Are we ignoring data quality to match the speed of decision making ?

What are the critical challenges a researcher faces in a world where too much information has to be processed at a lightning speed ?

Seth Grimes : The key research challenge remains what it has always been : To determine which insights will move the needle , that will make a difference , and then to design a study that will produce them . Sure , you can take a different approach – “ I have access to all this data . What does it say ?” – if you can afford to waste time and effort . Social media , in particular , is full of sound and fury , for research purposes signifying next-to nothing . So The critical research challenge is rational study design . Data and analysis choices follow , and those choices made , there ’ s loads of technology to help you do the job .

Is the speed of data analysis matching with the supersonic speed at which decisions have to be made at supersonic speed . Take some time . Study . Think . Model . If you know you ’ ll have to react fast to emerging conditions , prepare . Model for quick reaction , covering foreseeable eventualities . Have a plan . Map decision making to a decision tree . But when something truly unexpected happens – as something unexpected inevitably will – rely on your judgment .

Are you satisfied with the existing research methodologies that are being used to analyze data ? Will you suggest a new radical approach or still go with the traditional methodologies ?

Seth Grimes : All methodologies can be improved . Improvement does mean innovation and not just made faster execution with more data . Innovation : That ’ s new data sources , new data types , and new algorithms that draw on the diversity of data available . Innovation isn ’ t electronically tracked and geolocated behaviors ; and all that newly-usable good stuff . Make innovation part of your research methodology .

What gaps do you see in the way we collect and analyze data ?

Seth Grimes : We love to talk about text analysis . We marvel at facial coding and emotion analytics . We complain about social-media listening that doesn ’ t extend far beyond counting . So there are gaps in the way we collect and analyze data . We don ’ t take advantage of the opportunities – the data and analytical methods – available to use now . The biggest gap is therefore between word and deed .

What steps will you suggest to ensure that analysis of data is standardize ? How much active involvement of clients is required to ensure optimum quality ?

are to be made ? If not then what remedial actions needs to be taken to avoid misinterpretation of data in a hurry to take a decision ?

Seth Grimes : It ’ s the rare management decision that must be made purely on the fly , or rather those decisions should be rare . That is , I question the statement that management for over 200 years , dating back to Gauss and the emergence of statistical methods . What ’ s radical is abruptly abandoning proven approaches for unproven one , a jump that ’ s sometimes justified but most often not . So innovate by extending research methodologies to encompass online and socialmedia activities ; text , speech , image , video , and sensor data ; analysis should be standardized ? To the contrary , there ’ s so much diverse data out there and so many powerful analytical methods that if you standardize your analyses , you ’ ll miss insight opportunities . Rather it ’ s a certain analysis philosophy that should be standard : That studies should be well-rooted in insights needs ,