Going beyond collaboration to achieve data-driven success

Big Data Evangelist, IBM

Great data isn’t enough. No business can hope to succeed in the 21st century unless it has a culture of collaboration that’s adept at transforming data into fresh cognitive insights it can use for efficiency, modernization, monetization and even competitive disruption. 

But even a data-driven cognitive business culture isn’t enough. If you don’t empower every knowledge worker with the self-service tools, skills and applications to extract insights from data, you’ll fail to make competitive headway in a world where start-ups and established rivals leverage information assets to the max. One key enabler for business success is a cloud-based platform that enables data scientists and subject-matter experts to apply artificial intelligence (AI), machine learning, predictive models, natural language processing and other fruits of data science to any and all data.

Transforming business outcomes

Data science is the new IT,” says Rob Thomas, general manager, IBM Analytics, at IBM. “Many IT jobs will become focused on data science because data science is not about how you manage data as much as it is about how you get outcomes out of data. With the right tools you can look at a large set of data, both internal data and external data, and you can start to make predictions about what’s going to happen in your business. And more importantly, you can decide what you should do based on those predictions. So data science is really about changing business outcomes and doing it in a fast and iterative way.”“Data science experience is the first time we’ve ever made data science as a team sport,” says Thomas. “You no longer have to choose one specific tool or one specific language. You can work in an open environment, and you can collaborate with other data scientists regardless of which language they prefer. We have really democratized data science and made it available to everyone in the world. We have built tremendous technology around an open source core, and for the first time you can now integrate models from a variety of different languages and produce a great outcome as a data scientist.”

Unearthing opportunities

Where those outcomes are concerned, says Harriet Fryman, vice president, offering management growth, IBM Analytics, at IBM, the platform would “enable people to come together, novices or experts, to learn more, to become masters of their profession, to create and collaborate together. [They can] put algorithms to work to find opportunities for the business to exploit, like dynamic pricing, or they can find challenges that they need to resolve, like production yield problems or logistics problems.”  

What would that ideal cognitive analytics development environment look like in practice? At heart, it would need to provide several capabilities: 

  • A scalable, open source–based cloud data and analytics environment that helps simplify and automate data-driven business innovation
  • A single, cloud-based development environment that integrates all data for cognitively powered decision making and enables data scientists to consolidate their use of multiple open source tools and languages such as Apache Spark, Python and R
  • A self-service, task-oriented environment for teams to collaboratively develop, iterate and deploy sophisticated AI, cognitive computing, machine learning and other advanced analytics as composable microservices for deployment in cloud services, hybrid data and Internet of Things environments
  • An accelerator for rapid discovery, collection, ingestion and organization of data from all sources
  • A common platform for sharing, tracking and governance of data sets and algorithmic models among data scientists, data engineers and application developers
  • A set of solution blueprints that package the integration and smarts for specific scenarios—such as data lakes—thereby helping simplify development and speed time to value
  • A rich catalog of learning resources for teams of data science professionals to deepen their understanding of tools, techniques, languages, methodologies and other key success enablers.  

All these capabilities are supported on the IBM Watson Data Platform (WDP). According to Ritika Gunnar, vice president, big data and analytics offering management, IBM Analytics, at IBM, WDP is designed to drive cognitive-powered decision making across organizations. And it does so by facilitating cross-role collaboration on the development of cognitive data applications and automating their intelligent deployment.

Optimizing an open experience

A lot of what we do [in WDP] is based on open source,” says Daniel Hernandez, vice president, information integration governance, IBM Analytics, Platform Services, at IBM. “We’re taking advantage of Spark, we’re taking advantage of open source data products and more including Apache Atlas, which is our metadata system. Our view is that if we take very careful consideration through design for how these jobs—data science, data engineering—need to be done, we can offer an integrated experience that’s optimized for that discipline and that’s optimized for teams to get that work done better than anybody on the planet can.”

For the full value of WDP or any such platform to be realized in your business, you need to implement it in conjunction with a methodology such as IBM DataFirst Method. This maturity model of consultative practices helps organizations to assess the skills and roadmap needed to transform into a cognitive business. It helps businesses to assess the appropriate human capital, technology and other resources needed to achieve data-driven success. See Fryman’s recent blog post Like a gym membership, data has no value unless you use it for a discussion of the DataFirst Method maturity model.

“With the DataFirst Method, it’s going beyond just the technology,” says Kevin McIntyre, worldwide cloud sales and DataFirst Method leader, IBM Analytics, at IBM. “It’s looking at the culture, the strategy, the processes that really need to be understood to make any project or use case successful to drive business value to any customer. With that, we want to be able to bring in proven patterns of deployment of technologies where we’ve been successful with other clients. [We want to] bring in the right level of expertise for particular technologies for specific industries, bring in business value consultants and be able to surround that customer with expertise.”

Fast track your data and secure your competitive advantage with machine learning. Register for the June 22nd livestream.