Agile methodologies may save the day for digital transformation, McKinsey says

7 ways to open up the digital and data transformation process and get IT and businesspeople talking about what's really important.

Data -- or lack of the right data -- may be the Achilles Heel of the digital movement. Successfully making the move to digital nirvana requires data -- lots of it, and in the right places, available to the right people and systems at the right time.

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Unfortunately, organizations still do not have a handle on the data challenge. As expressed in a new McKinsey post, "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." There is also a great deal of uncertainty as to what the potential benefits once the data is surfaced and actionable.

"Critical business information remains trapped in isolated systems," observe the report's authors, Chiara Brocchi, Brad Brown, Jorge Machado, and Mariano Neiman, all with McKinsey. Further complicating things is the necessary talent -- both technical and managerial -- is in precious short supply, they add.

Brocchi and her team do offer a way to organize things a little better -- employ Agile methodologies to identify and get the data that matters to the business. Agile methodologies are moving beyond development shops exclusively and now provide strategic paths for the range of digital transformation activities -- especially data transformation.

Brocchi and her McKinsey co-authors make the following recommendations for bringing the Agile philosophy to the data management side of the house:

Adopt a business-driven approach to digital transformation and data management. This can be accomplished by creating "a master list of possible business use cases for advanced analytics, as well as opportunities for new or enhanced products and processes," Brocchi and her co-authors state. "Take inventory of the different types of data associated with those use cases and opportunities. Identify the most important customer characteristics and activities across a range of business domains."

Promote joint ownership. To break down cultural barriers, "representatives from the business side need to physically sit with members of the IT organization," Brocchi and her co-authors advocate. This helps advance "joint ownership of data-migration and data-management protocols that can help the organization define just-in-time data requirements, quickly validate the business case of proposed solutions -- rather than waiting for approvals to cascade through traditional channels -- and assure the quality of the solution."

Build and empower cross-functional, or scrum, teams. Scrum teams could include "representatives from the business units and IT -- for instance, data scientists, data engineers, business-information owners, IT developers, and quality-control specialists." It's also important to provide these teams "the leeway to make important decisions relating to data migration and architecture. Scrum teams must be fully focused on activities run in the data lab and committed to a test-and-learn approach; they cannot be 50-50 players, nor can they wait for approvals from colleagues or bosses outside the data lab."

Communicate, communicate, communicate -- then communicate some more. "Successes and failures, milestones reached, and impact realized should be clearly and frequently communicated across the company as the pilot progresses and the service is finally released," the McKinsey authors recommend. "In this way, best practices can be codified, and strong project champions can be identified and encouraged." In addition, Agile opens up new communication vehicles as well -- such as "semi-regular forums in which IT and business leaders can convene to discuss important industry trends and topics -- in areas of technical specialization, for instance, or competitive analysis -- and conduct postmortems on pilot projects."

Adopt emerging technologies. Digital and data transformation opens up opportunities for companies "to consider ways to improve current systems and technologies." For example, the creation of a data lake -- a catch-all enterprise repository -- "may require new platforms, tools, and skill sets that must be compatible with the overall enterprise IT architecture. It may also prompt IT leaders to reconsider their enterprise IT strategies, asking questions such as: How can we integrate the data lake with existing systems? How can the tools and platforms used to support agile data be leveraged in other circumstances? Should we introduce open-source technologies?"

Use pilot projects to build acceptance. "IT and business-unit leaders should identify a single project that is cross-cutting and of very high value to the business -- for instance, a data-based service that would help reduce customer churn or improve protections against fraud."

Develop and introduce key performance indicators. "Metrics that are directly tied to agile ways of working are critical for sustaining long-term commitment to agile data," Brocchi and her co-authors state. A key component is "work plans (also referred to as backlogs) are based on weekly or biweekly schedules called sprints." Such schedules "are strictly monitored by a project-management office," with regular updates send to business and IT leaders.

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