A playbook for the Chief Data Officer: Implementing Your Data Strategy Across the "Last Mile"
How an Analytics OS can operationalize your data strategy
Data leaders face a daunting challenge: implementing enterprise-wide data strategies that deliver tangible wins without sacrificing security, agility, and transparency. They must approach this by deploying an analytics solution that leverages existing investments while meeting changing organizational needs.
In many cases, the hardest part is not creating the strategy, picking the vendor, or even delivering new technology to end users: it's delivering the solution across the last mile of the implementation.
Success rests on a CDO's ability to manage an enterprise data strategy that uses flexible analytics technology that is not only aligned with existing processes, but generates genuine excitement among the user base.
The analytic deployment must be universal and provide everyone the ability to access and explore data without friction – no matter where they are or what type of device they are using.
In this issue, we explore how an Analytics Operating System can provide this universal analytics environment and ultimately deliver analytics across the last mile.
Regards,
Omri Kohl, Co-Founder & CEO, Pyramid Analytics
Pyramid Analytics
Leading a realistic enterprise-wide analytics strategy
Why do even the most carefully crafted and well-intentioned analytics implementations come up short? It's often because the implementation was executed strictly from either a top-down or bottom-up perspective. The key is to span the entire enterprise and analytics workflow with an Analytics OS that meets the core needs of technology leaders and business users alike. What is the Analytics OS, and how can it create this universal analytics culture?
Managing infrastructure and technology costs
Technology leaders are under incredible pressure to keep infrastructure costs as low as possible, without sacrificing agility and performance. Yet the organization is often held hostage by their technology vendors' technology limitations. What makes one provider's technology more suitable than another? The key is analytics software designed with efficiency—and scalability—in mind.
Administering content using an "analytics-as-a-service" model
In many organizations, IT is often the owner and administrator of the BI infrastructure. However, many IT professionals have become accustomed to repetitive, time-consuming tasks like building reports, administering security roles, and provisioning custom environments for new users. How can IT adopt an "analytics-as-a-service" model to increase user adoption rates and free time to spend on more strategic endeavors?
How to Create Data and Analytics Everywhere for Everyone: Top Insights for Digital Business
29 November 2017
Data and analytics leaders cannot master the opportunities and challenges of digital business transformation unless they devise a new model that both empowers analytics leaders in their local domains and leverages the shared best practices of the central organization.
Analysis
To succeed in digital business, data and analytics must be at the pulse of the organization – incorporated into all key decisions across finance, sales, marketing, supply chain and all other core business functions.
Data and analytics leaders often struggle to both bridge and coordinate an array of isolated, decentralized analytic solutions – in effect, analytic silos. Yet many vital business processes span multiple parts of the business – requiring a new business model. Domain analytics is an emerging approach intended to overcome these limitations by harmonizing the isolated analytic solutions into a leveraged, strategic discipline, as required.