Tag Archives: workflow

Box AI, workflow automation strategies about to unfold

Box AI and workflow automation advancements that users are waiting for, and which are instrumental to the content services platform vendor’s future, will come into clearer focus this month, according to CEO Aaron Levie.

With Box AI tools at the hub of Box Skills, the company’s still-in-beta system for customizing Box applications with machine learning technology from Google, Microsoft or IBM, AI will permeate Box’s content management systems, Levie said.

“We want to make sure we continue to automate and bring intelligence to your digital business processes,” Levie said in an interview.

New Box AI tools

Levie said the company will make announcements around Box AI and workflow automation, and generally, about how Box plans to “advance the state of the digital workplace,” at the BoxWorks 2018 conference in San Francisco Aug. 29 to 30.

“We’re going to talk a lot about AI and the power of machine learning,” Levie said. “And you’re going to see more of a roadmap around workflow in Box as well, which we’re really excited about.”

Indeed, workflow and digital process automation have been a perennial question for Box in recent years, said Cheryl McKinnon, a Forrester analyst scheduled to speak at BoxWorks.

Workflow automation progress

McKinnon noted that Box, which started out as an enterprise file sync-and-share company, has tried to remedy the gap through a partnership with IBM on the Box Relay workflow automation tool and other deals (with companies like Nintex and Pegasystems). Box also recently acquired startup Progressly to improve workflow automation.

We want to make sure we continue to automate and bring intelligence to your digital business processes.
Aaron LevieCEO, Box

“I do expect to see deeper investment in Box’s own automation capabilities as it puts some of the expertise from recent acquisitions, such as Progressly, to work,” McKinnon said.

“Content doesn’t get created in a vacuum — embedding the content creation, collaboration and sharing lifecycle into key business processes is important to keep Box a sticky and integral part of its clients’ internal and external work activities,” she said.

In addition to Box AI and workflow automation, Levie said Box is putting a lot of emphasis on its native-cloud architecture and persuading potential customers to move from on-premises content management systems to the cloud-based content services platform model that has distinguished Box.

Box CEO Aaron Levie
Box CEO Aaron Levie speaking at the BoxWorks 2017 conference.

“We’re really trying to help them move their legacy information systems, their technology infrastructure, to the cloud,” Levie said.

Box wants “to show a better path forward for managing, securing, governing and working content and not just using the same legacy systems, not having a fragmented content management architecture that we think is not going to enable a modern digital workplace,” Levie said.

Box vs. Dropbox and bigger foes

Meanwhile, its similarly named competitor, DropBox, completed a successful IPO this year and is angling for the enterprise market, where Box holds the lead. Dropbox’s stock price took a hit recently, but Levie said he takes the competition seriously. Box, too, sustained a decline in its stock price earlier this year, though the stock’s value has stabilized.

“I would not dismiss them as a player in this space,” Levie said of Dropbox. “But we think we serve more or less different segments of the market. They are more consumer and SMB leaning and we are much more SMB and enterprise leaning.”

Actually, Box’s most dangerous competitive threats are from cloud giants like Microsoft and Google, McKinnon said.

They are “investing significantly in their own content and collaboration platforms, and while Box partners with both of them for integration with office productivity tools and as optional cloud storage back ends, the desire to be the single source of truth for corporate content in the cloud will put them head to head in many accounts,” she said.

Box workflow gets upgrade with Progressly purchase

Box last week acquired workflow software company Progressly for an undisclosed amount, with the hopes of upgrading its own core workflow and automation to better suit its customers.

The acquisition of Progressly is an interesting one for document management company Box, as it just last year released Relay, a product it developed in partnership with IBM that is also meant to help companies with workflow management. The idea behind the Progressly purchase, according to analysts, is to beef up the Box workflow capabilities.

“While Box and IBM have been talking about Relay for years, I have not seen much traction of it in the industry,” said Alan Lepofsky, a principal analyst at Constellation Research Inc. “I think it will be good for Box to expand the capabilities of their own native workflow engine, increasing the number of triggers and actions that can occur both inside Box, as well as becoming an engine for processes within other tools.”

The importance of workflow automation for companies can’t be understated. Between onboarding, contract management and other business necessities, a company can save a lot of time and money with a capable automation process.

While the Box workflow capabilities were there, they were described as “rudimentary” by Holly Muscolino, a research vice president at IDC.

“The way [Box] is positioning it is that the software from Progressly, or at least the team at Progressly, will develop software that will go into the core Box product,” Muscolino said. “They have a rudimentary workflow in Box, but this will enhance that.”

Box also has partnerships established with other business process companies, like Nintex and Pega.  Muscolino said she sees this acquisition as potentially adding triggers to get partner processes automated within Box.

And improving the Box workflow capabilities is not expected to lead to a new product, according to Lepofsky, but rather enhance the existing features within Box.

Native workflow inside Box should be considered more of a feature and not an additional monetary channel.
Alan Lepofskyprincipal analyst, Constellation Research Inc.

“Native workflow inside Box should be considered more of a feature and not an additional monetary channel,” Lepofsky said.

Lepofsky added that customers can find more automation capabilities within competitors, like Microsoft OneDrive and Flow. And by beefing up the workflow with the Box acquisition of Progressly, the company is trying to better challenge the other players in the market.

“I think the driving factor was customer need,” Lepofsky said. “The opportunity to help automate content-centric workflows is a big step in helping people get their jobs done.”

While improving the Box workflow features seemed to be the main reason for the Progressly purchase, Box’s chief product officer, Jeetu Patel, also made it clear that the small team of 12 at Progressly was a factor in the acquisition, calling the team in a blog post a “group of highly talented individuals that have created a world-class product with a vision that is directly aligned with the team here at Box.”

Patel added that the Progressly team will “allow us to play a bigger role in how Box customers digitize and automate business processes.”

The terms of the acquisition were not disclosed.

New data science platforms aim to be workflow, collaboration hubs

An emerging class of data science platforms that provide collaboration and workflow management capabilities is gaining more attention from both users and vendors — most recently Oracle, which is buying its way into the market.

Oracle’s acquisition of startup DataScience.com puts more major-vendor muscle behind the workbench-style platforms, which give data science teams a collaborative environment for developing, deploying and documenting analytical models. IBM is already in with its Data Science Experience platform, informally known as DSX. Other vendors include Domino Data Lab and Cloudera, which last week detailed plans for a new release of its Cloudera Data Science Workbench (CDSW) software this summer.

These technologies are a subcategory of data science platforms overall. They aren’t analytics tools; they’re hubs that data scientists can use to build predictive and machine learning models in a shared and managed space — instead of doing so on their own laptops, without a central location to coordinate workflows and maintain models. Typically, they’re aimed at teams with 10 to 20 data scientists and up.

The workbenches began appearing in 2014, but it’s only over the past year or so that they matured into products suitable for mainstream users. Even now, the market is still developing. Domino and Cloudera wouldn’t disclose the number of customers they have for their technologies; in a March interview, DataScience.com CEO Ian Swanson said only that its namesake platform has “dozens” of users.

A new way to work with data science volunteers

Ruben van der Dussen, ThornRuben van der Dussen

Thorn, a nonprofit group that fights child sex trafficking and pornography, deployed Domino’s software in early 2017. The San Francisco-based organization only has one full-time data scientist, but it taps volunteers to do analytics work that helps law enforcement agencies identify and find trafficking victims. About 20 outside data scientists are often involved at a time — a number that swells to 100 or so during hackathons that Thorn holds, said Ruben van der Dussen, director of its Innovation Lab.

That makes this sort of data science platform a good fit for the group, he said. Before, the engineers on his team had to create separate computing instances on the Amazon Elastic Compute Cloud (EC2) for volunteers and set them up to log in from their own systems. With Domino, the engineers put Docker containers on Thorn’s EC2 environment, with embedded Jupyter Notebooks that the data scientists access via the web. That lets them start analyzing data faster and frees up time for the engineers to spend on more productive tasks, van der Dussen said.

He added that data security and access privileges are also easier to manage now — an important consideration, given the sensitive nature of the images, ads and other online data that Thorn analyzes with a variety of machine learning and deep learning models, including ones based on natural language processing and computer vision algorithms.

Thorn develops and trains the analytical models within the Domino platform and uses it to maintain different versions of the Jupyter Notebooks, so the work done by data scientists is documented for other volunteers to pick up on. In addition, multiple people working together on a project can collaborate through the platform. The group uses tools like Slack for direct communication, “but Domino makes it really easy to share a Notebook and for people to comment on it,” van der Dussen said.

Screenshot of Domino Data Lab's data science platform
Domino Data Lab’s data science platform lets users run different analytics tools in separate workspaces.

Oracle puts its money down on data science

Oracle is betting that data science platforms like DataScience.com’s will become a popular technology for organizations that want to manage their advanced analytics processes more effectively. Oracle, which announced the acquisition this month, plans to combine DataScience.com’s platform with its own AI infrastructure and model training tools as part of a data science PaaS offering in the Oracle Cloud.

By buying DataScience.com, Oracle hopes to help users get more out of their analytics efforts — and better position itself as a machine learning vendor against rivals like Amazon Web Services, IBM, Google and Microsoft. Oracle said it will continue to invest in DataScience.com’s technology, with a goal of delivering “more functionality and capabilities at a quicker pace.” It didn’t disclose what it’s paying for the Culver City, Calif., startup.

The workbench platforms centralize work on analytics projects and management of the data science workflow. Data scientists can team up on projects and run various commercial and open source analytics tools to which the platforms connect, then deploy finished models for production applications. The platforms also support data security and governance, plus version control on analytical models.

Cloudera said its upcoming CDSW 1.4 release adds features for tracking and comparing different versions of models during the development and training process, as well as the ability to deploy models as REST APIs embedded in containers for easier integration into dashboards and other applications. DataScience.com, Domino and IBM provide similar functionality in their data science platforms.

Screenshot of Cloudera Data Science Workbench
Cloudera Data Science Workbench uses a sessions concept for running analytics applications.

Choices on data science tools and platforms

Deutsche Telekom AG is offering both CDSW and IBM’s DSX to users of Telekom Data Intelligence Hub, a cloud-based big data analytics service that the telecommunications company is testing with a small number of customers in Europe ahead of a planned rollout during the second half of the year.

Users can also access Jupyter, RStudio and three other open source analytics tools, said Sven Löffler, a business development executive at the Bonn, Germany, company who’s leading the implementation of the analytics service. The project team sees benefits in enabling organizations to connect to those tools through the two data science platforms and get “all this sharing and capabilities to work collaboratively with others,” he said.

However, Löffler has heard from some customers that the cost of the platforms could be a barrier compared to working directly with the open source tools as part of the service, which runs in the Microsoft Azure cloud. It’s fed by data pipelines that Deutsche Telekom is building with a new Azure version of Cloudera’s Altus Data Engineering service.