Accounting has often been called the language of business and it is invaluable in managing the day-to-day financial costs, inputs, outputs, and outcomes associated with business activity. However, as companies start to understand the impact that non-financial drivers ranging from manufacturing outputs to headcount to service transactions to asset utilization rates affect the health of the business, executives have had to broaden the scope of considerations needed to track the health of the company.
As they have done so, businesses have had to shift even their financial departments to focus not just on dollars and cents, but to production units, employees, transactions, uptime, turnover, and loyalty. In doing so, the language of business has started to shift from accounting to a new paradigm of data.
Today, data is the language of business.
This does not just mean that executives should understand that data exists or should be used. Any executive who is confused about the value of data-driven decisions needs to join the 21st century. Rather, businesses need to be aware that using data as the lingua franca of a business means making fundamental changes that allow every department to access, analyze, and interrogate data. The ability to pull out specific contextualized data can be equated with the ability to speak individual words. But the ability to grunt “Food” or “Water” or “Fire” is not a fluent usage of language, but rather a prehistoric capability. This is the level of data usage that businesses have typically held employees to.
But there are higher levels of language usage and, analogously, higher levels of enterprise data usage that businesses should strive to. With the creation of data models, cubes, and warehouses, businesses can structure the data to allow employees greater access to analytics, including the predictive and algorithmic usage of data that allows people to find patterns and recommendations. This structured analysis of data allows users to engage in more mature collaborations focused on enterprise data, but still limits much of the conversation to whatever can be pre-built.
To truly support enterprise data as a language, businesses have to provide each department and each savvy data user with the ability to independently query, analyze, and explore their data and then be able to present these outputs and analytic definitions back to the rest of the business. This language needs to be parsable, easily learned, governed, and sharable or else it becomes yet another black box silo in the enterprise. In total, the progression shifts from data to controlled interactions to independent conversations.
This promise of using data as an enterprise language and the lingua franca of business was the potential that I first saw when I first ran into Looker and its SQL-based LookML language in 2013. At the time, Looker was no more than a handful of people and had just closed its Series A round. But the promise of Looker’s approach and the “a-ha” moment that showed up every time an analytic-savvy pro saw Looker for the first time showed that the product was going to take off among SQL-trained users and data-hungry companies that just needed the right tools to translate into analytically fluent organizations.
And, sure enough, this success has continued to this day when, on March 30th, Looker raised an $81.5 million D round to make data more accessible and available while becoming a truly global product. Over the past four years, Looker has grown to over 300 employees, 800 clients, and 40,000 users. Amalgam Insights (AI) expects that Looker will continue to expand commercially as well as explore the value of machine learning in providing contextualized and focused data exploration. Looker has tackled the problem of data and analytic fluency in a unique and productive fashion that has been good both for enterprises seeking to evolve to a higher level of analytic utilization and for a business intelligence market that needs to be pushed in supporting data-driven culture.
In the coming years, Looker still has challenges to face. Like other analytic and business intelligence platforms, Looker still needs to figure out how to best integrate machine learning, unstructured data analysis, and natural language processing into its solution. (Perhaps partnerships with the likes of SDL, Narrative Science, and IBM Watson Services are on the horizon?) And Looker still faces the perennial BI challenge of how to expand past the core 10-15% of analytic true believers to the rest of the enterprise. But AI believes that Looker’s fundamental approach will continue to be both important and influential in the enterprise analytics market for the near future and looks forward to Looker’s next steps with this new round of funding and market validation.