A- Understanding Cloud BI
Cloud BI or Cloud Business Intelligence refers to cloud-based tools which change raw data into information. This information can be used by businesses to help cut costs, increase revenue, streamline inefficiencies and formulate better organizational decisions.
As a business intelligence solution cloud-based BI offers many advantages over on-premises based BI. Cloud BI can be accessed easily, is less expensive, highly scalable and relieves users from many administrative tasks associated with data management.
Cloud BI performs any BI functions:
- Data Visualization
- Process Mining
- Data Mining
- Text Mining
- Online Analytical Processing (OLAP)
- Business Performance Managemen
- Statistical Analysis
B- Cloud BI Key Insights – Statistics Speaks
- In the next twelve months the use of cloud for BI and data management will increase to 78%.
- From 2013 to 2016, Cloud BI adoption increased from 29% to 43%.
- Public Cloud is among the most preferred deployment platform for cloud BI and analytics. The larger the organization, the more likely it is to use private cloud. 46% of organizations use public cloud for BI and data management, 30% use hybrid cloud and 24% use private cloud.
- The top three Cloud BI use cases (in comparison to on-premises) are Dashboard-based reporting at 76%, dashboard authoring at 55% and ad-hoc analysis and exploration at 57%.
- 48% of enterprises are adopting data integration between on-premise and cloud applications. This is the dominant use case across all company sizes.
C- Key Considerations To Consider Before Implementing Your Own Cloud BI Plans
C1: Why Cloud?
Choosing cloud just for the sake of being on the cloud is not at all a good strategy. Companies should check their requirements, identify their end goals and evaluate what is the best fit for their organization. With cloud becoming more widely adopted in the market in general, capabilities are more robust and can be comparable to on-premises analytics solutions.
C2: Where Does Data Currently Reside?
For organizations that already have their data in the cloud, shifting to a cloud BI solution is a natural transition. For example, companies which are already taking advantage of cloud-based operational solutions could choose to expand their use to include analytics. Other organizations have to integrate data from on-premises solutions, a lot more effort.
C3: Choosing Public Or Private Cloud?
IT departments may have a preference of how to deploy cloud. It can be done by a third party or by enabling cloud access but having the platform solutions managed internally. Though both options are secure, some organizations have sensitive data and therefore prefer private cloud options.
C4: What are the risks involved and what are the privacy and security considerations? And how will cloud affect any procedures or parameters which exist.
When looking at cloud-based BI, industry specific risk and security regulations puts added pressure on organizations to comply with requirements and therefore requires extra consideration. There are many cloud service providers who develop their platforms with the ability to support privacy and security parameters.
C5: Choosing Between Capex And Opex
Organizations need to identify whether to acquire new or additional hardware and pay for support and licensing long term or to pay a subscription fee and turn BI consumption into an operational expense. Investing in Capex projects will cost a little more initially but maintenance will be on the lower side. Opex expenditures on cloud may cost you less to implement but it may prove expensive depending on the scope and expansion of the project.
C6: How Will Data Be Integrated?
This is an extension of the conversation surrounding data governance.iData has to be sourced and stored somewhere and therefore identifying these requirements and complexities need to be dealt with beforehand.
C7: What Types Of Services Are Required?
Organizations should identify the capabilities required for success, namely, identifying analytics requirements. In terms of cloud environment, this means evaluating the level of managed services required vs. doing it yourself.
D- Why Cloud BI?
Cloud BI solutions offer a modular approach with robust capabilities which
- 1- Focuses on reducing costs
- 2- Streamlines processes
- 3- Improves customer responsiveness
Here are few more benefits listed to Cloud BI:
D1- Advanced Mobility
Cloud BI allows professionals to stay connected to real-time data for streamlined communications, decision making, and collaboration. Mobile users can access customer records, project requirements, client contracts much more from any mobile device. This improves productivity on a large scale.
D2- Lower Costs Means Higher Performance
With robust and scalable data analytics tuned, businesses are rapidly leveraging reporting tools in order to improve sales and marketing activities. This helps in increasing collaborative campaigns between sales and marketing teams, therefore, resulting in better sales forecasting, resource allocation, client engagement and customer support.
D3- Easy Data Interpretation
Cloud BI solutions allow faster and better business insights on sales performance, success and failure of marketing campaigns, customer behavior and also simplifies data interpretation.
D4- Agile, Adaptable Platforms
Scalability and multi-tenancy features of a Cloud BI solution automates everything from data discovery to data reporting. Businesses can integrate and analyze data on demand. Furthermore, cloud BI solutions can be easily modified to enhance reports, streamline processes, update dashboards and analyze data on demand.
D5- Data Security
Cloud BI solutions offer a wide range of data security protocols and features. Many of these solutions deliver high-performance analytics using data segregation, data encryptions, multi-tiered caching, regulatory compliances, investigative support network segmentation and security patches. Cloud BI offers strategic data security management.
E- What Large Corporations Have To Say About Cloud BI
If we consider Gartner’s definition of cloud BI, it explains that cloud analytics refers to any analytics effort in which one or more elements are implemented in the cloud, be it public or private. And these elements are data models, data sources, processing applications, computing power, analytics models, sharing or storing results. According to Gassman the problem arises when an organization decides to pursue cloud analytics but different divisions and departments define them differently.
Famous SaaS BI vendor like GoodData refers to themselves as cloud analytics vendors as does Coremetrics, a social networking analytics company. Of course, both offer different products. Then there are data warehouse vendors like Teradata and Kognito which are also referred to as cloud analytics companies and offer their analytic database in the cloud or in a hosted environment.
The problems come when people go down this road but fail to understand the scope. This results in customers not always understanding what they are getting.
Therefore, while significant opportunities for creating business value using cloud analytics exists, enterprises have to truly understand what the term means. Only after complete understanding should they pick a combination of six analytic elements deployed in the cloud that best meets their needs.
Organizations have to ponder if a cloud BI and analytics strategy is the right choice for their organization. This may depend on whether their data is already on cloud or on-premises.
Whether organizations look to the cloud for data warehousing, analytics or business intelligence it completely depends on where their data can be found. If they are using on-premise systems to handle the workloads being thrown at them, shifting data into cloud-BI system probably isn’t a good idea. Making unnecessary expenditures on a relocation process seems impractical if there is no real need. But if most of your data is already processed in the cloud, why not give it a try?
Large companies like Netflix are among cloud BI adopters. Netflix runs all of its data analytics applications in AWS cloud along with most of other systems. Hurt Brown, director of Netflix’s data platform, said the online streaming media company can have multiple Hadoop clusters sharing the same data in the AWS cloud. One managing the high-throughput data processing and another to support ad hoc querying.
So, whatever combination of cloud analytics your company chooses, make sure you agree beforehand on the definition of the term, it’s essential. Cloud analytics is not a uniform product or technology category. Therefore to gain any business advantage from it, enterprises must clearly define what they need from the cloud and what the term means to them.
References: b-eye-network.com, forbes.com, cio.com, businessintelligence.com, compudata.com, searchbusinessanalytics.techtarget.com