InsightOut: Multispeed IT drives fast business experiments and empowered citizen analysts

IBM Fellow and VP, CTO for Information & Analytics Group, IBM

This opening installment kicks off a series of perspectives that dissect the rapid transformations confronting enterprise analytics professionals and organizations. The intended focus is on the business impacts of such disruptive innovations as big data, machine learning, cognitive computing and cloud-based services. In addition, the series examines how organizations everywhere are adopting new best practices in how data and analytics are accessed, delivered and consumed. And it provides a forum for IBM analytics experts to share their thoughts on these key trends shaping enterprise data initiatives worldwide: 

  • Line-of-business buyers are increasingly at the forefront of organizations’ most strategic analytics-driven initiatives.
  • Knowledge workers are demanding self-service access to make evidence-driven decisions and engage in powerful analysis, exploration and visualization of big data.
  • Corporate IT is trying to manage the costs of delivering these capabilities while also maintaining compliance with the relevant governance and regulatory requirements.

A new best practice

Multispeed IT is an emerging best practice in which business users, data scientists and developers rapidly experiment with information to validate a hypothesis, investigate a specific anomaly, derive new insights, build new capabilities and create new customer-facing products. Other common terms for the practice include two-speed IT and bimodal IT. When these personas embark on a task, they may not be sure about what they are experimenting with and whether it will actually be of use. They may have time pressures that they cannot wait on, or they may simply not want to go through the exercise of getting a project funded through the normal IT process. Sometimes, multispeed IT is the result of friction between business and IT groups within their organizations.

Effectively, we are seeing these users increasingly demand a self-service model that does not require interaction with IT. However, to be effective they need to connect with and build off of capability that is provided or managed by IT, or they have to work within the confines of a governance or compliance policy.

The rise of the citizen analyst

Multispeed IT empowers knowledge workers, who are sometimes known as citizen analysts. These individuals have been adopting a new class of agile tool sets to satisfy the goals outlined previously. These tools range from true self-service analysis tools for the citizen analyst, such as IBM Watson Analytics, to traditional reporting and business intelligence (BI) capabilities and self-service tools for preparing data and managing diverse data analytics tool sets. Such tools have become more prevalent than ever within all classes of business—from small businesses to large enterprises.

As citizen analysts adopt self-service tools, they run up against the command-and-control approach of traditional IT delivery models in their organizations. The challenge they face stems from the desire to become highly self-sufficient, just as data scientists, application developers and data engineers have traditionally been self-sufficient in satisfying their own data analytics requirements.

Traditional IT perspective

Traditional IT is very much focused on the following characteristics and problems: 

  • Projects driven by budget and cost
  • Ongoing cost of managing and maintaining systems, acquisition cost of infrastructure and services, and fixed cost of improvement to these systems
  • Management of service-level agreements (SLAs) for the systems—availability, response time and cost
  • New work that is driven by projects or by specific requirements that can be self-initiated or driven from the business side
  • A strong focus—when it comes to data and analytics—on defining the data model and the extract, transform, and load (ETL) process required to populate the data model
  • Governance, compliance, trust and confidence, data quality and data classifications
  • Large focus on ensuring projects can be run in a production environment with a strong emphasis on satisfying overall SLAs and objectives
  • Projects that require multiple weeks to multiple months and years requirements of the citizen analyst

The flip side to the traditional IT perspective is the goal of the citizen analyst, which requires a much greater degree of agility. When citizen analysts start on a task, they are often driven by some hypothesis. At this point, they are not even certain the hypothesis will be valid or lead to a new insight.

In addition, the citizen analyst operates on a much more compressed time line than a project time line associated with the traditional IT approach. Delivery of projects is measured in days or weeks, and anything longer is simply not realistic. This citizen analyst approach enables the following:  

  • Finding data relevant to the hypothesis
  • Validating the initial hypothesis before provisioning data
  • Provisioning and shaping and preparing data into a form that will be well suited to further discovery
  • Achieving discovery over data, which includes further shaping and analysis for building out a specific analysis or report
  • Developing a collaborative model that allows sharing with a group
  • Using self-service access with a low barrier of entry and frictionless deployment and operation without dependencies on IT

Many of these projects are ad hoc, one-off tasks or tasks that will be used on an infrequent basis—such as those that actually lead to real insight. In many cases, the hypothesis is not proven out. A model with thousands of ideas begins to materialize that leads to hundreds of useful insights of which only a few may be ongoing critical new business insights. These new insights have to be deployed into the more formal business processes.

Self-sufficiency needs for builders of data analytics

Consider two fundamental questions for meeting the self-sufficiency needs of citizen analysts and others who create data analysis projects: 

  • How can organizations provide the technologies to support all categories of self-sufficient data analytics builders, including citizen analysts, data scientists, application developers and data engineers?
  • How can IT support centralized management of the actual production systems for data analytics technologies that are used by all self-sufficient data analytics builders?

When talking about central management of systems, we’re not implying that you have to have a central IT team that actually manages individual systems. That management can, and often will be, handled through managed cloud services. But you do need to have a group that ensures the architecture of these systems can satisfy the required SLA—response time, availability and cost. It also needs to satisfy governance and compliance around data and the analytics, the lifecycle of data, the lifecycle of analytics models and so on.

Going forward, the industry is evolving to be able to support a self-service paradigm that has a strong collaborative linkage to traditional IT capabilities. This paradigm needs to ensure not only satisfying challenges, but it also needs to ensure that there is strong trust and confidence in the tasks being executed by the self-service persona.

Be sure to continue exploring the technologies available on the IBM analytics technology platform discussed here. Click here to see the next installment of this series or here to see the entire series.