By Greg Silverman, May 15, 2020
In the past decade, businesses have gone from not having enough data to having too much. With more data than companies know what to do with, many have coined this influx as the saturation of information. It is the issue slowing down efficiency of organizations in the Big Data Era age, according to Forbes.
Large data sets, however, are not a burden to businesses despite what many people think. Rather, it’s an organization's infrastructure and modeling processes that are simply not equipped to handle the always evolving situations of the modern landscape that produce hoards of information. Instead of concluding that big data is not producing the desired results, many organizations need to shift their modeling tactics to manage more complex information.
A flexible approach to modeling large silos of information is the only way for businesses to stay competitive in the 21st century and beyond.
Data science as a practice emerged in the 1960’s with John Tukey’s novel filled with observations of the shifting nature of mathematics in business. With important technological developments like computers making their way into the mainstream, he acknowledged how the stored information and coding capabilities of a computer could merge with statistics to help solve complicated business problems.
As technology became more ubiquitous and its capabilities expanded, this way of thinking remained largely unchanged: data science was ideal for solving complicated problems. By creating constrained and stable models, businesses could solve problems such as “Is it more effective to market my product on TV or print?” With this straightforward approach to modeling, businesses analyze past trends to predict future behavior to dictate their investment decisions.
For decades, this modeling method has defined marketing and business decisions; however, it fails to acknowledge the flexible world we live in. Current tools don’t match the realities of the environment, and pre-existing complicated systems can’t meet the challenges of answering complex real-world problems. The perfect data set, even amongst loads of information is not a myth, it’s the stability of the competitive environment that’s the illusion that many organizations still stand behind.
Unlike complicated problems, complex questions have multiple moving parts and help businesses understand novel data, identify new trends, and model scenarios with the unexpected in mind. While complicated modeling answers straightforward questions, it fails to prepare businesses for unexpected outcomes, which are more common than ever on a global scale.
Historic data is extremely useful in predicting future behaviors when modeling correctly. However, with the use of complicated and constrained modeling systems, much of the underlying importance in the data mined by scientists goes to waste. Current tools may help businesses with their operations when market conditions are running smoothly, yet when unexpected events occur, are rendered insufficient in predicting behaviors.
Even changes on a specific topic, like a new emerging competitor or change in consumer attitude, perplex decision-makers unless their tools and process are flexible enough to handle these scenarios in near-time. Herein lies another problem with complicated modeling systems; they can take too long to produce useful results.
In a stable environment, spreadsheets and manual models would constantly hold true. While these complicated methods are helpful in sifting through large data sets to reach absolute truths, they typically take too long to develop. By the time these models are prepared, the reality is that variables and situations have changed, so the results are not representative of the current market.
Only an agile process that creates a flexible approach to modeling acknowledges that stability is a myth not only in the business world but also in life. In addition to historical data, assumptions formed with assistance from machine learning and artificial intelligence about the future append data to create a better hypothesis about the future. A complex model handles this combination of information and quickly tests a hypothesis with multiple parts to forecast accurate results about the market that are relevant to business decision-makers in the moment.
Gaining insight into the future of the market is, arguably, more valuable than continuing to pore over data from the past. When historical and transactional data are combined with educated assumptions, businesses can test complex hypotheses with a model equipped to handle multiple elements of the market for accurate predictions.
This is precisely what the Concentric model aims to achieve for every unique business. Market simulation is the next step in the progression of analytics. When statistics, spreadsheets, and even machine learning reach their limitations, simulation technology steps up to provide businesses with accurate results.
While statistical models evaluate past real-world situations and results, a rules-based probabilistic model leverages historical data to create a testable hypothesis. By running the simulation, businesses compare their forecast with actual results to determine if their model fits the future. Concentric delivers a platform that is a unified source of analytics for predictive modeling. It enables analytics teams to run multiple what-if scenarios in near-time for the entire company.
This flexible approach to modeling is as scientific as a statistical process, but it has the additional advantage of being validated by in-market results. Analytics teams have the power to identify if any new ideas or probabilities have emerged from their scenario and adjust company plans to meet business goals. They determine which rules have influence over the results and test additional interventions that forecast how hypothetical changes will perform.
Businesses are able to use their large stores of data for predictive modeling to help make investment decisions, prepare for different market scenarios and, per the Institute of Risk Management, mitigate risk that stems from the unintended consequences of purposeful action.
Begin gaining insight into the future and putting your large data sets to use with Concentric today. Our market simulation platform helps your business with flexible modeling, so you continue looking ahead instead of being slowed down by the information from the past. Contact our team to find out more.