GoodData has a wholly different take on the use of visual analytics and business intelligence. It believes analytics should be a demonstrably profitable activity, enabling businesses to monetize their data. Virtually all suppliers of visual analytics tools are pushing the notion that making these tools available to a wide audience is a good thing in its own right. There are no points for guessing why suppliers like this message – more users equals more license revenue. And of course users like the idea of having their own data analysis tools, and are particularly susceptible to the grossly overused ‘ease-of-use’ marketing mantra.
We need to take a small detour into the current myth of democratized analytics. Most employees are part of what we might call the production environment within a business. This means they tend to do similar things most days, simply because businesses are not large R&D organizations, but strive to reduce transaction and production costs through some level of automation. This applies to BI and analytics as much as anything else. So the notion that hundreds (and maybe thousands) of employees have the time and inclination to slice and dice data on a regular basis is fundamentally flawed. Analytics need to be part of the production environment, and that means they need to be embedded within production applications – sales, purchasing, accounts, HR, production – and so on. But there is also something else. The terabytes of data most businesses have acquired, at considerable cost, represent an asset in their own right. This means the data can be used to generate revenue and profits. Why else would be call it an asset?
So GoodData, somewhat uniquely, is focusing on the embedding and monetizing of analytics, transforming it from cottage industry to a mainstream profitable production activity. This is not a message that will appeal to anyone but senior management, and as such the sell is much more business focused.
GoodData provides a cloud based platform, with all the associated benefits – robustness, scalability, security and the avoidance of yet more capital expenditure and expensive supporting skill sets. The core platform does all the things one might expect of a visual analytics platform – charts, dashboards, maps, mobile, collaboration etc etc.
The profit center oriented approach of GoodData has grown out of its work with OEMs – businesses that provide information as a chargeable service. It wasn’t too much of a leap to realize that most businesses could provide chargeable services through productizing their own data.
More complex forms of analytics are also supported via an interface with the R statistical and analytics language. But in reality the technology is just an enabler. What GoodData brings to the party is the maturity and experience to turn data and technology into revenue and profit.
It would be perfectly feasible to use GoodData’s platform as a self-service analytics platform, and for some users this might be appropriate. Data discovery and exploration is a necessary process, but as I show in my Business Analytics Maturity Model, it isn’t a profitable activity in its own right, but just part of the data R&D that is a prerequisite for revenue generation and profitability.
Production-lining analytics is the first step to enhancing the top and bottom lines in a business, and this means embedding analytics into the production environment. This eliminates the context switching between production apps and a separate analytics toolset, and presents the most effective analytics right where they are needed. Most employees can benefit from embedded analytics, and as such the organization as a whole benefits. The number of people who need direct access to analytical tools is usually quite small, and even in the largest businesses it might be just a few hundred people. The number of employees who might benefit from embedded analytics is numbered in the tens of thousands in many large businesses. GoodData makes big claims for its scalability and production quality platform – this is very, very important. Downtime for a few hundred people slicing and dicing data does not have the same impact as downtime for tens of thousands. The quality of the platform is paramount.
The next level of analytics concerns itself with using data assets, both internal and external, to create data products – as GoodData calls them. I prefer the term information products, simply because information is the stuff that reduces uncertainty – and people will pay for information that reduces the unknowns in their business. The people who will pay for this information might be partners (a distribution network for example), suppliers and any other agents who might be able to profitably use the information services a business delivers. GoodData already works with a large number of major corporations in this way, and claims that 42% of the Fortune 500 use GoodData technology – albeit not all in the same way.
Finally GoodData is also starting to act as a data middle-man, bringing organizations together who work in related industries so they can create much higher value information products. This is one of the most exciting developments in recent years – federated data products creating a view on business activities that no single business can achieve.
The winners in any industry are always those that do something different. In the business analytics space it is GoodData that has moved away from the catchy, but largely meaningless marketing cliches such as ‘self-service’, ‘ease-of-use’, ‘big data’ and others that are starting to lose their sparkle – simply because they are not delivering. Having spent a decade in a hype wilderness, businesses can now start to production-line their analytics – the real work begins.
GoodData was founded around a decade ago by Roman Stanek. It is based in San Francisco and is a privately owned business with several investors. Roman Stanek previously founded NetBeans (acquired by Sun Microsystems), and is largely responsible for the unique positioning of GoodData.