Data Mesh: Understanding Its Applications, Opportunities, and Constraints

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Data has experienced a metamorphosis in its perceived value and management within the corporate sphere. Previously underestimated and frequently discarded, data was often relegated to basic reports or neglected due to a lack of understanding and governance. This limited vision, combined with emerging technologies, led to an overwhelming influx of data, and nowhere for it to go. There was little to no governance or understanding of what data they had, or how long they had it.

In the early 2000s, enterprises primarily used siloed databases, isolated data sets with limited accessibility. The 2010s saw the rise of Data Warehouses, which brought together disparate datasets but often led to bottlenecks. Data Lakes emerged as a solution to store vast quantities of raw data and quickly became swamps without adequate governance. Monolithic IT and data engineering groups would struggle to document, catalog, and secure the growing stockpile of data. Product owners and teams that would want, or need access to data would have to request access and wait. Sometimes those requests would end up in a backlog and forgotten about.

In this new dawn of data awareness, the Data Mesh emerges as a revolutionary concept, enabling organizations to efficiently manage, process, and gain insights from their data. As organizations realize data’s pivotal role in digital transformation, it becomes imperative to shift from legacy architectures to more adaptive solutions, making Data Mesh an attractive option.

The Basics of a Data Mesh

The importance of personalized customer experiences should not be understated. More than ever, consumers are faced with endless options. To stand out from competitors, businesses must use data and customer behavior insights to curate tailored and dynamic customer journeys that both delight and command their audience. Analyze purchasing history, demographics, web activity, and other data to understand your customer, as well as their likes and dislikes. Use these insights to design customized customer experiences that increase conversion, retention, and ultimately, satisfaction.

When discussing data architecture concepts, the terms “legacy” or “traditional” imply centralized data management concepts, characterized by monolithic architectures developed and maintained by a data engineering organization within the company. Business units outside of IT would often feel left in the dark, waiting for the data team to address their specific needs and leading to inefficiencies.

First coined in 2019, the Data Mesh paradigm is a decentralized, self-service approach to data architecture. There are four central principles that Data Mesh is based on: Domain ownership, treating data as a product, self-service infrastructure, and federated computational governance.

With Data Mesh, teams (Domains) are empowered to own and manage their data (Product). This requires stewardship at the team level to effectively manage their own resources to ingest, persist and serve data to their end users. Data stewards are responsible for the quality, reliability, security, and accessibility of the data. Data stewards bridge the gap between decentralized teams and enterprise-level governance and oversight.

While teams enjoy autonomy, chaos would ensue without a federated governance approach. This ensures standards, policies and best practices are followed across all product owners and data stewards.

Implementing a Data Mesh requires significant investment in both infrastructure and enhancing teams with the resources and expertise required to manage their own resources. It requires a fundamental change in companies’ mindset of how they treat data.

While a Lakehouse would aim to combine the best of Data Lakes and Data Warehouses, Data Mesh ventures further by decentralizing ownership and control of data. While Data Fabric focuses on seamless data access and integration across disparate sources, Data Mesh emphasizes domain-based ownership. On the other hand, event-driven architectures prioritize real-time data flow and reactions, which can be complementary to Data Mesh.

When and Where to Implement Data Mesh

Large Organizations with Data Rich Domains: With large organizations, departments often deal with a deluge of data. From Human Resources to Sales, each team has their own requirements for how their data is used, stored, and accessed. As teams consume more data, time to market and development efficiency suffer in centralized architectures. External resources and time constraints are often the biggest issue. By implementing Data Mesh, teams can work independently and take control of their data, increasing efficiency and quality. As a result, teams can optimize and enrich their product offering and cut costs by streamlining ELT/ETL processes and workflows.

With direct control over their data, teams can tune and tailor their data solutions to better meet customer needs.

Complex Ecosystem: Organizations, especially those operating in dynamic environments with intricate interdependencies, often face challenges in centralized data structures. In such architectures, there’s limited control over resource allocation, utilization, and management, which can hinder teams from maximizing the potential of their data. Centralized approaches can curtail innovation due to rigid schemas, inflexible data pipelines, and lack of domain-specific customization. Data Mesh offers organizations the flexibility to adapt to evolving data needs and utilize domain-specific expertise to curate, process, and consume data tailored to their unique requirements.

Rapidly growing data environments: Today’s digital age sees organizations collecting data at an unprecedented scale. The sheer volume of data can be overwhelming with the influx of IoT devices, vendor integrations, user interactions, and digital transactions. Centralized teams often grapple with scaling issues, processing delays, and the challenge of timely data delivery. Data Mesh addresses this by distributing the data responsibility across different domains or teams. Multiple decentralized units handle the influx as data inflow increases, ensuring timely processing and reducing system downtime. The result is a more resilient data infrastructure ready to meet both current demands and future needs.

When Not to Implement Data Mesh

Small to Medium-sized Enterprises (SMEs): While Data Mesh presents numerous advantages, it may not be suitable for all organizations or projects. Smaller organizations typically handle lower data volumes and may not possess the resources needed to manage their data independently. In these cases, a centralized data architecture would be more suitable to minimize complications in design and maintenance with fewer resources to manage them.

Mature and Stable Centralized Architectures: Organizations usually only turn to new solutions when they are experiencing problems. If a well-established centralized architecture is performing and fitting the needs of the company, there isn’t a need necessarily for Data Mesh adoption. Introducing a fundamental change in how data is managed is an expensive and disruptive undertaking. Building new infrastructure and expanding team capabilities changing organizational culture takes time.

Short-term Projects: Implementing a Data Mesh requires significant time and resource investment. The benefits of a Data Mesh won’t be seen when building or designing a limited lifespan project or proof of concept. If a project’s duration doesn’t justify the investment of a Data Mesh or the scope doesn’t require domain-specific data solutions, then the benefits of a Data Mesh aren’t utilized. Traditional data architectures are usually more appropriate for these applications and don’t need the oversight/governance that a Data Mesh requires.

Opportunities Offered by Data Mesh

Scalability: Data Mesh enables organizations to scale their data processing capabilities more effectively by enabling teams to control how and when their data is processed, optimizing resource use and costs, and ensuring they remain agile amidst expanding data sources and consumer bases.

Enhanced Data Ownership: Treating data as a product rather than a byproduct or a secondary asset is revolutionary. By doing so, Data Mesh promotes a culture with a clear sense of ownership and accountability. Domains or teams that “own” their data are more inclined to ensure its quality, accuracy, and relevance. This fosters an environment where data isn’t just accumulated but is curated, refined, and optimized for its intended purpose. Over time, this leads to more prosperous, more valuable data sets that genuinely serve the organization’s needs.

Speed and Innovation: Decentralization is synonymous with autonomy. When teams have the tools and the mandate to manage their data, they are not bogged down by cross-team dependencies or bureaucratic delays. They can innovate, experiment, and iterate at a faster pace, resulting in expanded data collection and richer data sets. This agility accelerates data product development, enabling organizations to adapt to changing needs quickly, capitalize on new opportunities, and stay ahead of the curve in the competitive market.

Improved Alignment with Modern Architectures: Decentralization isn’t just a trend in data management; it’s a broader shift seen in modern organizational architectures, especially with the rise of microservices. Data Mesh naturally aligns with these contemporary structures, creating a cohesive environment where data and services coexist harmoniously. This alignment reduces friction, simplifies integrations, and ensures that the entire organizational machinery, services, and data operate in a unified, streamlined manner.

Enhanced Collaboration: As domains take ownership of their data, there’s an inclination to collaborate with other domains. This cross-functional collaboration fosters knowledge sharing, best practices, and a unified approach to data challenges, driving more holistic insights.

Constraints and Challenges

Cultural Shift: Teams may not want to own their own data or have the experience to take on the responsibility. Training initiatives, workshops, and even hiring external experts might be necessary to bridge these skill gaps.

Increased Complexity: Developing an environment that supports a Data Mesh architecture is not without its challenges. As the Data Mesh model expands, managing the growing number of interconnected resources and solving integration issues to ensure smooth communication between various domains can be a considerable obstacle. Planning appropriately to support teams with access, training and management of a Data Mesh is critical to its evolution and success. This includes well defined requirements for APIs, data exchange, and interface protocols.

Cost Implications: Transitioning to a Data Mesh could entail substantial upfront costs, including hiring additional resources, training personnel, investing in new infrastructure, and possibly overhauling existing systems.

Governance: Data Governance has become a hot topic as data architectures grow and mature. Ensuring a consistent view of data across all domains can be challenging, especially when multiple teams update or alter their datasets independently. Tools to manage integrity, security and compliance are a requirement in a Data Mesh architecture. The need for teams to have autonomy in a decentralized environment is balanced with a flexible but controlled governance model that is the foundation for federated governance. This can be a challenge when initially designing the model based on team requirements, but it’s an important step to take as early as possible when building a data platform.

Skillset: Evolving with the Data Mesh Paradigm

With an evolved mindset, the Data Mesh paradigm demands expertise that may not have previously been cultivated within traditional data teams. This transition from central data lakes to domain-oriented data products introduces complexities requiring a deep understanding of the data and the specific use cases it serves, both internally and externally. Skills such as collaboration, domain-specific knowledge translation, and data stewardship become vital. As data responsibility becomes decentralized, each team member’s role becomes more critical in ensuring data integrity, relevance, and security. As data solutions evolve, teams must adopt a mindset of perpetual learning, keeping pace with the latest methodologies, tools, and best practices related to managing their data effectively.

Embracing the Data Mesh

In the evolving landscape of data management, the Data Mesh presents a promising alternative to traditional architectures. It’s a journey of empowerment, efficiency, and decentralization. The burgeoning community support for Data Mesh, evident from the increasing number of case studies, forums, and tools developed around it, underscores its pivotal role in the future of data management. However, its success hinges on an organization’s readiness to embrace the cultural and operational shifts it demands. As with all significant transformations, due diligence, meticulous planning, and an understanding of the underlying principles are crucial for its fruitful adoption. Embracing the Data Mesh is more than just a technological shift; it’s a paradigm transformation. Organizations willing to make this leap will find themselves not just keeping up with the rapid pace of data evolution but leading the charge in innovative, data-driven solutions.

Digital Transformation Trends that Future-Proof Your Business

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The core of future-proofing your business lies in the incorporation of cutting-edge technological trends and strategic digitization of your business operations. Combining new, transformative solutions with tried-and-true business methods is not only a practical approach but an essential one when competing in this digital age. Using the latest digital transformation trends as your guide, start envisioning the journey of future-proofing your business in order to unlock the opportunities of tomorrow.

#1 Personalization

#2 Artificial Intelligence

AI is everywhere. From autonomous vehicles and smart homes to digital assistants and chatbots, artificial intelligence is being used in a wide array of applications to improve, simplify, and speed up the tasks of everyday life. For businesses, AI and machine learning have the power to extract and decipher large amounts of data that can help predict trends and forecasts, deliver interactive personalized customer experiences, and streamline operational processes. Companies that lean on AI-driven decisions are propelled into a world of efficiency, precision, automation, and competitiveness.

#3 Sustainability

Enterprises, particularly those in the manufacturing industry, face increasing pressure to act more responsibly and consider environmental, social, and corporate governance (ESG) goals when making business decisions. Digital transformations are one way to support internal sustainable development because they lead to reduced waste, optimized resource use, and improved transparency. With sustainability in mind, businesses can build their data and technology infrastructures to reduce impact. For example, companies can switch to more energy-efficient hardware or decrease electricity consumption by migrating to the cloud.

#4 Cloud Migration

More and more companies are migrating their data from on-premises to the cloud. In fact, by 2027, it is estimated that 50% of all enterprises will use cloud services¹. What is the reason behind this massive transition? Cost saving is one of the biggest factors. Leveraging cloud storage platforms eliminates the need for expensive data centers and server hardware, thereby reducing major infrastructure expenditures. And while navigating a cloud migration project can seem challenging, many turn to cloud computing partners to lead the data migration and ensure a painless shift.

Future-Proof Your Business Through Digital Transformation with Kopius

By embracing these digital transformation trends, your company is not only adapting to the current business landscape but also unlocking new opportunities for growth. Future-proofing your business requires a combination of strategic acumen and technical expertise. This is precisely where a digital transformation partner, who possesses an intimate grasp of these trends, can equip your business with the resources and solutions to confidently evolve. Reach out to Kopius today and let’s discuss a transformational journey that will future-proof your business for the digital future.

A Step-By-Step Guide to Customer Experience Personalization

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Winning the interest and loyalty of customers means more than just offering a superior product or service. The secret lies in a powerful strategy called personalization – a dynamic approach that tailors the customer experience to meet individual needs and preferences. As businesses across industries strive to create lasting connections with their customers and meet their evolving expectations, the importance of personalization in the customer experience should not be overstated. Read on to explore the compelling case for customer personalization and a step-by-step guide on how your business can embark on this journey to elevate the customer experience.

Let’s face it, generic offerings are outdated. Today, customers yearn for something more; they want an experience that resonates with their unique tastes. Personalization is the magic ingredient that taps into this desire. By tailoring products, services, and interactions to individual preferences, businesses create a sense of connection that fosters lasting loyalty. And beyond that, research from McKinsey found that companies who implemented a personalization strategy generated 40% more revenue than their counterparts who placed less emphasis on this approach. All signs point to tailored customer journeys.

Data lies at the heart of personalization, offering insights into customer behaviors. More than ever, companies have access to a wealth of customer information, such as past purchases and browsing habits, that act as the building blocks to these insights. Leveraging advanced analytics and artificial intelligence, businesses can uncover valuable patterns and trends, guiding them to craft personalized experiences for their customers.

Building a successful personalization strategy requires thoughtful consideration and calculated execution. If you are just getting started, follow these steps to build an improved and tailored customer experience that will drive remarkable results for your business:

Step 1: Gather as Much Customer Data as Possible.

At the core of every successful personalization strategy lies a deep understanding of your customers. To lay this solid foundation, start by gathering valuable data from multiple touchpoints along their journey, including website interactions, purchase history, and customer feedback. Take advantage of powerful tools like customer relationship management (CRM) software, website analytics, and social media insights to gain a holistic view of your customers’ preferences, behaviors, and pain points.

Step 2: Divide Your Customers Into Audience Segments.

With an abundance of data at your fingertips, it is time to move on to segmentation. Divide your customers into distinct groups based on shared traits like demographics, purchase behavior, and interests. Audience segmentation empowers you to personalize your messaging or offerings, address individual customer needs with accuracy, and create a sense of relevance.

Step 3: Get Personal With Your Messaging.

Now that you have completed the segmentation process, it’s time to get personal! Start by creating interesting content with tailored product recommendations, and design exclusive offers that cater specifically to the unique preferences of each of your audience segments. By doing so, you will create truly personalized experiences that captivate your audience and leave an impression.

Step 4: Automate Dynamic Content Delivery.

Offer real-time digital experiences that resonate with your customers’ interests and past interactions. Embracing innovative technologies like artificial intelligence allows you to analyze customer data, predict behavior, and implement an effective personalization strategy that delivers tailored experiences on the fly. AI-powered chatbots take personalized support a step further, offering instant assistance to resolve customer concerns and boost overall customer satisfaction levels.

Step 5: Track Your Personalization Campaigns.

Monitor the impact of your personalization strategy on customer engagement, satisfaction, and business performance. Evaluate key metrics like conversion rates and customer retention to assess their effectiveness. Utilize any insights gained to identify areas for improvement and modify your approach accordingly.

The possibilities for designing a personalized digital experience are limitless. AI-powered chatbots provide real-time personalized support, making customers feel valued and cared for. Dynamic content delivery ensures website experiences are based on individual preferences. Personalization will enrich the customer journey, increasing engagement and conversion rates. If you are ready to deliver personalized experiences, Kopius is here to help. Let’s team up to create extraordinary customer experiences for your business!

Elevating Customer Experience Through Digital Transformation

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With customers having countless options at their fingertips, the importance of delivering seamless customer experiences cannot be overstated. Offering a great product or service is no longer enough. In today’s fast-paced online world, businesses must go above and beyond to ensure each touchpoint in the customer journey is exceptional if they want to stand out from the competition. One key solution that has emerged to help companies stay relevant and meet growing customer expectations is digital transformation. IBM defines digital transformations as a customer-driven, digital-first strategy that uses “AI, automation… and other digital technologies to leverage data and drive intelligent workflows… that drive faster and smarter decision-making.” But why should companies who are looking to improve the customer experience invest in digital transformation? The benefits of shifting to a digital-first business model are plentiful, impacting those both inside and outside of the organization. Let’s delve deeper into how adopting a digital transformation strategy can supercharge the customer experience and benefit your business.

Offer Seamless Omni-Channel Experiences

Meeting and exceeding customer expectations requires delivering a consistent and continuous experience, regardless of which channel they choose to utilize. By integrating mobile applications, social media, websites, and other online platforms, digital transformations allow customers to interact with businesses through their preferred channels. Providing this level of convenience means customers can start their journey on one channel and easily continue to another without skipping a beat.

Deliver Personalization and Customization to Increase Customer Engagement

Research by Accenture showed that 91% of customers preferred brands that offered relevant offers and recommendations. Digital transformations help businesses unlock the power of customer data, by collecting and analyzing vast amounts of information to derive meaningful insights in a matter of seconds. By tapping into advanced data analytics and artificial intelligence, companies can gain valuable knowledge about buying behavior, preferences, and trends. This helps pave the way for targeted promotional campaigns, individualized interactions, and tailored product recommendations, cultivating elevated customer satisfaction.

Decrease Downtime, Increase Efficiency

Inquiries, requests, or the occasional complaint are inevitable, but how your business handles them can be a make or break for your customer. Swift and efficient customer service and support is a must. Digital transformations empower businesses to optimize processes and minimize response times, resulting in expedited customer interactions. Through automation technology, AI-powered chatbots, and self-service options, companies can deliver immediate support around the clock. Thus, customers are able to engage with brands at their desired time and place.

Digitally transforming the customer experience is a necessity for businesses wanting to thrive in the digital era and offer seamless, tailored experiences. Ensuring speed, customization, and convenience across multiple channels leads to improved customer satisfaction and loyalty, driving tangible return on investment (ROI) for your brand. Are you ready to elevate your customer experience to new heights? Contact Kopius so we can create remarkable customer experiences that will drive your goals and revenue forward!

Data Lakes: The Foundation of Big Data Analytics

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By Kristina Scott

Data lakes are flexible and scalable architectures that are changing how businesses store and process data.

Businesses are generating and using more data than ever. This data comes from a variety of sources, such as customer interactions, social media, and IoT devices, among others. And it is often stored in different formats, making it challenging to use the data, analyze it, and gain insights. That’s where data lakes come in.

Data lakes are new to the world of big data analytics, and they are rapidly becoming the right choice for organizations. According to a report by MarketsandMarkets, the data lakes market is expected to grow from $7.9 billion in 2019 to $20.1 billion by 2024, at a compound annual growth rate of 20.6%.

Let’s dive deeper into the purpose of data lakes, explore their benefits, and look into their future.

What is a Data Lake?

Data lakes were introduced in the early 2000s by Apache Hadoop as an alternative to the limitations of data warehouses. A data lake is a storage system that allows you to store vast amounts of unstructured, semi-structured, and structured data at a low cost.

Simply put, a data lake is a large repository that stores raw data in its native format. Compared to a data warehouse, which stores data in hierarchical files or folders, a data lake uses a flat architecture and object storage.‍ While traditional data warehouses provide businesses with analytics, they are expensive, rigid, and often not equipped for the use cases companies have today, which is why the demand for data lakes is increasing.

Data lakes consolidate data in a central location where it can be stored as is, without the need to implement any formal structure for how the data is organized. That eliminates the need for preprocessing or transformation of data before storing it, making it an ideal storage solution for a vast amount of data. This raw data can then be processed and analyzed using a range of tools and technologies, such as machine learning algorithms, data visualization, and statistical analysis. Data lakes are built on Hadoop Distributed File System (HDFS) or cloud storage, such as Amazon S3, Microsoft Azure, or Google Cloud Storage.

Why Do Data Lakes Matter?

Often, a business has had big data and just didn’t know it. For instance, data goes unused because current business requirements only use a subset of the data a client or partner exchanges. Data lakes allow a business to consume and ingest vast amounts of raw data, allowing for data discovery in a cheap, efficient, and measurable way. Data-driven businesses tend to focus on future business needs, which require new insights into existing data and using newer technologies such as machine learning for predictive analysis.

Further, data lakes enable organizations to democratize big data access, making data-driven decisions a reality. The most significant advantage of data lakes is that they allow organizations to analyze data more effectively and gain insights faster to empower decision-making.

Data lakes enable businesses to become more data-driven, as they can access and analyze big data quickly and efficiently, shifting the culture to embrace data-driven thinking across the organization. And it pays off — a Deloitte survey found that companies with the strongest culture around data-driven insights and decision-making were twice as likely to significantly exceed business goals. Data lakes enable that big data-driven culture to thrive and be accessible at all levels of the organization. BCC research also found that companies that use data lake services outperform similar companies by 9% in organic revenue growth.

How are Companies Using Data Lakes?

“The one thing I wish more people knew about data lakes is that it’s a tool that has great potential but can be misused. It’s vital to have a strategy to keep your data organized and avoid turning your lake into a swamp.”
Michael Rounds, Director of Data Engineering and Analysis, Kopius

Companies across a variety of industries are using data lakes to gain insights, improve operations and gain a competitive edge. In a research survey by TDWI, 64% of organizations said that the main purpose and benefit of a unified data lake is being able to get more operations and analytics business value from data. Other top value adds include reducing silos, gaining a better foundation for analytics compared to traditional data types, and storage and cost savings benefits.

Here are some practical use-case examples of organizations implementing data lakes in business operations:

Retailers use data lakes to analyze customer behavior and purchase history to offer personalized recommendations and promotions.
Healthcare organizations leverage data lakes to store patient data from multiple sources, such as electronic health records and wearables, to better diagnose and treat diseases.
Manufacturers implement data lakes to monitor and optimize production processes and analyze product performance, thus reducing operational costs.
Financial institutions use data lakes to gain deeper insights into customers’ behaviors, analyze and detect fraudulent activities, improve risk management, and improve customer experience.

Overall, data lakes help companies make more informed decisions. By storing all their data in one central location, companies can find patterns and trends that were previously hidden. They are empowered to democratize data access, becoming more data-driven, agile, and competitive.

What is the Future of Data Lakes?

The future of data lakes is bright, as businesses continue to invest in big data analytics to stay ahead of the competition. With the increasing dominance of technologies such as artificial intelligence (AI) and machine learning (ML), data lakes can become more intelligent and powerful, able to create predictive models and automate decision-making processes.

McKinsey suggests that businesses take full advantage of data lake technology and its ability to handle computing-intensive functions, like advanced analytics or machine learning. Organizations may want to build data-centric applications on top of the data lake that can seamlessly combine insights gained from both data lake resources and other applications. Data lakes can be used to develop new business models and revenue streams, as businesses seek ways to monetize their data assets.

Ready to harness the power of data lakes in your business? Kopius can help build the future of your data-driven organization by streamlining your data architecture and delivering powerful analytics through data governance, machine learning, data visualization, and more. Learn about our Data Lakes solutions.

Explore Data Lake Solutions

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Using Agile Scrum When Working on a Nearshore Project

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What happens to the Scrum process when you are working with nearshore engineering teams?

Agile represents an overarching philosophy for software development, emphasizing the value of iterating quickly. You can read the Agile Manifesto here.

We use Scrum for project delivery, an Agile Framework that enables iterative and incremental product development. Scrum is a way to get work done as a team in small pieces at a time, with feedback loops and experimentation every step of the way so the team can learn and improve as they go.

Scrum allows teams to get things done at the right time, maximizing the value of what is delivered. Tasks are performed faster and with higher quality by self-organizing teams. Scrum is an excellent project management approach for the majority of engineering projects and is particularly well suited for nearshore and nearshore projects.

Nearshore projects are those where some project team members are based in Latin America. We typically provide consultants, project executives, and project managers out of the US, and then take advantage of the extraordinary nearshore engineering talent in Latin America.

Benefits Of Agile Scrum and Nearshore Projects

Decreased time to market – Scrum delivers value to the end customer 30 to 40 percent faster than traditional methods. Combined with the quicker engineering ramp-up time with nearshore teams, this is a significant acceleration.
Increased ROI – The decrease in time to market is one key reason that Scrum projects realize a higher return on investment (ROI).
Reduced risk – Mitigate the risk of absolute project failure (spending large amounts of time and money with no return on investment) by delivering the tangible product early for evaluation and scrutiny.
Better Quality – Projects exist to accomplish a vision or goal. Scrum provides the framework for continual feedback and exposure to ensure quality is as high as possible.
Higher Customer Satisfaction – Scrum teams are committed to producing products and services that satisfy customers.
Increased Collaboration and Ownership – When scrum teams take responsibility for projects and products, they can produce great results. Scrum teams collaborate and take ownership of quality and project performance.
Improved Progress Visibility and Exposure – Transparency and visibility make Scrum an exposure model to help the project team accurately identify issues and more accurately predict how things will go as the project progresses.
Increased Project Control – Scrum teams have numerous opportunities to control project performance and make timely corrections as needed

By leveraging Agile Scrum for nearshore projects, teams can rapidly design and build technology solutions through a series of sprints with each sprint delivering usable functionality. Periodically, completed sprint deliverables can be finalized and deployed as a production release.

Scrum Roles

The roles and staffing of a scrum project may be the most important decision made on a project.

Scrum Team – The Scrum Team is a group of collaborators who work toward completing projects and delivering products. The scrum team includes one scrum master, one product owner, and a group of developers. Within a scrum team, there is no rank or hierarchy.

Product Owner – The Product Owner is accountable for maximizing the value of the product resulting from the work of the Scrum Team. The Product Owner is one person, not a committee, and may represent the needs of many stakeholders in the Product Backlog. This person answers questions like:

What to create?
Why create this and not something else?
How to create this?
When to create it?

The product owner bridges the gap between product strategy and development, is responsible for the product backlog and organizing sprints, and answers questions from developers.

Representative Product Owner – Because being a product owner is time-consuming and is sometimes impossible for a client to dedicate to a project, we can provide a Representative Product Owner that alleviates the burden on the Client. Representative product owners have knowledge and communication of short- and mid-term project goals, deeply understand requirements, create and maintain product backlog, respond to team’s questions and requests, and assures the team’s understanding of requirements for upcoming sprints

Scrum Master – The Scrum Master is accountable for the Scrum Team’s effectiveness. The Scrum Master answers the question of Who creates it?

In summary, the Agile Scrum + Nearshore combination is exciting and powerful. We have seen it change the trajectory of client businesses since we started operating under this model more than a year ago.

To learn more about Agile Scrum + Nearshore combinations, reach out to us today! Kopius is a leader in nearshore digital technology consulting and services.

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Retail Technology and Innovation – a Conversation with Michael Guzzetta

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We recently spent some time with Michael Guzzetta, a seasoned retail technology and innovation executive and consultant who has worked with brands such as The Walt Disney Company, Microsoft, See’s Candies, and H-E-B.

Tell me about your background. What brought you to retail?

Like many people, I launched my retail career in high school when I worked in the men’s department at Robinson’s May. I also worked for The Warehouse (music retailer) and was a CSR at Blockbuster video – strangely, I still miss the satisfaction of organizing tapes on shelves.

I ignited my tech career in 2001 when I started working in payment processing and cloud-based tech, and then I returned to retail in 2009 when I joined Disney Store North America, one of the world’s strongest retail brands.

During my tenure at Disney, I had the privilege of working at the intersection of creative, marketing, and mobile/digital innovation. And this is where the innovation bug bit me and kicked off my decades-long work on omnichannel innovation projects. I seek opportunities to test and deploy in-store technology to simplify experiences for customers and employees, increase sales, and drive demand. Since jump-starting this journey at Disney Store, I’ve also helped See’s Candies, Microsoft, and H-E-B to advance their digital transformation through retail innovation.

What are some of the retail technologies that got you started?

I’ve seen it all! I’ve re-platformed eCommerce sites, deployed beacons and push notifications, deployed in-store traffic counting, worked on warehouse efficiency, automated and integrated buyer journeys and omnichannel programs, and more. I recently built a 20k SF innovation lab space to run proofs-of-concept to validate tech, test, and deployment in live environments. Smart checkout, supply chain, inventory management, eCommerce… you name it.

What are the biggest innovation challenges in retail today?

Some questions that keep certain retailers up at night are, “How can we simplify the shopping experience for customers and make it easier for them to check out?”, “How can we optimize our supply chain and inventory operations?”, “How can we improve accuracy for customers shopping online and reduce substitutions and shorts in fulfillment?” and “How can we make it easier and more efficient for personal shoppers to shop curbside and home delivery orders?” Not to mention, “What is the future of retail, and which technologies can help us stay competitive?”

I see potential in several trends to address those challenges, but my top three are:

Artificial Intelligence/Machine Learning – AI will continue to revolutionize retail. It’s permeated most of the technology we use today, whether it’s SAAS or hardware, like smart self-checkout. You can use AI, computer vision, and machine learning to identify products and immediately put them in your basket. AI is embedded in our everyday lives – it powers the smart assistants we use daily, monitors our social media activity, helps us book our travel, and runs self-driving cars, among dozens of other applications. And as a subset of AI, Machine Learning allows models to continue learning and improving, further advancing AI capabilities. I could go on but suffice it to say that the retailer that nails AI first wins.

Computer vision. Computer vision has a sizable opportunity to solve inventory issues, especially for grocery brands. Today, there’s a gap between online inventory and what’s on the shelf since the inventory system can’t keep pace with what’s stocked and on the shelves for personal shoppers, which is frustrating for customers who don’t expect substitutions or out-of-stock deliveries. With the advent of computer vision cameras, you can combine those differences and see what is on the shelf in real-time to inform what is available online accurately. Computer vision-supported inventory management will be vital to creating a truly omnichannel experience. Computer vision also enables smart shopping carts, self-checkout kiosks, loss prevention, and theft prevention. Not to mention Amazon’s use of CV cameras with their Just Walk Out tech in Amazon Go, Amazon Fresh, and specific Whole Foods locations. It has endless applications for retail and gives you the eyes online that you can’t get in stores today.

Robotics. In the last five years, robotics has taken a seismic leap, and a shift has happened, which you can see in massive, automated fulfillment centers like those operated by Amazon, Kroger, and Walmart. A brand can deliver groceries in a region without having a physical store, thanks to robotic fulfillment centers and distribution centers. It’s a game-changer. Robotics has many functions beyond fulfillment in retail, but this application truly stands out.

What is a missed opportunity that more retail brands should take advantage of?

Data. Data is huge, and its importance can’t be understated. It’s a big, missed opportunity for retailers today. Improving data management, governance, and sanitation is a massive opportunity for retailers that want to innovate.

Key opportunity areas around data in retail include customer experience (know your customer), understanding trends related to customer buying habits, and innovation. You can’t innovate at any speed with dirty data.

There’s a massive digital transformation revolution underway among retailers, and they are trying to innovate with data, but they have so much data that it can be overwhelming. They are trying to create data lakes, a single source of truth, and sometimes they can’t work because of disparate data networks. I believe that some of the more prominent retailers will have their data act together in a few years.

“Dirty data” results from companies being around for a long time, so they’ve accrued multiple data sets and cloud providers, and their data hasn’t been merged and cleaned. If you don’t have the right data, you are making decisions based on bad or old data, which could hurt you strategically or literally.

What do you wish more people understood about retail technology and innovation?

Technology will not replace people. In my experience, technology is meant to enhance the human experience, which includes employees. If technology simplifies the process so much that the employees become idle, they are typically trained to manage the technology or cross-trained to grow their careers. Technology isn’t replacing the human experience any time soon, although it is undoubtedly changing the existing work experience – ideally for the better, both for the employees and the bottom line.

Technology doesn’t always lower costs for retailers. Hardware innovation requires significant capital expenses when it’s deployed chain-wide. Amazon’s “Just Walk Out” is impressive technology, but the infrastructure, cloud computing costs, and computer vision cameras are insanely expensive. In 5 years, that may be different, but today it is a loss leader. It’s worth it for Amazon because they can get positive press, demonstrate innovation, and show industry leadership. But Amazon has not lowered its operating costs with “Just Walk Out.” This is just one example, but there are many out there.

Online shopping will not eliminate brick-and-mortar shopping. If the pandemic has taught us anything, online shopping is here to stay – and convenience is extremely attractive to consumers. But I think people will never stop going to stores because people love shopping. The experience you get by tangibly picking something up and engaging with employees in a store location will always be around, even with the advent of the Metaverse.

What are some brands that excite you right now because of how they use technology?

Amazon. What they have been doing with Just Walk Out technology, dash carts, smart shelves, and other IoT technology puts Amazon at the front of the innovation pack. Let’s not forget that they’ve led the way in same or next-day delivery by innovating with their automated fulfillment centers! They have the desire, the resources, and the talent to be the frontrunner for years to come.

Alibaba. This Chinese company is another retailer that uses technology in incredible ways. Their HEMA retail grocery stores are packed with innovation and technology. They have IoT sensors across the stores, electronic shelf labels, facial recognition cameras so you can check out with your face, and robotic kitchens where your order is made and delivered on conveyor belts. They also have conveyors throughout the store, so a personal shopper can shop by zone, then hook bags to be carried to the wareroom for sortation and delivery prep – it’s impressive.

Walmart and Kroger. Both brands’ use of automated fulfillment centers (AFCs) and drone technology (among many others) are pushing the boundaries of grocery retail today. Their AFCs cast a much wider net and have expanded their existing markets, so, for example, we may see Kroger trucks in neighborhoods that don’t have a store in sight.

Home Depot. They have a smart app with 3D augmented reality and robust in-store mapping/wayfinding. Their use of machine learning is also impressive. For example, it helps them better understand what type of projects a customer might be working on based on their browsing and shopping habits.

Sephora. They use beacon technology to bring people with the Sephora app into the store and engage them. They have smart mirrors that help customers pick the right makeup for their skin tone and provide tutorials. Customers can shop directly through smart mirrors or work with an in-store makeup artist.

What advice do you have for retailers that want to invest in technology innovation?

My first piece of advice is to include change management in the project planning from the start.

There are inherent challenges in retail innovation, often due to change management issues. When a company has been around for decades or even more than a century, they operate with well-known, trusted, and often outdated infrastructure. While that infrastructure can’t uphold the company for the next several decades or centuries, there can be a fear of significant change and a deeply rooted preference for existing systems. There can be a fear of job loss because of the misconception that technology will replace people in retail.

Bring those change-resistant people into the innovation process early and often and invite them to be part of the idea generation. Any technology solution needs to be designed with the user’s needs in mind, and this audience is a core user group. Think “lean startup” approach.

My second piece of advice is to devote enough resources to innovation and give the innovation team the power to make decisions. The innovation team should still operate with lean resources, focusing on minimum viable products and proofs of concept, so failures aren’t cost-prohibitive. The innovation team performs best when it has the autonomy to test, learn, and fail as they explore innovative solutions. Then, it reports its findings and recommendations to higher-ups to calibrate and pivot where needed.

In closing, I’d say the key to innovation success is embracing the notion of failure. Failure has value! Put another way; failure is the fast track to learning. Learning what not to do and what to try next can help a retail company to accelerate faster than the competition. Think MVP, stay lean, get validated feedback quickly, and iterate until you have a breakthrough. And always maintain a growth mindset – never stop learning and growing.

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3 Keys to Unlock Healthcare’s Digital Front Door

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The digital front door has become the common name in the healthcare industry for a mobile website or application that unifies the patient experience and connects patients to care across the continuum.

In short, a digital front door connects and scales the virtual care journey to give patients what they need, when they need it.

The trend toward self-service in healthcare was already underway when COVID hit, and the pandemic sharply accelerated the demand for digital access to healthcare information. Appointment scheduling is one important aspect of a digital front door experience, and studies find that 40% of appointments are booked after business hours, and 67% of patients prefer online booking. Further, $150 billion annually is estimated as the annual loss from missed medical appointments. (source)

Some of our company’s earliest and most enduring clients have been healthcare organizations, and we’ve noticed three keys to success when developing and deploying a digital front door.

Key to success: Get the right stakeholders involved

“This is more than a digital shift – the shift to a digital front door requires a culture shift within the organization,” says Yuri Brigance, Valence’s director of software engineering.

Experience has taught us that having the right people in the room can make all the difference in the success or failure of a major initiative. Especially considering the role that change management plays here – People don’t resist change, they resist being changed. So you need to engage stakeholders from all impacted groups, from frontline workers to back-office operations. This will improve requirements documentation, roadmap planning, and buy-in as the work rolls out.

Key to success: Users Drive the Design Strategy

“While a digital front door is a technology solution, it’s ultimately about humanizing the patient experience,” says Sam To, designer at Valence.

In the case of a digital front door, the users may be patients, families of patients, or healthcare providers. In nearly all scenarios, people value products that are easy to use, simple to set up, and have a logical progression. This is especially true in a healthcare situation, which may be hypercharged by personal and situational stressors.

Equitable design should be at the forefront of design decisions because the healthcare organization needs to design for a wide array of users and needs. You can read more about our approach to equitable design here.

The design phase of the digital front door project should include user interviews, feedback sessions, prototyping, and more. Giving the UX design team access to users early in the process can help to identify the best-case rollout strategy, reveal opportunities to differentiate from competitors, and deliver precisely the right content to users when they need it – all leading to better patient satisfaction scores.

Key to success: Develop a feature roadmap and strategy for rolling out updates

“When embarking on a digital effort in healthcare, it’s important to start by understanding which changes you need to see in the organization. Are you pursuing improved patient satisfaction scores? Physician satisfaction? ED/Urgent Care wait times? Quality and safety scores? Each area targeted for improvement may influence priorities differently,” says Malia Jacobson, healthcare content strategist at Valence.

Many healthcare providers are leaning into digital solutions to address patient satisfaction, reduce service demand, and reduce administrative overhead. In addition to standard features of a digital front door experience, providers should consider designing for experiences such as:

Bill pay
Self-scheduling and care coordination
Provider communication
Information libraries
Find a provider
Imaging library
Patient outreach
Capacity management
Census management
Forecasting
Infectious disease tracking
Discharge planning
Privacy and security to safeguard patient data
Strategies to increase adoption, such as gamification and push notifications
Support for population health initiatives
Analytics and insights to derive more value from data
AI features, such as chatbots, to reduce clinical burden and improve patient flow
Support for healthcare information exchange in compliance with FHIR standards and best practices.

It’s important to understand how these features interplay as part of a big picture roadmap with a rollout timeline and strategy. You don’t have to release everything at one time to be successful, and adding features as the platform develops and collects user feedback will future-proof the effort.

In closing, healthcare has always been heavily impacted by technology, but the patient experience lagged behind other healthcare innovations. That is changing.

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Cloud Migration and Cloud Services

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By Luca Junghans

A look inside these cloud capabilities

By joining forces, Valence and MajorKey offer an even greater set of cloud services for businesses that want to power their digital transformation with cloud technologies.

MajorKey works with clients to migrate business applications to the cloud, and Valence builds services on the cloud. This is one reason these businesses are such a powerful combined force.

The cloud refers to software and services that run on a (usually) regionally located server owned by the cloud service provider, instead of on an on-premise server owned by a customer. Cloud servers are in data centers all over the world. By using cloud computing, companies don’t have to manage physical servers or run software applications on their own machines.

It’s big business. In fact, one of our partners, AWS contributed 14.5% of revenue to Amazon’s overall business in 2021, which would have operated at a $1.8 billion loss in Q4 without it – and AWS revenue was up nearly 39% compared to 2020.

There are many ways to use and understand the business impact of cloud technology. We are breaking down the distinction between cloud services and cloud migration for you here!

Cloud Migration and Cloud Services

Simply put, cloud migration is what happens when a company moves some or all of its software onto cloud servers.

In other words, cloud migration is moving your software to a managed server operated by the cloud provider; and cloud services are technology solutions built on top of those managed servers. There’s a whole range of capabilities bridging the two.

Let’s take a closer look.

Cloud services range in how much they abstract away from the customer. A good example is Amazon Cognito, which is a user management cloud service. Amazon Cognito has implementations of basic user functions such as login, logout, sessions, and security, so a customer doesn’t have to worry about a deeper technical implementation of these features and can focus on managing users.

Cloud services are so flexible that there are seemingly infinite ways to deploy them for a business. Cloud services are the infrastructure, platforms, and software hosted by cloud providers, and there are three common solutions:

Infrastructure as a service: The renting out of virtual machines and space to customers, while providing a way to remotely manage the resource. When a company migrates to the cloud, they are using this service.
Platforms: Providers like AWS and Azure build specialized software on top of their own cloud hardware and offer the software to customers as a service. These are specialty services and can provide patterns for things such as Data Analysis, Compute, IoT, APIs, Security, Identity, and Containerization. We wrote about Digital Twins in a previous post, which referenced Digital Twin platforms offered by AWS and Azure.
Software as a service (SaaS): Software can be built on top of the platforms offered by the cloud providers. Software developers can also partner with other third parties to provide fully built instances of software that typically come with subscription rates, customer support, and personal configurations of the software. Examples of this include Atlassian Jira and Confluence, Dropbox, Salesforce, and G suite.

These services can be transformative for businesses in general, but it’s not always easy to know the best way for your business to use them. The added benefits to this migration range per case, and here are four examples:

Scalability: Cloud services often offer on demand scaling options that can satisfy unexpected or planned growth. Depending on your product, this can be a lot easier than upgrading on-premise hardware, but not always cheaper.
Cost: Although we expect the costs to be passed to the consumer in some way, the logistics of maintenance and upgrades to the cloud systems is handled by the provider. In many cases this can translate to a huge amount of money saved for the customers.
Performance: Performance-enhancing services like CDNs and regional hosting, when understood and configured properly, can have tangible and positive performance impacts.
Local Management: Being on the cloud means access to the digital portals to manage the services (most times). This creates a lower bar of entry for employees to manage and observe the resources.

Many businesses start their digital transformation journey by migrating infrastructure or applications from on-premises servers to the cloud. Notably, cloud migration can also refer to a situation where a business needs to bring the cloud resources they manage into an on-premises environment. It can also describe a situation where a business moves its data resources from one cloud provider to another.

Cloud migration to use cloud services is a process that presents many upsides, and is worth investigating! The process will add additional complexities – specifically, security and governance will generally be instituted upfront as a base for the rest of the migration. We design and engineer performant, scalable, and maintainable applications that save businesses money, fill in knowledge gaps, and provide users with a positive experience.

Here are two examples of cloud services that we’ve built for clients:

Building cloud applications with AWS lambda: We have bridged the gap between multiple third-party APIs and created new databases that consolidate data and deliver it to a web application. Cloud services remove the need for our clients to interact with these multiple services, which saves them time and money. At the same time, we used AWS Cognito to help our customer manage roles and users in a secure and trusted way. This removed the need for our engineers to write our own user management software, a cumbersome task.
Data pipelines: We identify problems in our customers’ current database providers and migrate data to a more performant and better structured database in cloud-to-cloud migrations.

We will continue to build and migrate while we investigate the future of the cloud. What are the new services and platforms? Who can benefit the most from them? How can we do it right? We will be prepared for the cloud migration and services needed from the real world to the metaverse, and beyond.

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Effective Digital Transformation

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The phrase Digital Transformation is commonly used today, referring to everything from an overhaul of a legacy system to leveraging online systems to engage customers. As champions of digital transformation, our team believes in the power of smartly planned and efficiently executed digital transformations to enhance business strategy; we believe that effective digital transformation is a cornerstone of business, and it is imperative that individuals understand the definition, potential impact, and processes that lead to success.

Effective Digital Transformation: How do we think about it?

Effective digital transformation puts business strategy ahead of digital strategy, whilst interweaving the two. Successful digital transformation solves business problems by focusing on the customer — for example, by decreasing costs, or increasing value — and using technology solutions that cut through business functions, industries, processes to affect change. In short, technology is a means to an end.

Digital transformation may help reduce product costs, but what does that do for the business? It provides resources to be routed into other aspects of the business. Leverage those freed up resources to enhance the customer experience and you are left with improved margins and happier customers and an effective digital transformation.

Consider Amazon — a company that digitally transformed its business of book selling to a Big 4 technology company. Amazon leveraged digital transformation initiatives to change its supply chain and operational efficiency in order to provide a better customer experience. Their culture (the world-famous 14 Leadership Principles) and business strategy are interwoven to focus on the customer: Amazon Prime has some of the fastest delivery options in the market and Amazon Web Services provides some of the best cloud solutions for enterprises. They digitally transformed their business and now provide customers with digital solutions to digitally transform theirs. From their website: “Amazonians… share a common desire to always be learning and inventing on behalf of our customers.” Leverage culture and technology to improve customer experience; digitally transform the business to help the customer.

Digital Transformation contains components of digital strategy, the use of digitalization, as well as digitization efforts. These terms, often thrown around interchangeably, are in fact pieces of the larger puzzle rather than equal to the overall process. Digitization is the process of moving from analog to digital, pen and paper to Microsoft Excel. Digitalization, according to Gartner, speaks of the use of digital strategies, technologies, initiatives to tap into new business opportunities or change a business model. If anything, one leverages digitization to digitalize, and the overall transformation of a business from one to another, becomes digital transformation. The definitions are debated and often vague, as discussed by Jason Bloomberg in this Forbes article. It is important to remain consistent in thinking of digital transformation as the overarching umbrella of strategic digital initiatives to improve the business with the customer at the forefront.

Digital Transformation: Consider “The Process” towards success

What does Digital Transformation success entail? What does it look like?

As enterprises restructure their strategy to evolve amid a changing technological and economic landscape while centering around the customer, it is important to consider the process and what it takes to succeed.

Key Stages to Success

According to Keller and Price in Beyond Performance: How Great Organizations Build Ultimate Competitive Advantage, successful transformation involves a few key stages — from goal defining, to organizational assessment, to designing and initiating transformation and sustaining it. It is critical to understand where the enterprise is and where it wants to go — and it is critical to be consistent and practical.

Ensuring Success

to move forward with a transformation initiative, it is imperative to align Keller and Price’s stages with McKinsey’s 5 themes to a successful digital transformation, which involve digitization to prepare an enterprise for digitalization:

Having the right, digital-savvy leaders in place
Building capabilities for the workforce of the future
Empowering people to work in new ways
Giving day-to-day tools a digital upgrade
Communicating frequently via traditional and digital methods

Think about the Amazon example again — they didn’t just leverage digital solutions to overhaul their business; they leveraged cultural practices to ensure that Amazonians are driven towards the integration of technology and customer centricity. McKinsey’s themes encompass a similar outlook: empowerment, communications, capabilities, leadership — core cultural understandings that can support a digital transformation initiative.

At Valence, we focus heavily on thinking about the future. It is critical to be ever ready for tomorrow, whether it means continuous learning, or building systems and solutions to prepare for what is next. These stages and themes will ensure enterprises are thinking about the next step, focusing on being proactive rather than reactive. At this important juncture of the 21st century, where we have crossed into a new decade and face the challenge of economic reinvention due to a global pandemic, it matters how we use technology to transform our enterprises to meet changing customer needs.

In summary, as stated by Jim Darrin, CEO of Valence,

“No industry or company can ignore the importance or impact of Digital Transformation, and must embrace a digital strategy in order to evolve into the next generation.”

Do you think your business is ready for a digital transformation? We can help you with the journey. Contact us for more information.

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