THE CASE FOR ANALYTICS AS A SERVICE
Analytics as a Service. This is the idea that the analytics needed by the business are hosted, managed, and delivered by a third party. Companies spend their time determining the right questions to ask while the service provider takes care of the environment, technology, and expert resources necessary for making it all work and delivering the answers.
PLATFORM. EXPERTS. INTERPRETATION.
At Sentier we know that a highly functioning analytics capability has three supports: platform, experts, and interpretation. This is where small, multi-disciplinary teams are enabled to make decisions. To move beyond simply hosting data and providing analytic tools, an AaaS must involve experts—real people—who know how to integrate data, model it, and customize the processes to meet the business’ needs. And when the insights are cultivated they must also be articulated to a broader audience. This means that columns and rows need to be displayed on a canvas so that they tell stories.
Many companies have failed to achieve a healthy analytics practice for many reasons. Often it starts with a fragmented ecosystem of responsible parties who must coordinate and over communicate. Then the desired outcomes (deliverables) that were set months prior have evolved, and organizations find it hard to pivot. This means more delays, and questions do not get answered in business time. Or worse: companies will spend more time trying to synthesize answers than actually getting them and using them. All of this leads to greater expense and a greater propensity for failure.
SEAMLESS INTEGRATION. RAPID RESULTS.
Analytics is a core capability for any organization to provide insights on new and changing conditions for the business. AaaS is the right approach because companies providing these services ensure that the fundamentals of analytics are seamlessly integrated from infrastructure and data integration to model definition and results interpretation. When done correctly all components of the service are set up to deliver rapid and accurate answers to dynamic questions. This means that the team can focus on identifying the most critical questions and answering them in days as opposed to weeks.
CREDIBILITY.
The piece that many companies deploying AaaS solutions overlook is the ability to see deeper into the numbers. This means that all of the source data and the underlying models should be transparent. If an analytics team needs to defend its thesis, they will need to support their findings with the original data assets. Anyone should be able to query and visualize the data lineage on demand. Anyone should be able to question the models that go into baking the results. All of these factors build credibility and trust in the analytics, and the business will be better for it.
