AaaS Part III: Analytics-as-a-Service is the new best practice.

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AaaS part I: Decision making and the Data to Insight conversion rate AaaS part II: The Tool-approach is something for the past AaaS part III: Analytics-as-a-Service is the current best practice Analytics-as-a-Service can be describes as a bypass to all of the blockers and hurdles exemplified in part II. The only thing required to benefit […]

AaaS part I: Decision making and the Data to Insight conversion rate
AaaS part II: The Tool-approach is something for the past
AaaS part III: Analytics-as-a-Service is the current best practice

Analytics-as-a-Service can be describes as a bypass to all of the blockers and hurdles exemplified in part II. The only thing required to benefit from AaaS is to expose the data to the AaaS-provider in a secure way, and to share business specific contexts. Best practice for sharing data is to utilize virtual private cloud (VPC) and with public/private keys the cloud becomes as safe as any firewalled intranet. The business context awareness is typically something analytics experts acquire already at the initial meeting with the business and decision-makers. Analytics experts (a.k.a. data scientist) are value driven by nature, hence they have a genuine interest to learn the fundamental business processes and quickly translate that into an analytics workflow.
An ideal AaaS case is when data is already in the cloud, or at least easy to upload into the cloud. The most obvious data streams to link to AaaS are the external ones like social media or machine-to-machine (m2m). Establishing a new AaaS-channel is done typically within hours, and the Analytics Expert can support decision makers with data-driven insights. This is how Analytics-as-a-Service drastically improves the data to decision conversion rate.
 
The 5 most common reasons to why an organization should embrace AaaS:

  1. Time-to-first-insight
    It only takes days or weeks from initial request to delivery of the first insight, in contrast to months for a license model that requires procurement and installation of hardware and software before even engaging in any analytics activity. Time-to-second-insight is just hours in both cases.
  2. Time to Return of Investment (ROI)
    Pay only for what you use and make every insight-retrieval an OpEx cost in contrast to a traditional license model where huge investment costs (CapEx) in hardware and software needs to be made before engaging in any analytics activity.
  3. Less Complexity in maintenance
    AaaS does not make your IT-landscape more complex. In contrast to the introduction of new technology in-house, which results in additional maintenance and integrations to the existing IT-landscape, not to forget the additional support to users.
  4. Better scalability
    An increased demand of insights is not limited by the number of licenses procured or hardware availability, in contrast to a license model that enforces fixed limitations on who and how the solution may be used.
  5. Availability of Capabilities
    You do not need to bother about acquiring, maintaining, and retaining analytics skills. The AaaS-provider takes care of all this and can effectively utilize the analytics expertise to serve multiple clients simultaneously.

These arguments are all very convincing, but notice that most of them are directed towards the IT-department. A much more important outcome is that AaaS directly improves the data to insight conversion rate, which is the primary value that the business is striving for. In fact, the IT departments often endorse AaaS at the same time as they choose not to engage with either the business or the provider in order to keep things simple for all parties. This approach truly unleashes the business, which then has the freedom to drive value. A recent example of this is a CIO at a world leading manufacturing company, who expressed it the following way – “if you as a provider deliver insights to our business in the cloud and/or as a service, then there is no point in involving my IT-department”.
 
There are a number of other factors that influence the effectiveness in the decision-making. One of these is the quality of the derived insights, but I’ll elaborate on this in another post. The thing I want to point out in this series is that the cost and availability of Analytics-as-a-Service is directly correlated to the value and demand in decision-making. That is why we at Capgemini drives Analytics-as-a-Service together with all the key technology providers. We want our clients to be competitive and have them harvesting the value out of their information assets – Analytics-as-a-Service is the new best practice to improve the data to insight conversion rate

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