We live in the age of big data, but big data is not showing up to the party alone. Fast data and open data are also coming along for the ride.
Together, they are creating growing mountains of data locked away in every recess of the enterprise -- from applications to relational and non-relational databases, to in-memory caches, public clouds, and Hadoop clusters. Furthermore, putting sensors on everything, as as both Cisco and GE propose, will increase the amount of data collected without necessarily increasing our ability to access and analyze it.
Access is the foundation
The growing size and diversity of data has made access harder, and access is the foundation of data analytics, mobile app development, and the Internet of Things (IoT). After all, the purpose of collecting data is not altruism -- it is to create value for the organization. It is about monetization. In many situations, this will only be possible if data can be shared easily.
At the root of the problem is an internal divide over who controls enterprise data. Whereas the data itself is overseen by information architects, access to that data is traditionally done through IT and almost always requires a formal IT project to make data accessible. This is not a recipe for easy and frictionless sharing of data either internally or externally. The enterprise has come a long way in automating IT and enabling self-service, but access to the core organizational asset of the 21st century -- its data -- remains disconnected and left to be solved through point solutions.
What is needed is an “as-a-service” model approach to data sharing. Data-as-a-service (DaaS) would enable information architects to select the scope of the data regardless of the actual data source(s), obfuscate or filter potentially sensitive information, and share it with the intended audience with a service level of their choosing, without having to write a query, design an API, or worry about how the access control or SLA will be enforced.
DaaS would abstract the actual data source to the user while talking to it natively. It would generate the appropriate query based on the selected data scope and represent the source data set as a REST API, which becomes the entry point for accessing the resulting data set. It would add fine-grained access and protection policies to ensure that only those authorized can get visibility to specific slices of data, while also protecting the data sources from attack and misuse. Data access through the API can be governed by service level agreements and monetized through metering.
Employing an as-a-service model to data would give organizations a way to share just the information that is relevant to a particular mobile, IoT, or big data analytics project and then share selectively with an app, cloud service, developer, or partner in a scalable and cost-efficient way. For manufacturers, service providers, and network operators looking at ways to share and monetize their data, a DaaS-based approach would remove friction and dramatically increase speed and time to market. The ability to focus on small data sets inside larger ones -- and to present these data sets as secure APIs, customized to specific customers or partners -- will make it possible for developers of all stripes to drive new revenue from their big data.
User Rank: Exabyte Executive 8/6/2013 | 1:23:01 PM
Re: Data-as-a-service True SharCo, but In the enterprises and business there are some data which can be shared with others. The thing is if it is shared for the public, what is the assurance, that the customer will not get any negative impact.
User Rank: Petabyte Pathfinder 8/5/2013 | 5:49:01 AM
Re: Data-as-a-service Good point, Pubudu. For sure, enterprises and businesses will obviously have to keep their information and data under wraps and private, for security issues and as a general practice.
User Rank: Petabyte Pathfinder 8/5/2013 | 5:48:26 AM
Re: Data-as-a-service I like how legalcio put it. It's an insightful perspective that DaaS is basically an extension of SaaS and what you choose to use will ultimately depend on what you want to do and achieve with your data.
Re: Data-as-a-service Even more so in a DaaS world I would imagine @dcawrey - pinpointing and restricting the data used will keep overheads down when paying the DaaS providers (on top of all your other IT outgoings). That said, will it not limit the space for more experimentation with data?
User Rank: Exabyte Executive 7/30/2013 | 11:33:08 PM
Re: Data-as-a-service Very true Pradeepta, I believe that most of the players will not like to share there data for public, But on the other hand all of the users use this data for only a better purpose this world will be a better place to live than ever.
User Rank: Exabyte Executive 7/29/2013 | 9:33:26 PM
Re: Data-as-a-service Being able to target or pinpoint data that is to be used for a particular purpose is going to become an imporant aspect of data science. I'm sure that there are some tools out there to do this, but it is going to become a sort of algorithm-based method in order to control the firehose, so to speak.
Re: Data-as-a-service The DaaS model started around 2000. My early mover company HostedDatabase.com was the first, soon followed by Intuit's Quickbase (through an acquisition), and today there is Database.com from Salesforce, plus many others. So in over a dozen years, must progress has been made to dispell early concers like security, performance, SLA terms, just to name a few. But then the whole SaaS model has grown since then, so it is natural that pure play DaaS would follow suit. I believe there are many advantages to this model, as noted in the article.
The big data market is predicted to grow six times faster than the overall IT market. The phenomenal growth in data analytics, business intelligence, open-source tools, and related technologies in 2013 looks set to continue.