83% Of Enterprise Workloads Will Be In The Cloud By 2020. LogicMonitor’s survey is predicting that 41% of enterprise workloads will be run on public cloud platforms (Amazon AWS, Google Cloud Platform, IBM Cloud, Microsoft Azure and others) by 2020. An additional 20% are predicted to be private-cloud-based followed by another 22% running on hybrid cloud platforms by 2020. On-premise workloads are predicted to shrink from 37% today to 27% of all workloads by 2020.
Digitally transforming enterprises (63%) is the leading factor driving greater public cloud engagement or adoption followed by the pursuit of IT agility (62%). LogicMonitor’s survey found that the many challenges enterprises face in digitally transforming their business models are the leading contributing factor to cloud computing adoption. Attaining IT agility (62%), excelling at DevOps (58%), mobility (55%), Artificial Intelligence (AI) and Machine Learning (50%) and the Internet of Things (IoT) adoption (45%) are the top six factors driving cloud adoption today. Artifical Intelligence (AI) and Machine Learning are predicted to be the leading factors driving greater cloud computing adoption by 2020.
66% of IT professionals say security is their greatest concern in adopting an enterprise cloud computing strategy. Cloud platform and service providers will go on a buying spree in 2018 to strengthen and harden their platforms in this area. Verizon (NYSE:VZ) acquiring Niddel this week is just the beginning. Niddel’s Magnet software is a machine learning-based threat-hunting system that will be integrated into Verizon’s enterprise-class cloud services and systems. Additional concerns include attaining governance and compliance goals on cloud-based platforms (60%), overcoming the challenges of having staff that lacks cloud experience (58%), Privacy (57%) and vendor lock-in (47%).
Just 27% of respondents predict that by 2022, 95% of all workloads will run in the cloud. One in five respondents believes it will take ten years to reach that level of workload migration. 13% of respondents don’t see this level of workload shift ever occurring. Based on conversations with CIOs and CEOs in manufacturing and financial services industries there will be a mix of workloads between on-premise and cloud for the foreseeable future. C-level executives evaluate shifting workloads based on each systems’ contribution to new business models, cost, and revenue goals in addition to accelerating time-to-market.
Microsoft Azure and Google Cloud Platform are predicted to gain market share versus Amazon AWS in the next three years, with AWS staying the clear market leader. The study found 42% of respondents are predicting Microsoft Azure will gain more market share by 2020. Google Cloud Platform is predicted to also gain ground according to 35% of the respondent base. AWS is predicted to extend its market dominance with 52% market share by 2020.
Key takeaways of the study results released during the webinar include the following:
Customer Analytics (72%), followed by supply chain, business optimization, marketing optimization (57%), risk and fraud (52%), and marketing (58%) are the areas in which respondents reported the strongest interest.
When the customer analytics responses were analyzed in greater depth they showed most interest in customer satisfaction (50%) followed by customer profitability (34%), customer retention/churn (32%), customer management (30%), and cross-sell/up-sell (26%).
Adoption was increasingly widespread and growing, with over 90% of respondents reporting that they expected to deploy one or more type of predictive analytics in the cloud solution.
Industries with the most impact from predictive analytics include retail (13% more than average), Financial Services (12%) and hardware/software (4%). Lagging industries include health care delivery (-9%), insurance -11%) and (surprisingly) telecommunications (-33%). The following graphic illustrates the relative impact of cloud-based predictive analytics applications by industry.
Adoption of Cloud-based Predictive Analytics by Industry
The most widespread analytics scenarios include prepackaged solutions (52%), cloud-based analytics modeling (47%) and cloud-based analytic embedding of applications (46%). Comparing the 2011 and 2013 surveys showed significant gains in all three categories, with the greatest being in the area of cloud-based analytic modeling. This category increased from 51% in 2011 to 75% in 2013, making it the most likely analytics application respondents are going to implement this year.
Comparison of Analytics Applications Most Likely To Deploy, 2011 versus 2013
63% of respondents report that when predictive analytics are tightly integrated into operations using Decision Management, enterprises have the intelligence they need to transform their businesses.
Impact of Predictive Analytics Integration Across The Enterprise
Data security and privacy (61%) followed by regulatory compliance (50%) are the two most significant concerns respondent companies have regarding predictive analytics adoption in their companies. Compliance has increased as a concern significantly since 2011, probably as more financial services firms are adopting cloud computing for mainstream business strategies.
Concerns of Enterprises Who Are Using Cloud-based Predictive Analytics Today
Internal cloud deployments (41%) are the most common approach to implementing central cloud platforms, followed by managed vendor clouds (23% and hybrid clouds (23%). Private and managed clouds continue to grow as preferred platforms for cloud-based analytics, as respondents seek greater security and stability of their applications. The continued adoption of private and managed clouds are a direct result of respondents’ concerns regarding data security, stability, reliability and redundancy.
Approach To Cloud Deployment
The study concludes that structured data is the most prevalent type of data, followed by third party data and unstructured data.
While there was no widespread impact on results from Big Data, predictive analytics cloud deployments that have a Big Data component are more likely to contribute to a transformative impact on their organizations’ performance. Similarly those with more experience deploying predictive analytics in the cloud were more likely to use Big Data.
In those predictive analytics cloud deployments already operating or having an impact, social media data from the cloud, voice or other audio data, and image or video data were all much more broadly used as the following graphic illustrates.
Which Data Types Deliver The Most Positive Impact In A Big Data Context