Each Buyer’s Guide Edition released by DCIG generates a tremendous amount of interest in the technology industry as a whole. However, as people consider each Buyer’s Guide Edition that DCIG puts out, many make the assumption that DCIG seeks to make each Guide Edition “all-inclusive.” This is a mistaken assumption which leads some to claim or position themselves as “in the know”, jumping to misleading and erroneous conclusions.
Over the course of the last year DCIG has refined its methodology for researching, preparing, and releasing its Buyer’s Guides to even more closely align with real world end user needs. When DCIG initially started preparing and releasing Buyer’s Guides focused on enterprise storage arrays a number of years ago, DCIG would research all of the products that met the definition of enterprise storage arrays and then produce just one or two Buyer’s Guides based on that research.
The challenge with this approach is that different storage arrays were intended to meet different user requirements. Creating a single Buyer’s Guide that included all of the products that DCIG researched, and then ranking them on the basis of a single use case, necessarily resulted in certain products receiving rankings that were viewed as “inferior” even though these products may have been fine for their intended use.
This was never DCIG’s intent. Breaking these products into multiple rankings was merely a tool to help DCIG and end users to distinguish between those products with the most comprehensive feature sets and most robust hardware resources as they pertain to a particular use case verses those with more limited feature sets and hardware resources. Indeed, many of DCIG’s Buyer’s Guides have included statements like the following…
“It is also important for users to note that just because a product scored the highest in a particular category or is ranked a certain way does not automatically mean that it is the right product for their organization. If anything, because of the scope of the models evaluated and analyzed, it may have features that are too robust for the needs of an individual department or organization.”
In spite of such disclaimers, some individuals came away with the impression that lower ranked products were automatically inadequate or inappropriate for their intended purpose.
To address these issues and correct this perception, DCIG recently adopted a body of research approach. This body of research method encompasses as many products in a particular focus area as DCIG can reasonably and reliably research. Once DCIG completes a body of research, rather than publishing all of the data about all of the products in a single guide, DCIG analysts use the DCIG Analysis Portal (which is accessible to anyone, vendor or end user) to create different views into the raw data based upon particular inclusion and exclusion criteria. These criteria are defined by DCIG in accordance with the use case for a particular Buyer’s Guide Edition.
Applying various inclusion/exclusion criteria to a body of research, DCIG can create multiple different Buyer’s Guide Editions that better align with real world markets and buying decisions. Further, DCIG discloses all of these criteria in both the blog entry announcing each Buyer’s Guide Edition and the text of the Buyer’s Guide Edition itself. Every DCIG Buyer’s Guide includes a section labelled “Inclusion and Exclusion Criteria” that discloses the criteria and the rationale that was used to determine them. In the case of the just released DCIG 2016-17 Midmarket Enterprise Storage Array, the inclusion/exclusion criteria for arrays included limiting the Buyer’s Guide to storage arrays that scale to no more than 500TB of raw capacity.
Due to these constraints associated with each Buyer’s Guide Edition that it produces, DCIG also includes language in each of its Buyer’s Guides that “one should not draw any negative inferences for any product or vendor not included in a particular Buyer’s Guide Edition and doing so represents a misuse of the Buyer’s Guide.” This language helps to protect the reputations of those products or vendors not included in a particular Buyer’s Guide Edition.
Taking this approach and using this language gives DCIG new freedom to focus exclusively on those products and vendors that best match a specific use case. It also helps end-users to quickly focus on the products most appropriate for their environment. In the case of the DCIG 2016-17 Midmarket Enterprise Storage Array Buyer’s Guide, DCIG can confidently highlight why Tegile’s products do truly stand out and which DCIG ranks as “Recommended” in environments where organizations need hybrid storage arrays that scale to less than 500TB (which DCIG believes to be a huge market segment.)
The evolution of DCIG’s methodology in the production of its Buyer’s Guides reflects a maturing of DCIG’s processes and understanding of how individuals use DCIG Buyer’s Guides. But perhaps more importantly, DCIG takes advantage of technologies that it now has at its fingertips to explore and visualize the data it has collected. DCIG’s decision to include in its Buyer’s Guides only products that are a good fit with the criteria for that Buyer’s Guide protects products from being unnecessarily and inappropriately called out simply because their technology may not fit a certain use case and the related inclusion/exclusion criteria.
DCIG believes this application of Big Data techniques to the analysis and presentation of its bodies of research puts DCIG at the forefront of where analyst firms can and should be going. Moreover, DCIG makes the data it has collected–along with all of the software tools necessary to draw these conclusions–available to individuals in any organization so that they may apply their own criteria and draw their own conclusions based on what is most important in their environment.
Some analysts and the media may find it entertaining and provocative to argue brand names and motives; perhaps to drive page views on their websites. DCIG differs in that it has always endeavored to solve the real world, day-to-day challenges that end users with limited time and resources face by giving them access and insight into the technical feature data they need to make better-informed technology buying decisions, faster.