Videos, Images, Numbers, Text: Blending Data Formats to Drive Decisions

Dr Katharine Johnson

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    Determining the correct methodology to use when faced with a research question can be daunting. There are many options to choose from, and often, researchers resort to a standard survey, but what if this doesnt give the best data? What would happen if researchers integrated several research methodologies or if they wanted to compare results with historical data or other data sets not in the same format? Would they gain more insight and be in a position to make more informed and thus better decisions if they used multiple sources?

    When faced with a business question, problem or even when wishing to trial something new, market research should be at the core of decision making within the company. To answer research questions with validity and accuracy, researchers need to establish the correct methodology and determine how to compare, validate or contrast results. Can a purely quant or qual focus at one point in time give the answers we need, or should researchers look to a mixed approach with both qualitative and quantitative techniques and comparisons to other data sets at other points in time?

    If more than one research methodology is used, then more than one data set will be sourced, interpreted, and analysed, so how can researchers manage and process all this data?

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    Blending multiple data formats can be challenging, but the benefits are definitely worth the effort. What can we do to make blending easier and ensure that all results are well-understood?

    The integration of these analyses is crucial to the understanding and subsequent outcomes of the business decisions. Data blending can help answer research questions at speed allowing business decisions to be made faster and efficiently. So, what is data blending and how can we blend data formats? What can researchers do during analysis to make blending easier, and how can we ensure the results are then well understood to drive those decisions within the business?

    Blending During Analysis

    The extraction of the relevant data from various sources, following the collection from several methodologies, plays an important role before combining data formats. Although many individuals can undertake simple data analysis it takes someone with more analytical experience or specialised software to extract the key information and interpret more complex data, big data, or data from various sources.

    Data blending is a process where data from multiple sources are merged into a single data set and, fortunately, there now many different tools available to make data blending of big data easier for the researcher, dramatically reducing analysis time. However, it is still key to ensure the person handling the data is confident about the different forms and file formats and they are able to clean the data and subsequently integrate these into a single form. Even if using automated methods for text analytics, speech analytics and video/image analytics for example, it is still important the person programming the results into the automated systems feels confident with the set-up of the methodologies so the results can be trusted.

    Blending Data Formats

    Non-automated analysis is time consuming, but at times it can be more efficient when assessing different methodologies and trying to integrate smaller amounts of data. However, the automated approach to blending data for big data sets is fast becoming the preferred option. Note though, automation will allow for general trends to be found, but may miss the nugget of information that can provide the valuable insight. Therefore, when blending data formats, dont overlook human analysis and, of course, the advanced knowledge of statistical analysis techniques and analytical software will still remain crucial for the understanding of the data and cross referencing the data to ensure reliability. The use of data blending software should supplement analysis rather than replace other analytical methods.

    As a business, the first decision should be to determine the most appropriate data warehousing system. Data warehousing is essentially a central repository of integrated data from one or more different sources. In 2012 Gartner Research introduced the term Logical Data Warehouse”, the idea being that you didnt have to have a single data store, but instead could leverage best-of-breed data store technologies and present them as a single aggregated data source without the need to necessarily ingest the data first. 

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    Data warehousing allows for a repository of integrated data from multiple sources and thus a better opportunity to blend resources for better insights.

    As many businesses need the ability to mix and match software options to suit their business needs, a single software option is no longer practical. Logical data warehousing solutions are more fitting to many businessesneeds, and thankfully there are many sites available that will help you to benchmark and compare the various data warehousing options to determine what is right for your business; whether this is a cloud platform such as Azure or AWS, or a visual analytic platform such as Tableau, Qlik Sense and Spotfire, the foundation of a best-of-breed data warehouse needs to be a database that can work across a wide variety of applications with multiple data formats and can be complementary to the systems already operating in the business.

    Driving Decisions

    Having a data warehousing system and software to allow the blending of multiple data sets in different formats whether it be videos, images, numbers or text offers great benefits and subsequently allows for greater insight which leads to more informed quality decision-making and often better efficiencies across the business. 

    If used and understood correctly it can achieve more than individual silos of information and allows businesses to cope with the expansion of data. However, to achieve this requires researchers to understand the system, the data and the capacity to what is possible.  Although automated, experienced individuals are still the key to turning the data into actionable insight.

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