Rob Woollen, CEO and cofounder of Sigma Computing, explains why there’s no doubt the cloud is in your future.
Are you preparing for a world of cloud data warehouses? Will you have the right skill set to take advantage of them? Rob Woollen, CEO and cofounder of Sigma Computing, explains why your enterprise should be focusing on moving to the cloud.
Upside: What technology or methodology must be part of an enterprise’s data or analytics strategy if it wants to be competitive today? Why?
Rob Woollen: It may be obvious, but I have to say the cloud — particularly a cloud data analytics stack. It is the only way for companies to efficiently and effectively combine data from all the myriad sources and analyze it together. Cloud data warehouses (CDWs) are experiencing incredible traction, and I believe we will continue to see a sharp trajectory for them and the various technologies associated with them, from the pipeline funneling all the data into the CDW to the cloud analytics and business intelligence (BI) software that enables you to access and analyze the data in real-time (like Sigma, the company I lead).
CDWs have taken off for a number of reasons: scalability, flexibility, lower costs, and connectivity, which are benefits presented by most cloud technologies. Furthermore, by storing all data in one place, organizations don’t have to deal with the complexity of searching various discrete business systems and data stores to locate the relevant data.
The ability to join data sets from discrete systems and analyze that data in real time is imperative to becoming data-driven. Companies need to be able to make decisions much more quickly and they need to make decisions based on data from across their company. Just about everything today is time-sensitive because the world is moving at such a rapid, always-on pace. We are entering the post-digital era in which both B2C and B2B customers expect personalized products, services, and experiences, and they want them on demand.
What one emerging technology are you most excited about and think has the greatest potential? What’s so special about this technology?
Until Fivetran came on the scene, everyone was taking an extract, transform, load (ETL) approach to getting data from its source into a warehouse, transforming the data while en route. Fivetran saw early on that there was an increasing desire to transform data as late in the game as possible because you were out of luck if the transform removed or changed something needed for analysis because the raw data never reached the warehouse.
The emergence of CDWs made loading all the source data in its raw form possible. Fivetran has turned the process on its head, flipping the L and the T (ELT) and waiting to transform the data until it is in the warehouse. Delaying the transform ensures no data is lost during the transform. This is extraordinary and I am sure there are more advancements to be made in ELT technology and the data pipeline space.
What is the single biggest data-related challenge enterprises face today? How do most enterprises respond (and is it working)?
I don’t know if it’s the biggest challenge enterprises face overall, but data literacy — the ability to read, write, and communicate data in context — is a tremendous challenge when it comes to becoming data-driven, and so many desperately want, and need, to be data-driven in order to remain competitive. LinkedIn’s U.S. 2020 Emerging Jobs Report shows that the data scientist role is becoming increasingly prevalent — it experienced the third-highest growth rate at 37 percent since the previous year. Companies are hiring data scientists to help achieve the data-driven Holy Grail, but this is not a scalable, long-term solution.
So much of our world today centers around data. We are rapidly approaching the point where data literacy will be a required skill, regardless of industry or job function, in order to truly be data-driven.
Is there a new technology in data or analytics that is creating more challenges than most people realize? How should enterprises adjust their approach to it?
Gartner predicts that 50 percent of analytics queries will either be automatically produced or generated via search or natural language processing (NLP) this year, but that still leaves 50 percent being generated through other means. Although the BI community is bent on cracking the NLP code to save time, money, and resources, the technology (as it currently stands) is not mature enough to solve all these problems.
Aside from the massive up-front implementation, ongoing maintenance, and expenses required to successfully implement NLP, the major shortfall remains in the technology’s ability to handle complex questions. No matter how mature the technology becomes, it will always be dependent on the kinds of questions the data team thought the business team would ask and trained the system to answer.
What initiative is your organization spending the most time/resources on today?
We are well into our “Sigma on Sigma” initiative and getting every department, every team, and every employee using Sigma as it is intended to be used. It has already proven to be fruitful in terms of identifying new efficiencies and creating a more holistic approach to strategy as well as ideas for improving the product to better suit the needs of specific teams.
Our internal team is our test kitchen and our loudest critics in some cases. We love the feedback, though, and always welcome it from our employees and our customers. We thrive on feedback — the good, the bad, and the ugly. It is the best way for us to understand our customers and their needs so we can make Sigma the best it can be.
Where do you see analytics and data management headed in 2020 and beyond? What’s just over the horizon that we haven’t heard much about yet?
Cloud has changed everything. There’s no denying that. The first generation of change brought traditional products into cloud data centers or hybrid approaches. Now, we’re starting to see the second generation of products emerge. These products are exclusively built for the cloud, not just running in the cloud, and specifically designed for elastic scale-out, which is quickly becoming something that is expected by all types of organizations. I think in 2020 and beyond we’ll see every industry not only include cloud in their strategy but start to take into consideration how the cloud will continue to impact the very way we work and open the door to new technologies.
Describe your product/solution and the problem it solves for enterprises.
Sigma is cloud analytics and BI software with a no-code, spreadsheet-like interface, empowering anyone to ask any question of the data in their CDW in real time. The increased hunger for data throughout organizations has analysts in ad hoc query and report factory hell. We help organizations accelerate time to insight by eliminating the bottlenecks caused by complicated solutions that only someone who can write code can use while the data team maintains security and governance over the data. It’s the perfect balance of control and freedom to explore. Bottom line: Sigma delivers on the promise of self-service analytics and BI.
Fuente: By James E. Powell (Upside)