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Establishing an agile data strategy that is customer-centric and builds on trust

To stay on track for future growth, forward-thinking companies leverage a crucial asset: data. By 2025, 75% of the world’s population will interact with data every day, with each connected person averaging 4,900 digital interactions daily, IDC estimates.

Increasingly, firms are looking to advance a data strategy that provides perspectives and helps their leaders and customers gain personalized, timely insights. This is easier said than done. We all know customer needs will change as markets evolve, so developing an effective agile data strategy is an imperative. Today’s data tools have matured to enable end-to-end agile data discovery, helping the industry match up with ambitious business plans and evolving markets.

While firms must explore enablers for an effective agile data strategy, they must keep a sharpened focus around prioritizing insights, managing culture, building trust and upskilling teams. Failing to incorporate these key points can result in losing growth opportunity to other market alternatives.

1. Prioritize insights that matter

Developing a giant data warehouse has worked well with high volumes of data — but not so much with the high veracities, velocities and varieties that companies must handle today.

As firms confront widespread disruption in the market and the need to innovate, they must prioritize the highest-value insights from their data. Development of such insights should be broken down into timebound modules. Work on these modules should leverage development of microservices architecture and evaluating if out-of-the-box industry solutions are a better alternative than building from scratch.

Often, adopting a fully functional, cost-effective external option vs. building in-house with longer timelines can provide firms with first-mover advantage in the highly competitive market landscape. This also allows them to build credibility for offering timely, innovative and forward-looking insights.

2. Culture, culture and culture

Developers must resist the urge to develop everything at once. Historically, if, say, a dashboard had to be developed, all the requirements first needed to be in place. Next, the developers would design and build the actual dashboard. In many cases, by the time the dashboard was fully developed, it was nearly obsolete or required substantial changes to stay relevant.

However, with an agile process, the core business and technology teams need to collocate, brainstorm and work together in the development cycle. Teams iterate through insights in an incremental manner with business users while being flexible to change.

The user community should dictate what data, insights and capabilities — such as self-service business intelligence — are required. Solutions should be codeveloped with business-automated test cases that verify the integrity and quality of the data as it is ingested.

Power users — those with knowledge of available tech stack and the business concepts — should also be part of the core team to help iterate through insights development. These users are an effective resource, as they are comfortable with tapping into both structured and unstructured sources and developing wire-frames required for analytics and insights. Power users are aware of the tool’s capabilities and are generally adept at augmenting artificial intelligence (AI) and machine learning (ML) uses to generate high-value insights.

3. Develop for data privacy and security

As regulators implement stricter compliance requirements, users become selective on how their data is used. And as firms look to build trust, securing data and implementing data privacy have never been so important.

While agility promotes data democratization, embedding data privacy and security as part of every sprint is paramount. Firms are also establishing agile data governance committees that evaluate all data access aspects for every sprint cycle. Leading firms are leveraging test-driven development to execute automated data privacy and security checks for every change and also in production on an ongoing basis.

4. Training

Often, it isn’t the direction the firm is taking that causes it to fail, it is the resources supporting the agile program. Professionals who are or will be adept in tools that support real-time data ingestion, inbuilt data governance, and complex and customizable data visualizations should be trained or hired. In addition, they should understand when to leverage AI, robotic process automation and ML.

It is also important for the core team to understand the out-of-the-box industry solutions available in the market to make build-vs.-buy decisions. Success of such agile processes hinges on whether resources are working together as teams while moving away from an individual contributor mindset.

There is a powerful incentive for firms to harness the true potential of data as they face an evolving marketplace and steep competition. In next five years, firms with an innovative data strategy that embraces change and adds value with timely, intelligent insights will be the disrupters, not the disrupted.

The views reflected in this article are those of the author and do not necessarily reflect the views of the global EY organization or its member firms.

(This column was co-written by Chetan Saluja, EY Americas financial services data and analytics senior manager).

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