Analytics as a Service

Analyze data and predict the trend for the future

 
 

Analytics as a Service

Analyze data and predict the trend for the future

Analyze, Predict, Build & Grow

Data value is from Analytics. We at DigitalGlyde provide analytics as a service, so you can focus on the value add in business. We can do the grunt work for you.

In the world of analytics the options are too many, you can use multiple algorithms and multiple cloud platforms to solve the same problem in a 1000 different ways, but the key is providing the business value. The key is that the solution is beneficial to business and should not be focused on 100% accuracy.

Analytics As Business Solution

With the growing data in database it is highly essential to analyze these data and predict the trend for the future is a necessity in current generation. Data without analysis is just a dump/garbage.

DigitalGlyde provides Analytics as a Service (AaaS) solutions to offer clients/businesses an alternative to developing internal hardware setups just to perform business analytics.

Analytics as a service is becoming a valuable option for businesses because setting up analytics processes can be a work-intensive process. Businesses that need to do more analytics may need more servers and other kinds of hardware, and they may need more IT staff to implement and maintain these programs. If the business can use analytics as a service instead, it may be able to bypass these new costs and new business process requirements.

Along with the appeal of complete outsourcing that analytics as a service provides, there is the option of going with a hybrid system where businesses use what they have on hand for analytics and outsource other components through the web. All of this equips the modern business with more choices and more precise solutions for changing business needs in markets that work largely on the availability of big data.

AaaSas Business Value

Knowledge is power, but traditionally that power came at the price of owning and managing on-premises analytics tools. Analytics as a Service (AaaS) can significantly lower that cost, but often those savings aren’t the only or even primary reason to consider these solutions.

AaaS is a term that’s often used too broadly. For example, some people use it to describe a bundle of services that includes cloud-based analytics tools and consulting, such as data cleanup and normalization. Those bundles currently are niche solutions, ones that take IT completely out of the picture.

The most widely used AaaS solutions are hosted environments for analytics. AaaS is an excellent way to gain actionable insights from the enormous amounts of data coming from sources such as the Internet of Things (IoT).

DigitalGlyde and AaaS

As with other hosted solutions, AaaS can be more cost-friendly than owning and managing on-prem analytics hardware and software. One reason why many enterprises turn to CDW when considering AaaS is because we can evaluate the economics to determine how much they can save.

Even when the potential savings are negligible, some enterprises take the plunge anyway because AaaS aligns with their company’s cloud-first strategy. In other cases, the big draw is access to the latest-and-greatest product features before they come to on-prem versions.

An aspect that hosted and on-prem analytics have in common is they’re only as good as the data they’re fed: bad data in, bad data out. This is another reason why many enterprises partner with CDW for AaaS. We can help develop strategies and processes for cleaning, formatting and more to maximize data quality.

Some enterprises are reluctant to use AaaS because they fear losing control of their data and thus potentially falling out of compliance with laws such as HIPAA. CDW often helps avoid those risks by developing procedures to ensure their data stays secure and private both at rest and in motion. We do that partly by using our hands-on, multi-vertical experience with HIPAA, PCI and other regulations for applications such as identity and access management, mobility and hybrid cloud.

A small U.S. bank that did all of its analytics manually became a recent success story for CDW. When assessing their strategies, we didn’t come in with a recommended vendor — or even recommend AaaS right off the bat. Instead, we focused on their business goals, including future state applications such as adding in external data, and the types of reports they wanted stakeholders, such as board members, to receive. All that led to a back and forth between multiple CDW partners with on-prem and AaaS solutions, with IBM Cognos eventually rising to the top.

If your lines of business are struggling to mine data for insights, AaaS is a proven way to help. And in the process, IT avoids the complexity and often cost of standing up an analytics environment on-prem. It’s a win-win for the entire organization.

What is Data Analytics as a Service (DAaaS)?

DAaaS Concept Customers will feed their enterprise data into the platform, and get back concrete and more useful analytic insights. These analytic insights are generated by Analytical Apps, which orchestrate concrete data analytic workflows. These workflows are built using an extensible collection of services that implement analytical algorithms; many of them based on Machine Learning concepts. The data provided by the user can be enhanced by external, ‘curated’ data sources.The DAaaS platform is designed to be extensible, in order to handle various potential use cases. One concrete case of this is the collection of Analytical Services, but it is not the only one. For example, the system can support the integration of very different external data sources. To enable DAaaS to be extensibility and easily configured, the platform includes a series of tools to support the complete lifecycle of its analytics capabilities Ascent/ Data Analytics as a Service: unleashing the power of Cloud and Big Data.

Architectural Aspects of a DAaaS PlatformAs

DAaaS platform can be an important venture, as it implies some interesting but complex engineering challenges. Here we analyze some potential approaches to the previous challenges. Due to space restrictions, we can’t cover them all in this White Paper. For example, security and privacy would by themselves demand a complete paper each.

DAaaS is not SaaS, but a Specialized PaaSAnalyzing the DAaaS problem as if it were another SaaS solution would lead to some very thorny and critical issues. As we saw previously, many of the steps in an analytical workflow can demand considerable customization due to the characteristics of the data sources but also of the analytic outcomes that are intended. If we take a basic analytical workflow, we will find at least the following steps:“Adaptation of the ETL (Extraction-Transformation-Load) processes from enterprise data sources.“Adaptation to the DAaaS Data Models and Metadata standards.“Modeling and configuration parameters of Analytical Apps, by defining workflows using Analytical Services.“Validation of results from end-users.“Integration of results back into Enterprise systems.“Configuration of GUIs for reporting and visualization.Even for simple cases, it seems obvious that the typical functionally-fixed SaaS solution doesn ́t provide enough flexibility.