devopsy.kinsta.cloud

Author: Lenley Hensarling

  • How to Navigate App Modernization Strategies for the Cloud

    How to Navigate App Modernization Strategies for the Cloud

    In today’s fast-paced digital landscape, organizations continuously seek ways to innovate and enhance customer experiences while optimizing efficiency. One of the most transformative strategies is modernizing applications for the cloud. App modernization isn’t just a technology upgrade. It’s a holistic business transformation. The cloud’s elasticity and scalability present opportunities to create deeper customer engagement, foster seamless partner integration and unlock valuable data insights. However, this requires a thoughtful approach that aligns with business goals and technological aspirations.

    Let’s look at some best practices for modernizing apps for the cloud and navigating potential challenges.

    The App Modernization Process

    It’s important to determine whether the cost of migration will lead to better efficiency and customer engagement that will yield an increase in return on investment (ROI). This approach allows businesses to address current issues and future-proof their data stacks.

    After assessing the cost and ROI considerations, organizations need to follow a systematic process to successfully modernize an app for the cloud. The first step is to evaluate the existing app’s strengths, weaknesses and areas for improvement. It’s crucial to assess an app’s compatibility with cloud platforms and set clear modernization goals.

    Following the assessment, design a cloud-native architecture that aligns with the cloud’s scalability, elasticity and distributed nature. This may involve rearchitecting the app to include such cloud-native components as microservices, containers, or serverless computing. Once the architecture is in place, migrate the app’s data from on-premises or legacy databases to cloud-based storage solutions to ensure data availability, scalability and ease of management in the cloud environment.

    The next step involves rewriting or modifying the app’s codebase to take advantage of cloud services and APIs. Specific modules or functionalities may need to be re-engineered to enhance performance, scalability and integration capabilities. Perform comprehensive automated load and security testing to verify reliability and performance in the cloud.

    Once the modernized app has been tested, deploy it on the selected cloud platform. Implement monitoring and logging mechanisms to gain insights into the app’s behavior, performance and user experience so it operates effectively in the cloud environment.

    Navigating App Modernization Challenges

    While the benefits of app modernization are significant, challenges can arise during the process.

    One of these hurdles involves dealing with legacy constraints. Legacy applications often have complex architectures that complicate modernization. Careful planning is required to update outdated technology stacks and move from a tightly coupled system to a loosely coupled one.

    Another obstacle pertains to data migration complexities. Transferring large amounts of data from on-premises systems to the cloud demands meticulous planning and execution to maintain data integrity, security and minimal downtime.

    Integration presents another potential difficulty. Modernized apps often need to integrate with other systems, both on-premises and in the cloud. Many cloud vendors offer tools and programs to aid in migrating data. These, coupled with credits to be used as apps are migrated, can significantly reduce costs.

    Security and compliance are critical considerations when moving to the cloud. Cloud environments require robust security measures to protect sensitive data, and organizations must comply with relevant regulations and industry standards.

    Effective change management strategies are another aspect to consider to ensure a smooth transition for users and stakeholders. App modernization is a business process modernization representing a significant change in business operations.

    In-House Vs. Outsourced App Modernization

    Choosing between in-house and outsourced app modernization depends on several factors.

    First, if an organization has skilled developers with cloud expertise, handling app modernization in-house is a viable option. On the other hand, if a team lacks the necessary skills, it may be more practical to outsource the task to a cloud service provider or a specialized development partner. It should be noted that if the development work is outsourced, it’s important to keep the app business owners involved to ensure that the modernized solutions yield the expected benefits.

    Another consideration is the availability of resources, such as time, budget and staffing. App modernization projects require dedicated efforts, and outsourcing becomes appealing if internal resources are limited. By entrusting the project to external professionals, organizations can ensure it receives the attention and resources needed without burdening the internal team.

    Evaluating whether app modernization aligns with an organization’s core competencies is also essential. If the application is not directly related to the core business, outsourcing can free up internal resources and redirect them toward more strategic initiatives. This allows an organization to concentrate on areas in which it has a competitive advantage and expertise while still benefiting from the app modernization expertise of external partners.

    Pioneering the Future

    Modernizing an app for the cloud is a transformative journey that holds the potential to enhance customer experiences, optimize processes and unlock valuable insights. Navigating challenges through meticulous planning, robust testing and adherence to standards are crucial, whether opting for in-house expertise or external collaboration. By embracing cloud-native principles and using advanced technologies, organizations can position themselves at the forefront of innovation, ensuring lasting success in the dynamic landscape of the digital era.

  • Predictions 2020: Five Real-Time Data Predictions

    Predictions 2020: Five Real-Time Data Predictions

    According to Gartner, 85% of big data initiatives end in failure. In 2020, organizations are out of budget and operational runway, and need to start executing and getting the big data recipe right.

    It is not just about big data; it is about using data differently. Rather than rearward-facing business intelligence, leading companies are driving differentiation and profit by using data to make fact-based decisions in real time. This requires the broader application of real-time information to make a difference in user experience, in prioritization of production and manufacturing schedules, and in spotting problems before they happen.

    Rather than reports for a few people, the new data-driven business processes are engaged continuously, and across all customer engagements, transactions and decision points within business processes. Scale means throughput across millions of transactions and with potentially millions of customers. Database systems have to be engineered differently, with different technology built to deal with real-time demands and high throughput across huge numbers of endpoints.

    As we enter the next decade, here are five predictions, centered on real-time data, that we expect to emerge.

    Machine Learning and AI Models Finally Get the Data They Deserve

    Most AI and machine learning initiatives are niche, nascent and not operating anywhere near scale. According to industry research (O’Reilly), hardware and data are the primary bottlenecks in getting enough data processed fast enough to harness the full promise of AI. That’s changing, with the arrival of new wire-speed hardware (from companies such as Intel) and clever data platforms designed to leverage hardware innovations for real-time efforts on a massive scale. It’s all about the amount of data you can process in real time to make AI and machine learning actually work. This is what is needed, and it’s becoming practical.

    Privacy Prompts a New Era of Data Innovation

    Businesses hoard data. And often, so much of it isn’t used due to infrastructure costs and technology bottlenecks. Now, as new privacy laws (GDPR, CCPA, etc.) show their teeth, companies really need to make all that data worth the risk. It will drive new real-time business processes that are not only safer but squeeze value out of data in new and exciting ways.

    Companies face a delicate balancing act. They must comply with the realities of new regulations, while also being able to use personal data in real time to serve customers what they want and expect. Companies that can satisfy the regulators and appeal to customers will win.

    If an Economic Downturn Occurs, Data Will Drive the Next Round of Economic Gains 

    An economic downturn is inevitable. When it happens, companies will be asked to do more with less. As a result, they will need to completely rethink their solution and delivery strategies.

    In that environment, with fewer markets opening or providing a sufficient return on investment, the next wave of gains is to optimize your existing segments and operations to the fullest extent. These data-driven initiatives require that companies understand customers better, and be able to instantly target them in critical moments—harnessing massive amounts of data to suggest and expand purchases. 

    5G Will Accelerate Customer Expectations

    As bandwidth increases and high bandwidth communication becomes available across devices (on cars, planes, homes, ships, buildings, etc.) and in more situations, we will see data being used differently. What may have been processed intermittently in batches with high degrees of latency will now be processed in real time, driving higher efficiency and removing risk from supply chains, customer interactions and equipment maintenance and performance. It will no longer be OK to know how you’ve done and correct it next quarter; rather, things must be corrected in near real time. New standards will be set by the highest-performing companies, and entirely new businesses will emerge that are leveraging a new level of availability to real-time data.

    Decline of Globalization Will Force Companies to Know Their Customers Better

    As global markets shrink, it becomes imperative for companies to up their game and drive for more market share within the available market. Companies that have been treading water and growing at the rate of the global market expansion are going to start to shrink. Differentiation through better customer experience and better management of supply chains will make the difference between continuing to grow versus stagnation or shrinkage.

    Companies that have invested in instrumenting their supply chains, who have a deep understanding of what comes from where and what the alternatives are, have already begun to optimize around the new web of tariffs. Those that are less agile are seeing reduced profits or reduced sales based upon the elasticity of their pricing options.

    The more real-time data one has, the clearer the picture of what options are available as the global situation changes. The faster a company can process that data means either missing financial targets or outperforming peers.

    Want to learn more about what to expect in 2020? Join us Jan. 23 for our Predict 2020 Virtual Summit  featuring discussions from some of the industry’s best and brightest offering up their visions for the future. Sign up today for this free daylong virtual event.

    — Lenley Hensarling

  • The Real-Time Innovation Dividend

    The Real-Time Innovation Dividend

    Time is compressing. The attention span of individuals and organizations is becoming ever shorter. There are only brief windows that can be devoted to any given transaction.

    Expectations around situational knowledge, updated in real-time, are compounding. Customers expect the applications they interact with know where they are, what they are looking for and what their preferences are in terms of price, value, design and timeliness of delivery. If the proper options aren’t provided immediately, the customer will move on to another provider.

    While the time available to an algorithm or application is contracting, the complexity of the tasks for successful interactions is increasing. Digital businesses must do more in ever shorter time windows. Differentiation in the digital world has a lot to do with a deep understanding of the customer based on multiple data streams and driving decisions that provide a deeply personalized experience.

    In business, we need to have a current picture of our situation. We build models and analytic dashboards, but these fall short in allowing us a real-time view. The latency of these models may be measured in seconds, minutes, hours, even days and weeks. As latency expands, the picture is not of the moment and that difference defines operating risk. The phrase “time is money” has never been more accurate.

    Brokerages require as close to a real-time view of positions allowing them to manage risk more closely, in terms of their aggregate position, based on a transactionally correct view of precisely what they have on their books to respond in real-time to global market changes.

    For retailers, it means knowing the content a customer has seen and the environment where they encountered it. Did they just come from a brick and mortar store? What digital journeys have they been on? What online content have they been perusing? Continually adaptive models use the answers to these questions to select active customers and recommend options that address what they are searching for.

    The digital shopping experience is composed of multiple linked processes, working in concert to continually understand the customer experience. Different system elements–user profiles, decisioning, recommendation engines and machine learning and AI–require data across different time windows. Some of these systems deal with terabytes of data and some petabytes. Some require absolute consistency and for some eventual consistency is good enough. In many of these systems, there is a demand for hyperscale within tight time windows.

    The ability to take in more data within a given time window feeds the currency of the context. It is not enough to have a lot of data, but you also need the most current view of that data. Operating at hyperscale enables an organization to deliver exponentially more data than traditional databases. This makes models more accurate and, by extension, more valuable.

    Scale Plus Speed Equals More Time

    In some sense the combination of scale with speed creates time, giving you the ability to do more within a given time window. Consider this a dividend, allowing the use of more data or allowing the process to be completed in less time.

    The systems that interact directly with either customers, employees or clients are edge computing systems, pulling in and processing data within relatively tight time windows. Edge systems may have expiry windows where data is deleted after a given time window where it is deemed to be stale.

    The data collected and processed at the edge may be moved to a system of record or core transactional store and kept for longer periods providing historical context. These data sets may grow to petabytes in size while still needing to support response times measured in milliseconds. Meeting these requirements requires hyperscale capability, where the performance of data access does not degrade as the repository of information grows.

    Here’s a real-life example. We must match the information collected regarding devices, identity and patterns that establish context against our database of user profiles. We want to run more sophisticated recommendation engines based on these patterns to ensure we are putting the closest thing to what the customer wants in front of them. We also want to make pricing decisions in real-time, to gain the largest basket at the best margins. We may have established a model that tells us customers with this profile are likely to make additional purchases after this transaction, so our analysis and optimization boundaries go beyond just this transaction. We will want to log this so we can track this behavior to validate the model. We have these processes going on, some in parallel, others in sequence:

    • Establish an identity profile of the customer.
    • Match that profile against a database of profiles that models likely desires and behaviors.
    • Based on that match, determine the products and/or services to recommend.
    • Select the content and media types based on the user profile.
    • Determine pricing based on the user profile and the transaction profile.

    The last element, real-time pricing, is something we want to modify to take into account the margin and profitability of the customer over time. We also know that if this impacts the overall window of the transaction the customer has a higher likelihood of abandoning the transaction and we can lose out on not just this transaction, but a whole set subsequent to this one.

    Doing Better for Both the Customer and the Business

    By building models based on hyperscale capability, 10 milliseconds are saved here, another 20 milliseconds there. This creates the time bandwidth in the overall process to add the notion of optimizing pricing around not just this transaction, but the value of this transaction profile and the relative margins of the various components in this sale and other subsequent sales that are likely to happen. We have created time to do this additional processing and do better–better for our customers and better for our business. And we know that as we add more data points to our profiles and scale up to include more data overall, we won’t experience performance degradation. These time benefits will remain. This is the real-time innovation dividend.

    — Lenley Hensarling