Traditionally, when data sources were small, the market research firms had various processes. Reports were generated by the manual entry of data into systems, and later by standard analyses, the results were visualized. But as the data volume increased online and offline (due to online resources), techniques have changed. In short, the processing and management of massive data are just like operating another market research company, forget to derive analyzes from it.
The way we think that the market research companies in a data-intensive setting can evolve ahead of the curve and stay away from the noise? Before this, let’s try to see why this data is needed first.
1. Brand monitoring
Data feed businesses. The larger the amount of data they have, the bigger their sample is, at certain points, to draw conclusions about products. For a certain collection of keywords, the data is gathered from all possible forums that discuss every brand they want to monitor. For instance, we had a client on brand monitoring who just wanted to hear people talking about manufacturers of sports equipment, for “good” keywords “poor” and “terrible”. Such findings are then made available to the public or the manufacturer to determine and respond, somewhat similar to the stock market scenario.
2. Sentiment analysis
This is possibly the market research parent on which it idolizes. The mode is to pursue positive/negative/neutral, and social media is the usual way to go.
3. Business dynamics review
That’s another famous suspect. It repeatedly declares trends like this if the industry moves towards big data for huge benefits. It’s just pure hype or if Samsung is the winner of the mobile race or iPhone. This includes again gathering facts and figures from multiple sources and expert surveys. For media and those who are not yet in, such analysis is instrumental and insightful.
Now that we know what the data around us can do for us, it is crucial to understand what lies behind these analyses. Almost all of these companies streamline their data collection process via outsourcing from the (DaaS) data as a service companies, who has nearly a real-time flair for extracting data from the deep web. Others try their own hands on it by using a data team, but at some point, turn back to the previous model. Let us discuss the harsh realities of using this model to help the research of your business.
Advantages of using (DaaS)
Coverage of data
This is particularly necessary because of the rivalry, but it is very challenging because of today’s content dynamism. Data as service providers have technical capability and expertise from their experience, so they have already exceeded this obstacle. Some market analysis firms have several data as a service vendor to ensure that nothing is amiss. Just remember it is resource-intensive to cope with evolving data when you plan to do this yourself; it is resource-intensive to collect changing data.
They do this same thing repeatedly for other businesses – as they already have developed a data as a service architecture and know-how to implement data as a service that caters to several other very similar firms, most of their efforts, as a rule, get very well balanced. While such an easier and cost-effective option is available, why reinvent the painful wheel.
Channeling other functions
Since data as a service (DaaS) providers always deal with different strategies and technologies, they can also provide much cleaner data just by adding some other components. Techniques like deduplication of data, close duplicate tracking and named entities are effectively performed on crawled data and supplied in an incredibly usable format. Hence, this process makes the life of the researcher easy.
Myth – 1: Reliability is a problem for external vendors.
The reality- This problem is true when selecting another vendor for any other task. Because most analysis is focused on the data’s backbone, an external agency may often be daunting. But with so much data flying around these days, you can find peace much better by not trying to do this yourself.
Myth – 2: after making a decision, I’ll be locked into the vendor and will have to use their services even if the quality does not satisfy me.
The reality- Yeah, this seems to be true for any other vendor you choose; therefore, proper evaluation of your vendor matter.
Myth – 3: Costs are extremely high. Particularly if I roll back from the seller as I lost time, spent money and went back to the square.
The reality- Actually, it’s the other way around. Most of the companies “as a service” are focused on a recurring cost model in which you pay as much as you do. And therefore, if research companies choose a vendor and want to stop its service after a few months, it is, in reality, the vendor that will be seriously troubled, considering all the initial investments they need to spend.
Myth – 4: You’ll give someone else the same data.
The reality- This is actually a misplaced concern that overtakes everything that has been addressed up to now. All the provided data is public, and when another company does not accept it from us, it may also be taken from some other vendor. Reserving exclusive data rights is also tricky since data as a service providers do not own it.
Finally, most market research companies have now been looking to robust (DaaS) data as a service provider list for all the reasons mentioned earlier, such as expanding data volumes, scalability challenges, technological limitations, and effort. The business results were highly praised due to the data quality and the simplicity of their platforms.