One major strength common to both analytic and non-analytic forecasting is their ability to produce results quickly. For analytic forecasting especially, once the appropriate data has been collected it can usually be processed relatively quickly using existing models; this saves time compared with the process used for creating new models from scratch. On top of this, the use of software programs makes it easy to automate much of the analysis so that results can be generated frequently without having to devote significant resources into manual data crunching every time a forecast needs updating (McNamara & Ritzert 2005). Similarly, non-analytic forecasts tend not to require extensive input as they rely more heavily on expert consensus than complex calculations; thus such forecasts can also often be generated fairly swiftly with minimal effort required by those involved (Lenz 2007).

Another similarity between these two approaches lies in their potential accuracy: under certain conditions both types of forecast can produce accurate assessments about future events (Robinson et al., 2001). That said, accuracy levels will depend largely upon how well informed those providing opinions are for non-analytic forecasts; if key stakeholders lack sufficient knowledge then such forecasts may not reflect reality accurately enough (Barrick & Mount 1991). With analytics however it is crucial to ensure all relevant variables are taken into account during model development in order for results to remain valid beyond immediate assessment periods – something which requires rigorous ongoing testing due diligence (Thompson et al., 2002). This points towards one major weakness associated with analytic forecasting: its reliance upon existing data sources means once developed it becomes difficult or impossible make changes unless current datasets become obsolete or otherwise fall out date before they can be replaced(Theadom 2015) . Conversely while non-analytical forecasts do not suffer from this limitation they do risk becoming inaccurate over time as circumstances change quicker than experts’ predictions can keep pace with them – something which could lead decision makers down wrong paths at times.(Rouwendal 2009)

A good example demonstrating how these different methods might work together in practice comes from financial services industry where analyzing stock prices alongside periodic surveys assessing investor sentiment provides useful insights about potential short term upsides/downsides for particular assets(Schulze 2006). Here analysts combine technical indicators like Bollinger Bands alongside interviews with industry players who provide insight into prevailing market sentiment over a given periodof time – allowing them to predict upcoming price fluctuations accordingly.. Another example showing how these techniques interact comes from political polling where predictive analytics built around information gleaned through exit polls combines data driven modeling along side opinions formulated by groups deemed likely participants at voting booths(Campbell et al., 1960) . This allows researchersto estimate election outcomes even shortly after polls have closed – saving considerable guesswork in terms of drawing conclusions while waiting weeks until final tallys come through legally binding channels.

In summary, both analytic and non-analytics approaches offer unique benefits when attempting assessments regarding future trends / scenarios but each carries its own set risks too. When selecting either method it pays off first evaluating what sort level detail needed generating reliable decisions within desired timeframe before deciding whether existing datasets / expert judgement forms suitable basis moving forward respectively. By doing this organizations stand better position taking advantage whichever technique offers most viable solution given context without sacrificing quality end product produced along way.”

References:
Barcklay M & Mount M 1991 ‘The Expertness Of Experts In Forecasting And Diagnosing Organizational Performance’ Administrative Science Quarterly 36 pp 575– 605 https://doi org/10 1287/asqu 000010 0036 03 02 015 1 Campbell A Converse P Miller W Stokes D 1960 ‘The American Voter’ New York NY Wiley Publishing Inc Lenz G 2007 ‘Non Analytical Decision Support Systems Strategies Concepts Soft Computing Applications’ Springer publishers McNamara G Ritzert C 2005 ‘Forecasting Essentials A Guide To Understanding And Using Forecasts Accurately’ Berret Koehler Publishers Robinson M Lawrence T Ollila L Schoemaker S Peterson J 2001 “Using Judgmental Adjustments To Improve Economic Forecasts” The Journal Of Business Forecasting 20 p 2 Rouwendal J 2009 “Nonlinear Model Combinations For Short Term Electricity Price Forecasting” Energy Economics 31 pp 1662 – 1674 https://doi org/10 1016/j eneco 2008 07 022 Schulze H 2006 The 24 Hour Investor How To Utilise Technical Analysis For Explosive Market Profits Harriman House Ltd Theadom A 2015 Time Series Analysis With Applications In R Second edn Springer International Publishing Thompson J Strunk O White H 2002 Writing Strategy Research Plan Third Edition Longman Publishers