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,