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Digital processes and data-driven decision-making company

Business environment

Every company is built on two main operational drivers: to create and capture value and to create flexible and robust operational model that will enable scope, scale and development (learning). In order to create value, companies should know who their target customers are, they should understand “job to be done” by their products or services and understand their customer needs (through collecting customer feedback) or uncover unknown customer desires (through collection of small data in combination with big data analytics). In order to capture part of that value and monetize it into company profit, companies must understand market potential, market environment and mechanisms for value capturing. If companies got the main part right – value creation, they should be able to execute both creation and capturing in the proper way in order to stay competitive. This is where operational excellence comes in play. If companies are not able to deliver on their promise and they don’t have the flexibility to adjust to changing environment, there will be others who will find a way to deliver their value proposition better, quicker and more efficiently.

For companies to be successful, they need to get four things right: customer value proposition; profit formula (revenue model, cost structure, margin model and resource velocity); key resources (people, technology, equipment, information, channels, partnerships and brand); key processes (processes, rules and metrics and norms).

In adjusting to changing environment, companies are not only fighting with their traditional competition, but also with new entrants. There are two distinctive competitive advantages that companies are using in order to: either get operational performance up or develop new value creation practices. One is digitalization of processes or even whole business model and second one is making better insight and improving decision processes with usage of bigger, better, more relevant data resources.

Digital transformation

If we are talking about digitalization of processes, that should improve operational capabilities, we can see that a digital representation is infinitely scalable – it is now possible to easily and perfectly communicate the pattern it represents, replicate it and transmit it at virtually zero marginal cost to a near infinite numbers of recipients, anywhere in the world. Moreover, digitizing the activity makes it easily connectable, also at zero marginal cost, to limitless other, complementary activities, dramatically increasing its scope. Finally, the digital activity can embed processing instructions – AI algorithms that shape behavior and enable a variety of possible paths and responses. Traditionally, the intrinsic scalability, scope amplification and learning potential of technology was limited by the operating architecture of the organizations that it was deployed in. Increasing levels of digitization, analytics and AI/ML can dramatically improve the scalability of a business, making the value curve increase more rapidly as a function of the number of users or their engagement. The essence of the digital operating model is avoiding direct human intervention on the critical path of the product or service-delivery process. While employees help define strategies, design user interfaces, develop algorithms, code software and interpret data, the actual processes that drive customer value are fully digitized. With this improvement the marginal cost of serving an additional user on many digital networks is, for all purposes, zero, apart from the small incremental cost of computing capacity, which is easily available from cloud service providers. When fully digitized, a process can easily be plugged in to an external network of partners and providers.[1]

With digitalization we can see emergence of new business models. We can talk about two main trends that are both based on network enabled business approach:

  • One is sharing economy and companies that are using non-traditional model of ownership in order to offer better utilization of resources with sharing approach. If we can solve three main challenges triangulation, transfer and trust more efficiently this model will grow even further. Main value proposition is access to excess capacity.
  • Second is platform business where we are seeing emergence of strong platform players like Amazon and Alibaba, that are developing their support business and are offering lower entry costs for large network of smaller players. It is true that platform owners are using their platform capabilities to move into different industries, trying to create different value capturing models and are using enormous data collection from platform generated activities to improve their value proposition and by doing so, they are becoming even stronger players in the economy. By doing so they represent certain risk for ecosystem development, but in this phase, they are more threat to existing traditional players in the industries that they cover with their ecosystem, then to the ecosystem it-self.

As mentioned, network business can represent a threat or an opportunity. Companies that are thinking about responding to this trend and moving into network-based business model should ask them self some questions like[2]:

  • What is the core service delivered?
  • What networks are key to providing that service and that what are their characteristics? Do they have strong learning or network effects? Are they clustered?
  • If network and learning effects are weak, how do you strengthen them over time? How do you increase the value delivered?
  • If the network effects are strong and there is very little value delivered until critical mass is reached, how do we get there?
  • What are the most important secondary networks? Can they enable additional network or learning effects?
  • Do we have challenges with network clustering? Multihoming? Disintermediation?
  • What are the best value capture opportunities?
  • Are there network bridging opportunities? Considering the data, you can accumulate from your core network, is it of value to another network?

Datafication

When we are talking about using data, not only for better understanding of existing business, but mainly to make better decision for new initiatives, better creation of value proposition and new ways of value capturing, we first need to understand what do we already have, how we can use it and what do we need from data perspective in order to reach our business goals.

Datafication is a way of building measurement into our work. Starting that project, we need to: do data audit, list our business functions and decisions we need to make and connect them with data we need, to do them. Then we should categorize that data. Prioritize the data we need and build ways to get data we don’t have. And at the end, we need to find a place to put the data in.[3]

As mentioned, we can use data either to understand business and make decisions based on it or move it into higher gear with incorporating data into our business processes and use them to create even more competitive advantage. Business intelligence is not the same as data science. It doesn’t carry detailed investigative analyses on the data, it simply describes what has happened, in a process that we call “descriptive analytics”. If we look at analytic value escalator, we can estimate four stages:

  • Descriptive analytics: What happened?
  • Diagnostic analytics: Why did it happen?
  • Predictive analytics: What will happen?
  • Prescriptive analytics: What should we do?

And when we move into the Data Science Process, we can talk about five stages:

  • Identify the question.
  • Prepare the data.
  • Analyze the data.
  • Visualize the insights.
  • Present the insights.[4]

When transforming a company into data-driven organization, it is important to get data strategy right in order to work on people, architecture and processes, based on it. Data strategy should[5]:

  • be owned by the CDO (chief data officer),
  • be endorsed and supported by the board,
  • be published and shared,
  • be in a form,
  • be organic document with integrated flexibility to change,
  • be aligned with business goals,
  • address people, processes and technology,
  • describe the end state of dynamic data-driven transformation and
  • have technical document being placed in appendices.

AI is the next step into creating value with use of new technologies and especially data. There are several different technologies that are all called AI. This can sometimes lead to terminological mess and can cause some complications in communication about usage of AI. In general, we can talk about seven technologies and their usage[6]:

  • Statistical mechanical learning – that automates process of training and fitting models to data and it is used for highly granular marketing analyses on big data.
  • Neural networks – that uses artificial “neurons” to weight inputs and relate them to outputs and is used for identifying credit frauds and weather predictions.
  • Deep learning – which is neural networks with many layers of variables or features, and it is used for voice recognition and extracting meaning from text.
  • Natural language processing – that analyzes and understands human speech and text and it is used for speech recognition, chatbots, intelligent agents.
  • Rule-based expert systems – is set of logical rules derived from human experts and it is used in insurance underwriting and credit approval.
  • Physical robots – that automate a physical activity and are used in factory and warehouses for different tasks.
  • Robotic process automation – is automating structured digital tasks and interfaces with systems and is used for credit card replacement, validating online credentials.

Companies that are successful already today with use of AI and have moved beyond only automation are following five principles that are connected to their organizational mindset, experimentation, leadership, data and skills (MELDS)[7].

  • Mindset – assuming radically new approach with using missing middle principle, people improving AI and machines give humans more power.
  • Experimentation – actively finding potential for testing AI and to actively use reimaged process with use of missing middle.
  • Leadership – making a commitment for responsible use of AI.
  • Data – building a data “supply chain” to fuel intelligent systems.
  • Skills – develop new “fusion skills” in order to enable reimagining processes.

Strategy and skill development in data and digital ready environment

When we are looking at maturity model of companies in their quest of becoming digital and data driven companies, we should estimate them based on their development in this fields[8]:

  • Strategy
  • Corporate governance
  • Leadership and sponsorship
  • Framework, process and tools
  • Policies
  • Information risk
  • Architecture
  • Organization, roles and responsibility
  • Skills
  • Metrics
  • Information guardianship behavior
  • Technology

So, moving company into digital and data realm, must be accompanied with new skills and management development. There are certain areas that will become more important than before:

  • One is ability of business lines to understand technology implications, to take responsibility for using new digital capabilities to improve processes, define new business practices, use digital approach for establishing new value creation practices and value capturing mechanism. In order to do that, it is business line responsibility to define business needs and work with IT group, to set up proper data and application framework for the most efficient workings on those needs. In order for business lines, to better understand, not only technology capabilities in the field of digitalization of processes and data usage, but even more important, creation of potential new business practices, that can be developed with use of new technological developments, companies should invest more into improvements of technical, data usage and process understanding skills of business line employees.
  • Second is creating new management. New breed of organization transforms the role of management. Management as supervision, especially of employees performing routine tasks, is finally over. Managers are designers. They should work on shaping, improving and controlling the digital systems that sense customer needs and respond by delivering value. They are also innovators, integrators and guardians that work to preserve the quality, reliability, security and responsibility of the digital systems.[9]
  • And third is creating new leadership. In contrast with the current wealth of data, analytics and AI, we still appear to be suffering from a shortage of managerial wisdom. There are four areas in which this leadership mandate is playing out[10]:
    • Transformation – it demands leadership commitment and there is no plan B for digital transformation. Qualification for leaders should start with an understanding of the digital systems they are creating and leading.
    • Entrepreneurship – AI is offering one of the biggest entrepreneurial opportunity in the history of civilization. Blockchain is one area where impact is lacking promise.
    • Regulation – regulators are racing to catch up with the evolution of technology. Collaboration between public and private is needed.
    • Community – communities are an increasingly important complement to regulation in providing checks and balances to digital firms. Think about the power of Wikipedia and open source software.

As digital firms increasingly shape our global economy, their management will be held accountable to a different standard.

Framework for decision making

Together with new skills development, we should take care of creating proper framework for using vast amount of data and ability of digital processes for better decision making. Decision model should represent a potential framework to better handle large information pool and faster change rates. The Empirical & Experiential Evidence (3E) Framework is an attempt to give management practices operational framework. It sees operational data at its core, but also see theoretical research informationally valuable. The 3E process begins with within-source steps: identification of distinct sources, then assessment of available and applicable data and aggregation of type-specific evidence; then you move to cross-source steps defining weights, agglomeration and then incorporation of evidentiary conclusion into the decision-making process. Every informational source on it owns can be imperfect, so we use informational triangulation, to come to better conclusions. 

Schema of 3E[11]:

3E framework

Consumer experience

With changes in business models, new business practices, based on digital processes and extensive data usage, will enable companies to focus more on delivering proper value through improvements in customer experience. New management and business skills should prepare business lines for better handling of information flows and usage of digital practices for more relevant, flexible and continuous delivery of improved value proposition.

One approach can be called connected architecture or connected strategy[12]. There are five main types, that are used across industries:

  • Connected producer
  • Connected retailer
  • Connected market maker
  • Crowd orchestrator
  • Peer-to-peer network creator

Connected strategy have two key elements: a connected customer relationship and a connected delivery model. Connected customer relationship is about four stages in customer journey: recognizing (the need), requesting (to satisfy that need), responding (company reaction) and repeating it over and over again. Connected delivery model is about: connected architecture, revenue model and technology infrastructure.

When looking to build strong customer experience, with a help of data and digital technologies, you should focus on four parts of CE framework[13]:

  • Doing It Right the First Time (DIRFT)
  • Encouraging easy access to service via effortless channels. (access)
  • Creating a complete customer service experience via every communication channel. (services)
  • Listening and learning by creating an effective VOC (voice of customer) process. (L&L)

People, processes and technology must be properly tuned in nine areas to create a flexible, customer-focused culture.

  • Clear brand promise – the brand promise is the foundation, which directs the people, processes and technology in the next eight areas.
  • Clear accountability for delivering the brand
    • Formalized structure
    • Leadership that leads by example
    • Peer accountability
    • Employee empowerment
  • Focused values translated into operation guidelines
  • Formal processes for all key activities
  • Measurement and feedback – direct data sources include customer satisfaction measurement, complaints, market research, social media and VOC processes. Indirect sources include quality measurement, operational data, online reviews sites and continual feedback on employees.
  • Ongoing communication to customers, channels and employees
  • Intentional emotional connection
  • Employees who deliver the brand – companies can teach people the necessary skills but cannot change their basic personalities.
  • Customized brand for market segments and geographies

Summarizing the steps for building better approach in digital and data driven economy

If you want to prepare a company and its people for digital and data driven economy, you must:

  1. Understand where, inside the business model, you want to improve your capabilities. Will it be on value side or operational efficiency.
  2. Understand and adjust your business to digital and data-driven reality.
  3. Improve ability of your people to tackle new requirements – data and digital literacy.
  4. Set up proper framework for decision making, move it beyond intuition.
  5. Connect new environment and technology with value creation in the form of better customer experience.

[1] Marco Iansiti, Karim R. Lakhani: Competing in The Age of AI; Strategy and Leadership When Algorithms and Networks Run the World

[2] Marco Iansiti, Karim R. Lakhani: Competing in The Age of AI; Strategy and Leadership When Algorithms and Networks Run the World

[3] Tim Phillips: Data-driven business; Use real-life numbers to improve your business by 352%

[4] Kirill Eremenko: Confident Data Skills; Master the fundamentals of working with data and supercharge your career

[5] Caroline Carruthers, Peter Jackson: Data-driven business transformation; How to disrupt, innovate and stay ahead of the competition

[6] Thomas H. Davenport: The AI advantage, How to Put the Artificial Intelligence Revolution to Work

[7] Paul R. Daugherty, H. James Wilson: Human + machine: Reimagining Work in the Age of AI

[8] Caroline Carruthers, Peter Jackson: Data-driven business transformation; How to disrupt, innovate and stay ahead of the competition

[9] Marco Iansiti, Karim R. Lakhani: Competing in The Age of AI; Strategy and Leadership When Algorithms and Networks Run the World

[10] Marco Iansiti, Karim R. Lakhani: Competing in The Age of AI; Strategy and Leadership When Algorithms and Networks Run the World

[11] Andrew D. Banasiewicz: Evidence-Based Decision-Making; How to Leverage Available Data and Avoid Cognitive Biases

[12] Nicolaj Siggelkow, Christian Terwiesch: Connected strategy, Building Continuous Customer Relationship for Competitive Advantage

[13] John A. Goodman: Customer Experience 3.0; High-Profit Strategies in the Age of Techno Services

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