Analytics Currently Defy “Ranking”

A Drive to Know Who is First and Who is Worst

At ARC we are having many discussions with buyers looking for clarity on how to make purchasing decisions for advanced analytics. Specifically, potential buyers are struggling to answer a fundamental question: How do I know what solution is right for my business? Comfortable with traditional means of comparison, many of these buyers desire an “analytics ranking” of solution providers. Advanced analytics currently defy laggard-to-leader ranking as a way for making comparisons. Analytics rankings overlook key dynamics of the current market.

It Starts with Market Noise

Why is that? It begins with the cacophony rising from everything analytics-related. New step-change solutions are being offered that are based on digitized operations and the availability to analyze high-volume, complex, and often real-time data. Added to this dynamic is a robust business intelligence market, with it's deeply rooted language and marketing—dashboards, databases, reporting, ETL, schema, data mining, simulation, and structured query, just to name a few. The result is an emerging market commingled with an existing one, with buyers wading into this onslaught of marketing noise.

As a result, there is a fair amount of complexity to sort out during the buying process, which is something we’ve discussed before. There are even levels of unintentional misinformation, as in this humorous example where ARC was mistakenly tabbed as a provider of real-time operational intelligence analytics. It’s really no wonder why buyers want traditional, easy-to-understand ways to separate “good” providers” from “bad.”

Conventional Rankings Overlook Key Elements

However, analytics ranking is difficult when most of the market is still trying to  understand how to define them. You can break underlying architecture, which does provide context for what analytics building blocks a solution contains—data connectivity, ingestion, storage, modeling, and visualization. Additionally, you can parse out the scope of the solution, including custom and commercial applications and platforms. Doing so provides clarity of what is being purchased/delivered and how it applies, but it doesn’t necessarily speak to value provided.

The problem is that most analytics rankings rely on a traditional IT lens, built upon years of assessing business intelligence and other software solutions. They are designed to measure more tangible dimensions over manageable time horizons. The dimensions include capabilities, geography and vertical penetration, roadmap and execution, etc., all which generally deliver a view of the provider stability, system functionality, and vision within a market. These dimension and rankings don't hold up as well when faced with the breakneck speed of development of the current analytics market and the mix of hard and soft ROI that solutions can provide.

In nascent markets like advanced analytics, the focused vendor often provides the deepest initial value for customers while the broader solution provider invests more for a tipping point of customer adoption and the eventual commoditization of the market. As seen in the image to the left. the current industrial analytics market demonstrates this in a contrast of solution flexibility versus fit. There are many large providers in the top left and niche, entrepreneurial  solution in the bottom right.

The interesting element is that there is a mad dash by providers across the spectrum to somehow provide both high flexibility and fit for purpose (to varying degrees) for customers. Many platform providers are adding application-like solutions while applications providers are moving away from customization by delivering more platform tool capabilities.

It’s clearly an indication of a market still in the defining stages both in terms of what capabilities are needed and what value should be expected. Using traditional methods of ranking, you could use a static set of dimensions to measure the market on a monthly basis for quite some time. You’d see considerable changes in the landscape every time you took a measurement. That’s likely to be the case until the market reaches another level of maturation.

Determining Value

To find a good fit, companies are best served by undertaking an honest internal assessment of where the organization is and is willing to go in its commitment to advanced analytics and the change that comes with it. Analytics rankings really don't have much to do with that.

There isn’t a right or wrong in terms of commitment, per se, as companies will move at the pace of their own market drivers. Instead, it’s more about assessing organizational readiness to embrace significant change. After all, a company implementing advanced analytics is embarking on a path of data-driven continual process improvement. To assess readiness to address internal issues related to data-driven change as questions, such as:

• How would you characterize your organization in when it comes to advanced analytics—discovery, excitement, development, transition, or maturation?
• How would you characterize the state of your data?
• Have you already defined use cases?
• Who within the organization will pilot your proof of concept? What are their prevailing attitudes toward analytics versus tribal knowledge?
• Where does analytics fit within the overall business strategy?
• Have you dedicated resources already, and how did you what you needed? Who determined that?
• What is your end vision and timeline for becoming an analytics-based business?

Once you have assessed your organizational readiness, it’s easier to match that more directly to capabilities and value provided by the wide range of solutions available in the market. Your organizational gaps and needs will be more evident to you and conversations with vendors will be more direct.

At our 21^st Annual ARC Industry Forum in Orlando in February of 2017, we will host multiple sessions that will dig more deeply into advanced analytics. Along with my colleague Peter Reynolds, I will also co-present ">a workshop on how to determine requirements for an advanced analytics request for proposal (RFP) process. For information on any of the sessions, please contact your client manager or shoot me an email.