When Big Data meets Cloud

Cloud BI – the modern approach

The CEO of one of worlds biggest IT giant Microsoft, Satya Nadella made it very clear in his first public comments that the future lies in the cloud.

“Our job is to ensure Microsoft will thrive in a mobile and cloud-first world”

This statement, from Nadella’s letter to the company, is interesting not only because of what it includes but also what it excludes. Windows and Office weren’t mentioned in his letter, while cloud services received plenty of attention.

At Ideata we share the same sentiments and foresee that providing data analytics as a service will provide unprecedented opportunities and allow our users to do more with their data.

Cloud computing has enabled many businesses to crunch the big data needed to draw big conclusions.Traditional BI vendors got too comfortable with their six-digit contracts and forgot to innovate, experts .

Gartner defines the six key elements of analytics as

  • data sources
  • data models
  • processing applications
  • computing power
  • analytic models
  • sharing or storage of results.

We provide combination of above elements as a hosted cloud platform on which you can perform all types of information discovery – reports, dashboards, ad-hoc analysis, predictive analytics and mobile.

The various advantages which big data analytics when combined with the power of cloud brings alongs are:

A global, decentralized enterprise can draw enough intelligence from all of its brands to make informed decisions through Cloud BI .A growing midmarket, concerned that it doesn’t have the money for an on-premises BI package that costs several hundred thousand dollars, do likewise and dervie value in SaaS BI solutions at all stages of the economic cycle – whether bull or bear.

BI on-demand will continue to be popular in niche cases, such as with   

 

Differences:

Special issues to consider:

The analysis tools may not have all the features that on-premise software products do – which may make them less complex and easy to use, but also less functional.

Some challenges which cloud BI deployment poses :

  • Sending corporate data beyond the firewall may raises red flags for some organisations
  • Orchestrating a sudden movement of data to the cloud
  • A crowded vendor marketplace that makes choice difficult
  • Data warehouse size and performance limitations
  • A lack of standard pricing models.

Apart from that there might be some technical challenges involved in securing data in transit

  • Gain access to the data where it resides
  • Transform it to a format suitable for reporting
  • Synch it with the source system/location on a regular basis so that it stays up to date
  • Creating meaningful output, with aggregations, metrics, measures, and all of the things business people like to look at.

Ideata SAAS BI:

To try to assuage those concerns, we can create private analytic clouds that run inside a customer’s firewall. We have a solid DR plan, which is frequently tested. A failover DR site is always created , and its location is flexible: It could be at the vendor’s site, the site of a third-party supplier or the customer’s own site.we also negotiate a service-level agreement that includes economic incentives for the provider to stay up 99.9% of the time.

Our Cloud architecture allows end users to use an instance of Ideata Server through a remote connection, provided by Ideata team in a short time, against the payment of a monthly fee. At this point, users will be allowed to access our instance, to tailor it to their needs, to upload their data and to deploy their analysis.

It doesn’t matter how big or how small your organisation is .You can be data driven through Ideata for Cloud.

Handle new class of data problems

In the last couple of years, we have seen data analytics move from being just a fad to a key to growth. It has impacted the business a lot more that we had previously imagined.

Information is now coming from every nook and corner of the organization, which is truly useful for driving decisions. With the newer distributed storage and processing technologies, organizations are able to store and query raw data there by enabling them to perform faster analysis. They are now able to get down to every single transaction out there and build correlations.

Role of Analyst

With this increased demand from business users, data analyst are now expected to curate more and more data from these variety of sources; perform cleaning and enrichment to bring the data into a format that will allow analytical tools to process it.

Analysts are required to build ETL workflows or SQL scripts which typically take months to implement. Moreover, the ETL process is not agile enough for analyst to handle changes in data and adding newer data sets. For smaller data sets, they take a rather painful option of cleaning it in excel sheets with complex formula and without any data lineage to recover from failures or errors.

Unfortunately, most of the businesses are still taking a reactive approach to data cleaning. With data aggregation and preparation taking about 80% of an analyst’s time, we need a more productive approach to transform raw data to actionable data. Additionally, business users should have the ability to work with disparate data sources and perform iterative data cleaning tasks and offload this trivia tasks from IT.

Ideata Analytics Approach

At Ideata Analytics, we are able to solve this problem with a truly redefined approach to handle data management challenges. With a variety of supported modern big data sources and traditional databases and flat files, users can now quickly get access to the data they need; use the suggestive visual data preparation to perform faster data integration, transformation and cleaning; and use a simple drag and drop interface to do ad hoc analysis.

With simplified and shortened load-combine-clean-analyze cycles, users can gain deeper insights into their business and reach to decisions faster. Register for a demo with us to learn more about how we can solve your data problems.