Today’s modern enterprise leverages a digital business platform as the foundation for digital applications. A digital business platform provides the agility to build and support the dynamic nature of modern applications.
So what do these platforms look like?
A digital business platform is based on three pillars: intelligence, orchestration and automation. It is a primary driver of business transformation, as it will help turn data into insights for making informed business decisions. It gives companies the opportunity to bring together business process execution with analytics to enable a smarter, faster, more streamlined enterprise.
Three pillars of a digital business platform
Intelligence
Orchestration
Automation
The digital business platform is supported by a hybrid cloud-enabled development and operations platform that encompasses:
1. Microservices: Microservices focus on doing one thing well and are contextual to specific business domains, enabling integration and facilitating rapid delivery of new capabilities (for greenfield applications) as well as modernization (for brownfield applications). And, because they are loosely coupled, microservices better enable continuous delivery activities.
2. Agile development: Efficiencies can be gained by using agile and rapid development that employs iterative and incremental steps and offers improved collaboration and continuous feedback.
3. Big data and IoT repositories: Leveraging IoT is all about gaining operational and product insights from sensor data but requires intelligence at the edge to process the massive flow of streaming events, as well as purposeful centralization of information. Data needs to be ingested, tagged and aggregated for use in streaming, predictive and preventive analytics insights that can enhance the decision support and “intelligence” of applications.
4. APIs: Business ecosystems are defined by the relationships among the participants, and information exchange brings the ecosystem to life. APIs provide the common interfaces and formats. Ecosystems based on APIs allow applications built on top of a digital business platform to extend their reach by leveraging internal and external data in an agile manner.
5. Easy-to-use intelligent automation: The platform will provide an easy-to-use ML/AI foundation that will not be limited to IT. Democratization means that employees everywhere can access the functions and data they need to write new application logic as descriptive business rules and to use ML/AI algorithmic services that improve their productivity.
6. Virtualization, containers and platform as a service (PaaS): These make it easier to create loosely coupled components for application composition and reuse, and for simplifying the implementing of key nonfunctional requirements of digital applications such as security, resilience and availability. These are game changers, allowing rapid provisioning and scalability of “infrastructure as code” services needed for application development, testing, release and deployment to production.
Over time, the digital business platform will evolve from human-derived rules to machine-derived rules, and its purpose will be to make data available and publish it (rather than process it). Consumption will be driven by serverless architecture and multimedia interfaces. And the platform will be in constant evolution.
Most government IT solutions were created only with the intention of automating the back office and focusing on efficiency. Requirements were gathered from case workers and then converted into functionality. The resulting IT solution is entirely focused on the internal operating model.
A similar approach has been taken with most government websites, which are often designed based on a government agency’s internal organisational structure, resulting in a poor user experience for citizens. Far too often, citizens start out on a promising home page, only to get lost in the weeds of dead-end pages, incorrect forms, and even other websites as they try in vain to navigate the unfamiliar organisational structure of the government agency.
But citizens no longer live in an analogue world and they’ve run out of patience. They expect governments to present digital citizen services in an easy-to-use, always-on, self-service, personal, and proactive way.
How to deliver a digital government experience
To deliver an experience that meets expectations, it’s clear that we need another approach — one centred around citizens. Requirements and functionality should be derived from the behaviour of the citizen as a customer – what information or service they need, what problem they need to solve, how they want to consume the content — and not from the organisational setup.
This also means that the government needs to provide a seamless and transparent interaction across channels. There is no time to develop a single application per service. Instead we must think in terms of platform models, where new services can be introduced quickly on top of existing services and a standard approach used to build applications.
This kind of transformation doesn’t just involve technology; it requires the transformation of the government organisation itself to improve how it provides services to its citizens via digital channels. It requires a strategy that is endorsed by the organisation’s leadership and mandates a transformation toward a new operating model, new capabilities and processes.
Balancing front- and back office digital programs
Going digital is not just about revisiting current processes and modernising legacy systems. It is also about balancing programs in the front and back office. This is a critical strategic point. Most digital programs are focused on improving the front office, i.e. the websites or apps that citizens interact with. That’s good insofar as it suggests a focus on citizen interactions. However, that model is unsustainable when the back office continues on as before – manual and labour-intensive, using the same legacy applications, and creating a backlog of requests.
Balance your digital programs with these five enablers
Some governments have already got the message and are redesigning their services with this model in mind. In the United Kingdom, for example, the government has published a set of best practices for designing a good citizen experience. The United States is following along the same lines.
Based on these design principles and drawing on our own experiences working with government organisations, Anteelo has identified five key enablers of successful citizen experience transformation:
Use design thinking. Also called human-centred design, design thinking is a creative problem-solving process that makes the citizen the central focus designing a better experience. It is ideal for tackling front-office related aspects.
Experiment in an agile way. Traditional approaches such as waterfall development take too long to deliver value. An agile, more iterative approach allows for the kinds of experimentation that can lead to process (and application) innovation in both the front- and back-office. This experimentation is a vital component of any digital journey and must be endorsed to get people, processes and technology aligned to optimise the workload.
Invest to drive automation. Governments can greatly benefit from introducing new technologies to automate administrative tasks and interconnect and then dynamically manage public infrastructure. Back-office applications can benefit from a surge in efficiency in applying RPA for example.
Get new digital capabilities. Having the right capabilities and people with knowledge and experience is key to executing a digital transformation program. No organisation will be able to introduce new technologies and change the operating model if it doesn’t have the right capabilities among its workforce.
Become data-driven. Government organisations that embrace data can transform services and become more predictive, proactive, preventive and personalised. Becoming a data-driven organisation also brings internal value. For one thing, greater efficiency means better utilisation of resources. Most of all, it brings value to citizens’ experiences by better understanding their behaviour and engaging them in meaningful interactions.
These enablers, of course, only describe a few key pieces of a more complex puzzle. We explore each enabler in considerably more depth and show how to turn each into concrete actions that drive better citizen outcomes, in our new white paper, Five enablers for governments to serve today’s digital citizens.
Digital Transformation / Industry 4.0 is on everyone’s mind. Investors are happy to hear from organizations that they are embarking upon a complete Digital Transformation / industry 4.0 journey. Investors love it, leaders advocate for it, directors have to make it a reality, managers have to design for it, but few understand what all it means in the grand scheme.
Hopefully, we can simplify this world for you.
What is Digital Transformation? Let’s keep it simple.
The simplest way to describe Digital Transformation is “Using Digital technology, innovation and intelligence to find better ways to do various things that organizations do today. It’s not about creating something new, but more about improving effectiveness and efficiency of existing processes for better business outcomes.”
Digital Transformation started as Industry 4.0 in some places. However, the idea remains the same. While Industry 4.0 started with the intention of transforming the manufacturing processes using Digital Technology, the principles of Digital Transformation now apply to all functions across the organization.
How does this theory apply in practice? Let’s study an example:
Step 1 – Current State
Map out the current process to uncover gaps that can be filled with better technology or intelligence.
Consider a global paper products manufacturing company. The manufacturing industry team is constantly trying to find opportunities to improve efficiency and productivity and reduce costs.
Energy consumption is a big area of focus for the manufacturing team. Currently, manufacturing industry reports and energy dashboards are used to track the consumption of energy across a few important machine parts.
Operators use these dashboards to identify sections of machines that are in green/red (good/bad) zones in terms of energy consumption and adjust the settings to optimize energy consumption.
These dashboards only track a limited set of machine parts that influence energy consumption.
Step 2 – Future State
Outline what the future should look like, after Digital Transformation.
Energy consumption of machines at the mill (specific reference to Tissue Machines) can be reduced by finding the key driving factors of energy consumption, determining their optimal settings while factoring in for the production constraints in terms of time, quantity and quality.
The following challenges will have to be addressed to get to the future state
There are a few hundred variables in a tissue machine that determine the energy consumption. These machine variables have to be studied comprehensively to identify the key influential factors for energy consumption. Relationships between these variables also need to be considered.
A detailed and statistically robust mechanism is created to generate insights/correlations across all relevant machine variables, to take proactive steps to minimize energy consumption.
Study the process characteristics that influence energy consumption and optimize them. E.g. machine speed, maintenance schedule, aging of parts.
Step 3 – Establish how technology, data and analytics can bridge this gap.
Select a machine, in a market, which can be managed and monitored easily. Maturity in terms of capturing data, and the groundwork that has already been achieved for manufacturing systems and lean energy dashboards provides an immediate feasibility in terms of execution and adoption.
Build a Driver Model to understand key influential variables and determine the energy consumption profile.
Identify Key Variables –
There are ~ 600 machine parts that drive the consumption of energy of a tissue machine. First, shortlist the top contenders and eliminate the non-influencer variables, using inputs from technical teams and plant operators.
Identify primary drivers among the selected machine variables using variable reduction techniques of Machine Learning.
Driver Model –
Building multivariate regression models to understand the impact of top drivers of energy consumption using techniques like linear regression, RIDGE/LASSO regression, Elastic Nets.
Optimize the engine to lower energy consumption.
Optimize energy consumption by identifying the right combination of drivers under the given production constraints – time, quantity and quality.
Create a mechanism to provide guidance during the actual production hours (In-line monitoring).
Track energy consumption of the machine parts and their active energy consumption states. Identify deviation from the standards.
In case of deviation, provide guidance to machine operators to bring the energy consumption to within defined limits.
Adoption
Real-time dashboards, refreshed weekly, provide charts on energy consumption, recommendations, and improvements achieved through proactive measures.
Post-live support to operations teams to enable adoption.
Scaling
Determine phased roll-out to other machines using
Strategic initiatives.
Machines or mills which utilize higher amounts of energy to target higher ROI.
Similarity in process and parts characteristics of tissue machines.
Data availability and Quality.
Readiness and groundwork for adoption by plant operators and energy management teams.
4 key stages in Digital Transformation
How should you, as a leader in an organization, look at Digital Transformation? Organizations should consider the 4 key stages of Digital Transformation, in order to create a sustainable impact on their organization. To make Digital Transformation a reality, all these steps cannot work independently. The philosophies of Design Thinking are embedded in the framework’s interconnected elements.
DEVELOPMENT PHASE:
Focus is on identifying the key areas and prioritizing the Digital Transformation efforts
Stage 1 – Discovery
Identify the key areas of opportunity or risk and related key stakeholders. Detail out the gaps in process, data, insights or technology, fixing which would help capture opportunities or mitigate risks.
Stage 2 – Design
Rapid iterations on design and implementation of prototypes helps reach optimal solutions faster. Build out Proofs of Concepts (PoC) to establish the theoretical validity of the approach. Validate the practical validity of the approach through a Proof of Value (PoV).
IMPLEMENTATION PHASE:
Implementation needs to account for limitations arising from human behavior and scale of the operations.
Stage 3 – Adoption
Building solutions that keep the user at the center of the design, is key to adoption. This means that users must be included in the design and feedback early on. In addition, there should be support for users post design, in the form of FAQs, training videos, chatbots etc.
Stage 4 – Scalability
If we can’t solve a problem at scale, then the solution does not solve organizational problems. The issues that we anticipate at scale, should be accounted into the design in the Development phase. This means considering the technology used, the infrastructure required, process automation possible / required and how to manage future developments.
Like Design Thinking would dictate, the Development phase of the Digital Transformation processes have to always consider the Implementation aspects.
Digital Transformation is no longer just optional.
Every organization is transforming the way they do business. Numerous organizations like BASF, Mondelez, KLM airlines, Aptar group, PepsiCo etc. are already making massive strides in this area.
If you want to zip past your competition, or even stay competitive, it’s about time you started thinking about how to transform the way to do business. After all, there’s no growth in comfort.
