In corporate management we use buzzwords like operational, tactical, strategic used to describe the decisions made. While operational decision-making is made on a daily basis, the strategic decisions are made over a longer period of time by the very top management. In our practice, we don’t like using these scientifically defined categories. We divide decisions into three groups as well, but not according to the management theory. Why? Because we know it usually gets difficult categorizing every decision according to lengthy and hard to digest management definitions and thus we rather let the company categorize the decision based on their specifics.
We describe decisions using simple labels „small“, „medium“ and „critical“. Our criteria of division have to do with who makes the decision, how costly and influential the decisions is and how long the decision-process lasts – as you see, simple criteria useful for any company on the market. Even though it very well overlaps with the management division, we feel it helps to look at it from a more practical side.
For each category, there is a specific approach to efficiently use data and make a data-driven decision. Now we’ll go through the categories and give our recommendations regarding data sources and analytical approaches.
1. Small decisions
While in management theory usually described as operational, small decisions tend to be done on a daily basis by the lower management. They usually don’t cost much and don’t affect a major part of the company. In corporates even some small decisions have to be validated by the superiors, in small companies and startups even bigger decisions don’t have to be validated by superiors. But most typically, small decisions are made quickly by the lower management.
To do efficient data-driven small decisions, it’s best to use the sources of continually processed data. Such sources may be found in dashboards, BI platforms, sometimes in your ERP and CRM systems or may be provided on a regular basis as reports, visualisations and so on. As small decisions aren’t influential enough, it wouldn’t be efficient to spend more time and money on more elaborate data background for each decision.
2. Medium decisions
Medium decisions are usually made by middle management according to the company strategy and consulted upon with the lower management as well. Such decisions usually take a little longer than small decisions to be made. The most important characteristic of medium decisions is their scope – they either get expensive, or either potentially or actually affect majority of the company.
To make a data-driven decision, it is vital to do a proper analysis before you get to the final stage of actually making the decision. As they get much more influential than small decisions, you should base your decision on deeper analysis, usually on statistical predictions and analysis, math modeling, AI and ML or any management consulting recommendations.
Such methods fall into data science as well, but definitely not all data analysts and data scientists utilize these methods. They require a lot of education in mathematics, statistics and business and also enough experience.
Data analysis on these decisions shouldn’t rely on descriptive statistics only, but should utilize different methods, compare them on the datasets, pick the most efficient one and deliver the final decision. However experienced statistician, he won’t get more information from the data than what already is in it.
In-depth analysis to make efficient decisions, save money on costs and also mitigate risks is usually a service the data science specialists and management consultants offer to their customers. If you need to back up your decisions with proper analysis, don’t hesitate to contact us and request a quote.
3. Critical decisions
Sometimes, more often than not actually, you company needs to make a big decision that both costs a lots of money (in investments, human capital allocation or a huge portion of risk) and has the potential to influence the vast majority of your company.
Those critical big decisions are usually made by the very top management together with the company’s shareholders, but require a lot of inputs from people throughout the company, so that the decision is data-driven and backed up with a well-crafted analysis.
At this level of decision making, you definitely need a proper analysis of data, consisting of deeper statistical analysis, predictions, math modeling, AI or ML and so on. But apart from that, you need to think on a business level as well. Even though the data analysis might recommend to make a move, it might not be a wise decision from other perspectives. Don’t forget it’s the long term, strategic level of thinking at this point.
A general rule of thumb here is the bigger the decision to be made, the more inputs you should take into your consideration process. When making small decisions, you might pretty much rely on your strategy and the data available. On the critical level, you should take the analysis as a recommendation, but never rely only on such analysis.
Do you need a well-crafted analysis for you decision-making process? Don’t hesitate to request a quote. We’ll offer you the high standard services done by our awesome statisticians.