By Cassandra Balentine
Organizations increasingly rely on business data to gain valuable insights to improve internal operations and predict and reflect on customer behaviors. Whatever the environment, data is an essential asset that must be managed, accessed, and analyzed to provide useful information towards a business goal or strategy.
As more technologies become available on the cloud, the ability to access both internal and external data is without exception. Data as a service (DaaS) and big data as a service offerings provide data on demand to address a range of business needs including business intelligence (BI) and analytics, improved access to on premises and legacy data, and the creation of standard interfaces for big data systems.
Paul Nashawaty, director of product marketing and strategy, Progress Software, describes DaaS as a cousin of software as a service (SaaS), allowing users to connect to a data source in real-time for easy access to highly transactional data. “DaaS is based on the concept that the product—data in this case—can be provided on demand to the user regardless of geographic or organization separation of provider and customer,” he adds.
In relation to BI, DaaS allows businesses to combine internal and external data for a new perspective, comparison, or insight. Hjalmar Gislason, VP of data, Qlik, offers the example of weather to illustrate the benefits of DaaS to BI through the accessibility of third-party data. “If you just want a chart showing the temperature in a given city for the past few days, you can go to Weather.com, but when you combine weather data with your sales data to analyze the effects of weather over time, you create valuable insight that is unique to you.”
Benefits of DaaS include affordability, controlled data, and agility.
Business Case for DaaS
The strongest use cases for DaaS in a BI scenario include retail, financial services, and marketing, but the opportunity exists for almost any case where analytics are utilized.
DaaS offers accessibility to business-critical data within an existing data center. Nashawaty explains that businesses use DaaS for real-time access to data without having to store that information for a long period of time. “For example, if a company is only using relevant data periodically, it doesn’t make sense to put resources into storage and infrastructure when most of the time data is not in use.”
Additionally, third-party data can be used to augment existing data to provided context when needed. “Imagine driving a car but only being able to look at your dashboard instruments and not out your windshield. Without external reference—i.e. what’s going on outside the car—driving the vehicle becomes a much more difficult task. In much the same way, DaaS allows you to ‘look outside’ to give you a more complete picture of your analytics,” explains Gislason.
IBM also offers an example, emphasizing the focus on weather previously mentioned by Qlik. Michael Dziekan, insights program director, business/offerings development and strategy, IBM Analytics, points to a collaboration between IBM and The Weather Company through WSI, its global business to business division. Announced earlier this year, the program involves the development of data-driven analytic solutions that integrate real-time weather insights to transform how businesses understand the impact of weather on their operations, anticipate weather events sooner, and take action to optimize those parts of the enterprise impacted by weather.
“In the public sector, real-time weather prediction is a critical component for governments preparing for or responding to natural disasters such as floods, hurricanes, earthquakes, and tornados. For example, in the case of a hurricane or tornado, governments can implement preparedness plans in the highest risk areas,” offers Dziekan.
Recent Advancements
Current technologies help facilitate the evolution of DaaS. Specifically, Nashawaty points to big data offerings such as Hadoop, Mongo, and SparkSQL as relatively new platforms that enable DaaS for rapid access to information from any place, despite physical location.
Gislason says a change in the mindset of technology effects the evolution of DaaS. “The open data movement affected the evolution of DaaS more than anything else. I mean that in the broadest sense, not only the opening up of governmental and intergovernmental organization data sets, but the realization that exchanging data in a machine-readable and -accessible format—whether for free or for a fee—can realize a lot of untapped value,” he adds.
The evolution of the Internet of Things (IoT) and the need to understand the relationships between billions of connected devices also drives a new set of opportunities for big data, analytics, and DaaS.
“Each connected object provides real-time data that needs to be understood and analyzed in the context of other data previously or simultaneously collected,” says Dziekan.
He notes a range of additional advances in both weather science and cloud computing that, “fundamentally impacted the process of collecting and analyzing data for insight, allowing us to harness more data, scale to meet demands, and be far more precise and accurate in forecasting. These advances in analytics enable us to do more with this rich data, namely helping businesses understand the precise connection between various data sources and business outcomes.”
Pricing Models
A variety of pricing models support DaaS platform offerings for both private and commercial available data.
Nashawaty suggests these pricing models are relevant whether a firm is a consumer of DaaS or one who provides data to others through such a service. “In general, DaaS pricing models are classified into two main categories,” he explains. These categories include volume- and data type-based.
Volume-based pricing models typically include options to pay by instance of data accessed as well as by quantity of data consumed.
Data type-based models are priced by the number of fields returned in a query.
“Frequently updated data lends itself well to subscription-based models, while volumous data that may not change very often lends itself better to pricing based on volume or detail levels,” offers Gislason. He says market data is a good example of the former, where timeliness may be of the utmost importance. Geographical data is an example of the latter, as most geographical data doesn’t change that fast, but tends to include a lot of details and geographic entities to report on.
Limiting Factors
Security is still a primary concern for cloud-based offerings, including DaaS.
“As an industry, users are not confident that data living in the cloud is secure enough to meet their needs. As more organizations move to adopt DaaS, companies need to put assurances in place within the cloud infrastructure to make users feel better about their data not being on premises in a dedicated environment,” suggests Nashawaty. “By implementing more robust security plans, DaaS companies can put customers minds at ease and prevent unauthorized access.”
Gislason points to a lack of standards for the exchange of data—and even more importantly meta-data—as the biggest barrier to wider DaaS adoption. “At the same time, the lack of standard formats and central ways to discover available data present the biggest opportunity for vendors in this space,” he says. He points to the saying, standards will emerge and there will be more of them. “The sooner you realize that fact, the sooner you see a huge opportunity in aggregating, normalizing, and optimizing that data—sometimes for quite specific use cases.”
Since each organization’s data needs are unique, the limitations of DaaS also differ. Dziekan says this is especially true when you consider the data sources available today. “Once a company decides on the type of data sets to analyze and how they will integrate that data into business processes, the areas that can hold this process up are the time investment to negotiate with data vendors and working through privacy limitations of the enriched data,” he adds.
DaaS Offerings
Select vendors in the data and analytics space provide DaaS offerings that continue to evolve.
IBM presents a comprehensive portfolio of data and analytics capabilities, which enable its clients to apply advanced analytics to the vast amount of data driven by billions of interconnected devices, mobile phones, and sensors to help solve pressing business problems with greater speed and agility than ever before.
The company offers a new generation of services that simplify the integration and analysis of data and the delivery of insights for knowledge workers—in real time, continuously, and at massive scale. “We can help accelerate our client’s success as data-driven organizations in three unique ways,” says Dziekan. These include simplifying and lowering acquisition costs of big data/analytics systems, decreasing the time to insights with pre-built solution offerings and data science infused services; making client data more accessible and consumable while further enriching it with new and diverse sources like the meteorological data, platform, and expertise of The Weather Company or social data from Twitter to create more meaningful business insights; and providing new, actionable insights about the business while respecting privacy by design.
Progress’ DataDirect Cloud is a premium data connectivity service that gives users a simple, unified way to access data—whether hosted in the cloud or on premises. “The differentiator is that we offer an innovative, patent-pending technology that provides secure, real-time access to in-network data like Oracle, Microsoft SQL Server, and IBM DB2—from anywhere. There is no need for extra steps or cashing your firewall configuration.”
The Progress DataDirect Cloud provides BI and analytics by enabling data-driven applications to connect to dashboards in real time, access to on premises data without the need to change a firewall, real-time access to legacy data from Salesforce1 and salesforce.com applications, and a standard interface to an organization’s big data systems.
The pricing structure of Progress DataDirect Cloud varies by user or data capacity.
Qlik DataMarket is a DaaS cloud offering that gives users ready-for-use data from external sources in a secure, affordable way to enable smarter business decisions.
Qlik DataMarket offers a tiered approach to pricing and packaging. The free solution, available to all users of QlikView and Qlik Sense, provides commonly used and freely available data such as weather and currency information.
At press time, the only paid-for offering is the Essentials packaging, which contains data such as currency, weather, and social economic indicators at the subnational level and is priced on a monthly subscription basis.
Evolving Data Needs
As the need and use of data continues to evolve, offerings such as DaaS become increasingly valuable to business cases.
Dziekan stresses that as organizations are able to incorporate more data gathered from the IoT, they will also be able to enhance enterprise decision-making with actionable insights that drive more positive business outcomes.
As much as the DaaS landscape has already evolved, more changes are expected. “Given that 85 percent of all data resides on premises, there will be a paradigm shift to the cloud to benefit from resources, economy sales, and utilization of equipment as needed,” says Nashawaty. He notes that essentially, it’s unrealistic to keep the massive amounts of data on premises. “Looking forward, we can expect data firms to grow significantly,” he adds.
The transition to cloud-based data offerings enables improved access to internal data, an avenue for third-party data integration that adds context to existing data, and the ability to improve data integration and migration processes from a development standpoint. SW
Dec2015, Software Magazine
