The Future of Data Analytics

Not too long ago, data collection was largely seen as a corporate chore, often required by law or policy, but something few companies wanted or understood the potential value of. Today, data is embraced as one of the most valuable company assets, because it is not only a record of what happened… it can be used to gain insight into what is likely to happen in the future. Companies that can effectively tap into and apply this knowledge will possess the most sustainable competitive edge of the 21st century, one that will rival even talent in importance and impact.

Industrialized Data & Analytics

As advanced analytics technologies mature and AI tools become embedded in an increasing number of core application suites, companies on the cutting edge are rebooting their data and analytics efforts as “industrialized” data and analytics capabilities that are end-to-end, repeatable, scalable and integral to core business operations.

Typically, they start by consolidating pockets of expertise into a center of excellence (COE). Some have even spun off data and analytics as a business services function, headed by a chief data officer. These new leaders – who are also data experts – are liberating information, addressing governance challenges, and blending disparate sets of data into a virtual horizontal layer accessible by the whole enterprise. They are also making major investments in developing and hiring staff with data science and analytics skills to extract and apply all potential value.

Reap the Rewards

Industrialized data and analytics needs to be viewed first as an enterprise capability that impacts the business at every level, from senior leadership to individual business units. They will transform core business processes through improved accuracy, efficiency, speed, eliminating manual work and enabling better-informed decision-making.

The following are principal areas of benefit in multiple domains:

• Service/operations management: Real-time data reporting and intelligence are must-haves for agile operations and smart automation of operating processes. Benefits: User experience, capacity planning/demand forecasting, capital expenditure performance, process efficiency and quality.
• Workforce empowerment and management: Anytime/anywhere data access and on-demand intelligence empower workers whether remote or in the office. Sentiment and productivity analysis inform HR assessments and strategy. Benefits: Worker experience, productivity, collaboration, innovation.
• Supplier/partner management: Data analytics provides visibility, tracking and anomaly discovery across all facets of vendor/supplier relationships. Benefits: Vendor efficiency, procurement and contract compliance, SLA tracking, vendor portfolio optimization.
• Enterprise risk management: Data & Analytics (D&A) systems inform forecasting, detect and report signs of fraud and cyber-attack, and sense service disruption indicators. Benefits: Forecasting accuracy, loss prevention, revenue assurance, collections efficiency and effectiveness, asset utilization, cybersecurity, service interruption, operating resiliency.
• Customer management: Customer data analytics create personas and digital twins that enable smarter testing, tailoring and targeting of services. Benefits: Customer experience, loyalty, higher spend, reduced churn.
• Market/sales management: Market data analysis and real-time reporting inform decisions and actions in an increasingly dynamic and disruptive marketplace. Benefits: Channel optimization, campaign performance, upselling, cross-selling.
• Product management: Product development decisions are facilitated by product, customer, competitor and market data and analytics.
• Benefits: Faster time to market, optimal product pricing, product profitability, portfolio optimization.

Pay the Consequences

If the positive impact of D&A, with its considerable scope and concrete, documented results do not resonate with the C-suite, perhaps another perspective may: The negative consequences for businesses that fail to fully develop a D&A capability.

These potentially include:

1. Missed business opportunities because of a lack of analytics to rapidly test and assess market reaction to new products and services.
2. Delayed reaction to changes in consumer or customer expectations that could have been revealed early by market data analysis.
3. Increasingly frequent and debilitating security breaches from an inability to analyze data patterns and markers that indicate imminent attacks.
4. More lapses in business continuity without real-time, automated data analysis that predict outages and prompt remedial action.
5. Perpetuation of suboptimal business tactics and decisions due to inability to recognize correlations between actions and increasingly inferior outcomes.
6. Forfeited advantages of an intelligence-augmented workforce because AI tools cannot operate effectively with data that is inaccessible or siloed, redundant, misidentified or otherwise inaccurate.
7. More damage suffered from disruptions such as the pandemic shutdown caused by delayed, incomplete or inaccurate data visibility of supply and demand pipelines.

These negative impacts will prove more damaging to businesses in the future than they have been in the past. Why? The performance bar is rising, set by companies that have fully digitized their operations across their G&A functions. This elite performance level, which we call “Digital World Class^™,” is well documented in our benchmarking efforts with hundreds of large global companies. These businesses enjoy a 29% lower overall cost of operations, have 83% higher net margins and 55% higher total shareholder return compared to the peer group (i.e., non-DWC organizations).

Keep Up or Risk Falling Behind

Compared to their peer group, twice as many Digital World Class technology organizations have used a formal, structured enterprise data and analytics roadmap to guide the maturation of their D&A capabilities. They have engineered D&A as an end-to-end capability, automating data collection and applying analytical predictions to ongoing decisions and tasks.

As business processes become more digitized and automated, more data becomes available about process performance and exceptions. This in turn informs further optimization of process design and execution. As a result of this positive feedback loop, gaps between digital leaders and laggards will widen at an exponential pace. Those without mature enterprise-level D&A capabilities will fall further behind.

Insights on Demand

The value of data and analytics comes from how insights can inform and empower decision makers. Historically, these decisions have generally prioritized the economic dimensions of organizational performance (e.g., delivering more cash, more profit, more predictive results impacting shareholder value). More recently, they weave in the complex considerations of ESG1 (environment, social and governance) standards, which radically expands the breadth and depth of the data domains that any organization must govern and draw insight from.

For any business problem, opportunity or goal that arises, there should be a process – modular, repeatable and scalable – that creates insights both on demand and as part of established enterprise performance management processes. The process should include resource commitment and target setting, and should operate on a calendar and rolling basis (e.g., strategic planning reviews, annual planning, forecasting).

We describe this “insight cycle” as an end-to-end process which operates horizontally, cutting across data stove pipes and data silos. The scope varies according to the data domains and decisions to which the insight cycle relates. Its goal is to deliver “fit-for-purpose” insights targeting the most impactful decisions in the enterprise. (Fig. 1).

Winner’s Cycle

Each stage of the cycle should have its own objectives, requirements for success and standard operating procedures. Without a robust, mature D&A capability applied to a broad set of data domains, business objectives will remain poorly served, business requirements will be more difficult to meet, and underlying procedures will be inconsistent and fragmented. At best, the resulting insights will be tactical, with an inherent risk of being unreliable.

Business and functional leaders should reflect on the following fundamental considerations in building each stage of the cycle for every use case relying on data and analytics:

1. Data sourcing: What data should be used to answer the most important questions in our business, function, processes and projects?
2. Information analysis: What techniques, methods (i.e., statistics) and tools should be used to deliver actionable insight from this data? How should we overcome any skills gap?
3. Information consumption: How should we develop our channels to distribute and consume data in order to impact our most important decisions in an efficient manner?
4. Data governance: What framework and controls should we put in place to ensure data relevance, quality and timeliness?

Refine and Repeat

The cycle itself is not a static process. Each iteration applied to the same or similar problem or opportunity drives an improvement in insight relevancy, speed and accuracy: Data is validated or corrected, and the AI tools performing the analysis learn from their previous experience. Over time, insights become more refined, more user-friendly, and more sophisticated. The cycle ultimately becomes an indispensable, intelligent utility, easily accessible for every business decision maker.

Our consultants are seeing this play out in practical applications at many client companies, where D&A efforts are already delivering rewards. While the majority remain in the early stages of their capability journeys, these organizations must embrace the importance of D&A and accelerate their investment. If not, they risk significant competitive consequences.