Strategies Beyond "In House" to Address Digital Transformation

This recent EY study on the Digital Deal Economy caught my attention. The survey of 600 non-tech corporate executives shows that 74% believe that digital transformation has either substantial or transformative impact on business operations, 66% on customer relationships  and 59% on vendors and supply chain. The implications is that there are still a sizable number of executives that still "don't get" the disruption and opportunities stemming from digital. That's scary if you work for one of these executives or businesses.

But what really caught me was this question and answer on growth

EY Digital Transformation Strategies

possibly implying that organizations only have these options to invest in digital transformation. You can either develop in-house (organic), outsource, or acquire digital capabilities. This is a simple, broad classification and larger organizations are likely to leverage all three depending on need and circumstance.

But it got me thinking about the language EY used around "organic" options.

What does "develop in-house" mean in a Digital organization?


Developing "in house" is an outdated term coming from when organizations could either build or buy technology. Buying effectively meant you were purchasing software and developing the business needs around its capabilities. Many organizations buying software tended to leverage "out of the box" capabilities because of the cost and complexity configuring and customizing enterprise solutions. Of course this often proved harder to achieve than expected since you needed talent trained and experienced to develop off these platforms and have practices to maintain the customizations whenever the software was upgraded.

The alternative of course was to hire programmers and develop custom solutions. Business executives often drove for custom solutions whenever they demonstrated a competitive advantage versus off the shelf software options, but often failed to fund ongoing development efforts. The result is that many proprietary solutions evolved to "legacy applications" because of the lack of investment and attention.

But there are other categories that may still be "organic" but I would argue against classifying them as having the same issues as "in house". You can license platforms that enable citizen development where operations and development of platform capabilities are outsourced to the SaaS vendor, and application development can be done internally, by acquiring applications from an app store, or by outsourcing. These solutions offer greater flexibility and fewer complications to maintain versus what we commonly associate with "in house" solutions.

Another "digital" solution is enabled by integrating multiple capabilities into a proprietary implementation. You might develop a solution using an IFTTT product to move data between platforms, selected functionality from platforms that are API driven, and RAD mobile tools to configure front end user experiences. None of this may require actual "development" (as in coding) and require more knowledge of how to integrate solutions and configure tools.

Businesses in the midst of a digital transformation need to look at these and other organic options. It's also time to bury the "in house" context and realize that there are many innovative solutions that don't carry the same issues as the "build or buy" solutions of the past.


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Why Agile Data Science Practices Drive Big Data Impact

With the hot weather in the US northeast this weekend, I was able to catch up on some reading and research around big data, data science, and agile. In thinking about the culture required to leverage big data capabilities and data science programs, I found some interesting data worth sharing.

The first comes from Computing Big Data 2016 -


Data Science Succcess

This is showing that the collaboration between technologists, data scientists, and business leaders is a key success factor to make data scientists thrive in an organization. It also helps to have senior backing and either a strategy or set of priorities identified. This isn't exactly surprising, but to address the collaboration required I have suggested using agile practices to perform discovery work and aligning data science and IT responsibilities in data science programs.

An Accenture Report on The Team Solution to the Data Scientist Shortage also accents agile practices as a method for growing and retaining data science talent

In addition, the time-proven wisdom about managing teams bears repeating: Data scientist teams, like others, flourish best when there is effective leadership, a strong mandate from above and clear goals. They require a path for taking projects from design through implementation. Like many projects in the IT world, they benefit from working in rapid, iterative sprints of preparation, analysis and review.

If agile is the answer to enable collaboration, then what is the problem or challenge to getting senior membership buy in? McKinsey articulates this well in their report Getting Big Impact from Big Data

Management teams frequently don’t see enough immediate financial impact to justify additional investments. Frontline managers lack understanding and confidence in the analytics and hesitate to employ it. Existing organizational processes are unable to accommodate advancements in analytics and automation, often because protocols for decision making require multiple levels of approval.

Agile Iterative Analytics Drives Buy In


Bottom line is that big data capabilities and data science programs take time to mature, but they don't necessarily require extensive efforts to provide business value. These teams have to demonstrate quick wins to senior leaders so that they don't lose interest in the program. Data science teams also have to take on some responsibility to help frontline managers to leverage the analytics and become more data driven.

If you want big impact from data science and big data, then think of demonstrating wins incrementally. Agile software teams perform frequent releases to drive incremental impact, capture feedback, and promote the next set of priorities. Data science programs should adopt similar practices.
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Digital Leaders Must Embrace the Lion

Become the Lion
What does this picture make you think about?

My first thoughts veer to a post I made last year, Agile Culture - Are You Developing Solutions or Solving for Business Opportunities? In that post, I suggested that agile product owners first must study the market and understand what problems that they can solve for customer and sell profitably. However, here's what I hear from less experienced product owners, "Can you build me something that does A, B, and C for Users X and Y and have it completed within the next couple of weeks." Now some product owners today are technical enough to come up with solutions, but their job is to articulate the business problem, market opportunity, and priority.

It reminds me of several examples when leaders hold onto sacred cows because they are subject matter experts and are ill prepared to collaborate when digital transformation is a necessity. By definition, when we are trying to transform we have to be able to question everything, challenge assumptions, bring in new data and facts, and develop new innovative thinking. 

It also makes me think of the impact of transparency. When you produce KPIs, operational metrics, agile development metrics, quality scores and other measures, are you more likely to get supporters and champions? Or do you get detractors who pick at a metric without the underlying context, claim you're doing something wrong, and try to use the data to promote their own IT solutions or their own way of doing things?

That's why this quote resonates with many CIO, IT leaders, and digital leaders. Become the lion or be the wolf?

Truth is, transformation requires a balanced approach. A lion, a wolf, a dove, a dolphin. The hard part is deciding what to be when.

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Leading Digital Transformation? Ask this question -> What is Everyone Working On?

What is Everyone Working On
The most simple of questions easily exposes the messiness of running IT organizations. The larger the organization, the more likely this simple question will lead to others that expose larger business, execution, talent, funding, process, governance and other issues.

It's the first question I ask as a new CIO and it's one that I repeat through monthly project portfolio reviews. It's particularly important to do in the months leading into budget season so you have the means to update roadmaps and promote new investments. It's a more critical discipline for organizations that are executing a digital transformation program, growing revenue rapidly, exploring strategic positions, or driving a business turnaround.

Why Portfolio Management is Important for Digital Transformation?


Portfolio management is a boring topic. Many CEOs loathe the bureaucracy and have a tainted view of the practice based on failures by the "Project Management Office", investments in management consultants, technology investments and other mechanisms to organize and prioritize initiatives.

So why is it critical for CEOs, CIOs, CFOs and CMOs to address this challenge? Here are a few reasons

  • Some of the best ideas come from employees and are not top down driven from strategic priorities.
  • Organizations need to pivot, speed up, slow down and stop initiatives with more frequency and agility as market conditions change, new customer opportunities are discovered, and competitive threats become disruptive.
  • Most strategic initiatives can no longer be executed well in silos and require staffing, expertise, and operational changes in multiple departments.
  • Improving customer experience is not just about front end customer interactions. Organizations also need to consider how to make operations more responsive, how to make data more accessible, how to address security or compliance requirements, and how to drive out costs in order to succeed at transformation.

So, if you have a portfolio practice are you updating it for the digital speed? If you don't have one, are you looking for a nimble way to implement it?

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Succeeding at Artificial Intelligence Requires a Commitment to Agile Experimentation

I went to graduate school and got my Masters in Electrical Engineering, but that's not what I studied. The University of Arizona had a strong program in optical sciences, medical imaging, information theory, and something called "machine learning" and I opted to take classes and ultimately complete a thesis on these topics. I remember learning the math and computing of neural networks, the computer vision algorithms behind facial recognition, and the underlying mathematics of mpeg encoding.

And I remember spending countless hours in a lab testing algorithms on a Unix workstation. Would a reinforcement learning algorithm work better than a two layer neural network? Should a genetic algorithm work better, or am I programming it incorrectly? Should I apply a fuzzy controller, perform an operation in the Fourier space, focus on heuristics or prove out the underlying mathematics?


AI Landscape

Most of what I remember is waiting for that workstation to spit out a result. There wasn't a supercomputer that I had access or a cloud environment where I could ramp up and run several experiments in parallel. In the end, artificial intelligence back then was a lot of experimentation between what data sets to test, what algorithms to apply, what parameters to configure, and how to best program them to get better performance.

Are you Ready to Experiment with AI and Machine Learning? 


I've been fortunate to have had some opportunities to develop artificial intelligence in business applications. I've developed or led teams to develop tools that enable comparing genetic and protein samples, natural language processing algorithms to extract search terms from newspaper classified advertisements, and document processing techniques for extracting building material names from construction blue prints and building specifications. What was common across all three applications is that it required significant experimentation, first to get the basic algorithm in place and then later to build up more intelligence to handle more disparate use cases with increasing quality and performance.

Is AI Today Fundamentally Easier?


The simple answer is yes, but the longer answer may be no. Today, a developer can access AI through APIs provided by IBM Watson, Google Prediction, Microsoft Cognitive Services, Amazon Machine Learning and many others MLaaS (Machine Learning as a Service) or AIaaS (Artificial Intelligence as a Service). The largest startups in AI have been funded north of $10M and the biggest companies are making multiple investments. There is a published Intelligent App Stack illustrating use cases, machine intelligence providers, and data prepping tools and other AI Marketplace overviews.

AI Technologies


But the longer answer is maybe not. As a business person with an opportunity to apply AI or machine learning or a development team that has the priority to implement, you have many options to implement. You need developers that can normalize data sets and plug into APIs or third party services. You need data scientists that have at least some familiarity with the basic algorithms from clustering to neural networks to deep learning. You need subject matter experts that can validate the output and suggest improvements. Most importantly, you need an agile experimentation process to try approaches, configure, run, and validate results.

Lots of choices, lots of talent, lots of time to implement. But the rewards can be significant.

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Defining Digital Transformation, Strategy and other Digital Terminology


    In my last post, I took a stab at defining the Digital Mindset from the perspective of different roles and responsibilities in the organization. In doing so, I used several terms, some industry standard and others that are emerging so with this follow up post I thought I'd provide more color on my definition.


    Digital Transformation


    Digital Terminology


    • Digital Transformation - See my older post older post, I provided my definitions for Digital Business and Digital Transformation.

    • Digital Strategy - If digital business defines an end state and transformation defines a path to get from current state to end, then Digital Strategy sets the scope, priorities and constraints of the transformation. It may state what customers segments you'll target, what digital capabilities are priorities, and what regulatory and other business factors are potential constraints. Strategy often communicates scope, priorities, and constraints in the forms of mission, goals, and values to help members of organization translate a digital vision to "what needs to be done".

    • Digital Opportunities - These are opportunities to grow revenue, new customers, and new markets through digitally driven products and services. It should be specific, so for example, "Investing in this [capability | new product | new service | product/service enhancements | new channels] we can grow revenue by [increasing prices | selling to a target customer list | growing market share in these areas]".

    • Digital Capabilities - As a technologist, I also use the word "Digital Capability" to identify technical attributes of products, services, supply chains, or business processes that strategically developed around a technical capability without assumptions on the underlying digital platform. This can include everything from reaching new customers through digital channels (a mobile digital capability, a social digital capability), the capability to process big data (big data capabilities), the ability to integrate with things (IoT), the ability to source work in a business process algorithmically (crowdsourcing, data processing services, AI as a service), or the ability to automate and integrate transactions (digital supply chain, blockchain). Digital capabilities are also what we deliver to the organization to make them more smart, efficient and collaborative and are properties of underlying systems that enable elasticity and automation.

    • Digital Products - These are products designed a digital customer experience first and add "brick and mortar", "paper" and other "analog" experiences later if needed. These products take advantage of artificial intelligence, location awareness, voice controls, interoperability with sensors, and access to a digital ecosystem of services to optimize the user experience and provide personalized conveniences.

    • Digital Disruptors - Are competitors that offer Digital products that can challenge or disrupt an existing business or product line. See my example of digital disruption in the newspaper industry.

    • Digital Interoperability - Implies that the data collected in one system, application or interface can easily be leveraged in other platforms. Some interoperability is achieved through a unified customer experience, for example, when an application provides access to all your data and provides similar experiences when accessing over web, mobile, and other interfaces. Sometimes interoperability is achieved when vendors agree on data and interface standards. Lastly, interoperability can occur through integration platforms such as ifttt, Zapier, and other iPaaS providers.

    • Digital Ecosystems - Digital ecosystems form when there are a wide range of services that can be easily interfaced that provide digital capabilities. Participants in the ecosystem often develop application programming interfaces (APIs) and other data interfaces to enable sanctioned consuming applications to access these services. The availability of these services implies that businesses can develop best in class applications by leveraging third party services in the ecosystem. They can also extend their customer reach and develop new businesses by making their own digital capabilities accessible. 

    • Digital Platforms - Are technologies selected by enterprises and organizations to be foundations for their digital businesses and to support digital transformation. I have a previous post on criteria to select digital platforms and proposed a process to evaluate transform enabling technologies.

    • Digital Processes - A business process that is heavily automated, robust to respond to evolving business scenarios, and is highly data driven. They can be contrasted with manual, paper driven, heavily stage gated processes that are inefficient, error prone, or have bounded scalability. McKinsey defines digital process innovation as, "A focus on the implementation of new or enhanced technology-enabled ways of working—or digital process innovation—can help companies simplify the technology landscape, reduce overall IT costs, and bring products and services to market quicker, thereby realizing greater earnings potential"

    • Digital Channels - Omnichannel is when customers have multiple ways they can interact with a product, service, business, sales person, or customer support. It also is when the sales, marketing, and support organizations have multiple ways they can interact with customers, prospects, leads, and users. A digital channel is when the collaboration, customer experience or communication occurs using digital tools, contrasted with in-person or physical channels.

    • Digital Marketing- Marketing activities to customers, prospects, and leads through digital channels. See building capabilities in digital marketing or benchmarking digital capabilities as good references. 

    • Digital Practices - Are attributed of the organization defined through roles, responsibilities and talent that implement digital strategy and transformation. MIT has a good write-up on these digital capabilities your organization needs and McKinsey's write-up discusses strategies for acquiring digital capabilities. Also see my post, What practices are needed for Digital Transformation.

    • Digital Talent - Digital talent refers to having individuals with digital skills. Common digital skills are in leveraging mobile tools, data analytics and visualization, social networking, collaboration tools, abilities to configure web tools, and even some basic coding. Beyond these basic skills, digital skills can be very role and organization dependent. If you are in sales, digital skills often includes understanding of CRM and sales automation. If you're in finance, you should have skills working in one or more ERP platforms. Here's a good study on developing digital talent.

    • Digital Workplace - From Gartner, the Digital Workplace enables new, more effective ways of working; raises employee engagement and agility; and exploits consumer-oriented styles and technologies.When I think of the digital workplace, I think of unified communication tools, collaboration environments, and enterprise mobile capabilities all aimed to help teams share information and be more productive.
    Did I miss any?


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    What a Digital Mindset Means Role by Role

    Digital Mindset
    We all hear and say that "Digital" requires a different mindset, a "Digital Mindset, but what exactly does that mean? If you think digital mindset is about having the latest gadget, or being the go-to person to configure your mobile device, or knowing the latest startup that is likely to disrupt an industry then you're missing the point. If you believe that digital natives have a leg up on having a digital mindset, then that might be the case when it comes to how they conduct their personal lives but not necessarily sufficient to be an active participant or role model when an organization requires a digital mindset.

    Having a digital mindset implies a new set of values and principles for the organization but applied individually based on role and priority. It starts with how customers perceive the organization's brand and their expectations to how fast and smart their product and services are relative to other options. It's services by an organization that also needs to be smarter, faster, safer, relevant internally to be able to deliver competitive offerings. This often requires individuals to understand and leverage digital technologies in new ways, and to shed old habits that are slower, inefficient, insecure, or digitally isolated from standardized practices.

    Do you have a Digital Mindset?


    Here is a summary of what having a Digital Mindset means role by role


    • As a customer, I want relevant, contextual insights that are important and useful. I want the algorithm to know my preferences and take action on my behalf where I permission.  I want freedom of digital selection anbd expect product and service interoperability. I require organizations to secure my information and want transparency on how it is used and shared.
    • As a developer, I want to be able to experiment with new digital platforms, develop prototypes, and test pilots without having to demonstrate business value and ROI up front. I want to work in an agile, low stress organization that enables me to develop solutions that empower customers.
    • As an IT engineer, I want to make sure that key operations to administrate applications are automated and that applications are secure in environments that can quickly scale up and down based on business demand. 
    • As a business analyst, I want to leverage six-sigma processes to document existing business processes so that they can be digitally reinvented using agile practices.
    • As a sales person, I want to make sure that products are competitive and continue to receive investment aligned with customer needs. I want to make sure that the CRM has the most accurate relevant information on my interactions with prospects and customers.
    • As a data scientist, I hope we safely collect data from our products, internal processes, and enterprise systems, ask questions, and leverage data in decision making.
    • As a product manager, I want to develop digital-first products that delight customers, enable their success, and grow revenue. I want to continually measure customer feedback and invest in new capabilities and partnerships that align with market needs.
    • As a marketer, I want defined KPIs around brand, leads, and customer plus the ability to experiment with digital marketing tools to optimize how we reach and what we message to customers and prospects.
    • As a customer service representative, I require appropriate access to all the information on the customer I am speaking to so that I can provide the best advice to solve their issue or present the most likely opportunities that service their needs.
    • As a financial analyst, I want to make sure that my forecasts are available to appropriate business users in the form of dashboards and data visualizations so that they can review KPIs, ask questions, and drill into the underlying data.  
    • As a change agent, I want to align the organization to future values, enable people to shed yesterday's ideals and practices, and promote behaviors and activities aligned with the organization's digital strategy. 
    • As a human resources leader, I want to make sure that we leverage digital tools to enable the workforce and ensure that we recognize and reward employees that are actively enabling the organization's digital future.
    • As a manager, I want the organization to make quick decisions. I want support of my colleagues when we have to do things fast and find ways to collaborate on solutions.
    • As a leader, I want to participate in creating a digital vision then make sure that we can review and adjust priorities based on customer need, market conditions, and our ability to execute.
    • As a shareholder, I want to make sure the organization's leadership understands and has plans targeting digital opportunities and digital disruptors while ensuring they meet digital security, privacy, regulatory and other compliance requirements. 

    But it's not enough for individuals to see their roles from a digital mindset. It takes a new collaborative, agile culture for individuals to work in teams aligned with vision an priorities.

    Good reading on the Digital Mindset



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