Four data- and analytics trends for finance

Today’s business environment is becoming increasingly complex, and while those of you who work in finance or economics try to make the best decisions possible in the shortest amount of time, the data on which you base your decisions is becoming increasingly irrelevant. Better data and analytics (D&A) is essential for making better decisions, and understanding the role and value of technology-based innovations in decision-making is critical. In light of this, this article discusses four important D&A trends you should take note of.

“CFOs can’t afford to wait and react to trends as they mature. They must proactively monitor, experiment with and exploit key data and analytics trends to respond to crises, innovate and rebuild.”

-Richard Ries, VP, Advisory, Gartner.

As the person in charge of finance and economics, you must understand D&A trends and their impact on the company’s investments and overall digital strategy. You should use increasingly sophisticated and diverse analytics across the organization to capitalize on opportunities and manage risk. In this article, we take a closer look at four key trends that should be considered in relation to the company’s strategy, based on extensive research by Garner. Continue reading to learn more about them and how they can benefit your business. If you want to read the original Gartner article, you can do so by clicking here.

Trend 1: Dynamic storytelling

Leaders continue to struggle with interpreting insights from financial analytics tools. Despite modern analysis and business intelligence (A&BI) platforms, insight frequently lacks context and is difficult for the majority of users to understand or respond to. New technologies that have been enhanced with machine learning (ML) and artificial intelligence (AI) can assist with this because they can now generate stories associated with data and build them into applications. The increased use of dynamic, context-based data dissemination for insight monitoring and analysis will reduce the amount of time users spend on predefined dashboards – as well as the amount of time financial planning and analysis (FP&A) teams spend manually filling out these dashboards.

Trend 2: Augmented data management

Data management encompasses everything from secure data collection to where data will be archived at the end. The goal is to ensure good and credible data that accurately reflects the company’s performance and satisfies all stakeholders. The company’s various stakeholders do not want to have to spend a lot of time organizing data in order to analyze and use it in decision-making. This means that decision-makers either ignore reports or cherry-pick data that supports their desired outcome. However, augmented data management techniques based on machine learning and artificial intelligence (AI) can help with this.

Augmented data management is useful in that it automates a variety of data management tasks, such as data security and identification, data source segregation and storage, as well as analysis. This is critical, because today’s digital growth has made previously mundane and simple tasks difficult for many people to manage. The need for augmented data management will also grow as more organizations move data resources to the cloud, and data and analytics teams struggle with a lack of proper technical skills and data confidence.

Trend 3: Pervasive cloud deployment

Cloud technology has several advantages, one of which is cost savings. However, while cloud technology is often more efficient than traditional technology in terms of performance, there are numerous challenges that can arise along the way. As more people use the cloud, data and analysis teams will face the challenging issue of data management and integration. It may appear simple to implement the solution, but achieving cost savings will be difficult unless you change both processes, expertise, and organization. This is why you must collaborate with other data and analytics leaders across your organization to develop a comprehensive and consistent approach to managing data as it moves to the cloud. It is equally important to recognize the value of smooth development, shorter time to market, and increased innovation.

According to Deloitte, when all costs and gains are considered, some businesses can achieve a return on investment in the cloud that is more than ten times the initial investment. In line with the aforementioned trends, the cloud enables standardization and simplification of processes through expanded data management, allowing you to access decision information in real time. This is a priceless benefit for many businesses.

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Trend 4: Coverging D&A platforms

Data and analysis processes (D&A), including analytics, business intelligence and computer tools, are becoming less and less defined as stand-alone tools for companies. Instead, there is now a convergence of these, and data and analysis processes will increasingly take place on individual platforms that include a variety of functions spanning the data life cycle, from data registration and storage to analysis and AI and ML. This enables a more holistic view, with clearer links between data and analysis investments, practices, processes, and important business results. This can help to create greater competitive advantages in a constantly changing market.

To capitalize on these opportunities, businesses and finance departments must first manage their fragmented data and analytics networks. Although data and analysis have become a top priority for CFOs in the last five years, much of the increased investment in D&A has been fragmented, with the finance department employing incompatible tools and systems. This has resulted in silos, making it difficult to produce analyses that contribute to effective decision-making. You must expand analytical capabilities, roles, and processes, anticipate changes in both products and practices, and plan for platform convergence to ensure compatible analysis tools and appropriate management. Furthermore, you must facilitate collaboration across the organization’s data and analytics environments.

A dynamic financial system in the cloud

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