Hugh Simpson, Global Lead – Data&Analytics, A.I.&Industry 4.0 at Ciklum. Hugh is assisting the clients to become data-driven organisations. Previously he worked for EY consulting clients on IoT and Data Analytics.
Comprehensive analytics is one of the main cornerstones of modern business. Since the advent of powerful computers in the late 1960s, analytics tools have been able to process large volumes of information in order to extract valuable insights.
As technology has continued to evolve, in part through more sophisticated software, blazingly fast hardware and greater access to larger amounts of data, analytics capabilities have grown into essential tools for businesses of all sizes. Data science has become a critically important specialty in and of itself, harnessing the minds of statisticians, librarians, and other computer scientists in order to mine information for better insights, trends, and other conclusions only made possible through a modern analytics solution.
Because analytics thrives on large amounts of quality data, the realm of financial analytics seems ripe with opportunity. Taking full advantage of a business’s financial data, financial analytics makes it possible to take in-depth measurements of an organisation’s financial status, conduct accurate planning and forecasting, and ultimately shape a company’s long-term goals.
Sales, profitability, and cash flow data fed into a financial analytics solution can all contribute to a clearer picture of where opportunity lies, allowing organisations to better align themselves with healthy and forward-thinking businesses practices.
However, as with all analytics tools, financial analytics solutions are often only as useful as the data they’re able to access. Without quality data, key insights can be riddled with inaccuracies and inconsistencies, delivering information that’s neither actionable nor useful. Even when data has been properly cared for, companies still need access to top talent and tools in order to extract the true amount of value from a financial analytics solution.
How do organisations use financial analytics?
Leading organisations use financial analytics to produce forward-looking strategic insights. By properly analysing large amounts of data, organisations using financial analytics can turn standard financial statements and reports into powerful tools that help build a better business.
Organisations may choose to use any and all of the different types of financial analytics tools, depending on their particular business need. Considering there is no one-size-fits-all solution that applies to any business, companies should determine which type of financial analytics solution makes the most sense before devoting significant time and resources to one.
What are some common problems organisations run into while using financial analytics?
All analytics tools require access to data, which may not be as simple as it seems at first glance. Random, inconsistent, or irrelevant data can lead analytics applications to deliver false conclusions. By relying on data sets that haven’t been prepared for use in financial analytics, businesses don’t just risk wasting time and money — they can potentially act upon bad information that can cause serious harm.
Data cleansing is an essential task that makes relevant data sets useful for analysis. Developing a comprehensive strategy to clean data, establish standards for how data is initially captured, and validate data to make sure it meets any necessary requirements helps ensure that financial analytics tools have access to accurate, relevant information.
That is, of course, if organisations are able to hire data scientists to cleanse data sets in the first place. Because of the sheer demand for data science skills, all industries seeking data scientists and other data professionals have run into a significant talent gap. Without access to leading data professionals, organisations seeking to boost their financial analytics operations may not be well enough equipped to ensure that analytics data meets a high standard.
Organisations also must be equipped with the proper tools to make the most out of financial analytics data. Relying simply on the standard edition of Microsoft Excel, for instance, can leave organisations without access to more comprehensive software tools for business intelligence or data visualisation. Even with a team of top professionals ensuring data sets are clean and accurate, companies seeking to maximise the most value out of financial analytics should have access to more powerful analytics tools.
How to forge a path to better financial analytics
In order to develop a comprehensive financial analytics strategy, here are some important considerations:
- Identify the right type of financial analytics software. Take time to determine whether a descriptive analytics, predictive analytics, or prescriptive analytics make the most sense — or if a combination of all three would deliver the right types of insights.
- Hire top data talent. Even though competition for top data professionals is fierce, it’s essential to have the right team members in place in order to make sense out of large data sets. Organisations that are serious about financial analytics deserve serious team members.
- Practice good data hygiene. No matter the source, data must be properly attended to before being fed into a financial analytics application. Follow good data housekeeping practices and carefully examine data ahead of its use to ensure its efficacy. As the old data maxim goes, “Garbage in, garbage out.”
- Use proper tools. It might be necessary to go above and beyond the capabilities of an application like Microsoft Excel in order to maximise financial analytics capabilities. Whether it’s through Excel plugins or standalone apps for business intelligence, giving employees access to the best tools available can help extract the most valuable insights.
Businesses seeking to implement financial analytics tools or improve existing solutions should take time to develop the right strategy for success. Without considering how data will be utilised, the manner in which it will be used, and who will be tasked with ensuring its success, organisations won’t be well-equipped to boost financial analytics performance.