On December 6, 2018, Looker announced that it closed a $103 million E round led by Premji Invest, a private equity firm owned by Wipro chairman Azim Premji. This round also includes new funds from Cross Creek Advisors, a venture capital firm focused on late-stage investments with current investments including tech darlings such as Anaplan,…
Companies Mentioned: Aberdeen Group, Actian, Alation, Arcadia Data, Attunity, BMC, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataKitchen, Datameer, Datarobot, Domino Data Lab, EMA, HPE, Hurwitz and Associates, IBM, Informatica, Kogentix, LogTrust, Looker, < MesoSphere, Micro Focus, Microstrategy, Ovum, Paxata, Podium Data, Qubole, SAP, Snowflake, Strata Data, Tableau, Tamr, Tellius, Trifacta.
Last week, I attended Strata Data Conference at the Javitz Center in New York City to catch up with a wide variety of data science and machine learning users, enablers, and thought leaders. In the process, I had the opportunity to listen to some fantastic keynotes and to chat with 30+ companies looking for solutions, 30+ vendors presenting at the show, and attend with a number of luminary industry analysts and thought leaders including Ovum’s Tony Baer, EMA’s John Myers, Aberdeen Group’s Mike Lock, and Hurwitz & Associates’ Judith Hurwitz.
From this whirwind tour of executives, I took a lot of takeaways from the keynotes and vendors that I can share and from end users that I unfortunately have to keep confidential. To give you an idea of what an industry analyst notes, following are a short summary of takeaways I took from the keynotes and from each vendor that I spoke to:
Keynotes: The key themes that really got my attention is the idea that AI requires ethics, brought up by Joanna Bryson, and that all data is biased, which danah boyd discussed. This idea that data and machine learning have their own weaknesses that require human intervention, training, and guidance is incredibly important. Over the past decade, technologists have put their trust in Big Data and the idea that data will provide answers, only to find that a naive and “unbiased” analysis of data has its own biases. Context and human perspective are inherent to translating data into value: this does not change just because our analytic and data training tools are increasingly nuanced and intelligent in nature.
Behind the hype of data science, Big Data, analytic modeling, robotic process automation, DevOps, DataOps, and artifical intelligence is this fundamental need to understand that data, algorithms, and technology all have inherent biases as the following tweet shows:
Over the next couple of months, keep an eye or ear out for Amalgam Insights as we show up at an event or webinar near you. Catch up with us at the following times:
- August 31: BrightTalk Webinar: Eight Telecom Expense Solutions Gartner Missed
- September 12-14: AI in San Francisco attending Looker’s Join 2017
September 26: Webinar: Machine Learning, Design Thinking, & the Role-Based Expert Enhancement Platform
September 27-28: AI in New York City attending O’Reilly Media’s Strata/Hadoop
October 3-4: AI in Indianapolis attending MOBI’s Untethered Summit
October 17-19: AI in Las Vegas attending Intacct Advantage
October 26: Webinar: Making the Leap from TEM to IT Management
What am I missing? Where else should I be? Let me know!
For more details on how to meet up with AI or to attend one of our events, look below!
Continue reading “Meet Up with Amalgam Insights”
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.