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Like the steam engine, electricity and the internet, analytics is accelerating innovation and reshaping businesses and industries. But while many companies are investing heavily and have high expectations, it’s far too easy to neglect the foundational elements necessary to scale analytics.
Technology can help achieve lasting business outcomes. But to scale such goals as increased operational efficiency, revenue growth, improved customer experiences, and better decision-making and agility, the foundations of business alignment, execution ability and accountability, and culture are equally if not more important.
Align analytics initiatives with business objectives to help realize maximum viable value
Start with identifying specific pain points and opportunities and designing applicable analytics solutions that have a customer-centric design approach and deliver tangible value. Instead of burning millions of dollars with the hope that analytics will magically produce ROI, it’s critical to first identify, spotlight and pilot high-impact use cases that are enablers of business value.
Taking a pilot-led approach can yield the most potential for increasing value and reducing sunk cost, but you should identify and use maximum viable value (MVV) as a key metric for determining which pilots to prioritize. Thoroughly analyze potential cost savings, revenue generation, customer experience enhancement and process improvement opportunities. Smaller-scale initiatives can enable organizations to assess feasibility, effectiveness and business value in a controlled environment. Pilots can provide deeper insights, help refine solutions and inform decisions about additional investments. Taking an incremental approach with smaller iterations reduces risks and enables continuous learning, feedback incorporation and course correction.
For example, a global insurance company wanted to modernize its data capabilities — a multi-year process — and stakeholders knew they had to realize business benefits long before full implementation. To demonstrate value early, they first identified a broad set of analytics initiatives and then concentrated on the low-hanging fruit. They started with enhanced underwriting analytics before reworking the underwriting technical infrastructure. And before scaling up any initiatives, they undertook a rigorous evaluation of the value each provided — MVV score — and then started with pilots to justify the investments.
To measure success and make data-driven decisions, establish clear KPIs tied to specific business outcomes. Continuous monitoring, enhancement, collaboration and integration further improves the long-term value of analytics.
Invest in modern data infrastructure
In another case, a global benefits broker had spent millions creating sophisticated and complex analytics for clients. However, when the CEO asked three different groups for figures for the same set of products, all three came back with different answers. The underlying issue wasn’t a lack of analytics solutions but rather the dependency on the archaic and siloed data infrastructure across the organization. A modern data infrastructure — one that encompasses storage, processes and accesses vast data sets, and confirms that AI algorithms have a steady supply of high-quality information — is the backbone of analytics value creation, especially as the volume and complexity of data grow.
You also should leverage cloud-based platforms and data governance frameworks; use advanced analytics solutions to collect, store and process high-quality data; acquire new and enriched existing data; and invest in scalable and efficient data storage and processing infrastructure. These components will often lead to more accurate and thorough algorithms.
Effective cybersecurity measures, encryption techniques and access controls are also crucial for data security and privacy in analytics applications. Compliance with data protection regulations such as GDPR or CCPA maintains trust and ethical practices. Investing in collaborative platforms and partnerships facilitates data sharing and coordination with external stakeholders. This enables access to additional data sets and expertise, and it enhances analytics capabilities through collective intelligence.
Set up the right operation and governance model for your organization
As you establish the appropriate operational model for analytics, it’s critical to define clear objectives, assemble cross-functional teams, and implement governance and ethical frameworks. Responsible analytics and AI should be a fundamental part of your organization’s strategy. Key areas include setting risk-based priorities; revamping cyber, data and privacy protections; addressing the opacity risk and equipping stakeholders with the right solutions for oversight. You should also monitor third parties, keep a watch on regulatory landscape, and maintain human-led and automated oversight.
Don’t put the operating and governance models last. That can lead to confusion, project delays and accountability issues. Even in industries that follow product or agile delivery models, bigger strategic and technical programs have more complexities than most of us are used to. Develop a governance framework to ensure responsible and ethical use of these technologies, addressing data privacy, bias mitigation, transparency and accountability.
In addition, compliance with regulations and industry standards maintains trust and ethical practices. Effective data management and security practices — including access controls, encryption and data protection measures — help safeguard sensitive data. You also should create cross-functional teams with specialists in data science, machine learning and domain knowledge to help facilitate holistic problem solving and effective collaboration.
Drive a culture of continuous experimentation and learning
Embrace a culture of swift, continuous experimentation — not so much “failing fast” as “winning quickly.” Consider how to provide a safe environment for trying new ideas that tie back to the overall value-based vision. For example, a multinational hospitality company that wanted to transform its customer data and analytics capabilities and improve customer experiences created an innovation hub to focus on use cases that typically wouldn’t have been considered. Rather than a prioritized “backlog” approach, the hub encouraged creative experimentation in parallel to core development. This helped foster a growth mindset, promote knowledge sharing and organize collaborative projects that cultivated curiosity.
Continue to push an agile approach with both development and ideas. Projects that are broken into smaller, manageable initiatives encourage early feedback and necessary course corrections. This iterative approach enables rapid experimentation, faster progress and improved outcomes. Regular communication, collaboration and feedback loops with key stakeholders, domain specialists and users also confirm that all input is incorporated and stakeholders are on board from the beginning.
Outside of key stakeholder groups, promote knowledge sharing through workshops, seminars and internal conferences and encourage cross-functional collaboration to leverage diverse perspectives, skills and experience. Provide learning resources and development opportunities — such as training programs and certifications — to keep teams updated with the latest advancements and most effective practices.
The bottom line
Analytics hold immense promise to reshape industries, generate innovative solutions and augment human capabilities. It’s essential, however, to go beyond the hype and build the foundational elements necessary for success. By investing in modern data capabilities, fostering a culture of experimentation, establishing the right operational model and ensuring business value alignment, organizations can unlock the true potential of analytics and reap their transformative benefits.
Mohammad Misbah, Director, Customer Transformation, PwC US, contributed to this article.
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