Business and IT often have conflicting core objectives. IT focuses on stability, repeatability, efficiency and operational risk management, while the business focuses on speed, agility, flexibility and business risk.
IT is responsible for deploying cost effective technology and managing enterprise data in a manner which minimizes risk via appropriate security and recoverability practices. These objectives are often measured via service level agreements (SLAs).
The business is responsible for achieving strategic corporate objectives with measurable metrics, such as x% revenue gain within a defined time frame. These objectives are achieved via initiatives and use cases which require analytics to be developed to measure success, i.e., BI reporting, dashboards, data analysis, machine learning, deep learning. Many of these use cases are speculative in the beginning, i.e. requiring some level of discovery to define the business and technical requirements.
Unfortunately, IT desires well defined requirements to ensure it can provide data and technology stability for new business initiatives. IT also must ensure there is no negative impact on any existing business processes running in production. How can IT ensure SLAs will be met, if the new business needs can’t be clearly articulated?
The business can’t wait until it knows the full requirements, they need to begin the discovery process now. As they work through a use case, they will prove or disprove their assumptions and decide to move forward or stop the effort. They want to fail as fast as possible to begin testing another approach or even a different use case.
When the business creates analytic processes/metrics which are valuable to repeat, we call them “repeatable analytics.” The good news: these analytics can have clearly defined requirements. The bad news: it’s already written and running in the sandbox. At this point the business wants IT to move it into production or perhaps considers it production. The business is ready to focus on the next analytic use case, so the conflict begins.
Recognizing the conflict is the first step in understanding the broader problem. No one is at fault and both are only focusing on meeting their objectives. It appears we are at an impasse, but there is a solution.
The goal is a win-win outcome enabled by Business and IT simultaneously achieving their objectives with the least friction possible. For this discussion, we will leverage the analytics capability framework in Figure 1 to focus on the different needs of the business and IT, and provide recommendations for meeting those needs.
The framework below depicts three foundational capabilities required for success in a modern analytic architecture
. These three capabilities are:
- Flexibility – The ability to choose the most appropriate software resources -- tools, languages and libraries, etc. -- to accelerate the user’s time to insight and minimize operationalization efforts.
- Simplicity – The ability to quickly provision and decommission analytic resources -- compute, storage and network -- in a simplified, manageable and cost-effective manner for business user and IT.
- Accessibility – The ability to efficiently find, secure and govern information and analytics within the entire analytic ecosystem without slowing down the business users or jeopardizing production.
Figure 1 - Analytics Capability Framework
Enabling Flexibility for Tools, Languages and Libraries while controlling chaos
Business and IT have different needs. When IT enables the business to operate in a self-service manner equipped with the proper tools, the quicker the business can innovate to find new insights. Many business users embrace the idea of discovering, learning and using the latest tools languages and libraries when they feel it enhances their ability to deliver results. Business users sometimes want the freedom of choice but don’t always understand the impact to a stable production environment. Often times, business users want to use the latest release of a tool, while IT wants to ensure production is a tested, stable release, to prevent outages and surprise break/fix calls.
A key role of IT is to provide that stable foundation by evaluating tools, languages and libraries to ensure that those tools are the best ones to meet the needs of the business. IT ensures that these components are strategic to the business needs, viable and maintainable for the long term at a reasonable cost.
Rationalization of technologies implemented in production is needed to ensure consistent product versioning, configuration supportability, disaster recovery and overall manageability. Bottom line: a key IT function is to support the business and ensure their needs are being met in a manner that ensures long-term system availability for all business users.
- Leverage a variety of tools, languages and libraries to perform analytics
- Perform in a self-service manner for speed
- Formal and self-paced training
- Ensure the production and discovery environment are accessible, sustainable, resilient, and maintainable
- Evaluate the supportability, cost, roadmaps and rationalize technologies supporting production workloads.
Recommendations for ensuring flexibility without chaos apply to both business users and IT.
- IT needs to research and establish an approved list of authorized tools, languages and libraries they can to support in the production environment. This should be done in concert with the business to ensure that business needs are addressed.
- IT is responsible for the promotion of sandbox data and analytics into production, which we will refer to as operationalization.
- In cases where new technology is needed, the business should get IT involved in the evaluation process as early as possible.
- Partner with user-based centers of excellence on technical training support
- Partner early with IT on new technologies to expedite the evaluation process.
- Establish user-based centers of excellence to collaborate and recommend self-service training material.
- Leverage production authorized technologies, whenever possible, to speed ability for IT to place new analytics into production.
- Choosing unauthorized technology should be at the user’s discretion, however, users should take a “buyer beware” approach. IT operationalization efforts may take longer or possibly requiring a rewrite, if the chosen user technology is not adopted.
- Allow business users to bring their own tools, languages and libraries into their own sandbox with the understanding it is strictly for exploration and they cannot directly promote their analytics into production.
Flexibility is the critical first step which enables users with a wide array of modern tools to meet their analytic needs. To fully accelerate innovation also requires focusing on simplicity and accessibility. In part 2 of this series we will discuss the importance of Simplicity.
Dwayne Johnson is a principal ecosystem architect at Teradata, with over 20 years' experience in designing and implementing enterprise architecture for large analytic ecosystems. He has worked with many Fortune 500 companies in the management of data architecture, master data, metadata, data quality, security and privacy, and data integration. He takes a pragmatic, business-led and architecture-driven approach to solving the business needs of an organization.
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Mark is a principal ecosystem architect at Teradata with 25+ years of data warehouse experiencing as system engineer, enterprise architect, and implementer of large data warehouses to Fortune 500 companies. He has performed consulting engagements at many large Fortune 1000 leveraging his analytic skills and technical knowledge to implement innovative data driven solutions focused on delivering value by optimizing efficiency or growing sales. He has spoken at the Teradata user conference on topics ranging from dual active implementations to workload visualization techniques. He holds a patent related to applying state machine concepts to managing high availability of systems.
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