All about Operationalized Analytics

Operationalizing Analytics

Organizations with a high “Analytics IQ” have strategy, culture and continuous-improvement processes that help them identify and develop new digital business models. Powering these capabilities is the organization’s move from ad hoc to operationalized analytics.

Seamless data flow

Operationalized analytics is the interoperation of multiple disciplines to support the seamless flow of data, from initial analytic discovery to embedding predictive and prescriptive analytics into organizational operations, applications and machines. The impact of the embedded analytics is then measured, monitored and further analyzed to circle back to new analytics discoveries in a continuous improvement loop, much like a fully matured industrial process.

An example of operationalized analytics is the industrialized AI utility depicted below. It enables automatic access and collection of data, ingesting and cleaning of the data, agile experimentation through automated execution of algorithms, and generation of insights.



Operationalized analytics builds on hybrid data management (HDM), an HDM reference architecture (HDM-RA), and an industrialized analytics and AI platform to enable organizations to implement industrial-strength analytics as a foundation of their digital transformation.

Operationalized analytics encompasses the following:

  • Data discovery includes the data discovery environment, methods, technologies and processes to support rapid self-service data sharing, analytics experimentation, model building, and generation of information insights.
  • Analytics production and management focuses on the processes required to support rigorous treatment and ongoing management of analytics models and analytics intellectual property as competitive assets.
  • Decision management provides a clear understanding of, and access to, the information needed to augment decision making at the right time, in the right place and in the right format.
  • Application integration incorporates analytics models into enterprise applications, including customer relationship management (CRM), enterprise resource planning (ERP), marketing automation, financial systems and more.
  • Information delivery of relevant and timely analytics information to the right users, at the right time and in the right format is enabled by self-service analytics and data preparation. This improves the ease and speed with which organizations can visualize and uncover insights for better decision making.
  • Analytics governance is the set of multidisciplinary structures, policies, procedures, processes and controls for managing information and analytics models at an enterprise level to support an organization’s regulatory, legal, risk, environmental and operational requirements.
  • Analytics culture is key, as crossing the chasm from ad hoc analytics projects to analytics models integrated into front-line operations requires a cultural shift. Merely having a strong team of data scientists and a great technology platform will not make an impact unless the overall organization also understands the benefits of analytics and embraces the change management required to implement analytically driven decisions.
  • DataOps is an emerging practice that brings together specialists in data science, data engineering software development, and operations to align development of data-intensive applications with business objectives and to shorten development cycles. DataOps is a new people, process and tools paradigm that promotes repeatability, productivity, agility and self-service while achieving continuous analytics model and solutions deployments. DataOps further raises Analytics IQ by enabling faster delivery of analytics solutions with predictable business outcomes
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