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Using agile to accelerate your data transformation

Cubility - AI - Data Transformation

Data-management issues can undermine companies’ ability to create value from analytics. Some businesses are using agile principles to make sure data are available when and where they are needed.

Data have become core strategic assets in most organizations, and data management has become a top priority for most C-suite leaders. The structured and unstructured information companies collect about people and processes has the power to spur cutting-edge customer acquisition and retention strategies. It can reveal areas where processes could be made more efficient, and it can help business leaders make better decisions that reduce overall organizational risk. Data are the cornerstone upon which companies are launching their digital transformations—investing in analytics capabilities, machine learning, robotics, and other technologies to boost their odds of success.1

Indeed, companies are spending hundreds of millions of dollars to transform their data-related IT infrastructures and processes. But for most, the benefits of doing so have been limited to discrete areas of operation. Data-migration solutions created for specific business units or functional areas—such as the ad hoc repositories built for financial or administrative data—have been difficult to replicate company-wide because there is no end-to-end logic or central governance associated with them. Critical business information remains trapped in isolated systems.

Additionally, most companies face a big talent gap when it comes to data management. There is typically limited expertise within IT and business groups about newer data-migration technologies, capabilities, and architectures, as well as approaches to data delivery. And the relevant subject-matter experts, who could help define best practices in data migration and transformation, tend to be as siloed as the data flows they oversee.

In too many companies, the benefits of data remain undefined. About 60 percent of banks, for instance, say they have never quantified the potential value to be gained from investments in data-migration tools and capabilities.2 Without a clear vision and outcomes-based metrics to guide executives’ strategies and decisions, data-transformation projects can drag on for years.

Businesses must generate analytics-based insights much faster than that. They need a coordinated data-management program that explicitly involves the business and can be deployed across multiple functions and business units.

Some leading-edge companies are using an agile approach to run their data programs. Agile is a time-tested methodology used in IT organizations to build software or manage processes more effectively. Broadly, it is a collaborative approach in which cross-functional teams design and build minimally viable products (MVPs) and features quickly, test them with customers, and refine and enhance them in rapid iterations.3 Agile data similarly relies on a joint approach to development and delivery: cross-functional teams comprising members of business and IT work in “data labs” that are focused on generating reliable insights that allow the company to address its highest business priorities and realize positive outcomes quickly.

In this article, we explore the principles of agile data and the steps companies can take to introduce it in their organizations. The companies that deploy agile data can realize significant process and product improvements in the near term and set the stage for future advances and experimentation in big data infrastructure.

Understanding agile data

An approach to agile data necessarily relies on several core principles and organizational capabilities. The first is a business-driven approach to digital transformation and, hence, to data migration and management. Under this approach, companies create a master list of possible business use cases for advanced analytics, as well as opportunities for new or enhanced products and processes. They take inventory of the different types of data associated with those use cases and opportunities. During this process, they identify the most important customer characteristics and activities across a range of business domains. An insurer facing disruption from digital entrants, for example, may consider ways to conduct more detailed analyses of factors such as customer purchase behaviors or time to serve customers. In this way, it could improve its underwriting processes, reduce costs, and increase quality of service.

Teams then rank-order the opportunities identified and consider, for each, the levels of data governance, architecture, and quality required—customers’ preferred channels of consumption, for instance, or the “golden” (or irrefutable) sources of data required, or the latency of data. The result will be two detailed, aligned road maps—one highlighting digital business objectives, budgets, and time frames and milestones; the other defining the data requirements to build an effective big data architecture and provide seamless analytics support.

Article originally published by McKinsey


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