Replacing Legacy Systems

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Episode 16

At what point does a new vehicle become used? When it rolls off the lot? After an acceptable number of kilometers? When the new car smell has faded away? The same question can be asked with IT systems. At what point does a system become a legacy system? When it is released into production? After an acceptable number of years in production? When the user experience no longer feels modern?

A legacy system is an old method, technology, computer system, or application program that is still in use. There is the idiom “Do not fix what is not broken,” but with IT systems it’s not entirely clear when a system has reached a broken state. However, these systems may underpin the entire organization and therefore it is unwise to wait for a failure as the trigger to act.

There are common action triggers, and technical debt usually plays a significant role. Like a new car, the IT system requires regular maintenance in order to function effectively. Technical debt can accumulate and if not regularly paid down, the accrued interest can become so large that the effort to maintain is greater than performing a full refresh. Much like a used car, at some point, the IT system is sent to the proverbial scrap yard. Another trigger is an existential crisis – or in today’s buzzwords, “disruption”. Consider small professional service firms being disrupted by freelancing job boards. These firms must now compete both for talent and revenue. The modern and easy to use platforms are forcing firms to refresh their processes and technology platforms in order to stay technology relevant. A third trigger is based on platforms falling out of service. Many solutions are built on platforms developed by large service providers like Microsoft, Oracle, or IBM. These platforms are developed, maintained, and eventually replaced. When these products reach end of life, they stop receiving updates for functionality and security leaving users vulnerable if they do not switch platforms. Lastly, some systems are replaced as part of a managed refresh set out by leaders under the guidance of a strategic planning process.

Vintage Car Parked Inside An Auto Garage

Regardless of the trigger, we need to consider what is important when replacing a legacy system. Modernizing while maintaining the exact same functionality is a waste of resources. Take for example a paper application process. The tool employed is a physical item that allows a user to record information so that an agent can interpret, make a decision, and take an action. On a whole the system is not very intelligent and if the paper form is replaced directly with an electronic form, the system has not gained any intelligence. By digitizing a paper application process, the IT system can have intelligence introduced improving the experience for both the user and agent. Are all fields required? What information in one field can be used to fill out other fields eliminating steps and improving the user’s experience. Can the entire application be replaced with the users sign in credentials? While paper processes are now the minority, with each system refresh the tools of the day are exponentially more intelligent than the predecessor. When refreshing your system, there is the opportunity to revisit existing processes and reimagine them with more intelligent solutions. We see this today with firms racing to incorporate machine learning and automation tools into all areas of their businesses.

Now you know that you want to incorporate more intelligence in your processes – what next? In many cases technology makes it easy to expand the scope of influence and reach. It is important to understand that when building a system, it is the exceptions that will kill you, so kill the exceptions. In robust system design, it is the exceptions that cause cost overruns; and accumulate the initial rounds of technical debt the organization must manage. Eliminating the need to process an exception will provide tremendous savings.

Like new cars, new IT systems are expensive. The value that can be derived is a function of the design, how it is used, and how well it is maintained over the product’s lifetime.

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Customer Centric Processes

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Episode 15

The business game is hard. Coordination of people, processes, and technology requires an aligning vision or purpose to guide priorities, establish culture, and foster the development of a brand image. To gain better market understanding and differentiate from their competition, many organizations are shifting to a customer centric view. But organizations have always cared about customer needs, so what makes this shift different? How can it be applied?

Customer centric processes are different because they start at the very foundation of the organization. A customer focus will be explicitly stated in the organization’s vision and mission statements and is commonly held as a core value. Ideally, all customer centric decisions can point back to the vision, mission, and values. A common example of customer centric values would be “The Customer is Always Right”, with more modern interpretations like Amazon’s “Customer Obsession” motto.

The largest difference in customer centric processes when compared to traditional business norms is through redefining the brand to focus on the customer experience, not the product or service the firm provides. The shift from product focus to customer focus has numerous ramifications. Does the best possible product provide the best possible experience? Does the best possible experience require the best possible product? Now ask the same questions but with price, place, and promotion. It is not necessary to have the lowest price, best product, best location, or most engaging advertisement to provide the best possible customer experience. Everyone has a situation where they paid a little more, travelled a little further, or waited a little longer because they felt “product B” would provide a better experience. And now in the world of social media, individual user experiences uploaded to platforms like YouTube can sway purchase decisions.

A Person Standing On The Edge Of The Grand Canyon

There are other dimensions to consider, let us look at supply versus demand driven; reactive versus proactive; standardized versus personalized. Marginal cost of production in many industries is a race to zero and this relationship is no more apparent than in consumer electronics. Firms do not flourish when the lowest cost wins. Economies of scale powered by standardization have provided us great abundance; however, as margins get squeezed firms can fall into a trap. It is a self-defeating trap when the focus is purely on squeezing value from the supply chain. The trap is further magnified when and the only reaction to changing market forces are when the firm is forced to, because change is viewed internally as an unwanted expense. To break the habit, leaders must look back at the vision, mission, and values, and ask: what will provide the best client experience? To provide the best experience, demand must be anticipated, change must be proactive, and the product or service must feel personalized. The business game has shifted from maximizing transaction volumes to maximizing lifetime customer value. Lifetime value is driven by the chain of positive experiences derived from your brand.

Now you’re thinking, sure, experience is important, but providing a personalized experience is well beyond the capacity of our organization, plus all that hands-on customer service cannot be cheap. Most organizations are sitting on gold mines of data waiting to be turned into insights. Take for instance a parts supplier that can proactively service its products for its clients, eliminating the typical reactionary process of the client calling customer service when the part fails. Sensor data can be streamed from devices in the field and preventive maintenance routines can be developed. Technology is enabling organizations to predict demand, provide proactive responses, and personalized experiences at scale. It is that great feeling when the barista knows your name (with correct spelling) and has your quad long shot grande in a venti cup half calf double cupped no sleeve salted caramel mocha latte ready by the time you are at the counter. Cameras near the entrance remember your face and recall your last order, instead of placing your order with a barista, you walk to a counter, and confirm your order with a thumbs up, and proceed to pick up your order from a service stall with your customer number.

Humans are interesting creatures, details like names and activities can long be forgotten, but we will always remember how that last brand experience made us feel. To stay in the game, it is about the processes that provide for the customer, the best possible experience.

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Technical Debt – The promise and peril of low-code applications

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Episode 14

Technical debt is a concept in software development that reflects the implied cost of additional rework caused by choosing an easy solution now, instead of using a better approach that would take longer. Technical debt is repaid by spending development time on refactoring the project at the cost of adding new features or functionality. Technical debt can be viewed similarly to financial debt, if not repaid it will accrue interest, making future repayment more difficult.

Technical debt is an inescapable phenomenon of our modern digital age. As technological progress increases, the time a solution spends in development shrinks. Development principles like Agile and DevOps advocate for continuous deployment and integration of working products; deliver value as fast as possible. Striving to have production code in the hands of a user as soon as possible is a situation that is ripe for generating technical debt. There is great temptation to cut corners to meet the release schedule. With business staff (not developers) now driving the development process, low-code applications add another dimension of complexity. Low-code platforms promise high productivity that empowers everyone in the organization to achieve more. But is there a hidden cost to such a powerful set of tools?

A distinction must be made while we discuss low-code applications, they should not be viewed as Shadow IT. We are going to assume that low-code applications have oversight by the organization’s IT department or technology partner. Without this structure, it is highly likely that productivity gains will evaporate as systems lacking coordination fragment. There is a trend toward having the leanest technical stack possible. Taking “lean” ideas from the manufacturing and supply chain worlds and applying them to IT infrastructure and processes. A “lean” IT system will have only the resources it needs, be responsive to changing requirements, and be resilient to shocks. These principles applied in the wild manifest themselves as API focused integrations, business analyst led development, and limited use of enterprise applications. While powerful, enterprise systems can be technical debt traps.

A Screenshot Of Website Code Being Edited

The promise of placing development in the hands of those closest to the problems removes several links of communication where information can be misinterpreted. Lets look at Microsoft’s Power Platform as an example. We have Flow, Power Apps, and Power BI giving non-developers the power to automate workflows, build custom apps, and drive data insights throughout their organizations. Additionally, these applications are supported by rich marketplaces with templates, service add-ons, and community discussion groups. When one can build a custom application in hours, the pace of iteration explodes, allowing for quick failures and rapid discovery of effective solutions.

But is there a cost to all this power? If business users are empowered to build their own tools and can build them rapidly, we are trading one form of technical debt for another. Take for instance traffic: if we improve roads, we do not get better traffic conditions, we get more cars. By empowering business users to build applications, we do not solve technical debt challenges, we get more applications.

Rapid iteration is invaluable. When success is found, it is natural for organizations to formalize the solution. Low-code applications developed internally, will likely be lacking documentation, have weak quality assurance processes, and often have only one individual with working knowledge of the application’s architecture. Envision a large multinational organization with many similar regional offices, where users all independently start creating solutions. Which success should be adopted globally? Who decides what to use? Should each office maintain their own solutions? How does the company support thousands of similar applications?

Power tools carry large responsibilities. At MERAK, we think it is best to see low-code applications as credit cards. The rapid iteration with inclusion of business users is instant gratification at the expense of future technical debt payments as organizations take discoveries and build the proper infrastructure to support the solution going forward. Prudent organizations will recognize this relationship and strategically accumulate technical debt, understanding that the creative ecosystem enabled by low-code development platforms can produce core products better aligned with internal needs and client needs followed by technical debt payments.

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Adrift in a sea of data

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Episode 13

There is a saying in management, “you cannot manage what you do not measure.” On first pass, this sounds straight-forward. The quote is most often attributed to two individuals W. Edwards Deming, the statistician and quality-control expert credited with having launched the Total Quality Management (TQM) movement, and Peter Drucker, a very well-known management consultant 1. Despite the simplicity, the statement is misleading. Gathering data for data’s sake provides zero value. Data analysis with no vision is like being on a boat set adrift. With IOT device proliferation, cheap computation and storage, there is no easier time than now for organizations to take measurements and collect data. This blog post addresses the importance of having a plan for the data you capture to obtain valuable results for your organization.

The human mind is amazing. We are incredibly good at using our intuition to make decisions with incomplete information. The brilliant minds working on machine intelligence are reminded daily how amazing our talents are. A mindless task like walking through a doorway is a ridiculously hard problem for a machine to solve. Now consider skills and more human elements like empathy, compassion, or friendship. Businesses and organizations are managed by people that must interact and coordinate with one another. We have limitations though, our memories are fallible, we are susceptible to biases, and we experience the world with limited bandwidth on many dimensions. Current data systems can track an incomprehensible quantity of information, spot unintuitive correlations, and operate with high levels of information throughput. It is logical to have technology track and build datasets that are beyond our abilities. There are definite areas where human and current machine competencies complement each other.

Old Fashioned Pirate Ship Sailing Into The Sunset

Today we have the ability to capture unimaginable amounts of data, IOT sensors costs have been falling exponentially and we are all familiar with Moore’s Law for computation. The value of data is not the information that it contains, but the story it provides informing the ultimate decision to take action.

At the end of the day (at least for now), businesses are managed by people. We all make decisions based on our intuition from the story generated by the data the organization has captured, processed, and analyzed. Like a physical supply chain, it is the system that provides value, not the parts. What does this all mean? Data must be linked to a future decision to act. What is the vision for the organization? What areas of the organization does the vision impact? What parts of operations or processes can be optimized? How is the data captured, processed, and analysed? Does my organization have the data science capabilities internally? What is an effective baseline for which future progress can be judged? What governance structures need to be established to manage information flow, approvals, and reporting?

For instance, a resource development firm in the energy industry might seek out environmental data to monitor impacts during construction and operation of a remote facility. The vision could be to ensure regulatory compliance to prevent fines and improve social acceptance. Data could be imagery, site samples, inspection/audit reports, construction event logs, weather, etc. The data could be prepared as reports and dashboards for operators, field managers, regulators, specialist contractors, and executives. Governance could be structured by executives by setting quarterly performance targets. Operators and field managers are then responsible for adapting processes to meet these targets using the established data supply chain for feedback and validation.

To stay in the game, firms have found success using data driven decision making. However, invisible to the outsider is the time and effort invested to not only to build this sea of data, but also to establish the supply chain linking a vision, data management processes, and governance to a decision-making process.

  1. Why “You Can’t Manage What You Can’t Measure” is Bad Advice by Don Peppers
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Intelligent Systems & A.I. In Content Creation

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Episode 12

“Humanity is a kind of biological boot loader for A.I.” – Elon Musk

Humans have long thought that machines will never be as creative as us, yet as we build more sophisticated systems, we are surprised by what our machines produce.

Our last blog was written by a neural network built to detect fake news content written by other neural networks. In this blog we will explore the ramifications when humans are no longer the primary actors responsible for media content generation.

Internal Gears Of A Watch

Dramatic Reduction in Production Cost

Last week’s blog took 1/5th of the time to produce than other blog posts published this year. The model generated content had near perfect grammar and the narrative was on point; it would only require minor effort for a human to clean up and send off to production. It is still early days with this technology and if machine written content improves like image recognition and editing, there is no doubt that human and machine content will be indistinguishable to the untrained eye. This potential for collaborative and fully automated forms of content generation will make it extremely inexpensive to pump out content. The effective price of machine written content will be the compute time to generate it, which for today is pennies. Audio and video are not far behind following the same evolution we see in text. When content is so easy to produce, the core question to ask is; who will have the time to consume it all?

Digital Celebrities

With face filters advancing to a state where they are almost indistinguishable from reality, we are not far from a time when we will see purely fictitious characters rise in popularity. Reanimating popular figures of the past may also see a rise in popularity. The line between what is real or not, will fade. How long before a development team behind a digital actor is given an academy award?

A Future Full of Personalized Content

Recommendation engines are the backbone of some of the most valuable companies in existence. Today content is selected for you, soon, content will be created for you. The dramatic cost reduction in content creation thanks to machine learning systems will allow content to be written and produced on demand. Think Black Mirror’s: Bandersnatch but on steroids. Imagine an entire series based on your preferences and decisions.

How to Stay in the Game

The future will be filled with more and better targeted content chasing the same eyeballs. Mountains of content will be produced and never experienced. Using chess as an example, the best teams are composed of both players and machines, therefore, winning firms will leverage the strengths of A.I. with a human touch.

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The Truth About Artificial Intelligence and Marketing

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Episode 11 – All the content below was generated by a model developed to spot fake news generated by other neural networks. Using a headline as a seed for the content of the blog, I arrived at something that covers the core idea I wanted to explore after three attempts. Once I had the content, I asked the model to generate its own title. All credit goes to the team at the Allen Institute for Artificial Intelligence. The link to the tool and their publication is below.

Publication,

Truth be told, artificial intelligence (AI) has seen fit to enter the marketing communications domain. Automation and AI are unlikely to replace, completely, the human factor, but some technologies will represent disruptions to human creativity and dissemination of ideas. I’m sure you’ve seen the graphs: while job security has been an issue with brands, the idea of automation that facilitates creativity has its advocates.

What differentiates us as a technology-informed audience is how AI will affect the distribution and use of content in the digital world. Social media provides an outlet for content to reach end consumers or influencers; display advertising enables brands to target demographic audiences based on keywords; and the semantic web sites allow the delivery of targeted emails to prospects.

Trends are already showing that we humans, no matter how large we are, tend to think on our feet and move at pace. While AI is best in empathy, it’s easy to apply it only part of the time or partially to a product or service – or to our own personal experiences. CMOs must, however, decide how AI will be applied to their own brand and how it fits into the overall distribution strategy.

While most marketers would agree that AI is first and foremost beneficial to big, national brands with budgets and content levels comparable to the C suite, what about smaller brands? What about micro-influencers, social influencers, and influencers in emerging industries? What about their audiences and their needs? Is AI something that their audiences and their industries can utilize?

There are already examples of AI-assisted content production, ranging from auto brands to fashion ones. Transparent, a global automobile brand, has developed a car racing product that allows the driver to call upon an automated driver that can react instantly. In response to an alert from an AI driver, the car is programmed to pitch the driver to a “race seat”.

A Laptop On A Computer Desk Displaying &Quot;Fake News&Quot;

Another example is Pinterest’s 2015 launch of a product search feature that can now be enabled to the same Pins. The AI tech is in the form of an image recognition tool that leverages the user’s location and browsing habits to refine the search results. The AI engine determines the nature of the Pins based on previous searches; however, it remains to be seen how many brands actually choose to adopt AI-powered content production tools.

As much as we’re seeing brands’ marketing and marketing content evolve, there are still companies that are uncertain of how artificial intelligence will influence their audiences. The AI ads that pop up all the time, paired with an algorithm that determines whether someone will view an ad or not, is one thing. Images tied to search terms, along with other data captured through keywords and other variables, might be another. How will these two types of AI ads operate when there are infinite combinations of text and images? More importantly, how will this AI affect how brands generate marketing and creative content?

My inbox is flooded with this question and many, many more. The time is now. Where will brands’ audiences turn to when their needs and wants can’t be addressed by their existing audiences? The holy grail that many advertisers have tried to reach, like audiences targeted at specific demographics and interests, is now within the reach of brands.

My suggestion? Forget the non-existent challenger brands in your global marketing plans. Now is the time to focus on marketing and creative content crafted with your local, micro, and micro-influencers. After all, AI has already shown us that information is no longer passed on by gut instinct; algorithms have become the relevant agents of knowledge. As such, these AI connections are the bridges into a larger audience reach and the savviest among us should realize the power that lies within these relationships.

AI will continue to be featured in everything from marketing and advertising content to private communications, but as automation enables greater access to data and information, the ways in which we view our relationships with brands will dramatically change. It’s time to throw out your current communication strategy; it’s time to embrace AI.

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Digital Transformation

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Episode 10

Who doesn’t like a good buzzword? We love buzzwords. We love buzzwords as they communicate complicated ideas between stakeholders, so everyone can move forward on their action plans. Digital Transformation is a doozie. It is all the rage in the technology sector, second only to disruption. The hidden danger with buzzwords is when they are dropped into the conversation without clear context, it is easy to just go along without really understanding the implications of what was just spoken. But what does digital transformation mean? Let’s break it down into a couple components.

Your organization is composed of competencies, which are defining capabilities or advantages that distinguish an enterprise from its competitors. Transformation begins at the conception stage in competency development. Your organization’s competencies (processes, resources, human talent) were developed with the skills and technologies of the day. For organizations greater than five years old, the tools available today are radically different than 10, 20, or 50 years ago. Yet the competencies that make your organization great were likely developed very early in the organization’s history, and probably have remained the same since that development period. Where transformation becomes relevant is when you look at the tools available today and reimagine your organization’s competencies. This is a different approach to incremental improvement, whereby small changes are phased into a core process that remains relatively static. In practice it is important to keep in mind that overnight transformation is nearly impossible; therefore, transformation is dependent on a transformational vision, but is executed incrementally.

A City With Alien Space Ships Hovering Above

Innovation is typically not a product of brute force creativity, but rather combinatorial. The smartphone is an excellent example, microwave technology, LCD screens, transistors, and Li-ion batteries have enabled us all to carry the totality of human knowledge in our pockets. Your digital transformation journey begins with considering the tools of the day, but these tools should not be considered in isolation. Adding cloud, mobile, machine learning, block chain to any process as isolated solutions will not yield beneficial results. Blockchain is a great example of a technology of the day being injected into a yesterday process, yielding little value for most implementations.1 We are not looking for a better mousetrap, but questioning the idea, is a trap the best tool for the desired outcome we want to achieve?

In the last blog, technology as a differentiator, I argued that technology can be used to redefine the customer experience or supercharge creativity. A vision for a home decor retailer using augmented reality to transform a customer’s own home into a showroom speaks to a vision that would transform the face of retail if successfully implemented. Transformation begins with determining a clear vision for your organization, then establishing the competencies required to fulfill that vision. Transformation is not instantaneous, like the name suggests, it is incremental, but to stay in the game, the vision must be transformational.

  1. “The Top 5 Mistakes In Blockchain Project Implementation”
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Technology as a Differentiator

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Episode 9

Any organization that provides commodity products or services is in a race to zero. When competition is focused on price, there is no room for differentiation and investments are driven to find efficiencies and reduce cost. Technology in this environment has a very limited scope. What if technology could be a differentiator? Can we move beyond cost reduction?

Technology is both an investment and a competency multiplier. Organizations miss out when they view technology not as an investment but as an expense. Let’s look at other common differentiators to expand on the argument. “Location Location Location.” Where your organization is located can have as much influence on success as your product and brand. It is often cited that McDonald’s, “is not a Fast Food franchise”, but a “real estate company”. To McDonalds, location is one of the most important differentiating factors in their success. “We have the best people.” No professional services firm will advertise how amazingly mediocre their staff are. Some organizations find differentiation in their culture and from the qualities of individuals they recruit and retain.

In these two examples, location and human capital are viewed as investment vehicles that provide the competitive advantage over their competition. Sure, whatever, technology does cool stuff, but we administer government programs, fix air conditioners, manufacture saw blades, etc.; we are not a technology company. Every company is a technology company. Two areas that technology can differentiate your organization are changing the customer experience and scaling creativity.

An Iphone With A Fantasy 3D Castle On Top Of The Screen

Easier, immersive, and customized. Employing technology to improve in any of those categories can allow an organization to differentiate from its competition. Let’s take home décor as an example. Many retail locations will have showrooms. These rooms provide a creative representation of what is possible from the mind of the designer. Customers will ask specific questions. Will this fit in our place? How does green look here? What if it was just a little taller? In conventional showrooms, these answers are left to the imagination of the customer. Leveraging augmented reality, massive areas of retail space can be compressed down to a few demo rooms (even made mobile, or moved into the customers home directly) where customers could immerse themselves in a creative exploration of what is possible. Room configuration is done digitally, customers are immersed, and the experience is customized to the customers wishes.

It is undeniable that automation has revolutionized the world and machine learning has matured to a state where its impact is felt everyday. Creativity has been thought of as a bastion of human talent, but this assumption is evaporating before us as machine learning systems can “complete sentences”, “draw landscapes”, and “write music”. It is easy to get apocalyptic, but like precision machining, printing presses, and steam engines; machine learning is another tool that (if used responsibility) will contribute to human flourishing. To stay in the game, organizations that are able to find strategic partnerships with machine learning systems will find creative competitive advantages over firms that only rely on humans. With the most valuable companies in the world technology driven, it is a clear signal to the rest of the market that a competitive advantage is derived through a mindset that views technology as a competency multiplier, rather than a cost.

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Technology Sales – Solution Versus Problem

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Episode 8

For common problems a technology solution likely exists. “Start-up” culture and values have permeated business thinking. To drive sales, technology firms proactively develop minimal viable products (MVPs), with the purpose of gathering early feedback. User feedback is essential to discovering a product that customers will adopt and love. The discovery process is messy, and failures are common, therefore, it is normal for technology solutions to be developed with little initial evidence to indicate future success. As a result, we live in a world where there is an app for everything. If success is found, to fully monetize the development cost, there is an incentive to modify the existing solution to meet the needs of new clients. These forces have culminated into the manifestation of platform solutions primarily hosted on “cloud services”. Platform solutions are built for everyone, are highly scalable, and can be heavily customizable; a powerful and easy to use solution for all your technology needs. For a low monthly subscription fee, take your business to the next level by unleashing a technology transformation. Amazing! Welcome to solution sales.

Lego Batman And Superman

Despite being faster than a speeding bullet, and able to leap over tall buildings, solution selling has its kryptonite. Any mild-mannered journalist will know, the devil is in the details. When developing a product for everyone, at the design phase, developers must envision the most common problems and use cases for their target audience. It is impossible to predict how users will use your product. Technology development has three aspirational pillars: easy to adopt and use; low cost; and fully featured to the client’s needs. In the best-case scenario, there will only be enough resources to cover two pillars well and compromises must be made. Despite the limitations, solution selling dominates. Selling a super solution reduces the friction at the most critical stage of the technology sales cycle. It is hard to say no to something so powerful, inexpensive, and easy to use. Solution sales is super.

Problem focused sales turns the process around, and then takes three steps back all the way to the client’s unfortunate childhood. The goal of problem sales is to understand the need before offering a solution. We can envision a problem focused solution as a tool on a utility belt. A focused solution targeted to the specific task at hand. Scaling up problem sales into a “technology partnership” the result will be a collection of solutions like the utility belt itself. With the utility belt, ordinary is transformed into super. Problem focused sales have challenges too, understanding the core problem requires introspection and dialogue, and this process will be a source of friction throughout the entire process. Humans naturally do not like change, and are also defensive of past decisions, not wanting to appear incompetent or incapable. It is far easier to accept a super solution that can do everything, than to unpack the complexity of, and clearly define messy problems.

“People do not update like software”; it is important to set expectations when adopting the super solution as there is a natural incentive to overpromise. When starting with the problem first, expect friction and challenges. If a process is ineffective, simply adding technology into the mix will not solve the actual problem, true solutions necessitate a deeper dive to clearly define the problem and needs to be addressed. The journey of matching solutions to problems that developers and clients take is a messy one. Understanding the two paths of technology sales will help “keep you in the game”.

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Technology Partnership

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Episode 7

Great teams are diverse and multi-disciplinary, using a wide array of skills and perspectives to deliver successful projects. The same idea scales up to the organizational level; organizations derive a competitive advantage by combining the differing competences within. A great organization might not have the competencies that allow for effective development and management of their technology stack. This is where a technology partner comes in. So how can you determine if a technology partnership is right for your organization?

Strategy 101

Deliver value by producing a product or service worth more to your client than it cost you

Sounds simple right? There are two core elements required to make a business model work. The first is a market need. It does not matter how amazing your product or service is, if no one has a need for it. Once a need has been identified, the second requirement is assembling the skills and resources necessary to produce the product or service. These skills and resources are known as core competencies. Core competencies are a defining capability or advantage that distinguishes an enterprise from its competitors.

A Person Shaking Hands With A Virtual Person

Get Good

Your organization is defined by its core competencies

It is critically important to understand what makes your organization great. Human talent, processes, and capital investments make up the critical components that differentiate your organization. Unfortunately, this simplified model of assumptions ignores resource constraints. It is impossible to do it all. Compromise from the ideal vision is inevitable; however, constraints are the forge that define elements of your organization. What makes your organization great? Changing technology is forcing a rapid transformation of human talent, processes, and capital investments. How much does what you are “great at” overlap with the technology that enables being great? Recognize that there is a difference between being fluent at operating versus being fluent in development and administration. Time and resources spent in development and administration can diminish the effectiveness of our core product or services. It is impossible to be great at everything.

When a Technology Partnership makes sense

Create something greater than the sum of its parts

The most critical element to the idea of a technology partnership is understanding that the environment your organization operates in, is not a zero-sum competition. The cake is a lie, competing for a bigger slice will ensure that the scope of opportunity (the cake) remains fixed. A classic example of this worldview is the merger of directly competing firms, instead of combining differing competencies, it is a merger “of equals” to provide the same competencies at a larger scale. A bigger slice of the same cake. Technology partnerships are not outsourcing either, unlike outsourcing a partnership is not transactional, it is mutually additive.

What if you want a bigger cake, or maybe pie is more your thing? A technology partnership links differing competencies whose unique composition creates something greater than the sum of its parts. It is important to note that technology partnerships require a high-level trust relationship to flourish. With differing competencies, expect that cultures will differ; therefore, spending more time upfront to have a clear shared vision and core purpose defined is essential. For some organizations the technology they use is core to their business model. While for others technology is just an enabler. Technology partnerships make sense if more energy and resources can be focused on what makes your organization great.

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