamental goals: to increase revenues and to reduce costs. DaaS helps with both. On the one hand, structuring the work with data enhances efficiency and speeds up business processes, thereby simultaneously reducing costs while also improving the top line without even reinventing the bicycle. On the other hand, the DaaS methodology allows you to detect bottlenecks and, at the same time, potential growth points in the company’s production cycle, such as introducing predictive analytics and optimizing logistics, which can result in real, game-changing increases of the bottom line. It is worth saying that DaaS is used both for the company’s internal needs and for the fulfillment of tasks set by customers. Moreover, in both cases, DaaS structures the workflow and accelerates the receipt of the result. As practice shows, the DaaS concept works best when implementing the following six stages as part of a single, sequential chain:
- Creation and/or collection of data. Data can be obtained from external sources (media, social networks, data from mobile devices) and internal (databases, CRM, ERP, and most importantly, IoT).
- Transportation. There are dozens of standard and non-standard ways of wired and wireless data transmission – modern providers can make networks out of almost anything.
- Storage. Cloud storage solutions have become the standard of work today – there are various virtualization technologies and a huge selection of hardware solutions on the market.
- Data analysis. This is the most difficult stage, where all the power of technological capabilities and the latest advances in mathematics are realized. At this stage, the most expensive specialists are involved, and most of the company’s primary data finally turns into useful (convertible into money), and, most importantly, presentable and readable information for clients.
- Information security. Elements of this unified system must be present at every stage of the DaaS model.
- Integration and implementation of the obtained results. The final stage, preceded by preliminary testing of the solution and the development of a pilot project, clarifies whether the solution has a positive effect and is a success.
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How does DaaS work?The Data-as-a-Service platform is a complex solution in which various data sources and tools (for instance, self-service reports, business intelligence, and applications) interact. Once the platform is deployed, end users can access data using standard SQL over ODBC or REST. Companies can also use external DaaS services to access data. Many companies provide DaaS services through simple APIs. An example is the leading providers of company data: Opencorporate, Crunchbase, Orbis, etc.
What are the benefits of Data as a Service?The potential impact of data as a service (DaaS) is enormous. And not just in terms of revenue, DaaS can benefit the entire organization and its customers when used correctly and successfully. Listed below are some of the main benefits that DaaS can bring to a business. Data monetization. Many companies today have a lot of data. But they are experiencing some problems in organizing and using this data. Using DaaS, companies can not only help cope with the basic challenges but also start monetizing the data. This approach will make the data more accessible. Cost-cutting. DaaS means that you buy processed data as a service. Instead of spending on data management and analysis software and employing data scientists to store their own data, companies pay one price to do it all in one package. They still get access to the data, but it’s already cleaned and processed for them! DaaS means making decisions based on a data-driven approach, not making subjective or impulsive decisions based on emotion and behavioral bias. But rather, based on hard facts backed by numbers. Thus, you can save resources by conducting fewer tests to prove a certain theory. Improving user experience. DaaS can help companies develop personalized customer experiences with predictive analytics to understand consumer behavior and patterns, better serve customers, and build loyalty.
- Providing access to data anywhere (42 percent)
- Enabling better flexibility (37 percent)
- Reducing the support burden on IT staff (36 percent)
Challenges to Consider When Using DaaS
Who can use DaaS?Data as a Service is economically viable with the possibility of reducing costs or increasing revenues by 5% or more. For a company with billions of dollars in turnover, this 5% can translate into a herculean dollar amount and can make a huge difference to measures of profitability. In terms of scale, DaaS benefits businesses of all sizes with a well-thought-out strategy that includes evaluating the workflow in terms of two basic concepts: the tasks to be solved and the data that either already exists or needs to be obtained.
You can have data without information, but you cannot have information without data. Daniel Keys Moran
How to get started with data as a serviceGetting started with DaaS may seem impossible. The main reason being that DaaS is still a relatively new type of business management solution. However, the task is not impossible or overly difficult, but what it is is – irresistible. Resorting to DaaS eliminates much of the setup and preparation work associated with building an on-premise data solution. And with the ease of deployment of the DaaS solution and the availability of technical support services from DaaS vendors, this process does not require specialized personnel from the company. Basic steps to get started with DaaS include:
- Choosing a DaaS solution. Factors to consider when choosing a DaaS offering are cost, scalability, reliability, flexibility, and how easy it is to integrate DaaS with existing workflows;
- Registering and activating your DaaS platform;
- Transferring your data to the DaaS solution database. Depending on how much data you need to transfer and the speed of the network connection between your on-premises infrastructure and your DaaS, data migration can take time;
- Enjoying the DaaS platform that is ready for use.
What are the leading tools that are used to enable DaaS?Generally, data as a service consists of the following types of technologies:
Data integration toolsData integration tools select, prepare, extract and transform data. The tools also collect data from different sources to gather in one centralized place, the most common such tools are:
- Talend data integration software. Which integrates corporate data to connect, access, and transform any data in the cloud or on-premises.
- Informatica Powercenter. This data integration tool allows you to access, retrieve, and process data from various sources.
- Data virtuality. Is an integration and data management platform for instant data access, easy data centralization, and data management.
Database Management Systems (DBMS)DBMS is a complete software system to define, create, update, manage and query a database.
- IBM Db2: an AI-powered hybrid database management software to manage structured or unstructured data either on-premise or in the cloud. Db2 is built on an intelligent common SQL engine designed for scalability and flexibility.
- Microsoft SQL Server: a relational database management software to store and retrieve data used by other applications.
Self-service data preparation toolsSelf-service data preparation tools help organizations democratize data. Empowering them by arming them with analytics capabilities, enabling business leaders to explore complex data at scale and have a greater understanding, and hence control of the organization through these insights and exercise of appropriate levers.
- Pentaho 7.0: Pentaho provides open-source BI and data integration products that bridge the divide between big data and data preparation.
- Datawatch Managed Analytics Platform: The platform is designed as an enterprise solution for self-service data preparation and visual data discovery. Its data preparation capabilities include: disparate data set manipulation, filtering, enrichment blending, and combining data.
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