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The Future of Data Analytics in Finance

The advancement of data analytics is changing the corporate landscape. This change has made its way into every department of a company, including finance.

Today, analytics can help transform products and services to better serve customers and enable employees towards more purposeful work. Analytics can also enable organisations to anticipate future trends and behaviour, enabling CFOs to streamline business functions according to demand and allocate resources more efficiently.

With a highly competitive environment, finance is expected to provide more than just accurate financial statements and reports. They need forward-looking, predictive insights that can help define the business strategy of the future and improve day to day decision making. In short, finance needs to be better at analytics.

Over the past few years, organisations have seen the value that finance can bring to many aspects of the business. This presents a huge opportunity but can also be a challenge. Even though finance has always been a critical department in any business, it can create an impact in current times like it never could. But to be able to do this, finance teams need to understand the need and proper usage of analytics in various organisational processes.

Finance needs to prepare, model, and interrupt data beyond their traditional financial data sources to provide insights that support decisions and can positively impact the organisation. Advanced analytics can help CFOs with tools to declutter the barrage of data that they have within and around them. Combining internal financial information and operational data with information from external data sources can enable businesses to address critical questions with ease, speed and accuracy.

Where does finance stand currently in terms of analytics?

With the ever-changing landscape in the business environment, data and analytics seems like a promising solution to meet the challenges of digital transformation. According to a recent survey by Harvard Business Review Analytic Services, 91 percent of respondents said that effective data and analytics strategies will be essential for successful business transformation initiatives in the next two years. 87 percent of respondents in the same study said that analytics proficiency will be a key competitive differentiator in their industry in the next two years.

These numbers indicate that leaders understand the importance of analytics and their role in achieving desired business outcomes. However, very few organisations are ready to implement analytics in their business. Most of them do not have the technology, processes or expertise in place to become data-driven.

The understanding of analytics and its successful application to achieve significant business results requires a strategic approach. To build the right analytical strategy, organisations must first understand what are the specific challenges that they face and how analytics can help them address those challenges. However, most organisations are far from having this kind of an approach to adopt analytics.

Current Challenges to Adopting Analytics

Here’s a look at the challenges that are preventing finance departments from becoming data-driven.

1. Analytics being used for basic processes

Most organisations are using analytics in some form or the other. But most of these uses are for basic processes like producing cyclical reports, providing basic budgets, forecasts etc. These make basic operations easier but do not lead to any valuable insight or pave the way for more agility or strategic direction. As a result, organisations fail to build a competitive advantage for themselves through their use of their data and analytics.

Most organisations also fail to utilise their data to derive insights that are rich and can enable to improve business operations and processes. Organisations lack the understanding of analytics and visualisation techniques that can help them enrich the effectiveness of their data strategies.

Businesses processes in finance such as Quote to Cash (Q2C), Procure to Pay (P2P), Record to Report (R2R) and Budgeting, Planning and Forecasting (BPF) have a lot of data. These processes are still viewed as purely financial, and their data is not analysed in real-time. This data can help in identifying insights into customer behaviour as well as supplier data.

However, most of this data is ignored, missing opportunities to predict which customers are in danger of going to competitors and if suppliers will be late or more costly.

2. Lack of understanding of data

When it comes to effectively utilising analytics, it’s imperative for organisations to understand their data, how to prepare it and most importantly, how to use the data to generate meaningful, real-time and actionable insights. Organisations have an enormous base of data which is extracted from multiple systems like HRMS, Payroll, Accounts, eCommerce platforms etc. Each of these are handled by different teams and mostly function in silos. While having data is not a problem, accessing and interrupting this vast amount of data in a way that enables growth is the main challenge.

Organisations are either overloaded with data or constrained by access to data. There are very few organisations that actively manage data as a corporate asset. For organisations that do understand the importance of incorporating data analytics, they might face a difficulty in acquiring talent with the required training or skills to make the right use of the data available with them. Without the right understanding in place to utilise data, even the best of investment in the latest analytical tools and resources may end up being underutilised or not used at all.

3. Lack of a data strategy

The amount of data is increasing exponentially every day. Organisations that combine human judgement with systems that put data and insights at their fingertips are in the strongest positions to compete successfully in the marketplace. For this, they need a strategy that combines advanced analytic tools and legacy technologies they already use.

Analytical success requires the use of the latest technologies, such as business intelligence tools, artificial intelligence (AI) and machine learning. However, just investing in these technologies isn’t enough. Having the right strategy to use this data in the most optimum way and making it available to all levels of decision-makers is what makes the main difference.

There is also a need to leverage traditional technologies such as data warehouses to bring together data from different formats, sources and applications. This can help derive insights that are richer with self-service capabilities from a single source. Achieving this requires an integrated approach combining the need for improved data governance, development of new skills, cultural change and shift to organisational management mindsets.

4. The rigid structure of legacy systems

The biggest barriers aren’t related to technology, but around the transformation, from a traditional legacy business to a data-driven organisation. Surveys suggest that one of the biggest causes of the failure to adopt a data analytics strategy is the dependence on siloed systems or analytics.

For instance, data silos and management of data coming from multiple disparate systems are still prevalent. This limits organisations from being more agile in terms of their data strategies. Siloed data across disparate systems creates confusion and locks information inside specific departments. This automatically blocks the efforts towards developing deeper and more insightful analyses to generate better value-creating opportunities for businesses.

Disparate systems also lead to a lack of collaboration between different departments. A key feature of being a data-driven organisation is effective cross-functional collaboration within as well as outside the organisation. Business and IT staff need to work together and enable better interactions between technology and employees. Only an agile and collaborative environment can ensure that new digital tools support business goals.

The way forward

With so many challenges, it will take a while for organisations to make a serious move towards adopting a data-driven culture. The good news is that most organisations plan to launch or expand company-wide strategies for making data & analytics an integral part of their decision-making processes.

The first step to making this a reality would be for finance leaders to identify their specific business goals and then define what data will be needed to support those efforts. Once these goals are clear, the next step would be to establish a solid data platform for a particular line of business and build it out with time. Instead of launching across the entire spectrum, start with a pilot by identifying areas that could generate a high amount of value with minimum effort and investment.

Focusing on critical business areas and strategic challenges can be a starting point. And the lessons can be learned and applied to other areas over time. As you gain experience with analytics, continually look for ways to leverage it more effectively and strategically. Monitor results and continually adjust the questions, data, analyses, and tools to increase their relevance and impact. It is a process that would require an investment of time and resources, but the effort will be worth it and eventually enable your organisation to grab that competitive edge for the future.

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