I recently heard a joke that went something like this –

 Maria: “What forms do you need me to submit?”
     Insurance Rep: “Please fax your proof of insurance and a copy of the police report to the number listed on your insurance card.”
     Maria: “Can I email you those items? Faxing isn’t supported where I live.”
     Insurance Rep: “Oh, where do you live?”
     Maria: “2016”

I’m starting to feel the same way about Cloud Analytics. If your organization, in 2016, doesn’t have a Cloud Analytics strategy, then you are unfortunately behind the curve.


A Cloud Analytics strategy is one that:

  • Supports the use of platform-as-a-service tools to deliver data-based analytics to discover key business trends
  • Predicts positive and negative business outcomes and suggests how to achieve and/or avoid them
  • Drives actionable results with data-backed decisions

In this article, you’ll learn why it’s important to have both an Analytics Strategy AND a Cloud Strategy, and why they should intersect.

You need an Analytics Strategy

An analytics strategy helps to define a plan for how your organization is going to implement data-driven support to produce better outcomes.  Without analytics, you’ll be forced to react to a poor-performing customer promotion AFTER the damage has already been done. But with analytics, and the ability to use them appropriately, you will be able to proactively detect that a promotion is likely to be poor-performing and make the necessary changes BEFORE it happens, effectively turning a negative situation into a positive one.  Which scenario would you rather be part of?

Your analytics strategy doesn’t need to be complex and it doesn’t need to involve building a massive team of data scientists. It does, however, need to:

  • Align with a defined list of business goals
  • Identify an approach for data exploration within your organization
  • Include tools for data exploration, analytic modeling, and data visualization
  • Lead to a path that includes machine learning and predictive capabilities

Building your first analytics strategy should include the following plans:

  • Define what answers you need to know BEFORE an action is made. Having a key set of questions and   answers that you’ll need are important to building an effective analytics platform.
  • Design a plan for acquiring the data you will need to answer those questions. Analytics aren’t meaningful without the right data. Modern tools let you integrate curated data with external data sources – you’ll often find yourself looking for broader sets of data as you mature your analytics.
  • Identify your measure of success in predicting the future. Knowing how close you’ve hit the mark is a   good indicator of success. While no analytic model is perfect, you need to know what “good enough” means for your organization’s solution.


You need a Cloud Strategy

Your organization doesn’t have to be in the middle of a full cloud migration to have a good cloud strategy.  Hybrid cloud implementations – those than involve keeping infrastructure resources on-premises while supplementing with cloud platforms – are a great approach to incorporating cloud agility and scalability into your overall strategy.

In addition to the obvious benefits of increased scale and decreased cost, cloud solutions provide a high level of agility. Cloud or Hybrid Cloud-based projects that are a developed with a “solution first and infrastructure last” methodology are often able to be more nimble and can be quickly adapted to new business requirements and changing industry norms.

A hybrid cloud strategy doesn’t need to include sweeping changes across your technology organization. Instead, it needs to focus on filling gaps that your current environment is not able to support.  It should include:

  • Identification of what tools and capabilities you are lacking now that could be provided via a cloud platform.
  • Identification and description of sources of data that can live in the cloud and, also, those which need to remain on-premises.
  • Focus towards platform-as-a-service offerings that reduce the necessity of direct administration and maintenance.
  • Inclusion of tools that provide capabilities that your organization currently does not possess.

Three important considerations to include in your cloud strategy are:

  • Build in support for both corporate-wide and departmental-focused deployments. The former requires strong integration with current infrastructure, but the latter can be more flexible and dynamic based on business needs.
  • Major cloud platforms, like Microsoft Azure, are secure by design. Include data security as a key priority, but don’t let overarching security mandates hinder your progress.
  • Identify key personnel who will own a specific cloud-based tool. Feature updates often happen quickly in the cloud, and having a single point-of-contact who keeps up on new features and updates is key to adoption success.

Your Analytic Strategy & Cloud Strategy Should Intersect

Analytics is a cutting-edge industry, with lots of new tools evolving quickly. Cloud platforms, like Microsoft Azure, are also cutting edge and enable a level of agility that is not possible with on-premises installations. This fact allows analytics and cloud to fit together very well.  For most of our customers, their analytics strategy and cloud strategy intersect at some level.

Some factors causing the two strategies to intersect include:

  • Many analytics tools are complex, with a large ecosystem of community-built packages. Large ecosystems require constant administration. Going to a cloud platform minimizes and/or removes administration requirements.
  • As analytic models mature, the tools mature with them. Cloud technology moves quickly, with updates coming more often than on-premises architectures.  New features arrive weekly or monthly.
  • Analytic models are subject to change as business requirements are refined, or as activities happen in the real world. Using a cloud platform imbues your analytic model with the agility it needs to react to an ever-changing workload.
  • Analytic solutions built using a cloud platform generally benefit from a faster time-to-market than those built on-premises using traditional infrastructure. Because these solutions minimize the focus of acquiring and configuring infrastructure, projects are often “off the ground”, quickly leading to realized business value during the first weeks of a new project.
  • Scaling your solutions doesn’t require a budget council meeting. Cloud is hyper-scale. When you need to grow your solution from 100GB to 100TB, you don’t need to ask for a large briefcase of money. Operational costs are easier to budget for.
  • Data Analysts and Data Scientists tend to work on the cutting edge. Cloud platforms also tend to stay on the cutting edge, meaning your analyst teams are always able to work with the latest technology.
  • Integration between development cycles and deployment cycles are built into the cloud platform. Often, the deployment process is built into the development process of cloud platforms.  Your business analytics team will be able to manage their own release schedules and ensure that the right answers are always ready.

What’s next? Join our Webinar

Hopefully you’ve read this and are thinking “I really need to get that cloud analytics strategy started!” – or, maybe you have already started and just want to make sure that you’re going down the right path. In either case, 3Cloud is here to help you feel confident that you’ll be successful with an upcoming free webinar discussing the Microsoft approach to a Cloud Analytics strategy.

In this webinar, you’ll understand why Microsoft has become a leader in enabling success in the analytics space over the last two years. You’ll learn about the methodologies that highly successful teams have implemented. Additionally, you’ll learn about the tools available in the Microsoft Cortana Intelligence Suite; a complete Cloud Analytics platform that includes the data management, data transformation, analytic modeling, data intelligence, consumption, and collaborating tools that are required to implement a well-designed Cloud Analytics strategy.