The volume of data from all business units is growing significantly, requiring more reconciliation and consolidation than ever before. If tackling volume wasn’t enough, this data is often stored in a variety of locations and in multiple formats, resulting in precious time wasted scouring a network of systems for the most up-to-date file. 

What organisations urgently need to do is find a way to enhance efficiency and free-up time for value-adding projects. But how? It boils down to transforming data and analytics processes in three key challenge areas.

Streamline Regular Reporting, Save Hours of Time

Monthly, quarterly or annual reporting are key components of the job. But when data sources are owned by different teams, the process of analysis and synthesis becomes increasingly complicated. This results in manual sourcing, refining, and verifying of data until you’ve compiled it all to the standard needed for including in reports to stakeholders. 

By employing a centralised analytics platform, teams can gain the freedom to experiment with figures in a verifiable environment where the base data is both consistent and accessible across teams. With teams working off the same sets of data, you can shift project parameters as and when needed and build on existing data without any excessive rework. With this data foundation cemented, there is the potential to introduce AI into the process with real-time predictive forecasting, enabling you to automatically generate your monthly forecasting budget and fire this over to your team for review. 

Handle Ad Hoc Requests With Ease

There’s no shying away from the fact that ad hoc requests eat into valuable time and disrupt your team’s work. When juggling these ad hoc requests with monthly reporting, there is finite time for achieving deeper data analysis, experimenting with new ways of working or uncovering new trends. 

So, when assessing how you approach ad hoc requests, it’s worth asking the following questions. Can you respond to sudden requests from executives on the same day? How quickly can you access source data for financial analysis? How much time does your team spend in getting to step one of the ad hoc request?

Again, fragmented systems and methodologies inhibit finance teams being able to deal with these requests swiftly and effectively. What you need is a unified hub of data that brings together all of the various data sources and systems into one platform. If different departments can access and also contribute to analytics projects within the platform, it allows for seamless collaboration and efficiency. 

This wide-scale connectivity can allow you to intersect your financial ledger datasets with a variety of historical trends and analytics, for instance, or coordinate with a range of individuals in the business who can validate these figures. This allows you to deliver richer and more meaningful insights quicker and, crucially, with less impact on your team’s capacity. 

Reduce Operational Risk

With masses of financial data come financial regulations and the potential of auditing. If you’re only spotting and rectifying any mistakes at the last minute, this could be down to a weak internal control environment. These mistakes could arise from collating data from several resources for an audit and building summaries for regulatory analysis off this. 

If your internal environment is still constricted to a system of manual spreadsheet data entry and notes, it can be susceptible to errors, a lack of consistency and cloudy traceability, significantly affecting your ability to accrue hard evidence for your reports. 

Again, with a centralised AI-powered analytics platform, this operational risk can be reduced. One of the tool’s major benefits is that it offers automatic, inherent data workflow traceability. This means you can see what is being done when, in what order and where inputs and outputs fit in the overall data flow. 

Transforming Analytics

The nature of finance means that data has always been the bedrock for finance teams. But taking the step to transform to more agile, responsive and robust analytics can enable you to deliver accurate and valuable business insights quicker. By working off the same central analytics hub, finance teams can save masses of time while gaining the freedom to adjust data as needed, respond rapidly to ad hoc requests and reduce the audit risks that come with mistakes.