Blending Data Formats to Drive Decisions

It can be difficult to decide the correct approach to use when faced with a research issue. There are several choices to choose from, and researchers often resort to a standard survey, but what if this does not provide the best information? What would happen if multiple research methodologies were combined by researchers or if they decided to compare findings with historical data or other data sets that were not in the same format? If they used several sources, would they gain more information and be in a position to make more educated and therefore better decisions?

Market research should be at the heart of decision-making within the organization when confronted with a business issue, challenge or even when trying to try something different. Researchers need to develop the correct methodology and decide how to compare, validate or contrast findings in order to address research questions with validity and accuracy. Will a strictly quantum or qualitative focus offer the answers we need at one point in time, or should researchers look at a mixed approach at other points in time with both qualitative and quantitative methods and comparisons to other data sets? If more than one analysis technique is used, then more than one data set is sourced, interpreted, and analyzed, so how can all this information be handled and processed by researchers?

For the interpretation and eventual consequences of business decisions, the inclusion of these studies is important. Data blending will help to react rapidly to research questions, enabling business decisions to be made more quickly and effectively. So, what is data mixing and how can data formats be blended? What can researchers do to make blending simpler during study, and how do we ensure that the findings are then well known to guide certain decisions within the company?

Blending During Analysis

Before merging data formats, the extraction of the relevant data from different sources, after being obtained from many methodologies, plays an important role. It requires someone with more technical expertise or advanced software to extract the main information and analyze more complicated data, big data, or data from different sources, although many people may perform basic data analysis.

Data blending is a method in which data from various sources is combined into a single data set and, luckily, several different methods are now available to facilitate the researcher 's data blending of big data, significantly reducing analytical time. It is still necessary, however, to ensure that the person handling the data is confident about the various types and file formats and is able to clean the data and then consolidate it into a single form. Even if, for example, automated techniques for text analytics, speech analytics and video / image analytics are used, it is still crucial that the person programming the results into the automated systems feels secure with the methodology set-up so that the results can be trusted.

Blending Data Formats

Non-automated analysis is time consuming, but when comparing various methodologies and attempting to combine smaller quantities of data, it can often be more effective. The automated approach to data blending for big data sets, however, is increasingly becoming the preferred choice. Remember, however, automation can make it possible to discover general patterns, but might miss the nugget of data that can provide useful insight. Therefore, when combining data types, human interpretation is not neglected and, of course, in order to ensure reliability, advanced knowledge of statistical analysis methods and analytical tools will also remain essential for interpreting the data and cross-referencing the data. Rather than replacing other analytical approaches, the use of data blending tools can complement analysis.

As a corporation, deciding the most suitable data warehousing system should be the first decision. A central repository of consolidated data from one or more separate sources is basically data warehousing. In 2012, the term "Logical Data Warehouse" was introduced by Gartner Research, the concept being that you did not have to have a single data store, but could actually exploit best-of - breed data store technologies and view them as a single aggregated data source without first necessarily ingesting the data.

A single software solution is no longer feasible, as many organizations need the flexibility to mix and match software options to meet their business needs. Logical data storage solutions are more appropriate for the needs of many businesses, and fortunately there are many sites available that will allow you to benchmark and compare the various data storage options to decide what is right for your business; whether it is a cloud platform such as Azure or AWS, or a visual analytical platform such as Tableau, Qlik Sense and Spotfire, the foundation of a business

Driving Decisions

Having a data warehousing infrastructure and software to allow multiple data sets to be mixed in various formats, whether videos, images, numbers or text, provides great advantages and subsequently enables greater insight that contributes to more informed decision-making of quality and often better business efficiencies.

It can do more than individual information silos if used and interpreted correctly and helps organizations to cope with data expansion. To do this, however, researchers need to consider the method, the data and the desire to do what is necessary. Although automated, knowledgeable people are still the key to translating the data into actionable knowledge.