For businesses, artificial intelligence is set to become mainstream as it is embedded into transactional applications rather than being developed as bespoke solutions by enterprises in-house. This will require businesses to enact new data science models and push those into core business processes to achieve business value. It also stands that open data will continue to provide new and unique opportunities to exploit artificial intelligence technology and will be a key driver for start-ups in the AI space.
To understand more, Digital Journal spoke with Glen Rabie, the CEO of Yellowfin.
Digital Journal: How important is digital transformation for business?
Glen Rabie: Every business needs to be a digitally led business. Sure there are some exceptions – mostly mom and pop style businesses. But if you are an organization of significant size and scale you need to be on the path of digital transformation. From procurement to customer delivery almost all aspects of the value chain are being disrupted by new technologies that are improving the customer experience and putting the customer first.
DJ: How is artificial intelligence shaping businesses?
Rabie: Gartner believes that the future of analytics is augmented. That is, analytics will be AI-driven and all end-to-end use cases will be automated. I also believe it won’t be long before analytics is no longer on our desktops – instead it’ll be embedded in applications. We’re already starting to see this happen, which means any software vendor that wants to take advantage of AI through analytics has no time to lose.
DJ: Do you think AI is going to become more mainstream?
Rabie: Many businesses are already able to use AI through tools like TensorFlow from Google, the AWS stack, or IBM’s Watson. This has paved the way for entrepreneurs to come in and build products that solve more specific problems. This is the phase we’re entering now – AI is going mainstream. Rather than using a generic platform, software vendors can now embed specific AI into their own applications. This process is seamless to the end user and allows software vendors to deliver AI capability into their workflows.
The biggest difference between the development of AI and other software platforms is the speed of the transition. While ERP platforms took decades to reach this stage, AI has probably had the fastest transition from in-house R&D to mass market that we’ve seen in software development. I believe this is because organizations realize that AI can really give them a competitive advantage, so there’s no time to waste.
DJ: Will this lead to more companies bringing management solutions in-house?
Rabie: At the moment, some independent software vendors are starting to tinker with AI and trying to work out how they can make it work for them. If you’re an independent software vendor, the biggest mistake you can make right now is to start investing in AI capability in-house. That’s because you don’t have the time to catch up to the bigger players who are already well down the software development path.
Any new form of software development goes through three phases. It starts off being the domain of expert organizations who handcraft tools in-house from scratch. Some of the leaders in AI right now have done just that. Google and Amazon have spent a lot of money on data scientists and other experts to create their AI software in-house. So if you’re just starting to think about letting your Dev team spin up their own data science organization, you’re too late. It’ll take too long to develop your AI capability and soak up all of your profits before you can catch up to the rest of the market.
Many of these mega-vendors are already in the next phase of software development. This is where they take their R&D and create a set of tools or platforms that are generic enough to apply to multiple problems. They’ve taken the risks so they can define the market and profit from it. Many businesses are already able to use AI through tools like TensorFlow from Google, the AWS stack, or IBM’s Watson.
DJ: How will businesses begin to use data science models?
Rabie: Data science traditionally lives in an ivory tower with quants crunching numbers in a backroom somewhere separated from the business. Because the analytics team works in separate organizations and on different systems, making data science relevant to the business remains the greatest challenge of analytics today.
Think about how data science makes its way from the data scientist to the business user. The data scientist is the explorer, innovating, writing algorithms, creating new models, working 20 to 25 models at any given time. The data miner experiments, using data mining techniques to identify the top five to seven models. The data analyst validates by checking data quality, ensuring governance, and graduating the top three models to the business. The business analyst looks at valuation, assessing business value, and graduating the top model into production. The business user interacts with the model embedded in a business process, creating value for the organization.
When this entire team works on a single platform, as they do in Yellowfin, the whole process is accelerated. An average organization can cut days or weeks out of the process that moves the best models into production. The bottom line: all-in-one platforms create more value, faster.
In addition to speed, when the entire analytics team is on the same system, cross-organizational collaboration and understanding happen organically, especially when the system supports collaboration. The system brings the organization together and the team members learn more about the roles and responsibilities of others on the team. This kind of communication only comes when users along the analytics supply chain work on a common system with collaboration built-in.
DJ: What are the benefits of open data and how is this changing businesses processes?
Rabie: Open data provided all businesses with access to a wide range of data sets. They can use these to augment their own data and thereby gain a better understanding of their own business or even use open data to create new products and services that they can sell to their customers. For example:
Populus is an emerging player from San Francisco in the smart city space that amalgamates data from a variety of ride-hailing, car, bike sharing and e-scooter operators to help city authorities plan their traffic and parking strategies more affectively. It adds value by being an aggregator for these data sources and presenting it in formats that are easy for city planners to work with.
