September 30, 2021
September 30, 2021
Contributor: Jackie Wiles
As data and analytics become increasingly critical to good decision-making, CFOs must take note of these key technology-enabled trends.
In today’s disrupted operating conditions, we are trying to make the best possible decisions in the shortest possible time, but the historical data on which we’ve typically relied is less and less relevant. Rapid advances in data and analytics (D&A) can help, assuming CFOs understand the role and value of these technology-enabled innovations.
As drivers of digital strategy, it’s on CFOs to translate D&A trends and their impact into must-make investments for the enterprise. You must deploy D&A innovations to leverage the opportunity and manage the risks of the increasingly sophisticated and varied analytics in use across the organization.
“CFOs can’t afford to wait and react to trends as they mature,” says Richard Ries, VP, Advisory, Gartner. “They must proactively monitor, experiment with and exploit key data and analytics trends to respond to crises, innovate and rebuild.”
Download now: The Top 4 Data & Analytics Trends in Finance
These trends should be considered and reevaluated in the finance function's strategic planning, based on the extent to which they enable shifting and urgent business priorities.
More organizations are starting to share data by default, thus democratizing access to this key resource. In this environment, four trends are emerging as particularly relevant to the finance function, which is both a producer and consumer of large amounts of data.
Dynamic data storytelling is replacing traditional predefined dashboards.
Increasingly, AI and natural language generation will automatically analyze data and generate insights in a narrative format.
By 2025, data stories will be the most widespread way of consuming analytics. Augmented analytics techniques will automatically generate 75% of those stories.
Augmented data management
Metadata management solutions will further organizations’ understanding of data and enable finance to execute automated data management.
By 2023, augmented data management will reduce reliance on finance analysts for repetitive and routine data management tasks, freeing up to 20% of their time for collaboration, training and high-value analytics tasks.
Pervasive cloud deployment
A greater share of enterprise data will be managed through cloud applications, furthering the decentralization of data and analytics capabilities.
By 2022, public cloud services will be essential for 90% of data and analytics innovation.
Convergence of data and analytics platforms
Data and analytics processes will increasingly occur on singular platforms that incorporate multiple capabilities across the data life cycle, from data entry and storage to analysis and AI and ML.
By 2023, 95% of Fortune 500 companies will converge analytics governance into broader data and analytics governance initiatives.
Leaders across organizations continue to struggle to interpret insights from finance. Despite modern analytics and business intelligence (A&BI) platforms, insights often lack context and aren’t easily understood or acted upon by the majority of users. New technologies, augmented with machine learning (ML) and artificial intelligence (AI), can dynamically and automatically generate personalized data stories and embed them into applications.
The increased use of dynamic, in-context data stories for insight monitoring and analysis will reduce the amount of time users spend in predefined dashboards — and the amount of time financial planning & analysis (FP&A) teams spend manually populating those dashboards.
Read more: Finance AI Is Critical to CFO Digitization Efforts
Decision makers want data to be current, complete and consistent across the range of views that different stakeholders take. They also need to trust that the data reflects business performance, and they don’t want to expend a lot of effort organizing the data in order to analyze and use it in decision-making.
Gartner data shows that 54% of finance organizations still struggle to meet these needs and provide data and reports stakeholders can rely on to inform their decisions. That means decision makers either ignore reports or cherry-pick data. Augmented data management, which leverages ML and AI techniques, can help.
Augmenting data management aids in automating data management tasks. That’s critical, because data proliferation and diversity now make even previously mundane and easy-to-accomplish tasks too numerous to handle — at the speed and scale required for digital business, at least.
This need for augmented data management will only increase as organizations move more data assets to the cloud, and data and analytics teams struggle with data-trust issues and a scarcity of technical skills.
As more data components head to the cloud — albeit at an uneven pace — finance data and analytics teams will have to deal with the thorny issue of data governance and integration. That’s why you must coordinate with other data and analytics leaders across your organization to develop a holistic and cohesive approach to managing data as it moves to the cloud.
Cloud is also a key driver of cost optimization, but only when approached with a financial- governance mindset. An informed finance function can ensure the relative tradeoffs between price and performance, rather than just cost, drive cloud-service decisions. Finance is also well positioned to track and adjust price and performance metrics over time as cloud providers improve performance and tool capabilities.
New features and capabilities such as AI and data mining are increasingly offered as “cloud-first” and will eventually become “cloud-only.” Finance leaders will likely need to reallocate staff away from data management operations and toward activities that generate more business value. Individuals tasked with managing finance’s data infrastructure will then transition to cloud provisioning and act as data infrastructure experts, for example.
Analytics, business intelligence and data science tools are becoming less defined as tools. This overlap potentially creates more complete and effective links among data and analytics investments, practices, processes and key business outcomes. This, in turn, speeds D&A maturity, which translates into greater resilience and competitive advantage for organizations.
To capture these opportunities, finance teams must tackle the fragmented state of their data and analytics networks. Although data and analytics has grown as a priority for CFOs over the past five years, and spending on it accounts for a significant part of the finance budget, much of that investment has occurred in a piecemeal fashion, with finance adopting individual tools and systems that are incompatible. This has left analytics capabilities in silos and made it more difficult to create comprehensive analysis to inform effective decision-making.
To ensure a constructive convergence of analytics tools and governance, you will need to expand analytical capabilities, roles and processes, anticipate changes in products and practices, plan for this convergence of platforms and facilitate collaboration between data and analytics communities across the organization.
If you are responsible for data and analytics strategies:
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Recommended resources for Gartner clients*:
Top 4 Trends in Data and Analytics for Finance, 2020
Finance’s Role in Improving the Business’s Financial Data Literacy
Key Master Data Management Principles for Integrating Financial and Nonfinancial Data
Components of Modern Analytics and Business Intelligence Architecture for Finance
*Note that some documents may not be available to all Gartner clients.