Digital Transformation
Playbook for business transformation
Our playbook is drawn from successful patterns at more than 100 engagements over three years.
As organizations globally frame their business transformation strategies and the enabling technology solutions for 2021 and beyond, they have to factor in massive, fundamental shifts underway in their markets. While COVID-19 has disrupted where and how nearly everything is made, delivered and consumed, the top themes industry experts have identified include organizational resiliency, maximizing customer experience and digital transformation.
Keeping in step with those shifts, we at Iris have developed a “Transformation Playbook” for our service offerings based on common patterns at more than 100 engagements over the past three years. This playbook is a confluence of five service categories: Interactive, Integrations, Data & Analytics, Cloud and Automation.
The playbook anchors on two core themes – customer experience and core engineering – which are enabled by the five technology service categories listed above. These have been the most success factors for our customers across the board.
How we engage
Four themes are common across any of the services that we provide.
- Agile program management
- IT strategy, architecture and design
- Application and quality engineering
- Innovation pods
Each of the five service categories has the following sub-services:
- Interactive: user experience (UX), user interface (UI), and mobile apps
- Integrations: API engineering and management, and middleware/ ESB
- Data and analytics: data transformation, business intelligence, and data science
- Cloud: cloud foundation, application migration, application modernization and cloud-native, and data analytics on the cloud
- Automation: DevOps automation, intelligent automation and test automation
Customer experience
Customers begin their planning and execution from the perspective of customer experience, which is the only differential today in their markets for their products and services. Therefore, all technology programs are designed with customer experience as the guide.
Customers typically use a combination of services. For example, they might start by drawing from a broad business strategy, and then weigh the desired business outcomes, before designing customer journeys. They might consider scenarios such as — what happens during an onboarding of a customer? Or, what happens during a trading lifecycle?
Once they map their customer journeys, they typically define the desired user experience in terms of the most intelligent and intuitive way of designing the channels. After they define that user experience, we design the channels, depending on aspects such as the customer persona and the key performance indicators or KPIs. Those channels could be a web channel, a mobile app or a chat bot. Intelligent automation is also used in designing the UIs; many of our services are about predicting customer needs, and proactively sending them, say, push notifications on SMS.
Business intelligence is another key factor in planning for maximizing customer experience. Here, we attempt to secure the right level of business intelligence with modern ways of data visualization. The insights from the data in terms of timeliness, value and intelligence are crucial in planning for the user experience. In the area of customer relationship management, we provide integration services for creating contact centers or for obtaining 360-degree views of customers.
Core engineering
The maximum value is achieved in core engineering. Here, the dominant technologies customers use are in automation and application development, API integration, data transformation, Artificial Intelligence/Machine Learning or AI/ML, cloud, DevOps and RPA (robotic process automation). Most projects have cross-functional parts, or mix and match of the various technologies.
The core engineering elements are the tools that maximize the outcomes in terms of customer experience. They address the customer experience, on which the IT roadmap or strategy is defined. That is how the backend is created for the front end. In IT industry parlance, the term “back-end for front-end” refers to how deeper technology systems are engineered according to what is needed in terms of customer outcomes and journeys.
The first aspect of core engineering work involves deep dives into application engineering; low-code and no-code — an evolving area, where with automation and application development, business users could directly define user requirements.
That leads us to the second area, which is evolving significantly — APIs and integrations. Many a time, once we have the definition of the desired user experience, modern APIs and integration layers can deal with information exchange from even the legacy and monolith systems very efficiently.
The third element of core engineering — data transformation — is about monetizing existing data and drawing the next level of analytics.
The fourth element has to do with AI and ML technologies. Once the data is well organized and transformed, we have seen the maximum impact, or the ability for AI and ML, to get the best insights. These are not just prescriptive analytics, but also predictive analytics using the modern machine learning and AI techniques.
The fifth element — cloud applications — is becoming pervasive. Most of our customers are either already on their cloud journey or have gone quite ahead in migrating their applications on to the cloud. That is not just in terms of infrastructure-as-a-services, but also in maximizing software-as-a-service and platform-as-a-service to get the best out of cloud platforms.
DevOps and Test Automation — the sixth element — have become the core in terms of connecting all of these engineering activities. These involve a high degree of automation, and they help in maximizing time to market.
RPA, the seventh element, is in effect an enablement layer for intelligent automation. Many of our customers have adopted RPA platforms like UiPath or Blue Prism to streamline many of their business processes. Once these processes are streamlined, the evolution of RPA towards intelligent automation occurs. At this stage, the effort is aimed at eliminating waste in processes and moving towards intelligent automation.
Cybersecurity technologies have also become pervasive. So every time we build applications, we create data structures capable of ensuring cloud security.
The Iris Garage
The Iris Garage represents a set of accelerators that complement the Transformation Playbook. Essentially, it shows how we engage with our customers. Our strategy here is to create for our clients a resource base of reusable assets, extracted from across our customers’ use cases.
We have about 20-plus accelerators, built on either open source or most prevalent platforms, along with API-based adaptors and deployment packages. We call these “open source-based reusable solutions” that are readily available for use by our customers.
Our accelerators
In customer experience, the Iris Garage accelerators include:
- Conversational AI: easy-to-use AI-based conversational interfaces.
- iASQ: an automated service requestor with ML-based channels and ITSM real-time response.
- AURA: automated research accelerator, which contains instant research summaries from hundreds of different sources.
- Report analyzer: this helps draw insights from complex data, rendered in summaries and charts.
In core engineering, the Iris Garage accelerators include:
- Report rationalizer: a structured and automated process to identify similar or duplicate reports and create master report templates.
- Data catalog: this enables contextual/domain perspectives, which enables customers to use data efficiently.
- Entity extractor: this helps navigate complex and lengthy legal contracts and extracts key entity by understanding its semantics.
- Self-heal UX test: this automatically adjusts UI and other functions in scripting.
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