Chad Meley, Vice President, Marketing, Artificial Intelligence, IoT and Customer Experience Solutions Cloud
- Public clouds will move from competing on commodity compute and storage to competing on full stack data analytics offerings. AWS, Azure, GCP, Alibaba and other public clouds will aggressively partner with enterprise data analytic vendors to offer high value solutions optimised for their cloud and integrated with other components. In house data analytic offerings will become less relevant to large enterprises looking to avoid cloud lock-in (I’m looking at you Redshift and Big Query). These in-house data analytic offerings served their purpose when cloud providers needed to engineer these tools for the cloud but have since been out innovated. Enterprise players in the data analytics space have moved past the strategy of creating their own cloud and are now looking to partner with all of the big public cloud providers.
- After a few successful AI pilots over the last couple years, enterprises will put a renewed focus on enterprise data management and integration to serve as a foundation to scale up to hundreds and thousands of narrowly defined AI use cases. Every sort of machine intelligence that surrounds us today is narrow AI. Narrow AI operates within a pre-defined range. Narrow AI works within a very limited context and can’t generalise to take on tasks beyond its field without significant rewrites and retraining. So, you can’t expect the same AI algorithm that detects fraud to detect customers at risk of churn. That’s the task of a different narrow AI algorithm. A successful enterprise AI initiative will spawn hundreds, if not thousands, of use cases, each supported by a narrowly defined algorithm. Once that’s understood and anticipated, it’s evident that large enterprises need a common data foundation to scale their AI ambitions.
Customer data platforms
The Customer Data Platform (CDP) space will be exciting to watch in 2020. CDPs are emerging to solve the challenge of fragmented customer data and disjointed customer experiences by enabling nontechnical business users to easily and quickly extend the customer profile, generate customer insights and deliver finely tailored instructions to the last-mile tools that execute personalised messages. While CDPs are spot on in defining the challenge, the current crop of CDPs fall well short of solving the challenge for large enterprises. All the technical ingredients are there for this category to mature fast: citizen data integrator tooling, no-code machine learning and autonomous real-time personalisation. When it does, it will generate massive value and usher in a step change in the way enterprises sense and react to customer opportunities.
There will be immense interest and adoption of “no-code analytics.” We’ve seen a steady democratisation of advanced analytics by automating away certain laborious aspects such as feature engineering and model selection. But advanced analytics become truly pervasive when machine learning and other advanced procedural analytics becomes something that requires absolutely no coding or SQL skills. No-code analytics will become embedded in workflows or invoked through simple drop-down menus. They won’t make coding obsolete in the analytics world but will increase the number of use cases benefiting from analytics in large enterprises by a factor of one hundred.
Data privacy & governance
New regulations like GDPR, CCPA and dozens of new country and state laws making their way through legislative bodies are similar in their intent to protect customer’s rights to their data but differ in prescribing what actions companies must take. Enterprises will react in one of two very different ways. Some will take a risk averse posture by curtailing the collection and use of customer data to avoid exposure while they monitor enforcement. Others will go the other way and embrace transparency above and beyond new legal mandates. Companies like Apple are already showing the way by using this as an opportunity to create competitive advantage with services that put the consumer in control of data and privacy, including the ability to request a copy of all data associated with your Apple ID.
Brian Wood, Director of Cloud Marketing
- Enterprises will become cloud-first in the deployment of all new analytic workloads. IT departments will be expected to default to the public cloud to support any business initiative not considered mere capacity expansion for existing infrastructure. “Use it or lose it” bulk purchase agreements with public cloud vendors will spur enterprise IT departments to blindly prefer cloud deployment location over solution fit, much to their leaders’ eventual regret. The frenzy of meeting short-term budget objectives will trump the measured wisdom of considered planning and strategic investment.
- As-a-service offers will move up the stack to incorporate a slew of capabilities formerly considered custom to provide increasing levels of commoditisation and duplication. Containerisation and solution portability will become the new battleground for enterprise IT; vendors having “the best” deployment-specific point solutions will lose out to competitors that can span multiple domains (e.g., public cloud, private cloud, on-premises) with ubiquitous offerings, thereby providing freedom and leverage against lock-in. Advertising claims will soar.
- Hybrid cloud deployment will dominate. Just as organisations have come to embrace the advantages of combining short-term contract workers with fulltime staff or recognise the benefits of pairing AI-augmented insights with warm-blooded subject matter experts, enterprises will diversify IT portfolios to match existing, workhorse on-premises systems with agile, cloud-based deployments for all new projects. The empowerment of AND will reign supreme over the trade-offs of OR.
- Profitability will reassert itself as a desirable – indeed required – outcome in the IT vendor landscape. Global economic, political, and regulatory uncertainty will spur business leaders to choose safety over speculation, in turn driving a wave of vendor consolidation in which weaker firms with promising innovation will become the prey of behemoths with bulging balance sheets. As is the case near the peak of any bubble, silly sky-high valuations reflecting the scale of ego, greed, and hubris will make early investors giddy and cause hapless employees to cling to what were formerly opportunistic side hustles.
Cheryl Wiebe, Practice Director, Industrial Intelligence
- AI will begin to improve the process of data management itself, such as for system resource allocation, automated feature engineering, operational metadata collection and better knowledge management (tagging, etc.)
- What the world is calling AI will split into several areas, which someone in marketing will create pithier names for. A few examples of them are:
- Robotic process automation, which is the marriage of policy engines + workflow engines + machine learning models for “next best action”
- Automated feature engineering and selection
- Perception AI, which is the automation and refinement of physical perception (yes, your five senses) into superpowers, using specialised, multi-channel sensors and AI to better see, taste, smell, hear and feel
- Resource allocation AI, the marriage of optimisation technologies (such as linear programming and such) to sense and respond in real time to demand (for example 5g network optimisation).
David King, Retail Industry Advisor, Teradata
Customer experience/Customer journey
- Personalisation scales: Leaders will connect large, disparate data sets such as clickstream, retail journey, social and IoT and leverage AI and ML to orchestrate and ease customer journeys across touchpoints. Focus will be on product and service curation, and localisation across offers, purchase, setup, service and support. If they build trust and avoid being creepy, companies will find customers willing to exchange information in return for better experiences.
- Help me help you: Mobile enablement for retail associates will take emphasis in order to empower store teams to know or predict customer behaviour, purchase history, service history and preferences across channels. The key is in curated real-time insights that store associates can have at their fingertips as they engage with their shoppers in store in 1:1 conversation.
- Augmented reality becomes, well, reality: “Try on before you buy,” “see what your friends think,” “explore possibilities like recipes” and “find it in store” as smartphones become AR devices that will help decrease product returns and facilitate happy customer journeys. Augmented reality will help customers make better informed choices, both at brick-and-mortar and online.
- Supply chain leaders will balance constant cost pressure with expectations to manage multi-channel demand, real-time forecasts and exact inventory tracking. They will leverage IoT, RFID, sensors, cameras and eventually blockchain. Major investments in advanced analytics, warehouse relocation and automation both in distribution centres and stores will be essential to survive the arms race with digital retail giants.
David King, Teradata