New technology enables the office of finance to move beyond ROI models and spreadsheets to alter the fabric of what “strategic” means for their company. Consider that AI and machine learning have opened up entire new worlds of data analysis previously inaccessible. This data can be used not just for traditional applications like risk management and profit optimization, but also in forecasting consumer trends and developing new products—both things which once fell into the domain of strategy development.
By analyzing data, companies can identify opportunities for growth and improvement and make decisions that are better aligned with their goals and objectives. By basing decisions on hard data, rather than subjective opinions or assumptions, the Office of Finance can reduce the likelihood of making mistakes or making decisions that are not grounded in reality.
Here are 5 ways a company can effectively leverage data to take it to the next level:
- Addressing the current topic of black-swan events.
Events like the Covid-19 pandemic, war in Ukraine, geo-political tensions, force companies to rethink the probable vs. possible equation. In stable environments we plan for probable outcomes. We proactively prepare what is likely to happen. And that makes sense. But over-reliance on the probable, leaves us exposed should improbable events come to pass. To ensure robustness, we need to produce scenarios based on “possible” not just “probable” events. Then we would have multiple scenarios covering a wide range of material factors, readily adaptable to current circumstances at a moment’s notice. The next best thing to being proactive is to be immediately reactive. And this means building planning contingencies based on what the data tells us. Imagine a comprehensive algorithm calculating raw materials, work-in-process, and finished goods inventory reorder points as a function of shifting supply and demand, capacity and production processes. This ambitious calculation would balance intra-data dependencies (e.g. innovation driving efficiency), competing KPIs (e.g. enough inventory to support production, but low enough to reduce over-stock investment and carrying costs), supply chain redundancies, and consumer demand. We have the data for all of this to happen.
- Using predictive analytics and forecasting in business planning.
Let’s follow up with a major specialty paint producer for high-tonnage trans-oceanic ships. They need to forecast future contracts not yet on the books, meaning they have to plan paint production (measured in tons not gallons), ensure prompt delivery to one of the worlds few dry-docks facilities capable of lifting such a ship completely out of the water, and then co-ordinate the painting process to reduce docking fees and ensure the ship is quickly returned to service. They need to anticipate ordering manufacturing raw materials, production processes and capacity planning, transportation logistics, and paint application processes all while accounting for weather, disruptions, and fall back positions at every stage. Thankfully, they can rely on vast historical data that accurately afford 70-80% predictable accuracy, and scenario analysis to cover uncertainties inherent to the last 30% reactive processes outside of their control. They are essentially herding data into a corridor of viability that will minimize chances of mis-planning.
- Switching gears to qualitative assessments. Generally we are conditioned to err on the side of quantitative analysis, but qualitative considerations are just as important.
The tricky part is how to leverage non-number data to improve your decision making process. This has always been an important obstacle in pushing Balance Scorecard initiatives or as an example, fully accounting for exit interviews findings to improve employee retention. Thankfully technology is giving us the means to fully combine number data and qualitative data into value equations. Inulta has made very efficient use of the AIH (Analytic Information Hub) platform from Wolters Kluwer CCH-Tagetik, to unify million data points at the lowest granularity point into an easily mined, actionable, real-time accessible tool to take into account both data and qualitative aspects to ensure a higher level of relevancy. We can now mathematically weigh-in employee feedback, competitive market analysis reports, or consumer mood indices in a measurable way. As an example, imagine the use an investment company can make of this predictive power in producing Investment Status reports that capture not only data, but analysts’ perspectives into ensuring a completely balanced and accurate view-point.
- Building on unifying number and non-number data, we segue into the concept of “impact accounting”, an example of which is ESG reporting.
This is nothing else than measuring the full weight of company decisions on all societal aspects. This is not as simple as affixing a “Company Good” or “Company Bad” label, but rather assigning monetary values on the impact internal asset allocation and governance decisions have on stakeholders, society and environment. This is an extremely complex (and fluid) reporting requirement that goes much deeper than the three pillars of governance, environment and society. It is also rendered more difficult by the need to balance often competitive goals but also uncover opportunities for alignment. Ethical business practices (governance) can lead to supply chain labor standards (social) and raw material sourcing (environment). But before a company can improve its standing it must measure its current position, then devise feasible plans of improvement without affecting stakeholder value and maintaining the company’s business viability. This is all data driven. Gut decisions, even if well intended, run the risk of producing unintended consequences, often negating the initial intent. Understanding data means understanding the cause and effect interplay.
- Finally, let’s round up this top five by looking at planning in general. So what is planning?
Well, very simply put, it is the process in which smart people set some targets, decide how to allocate internal resources (cash, human capital, production capacity, market conditions, etc) to maximize and optimize the chance of reaching aforementioned targets. But the reality is more complicated than that. Say, you reached your targets. Is that good? Or maybe targets were set below attainable levels. Say you did not reach the targets. What incremental changes must one make to ensure success in the next round. And what if next year, the CEO vision changes and new KPIs are prioritized? The answer to all of these questions and pitfalls is data. Inulta has long used predictive analytics, AI algorithms and specific ML simulations to extract planning “intelligence” from the AIH (Analytic Information Hub) data set. This not only maps data dependencies and contingencies, but provides a pre-populated, unbiased, starting point to act as a foundation for all analysis to follow.
It’s no longer sufficient to be a good financial mind.
You need to be able to analyze your company’s data holistically and come up with innovative strategies based on data analysis.
Technology enables the CFO to set attainable targets, immune to human bias and error by drawing data from multiple sources, identifying patterns, and providing valuable insights to decision-makers.
The right CPM platform can help you process data faster, analyze granular information, and accelerate report production. You can now plan, budget, and analyze data across unlimited dimensions and at any level of detail — without depending on IT:
- Transform granular data into actionable insights
- Drill-down to details for a full organizational view
- Connect finance and business with trusted data source
- A basis for machine learning and predictive analytics
- Finance-owned, reducing dependency on IT