Monetizing Data: 4 Datasets You Need for More Reliable Forecasting

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In the era of big data, the focus has long been on data collection and organization. But despite having access to more data than ever before, companies today are reporting a low return on their investment in analytics. Something’s not working. Today, business leaders are caught up in concerns that they don’t have enough data, it’s not accessible or it simply isn’t good enough. Instead of focusing on making data sources bigger or better, companies should be thinking about how they can get more out of the data they already have.

Contrary to popular belief, a high volume of perfect data isn’t necessary to drive strategic insight and action. While that might have been the case with time-series analysis, forecasting using simulation allows companies to do more with less. With simulation software, you aren’t constrained by the hard data points you have for every input; it allows you to enter both qualitative and quantitative information, so you can use human intelligence to make estimates that are later validated for accuracy with observable outcomes. Companies can then use these simulations to test how the market will respond to strategic initiatives by quickly running scenarios before launch. Also, most businesses already have enough collective intelligence within their organization to create a reliable, predictive simulation.

By unifying analytics, building forecasts and accelerating analytic processes, simulation helps companies build a holistic picture of their business to optimize strategy and maximize revenue. Here are the four types of information that companies need to fuel simulation forecasting and monetize their data investments:

1. Sales Data: Define success

The first set of information needed for simulation forecasting is sales data. In building a simulation model, sales data is used to define the market by establishing the outcome you’re trying to influence. That said, simulations can forecast more than sales outcomes in terms of revenue – they can also simulate a variety of other outcomes tied to sales such as new subscribers, website visits, online application submissions or program enrollments. Whatever the outcome is that you’re measuring, it’s helpful to have the information broken out by segment. If you don’t have this level of detail to start, you can continue to integrate new data into the model to make it more comprehensive over time.

2. Competitive Data: Paint a full picture of your market

With simulation forecasting, you are recreating an entire market so you can test how your solution will play out amongst competitors. In order to understand how people within a certain category respond to all of the choices available to them, you will need sales and marketing information for your competition. Competitor data is usually accessible from syndicated sources. If you don’t have access to competitor data, you can use approximate information available from public sources, annual reports or analyses from business experts to build out the competitive market in your simulation.

3. Customer Data: Understand how your consumer thinks

The third area of information needed for simulation is customer intelligence. In order to predict the likelihood a consumer will choose one option instead of another, you need to understand how they think. This requires information around awareness, perceptions and the relative importance of different attributes in driving a decision. These datasets are often collected and available through surveys. But even if there isn’t data from a quantitative study, your brand experts can use their judgment to make initial estimates of these values, and the values will later be verified through calibration and forecasting of observed metrics like sales.

4. Marketing Data: Evaluate the impact of in-market strategies

Finally, to drive simulation forecasting, companies need data on past marketing activity. This information is essential to understand how messaging in the market has influenced consumer decision making. This can be as simple as marketing investments and impressions broken out by paid, owned and earned activity, or it can be as granular as the tactics and specific media channels within each area.

Once a company identifies sources for these four types of data, it’s time to find an effective way to monetize it. The best way to get value from your big data is to identify unanswered business questions. With simulation forecasting, reliable answers are accessible – and you may need less data than you think to get meaningful, trustworthy insight.

About the Author

John Pasinski is VP of Analytics at Concentric, where he leads analytics initiatives for the company and its customers, including Microsoft, Toyota, Scripps and Whirlpool. Prior to Concentric, John developed predictive models on the risk management team at Bank of America. With a passion for problem solving, John’s always thinking about how simulation software can power better business decisions by equipping companies with quick, trustworthy answers. He holds a bachelor’s and master’s degree in biomedical engineering from Washington University in St. Louis.


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  1. There are many challenges regarding simulation as you describe, as I’m sure you are aware. Even in possession of perfect data, the cause and effect relationships between the actions our business takes and the actions competitors take create a dynamic that we must account for in some way. One approach is to use methods such as game theory to model the competitor response. Also, there are market dynamics which can be modeled by constructs such as Michael Porters five (or six) forces model. There are also lags and reservoirs in the system which can give rise to complex time-based dynamic behaviors which can only be modeled by dynamic simulators or modeled as sub-systems which dominate the dynamics. An example is on the supply chain side, there is a known dynamic sometimes called the bullwhip effect, which amplifies instabilities down through the supply chain which are inputs into demand planning.

    Your definition of four data types is very accurate. I tend to add market data (somewhat equivalent to summing competitor data), and economic data relevant to the markets in which you are engaged. There are vast supplies of public data available which can add great values to simulations.