What Impact does Big Data have on Finance?
2.5 quintillion, or 2.5 billion trillion: this is the number of bytes of data produced each day on Earth according to IBM. An absolutely astronomical data production which took off in the early 2000s and which will be further strengthened by the development of the Internet of Things (IoT), including in the financial professions. Bigger, more complex, more diverse: data has become "big", and today allows new and even disruptive use in the service of businesses and their transformations. Big data, a new turning point in the history of data with a capital H, has already started to transform the finance professions.
From papyrus to big data: the formidable history of data in finance
From papyri used by the scribes of ancient Egypt more than 3,000 years ago, through Mesopotamian clay tablets, to the databases and computer software that we know today: financial data has always known how to marry, in each era, the format that suited it best. The Third Industrial Revolution at the start of the 21st century saw data gain in volume, complexity and diversity, and pass massively from paper to digital formats. Consequence for the finance professions: the opportunity, finally, to develop a better understanding of one's data, which is still too often located today in information systems that are not very communicative and stuck in their own logic. Old proprietary systems, ERPs, dedicated business or function software packages, decision-making tools, collaborative tools, etc., all these tools create detailed data, often under-used, and not always consistent with each other.
With the arrival of data scientists, the emergence of an "inverted" model
The 2000s witnessed an explosion in data storage capacity and computing power. In 2010, cell phones and game consoles had the capacity to perform one billion calculations per second. Today, the latest microprocessors are capable of performing ten trillion calculations per second. This revolution has effectively pushed back technical barriers and the ability to work with large datasets and has brought about a paradigm shift.
Until now, the data has been used by business experts, usually specialized, who draw conclusions by applying a set of rules developed and optimized over the years.
With Big Data, a reverse approach is developing, which applies advanced statistical and algorithmic tools on gigantic and above all multi-business databases, in order to identify interpretations and conclusions that until then remained under the radar of traditional approaches. These advanced techniques involve new players alongside financial experts: data scientists.
Far from being in opposition, this data approach is perfectly complementary and compatible with the "business expertise" approach. Its vocation is not to replace it, but to complete it, which will, of course, involve bringing together the cultures of “business expert” and “data scientist”.
Big data, a new Business Partner?
Optimizing, efficient and predictive: three words that can qualify the contribution of big data in the finance professions.
For highly operational finance professions, like other professions such as production or marketing, big data techniques can be directly exploited to improve the efficiency of certain activities and processes specific to a “Business Unit”. ". This will be the case, for example, for treasury, by having recourse to detailed “Order to Cash” and “Purchase to Pay” transactions to improve cash management, or for the accounting qualification of fixed assets in large multi-site organizations. Budgets and forecasts will also gain in finesse and accuracy by the combination of internal and external data analyses made possible by this new approach.
In its role as partner and operations controller, Big data also enables Finance to scan and identify areas for improvement and areas of potential risk.
Through its formidable analysis and forecasting capabilities, big data opens up countless opportunities for the finance professions: operational and functional optimization, investment decisions, optimization of working capital, support for sales teams, etc., and also detection of risky patterns in the detailed data of a BU, detailed analysis of the a posteriori profitability of "small" investments, etc.
A new eye, an additional string to its bow, Finance can and must use big data both for its own operational needs, and to inform medium-term strategic and short-term operational choices.