{"id":6416,"date":"2024-01-18T13:21:55","date_gmt":"2024-01-18T18:21:55","guid":{"rendered":"https:\/\/solutionsreview.com\/data-management\/?p=6416"},"modified":"2024-01-18T16:33:51","modified_gmt":"2024-01-18T21:33:51","slug":"2-key-considerations-for-simplifying-your-application-stack","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-management\/2-key-considerations-for-simplifying-your-application-stack\/","title":{"rendered":"2 Key Considerations for Simplifying Your Application Stack"},"content":{"rendered":"

\"\"<\/p>\n

Solutions Review\u2019s Contributed Content Series<\/u><\/a> is a collection of contributed articles written by thought leaders in enterprise tech<\/a>. In this feature, EDB<\/a>‘s Chief Product Officer Chandler Hoisington offers key considerations for simplifying your application stack, to consider.<\/strong><\/i><\/span><\/p>\n

\"\"<\/a><\/span>The application stack has gotten complex. Despite app consolidation initiatives and budget constraints, the typical large enterprise still has an average of 664 applications in its tech stack according to one report<\/a>. Every few years, new technologies emerge promising to make business infrastructure more efficient, scalable, and cost effective. Things like the advent of cloud computing, containerization, NoSQL, machine learning, and, most recently, the explosion of artificial intelligence (AI), have drastically changed\u2014and complicated\u2014the application stack.<\/p>\n

Things look very different from just ten years ago when simple, monolithic applications and databases were the norm; think of just a Java application or an Oracle database. Now, we\u2019re running all (or many) of our applications in the cloud, deploying them automatically through continuous integration and continuous delivery\/continuous deployment (CI\/CD) systems, packaging them in containers, running them on distributed systems, with multiple relational database management systems (RDBMSs) and open source technologies in place.<\/p>\n

This shift has empowered companies to innovate like never before, but it has also caused its fair share of headaches for the simple fact that things are now much more complicated. Organizations have tried to solve these challenges as they arise, but the complexity has invariably caught up with anything designed to simplify it.<\/p>\n

And now, emerging technologies like AI and large language models (LLMs) are offering huge promise, yet many organizations are still trying to figure out how to move to the cloud or how to integrate new databases to maximize the data their business generates. We\u2019re in an \u201carms race\u201d of sorts, in which every organization is trying to adopt new technologies as they crop up, but many still have not yet mastered the basics.<\/p>\n

Getting a hold of this complexity and simplifying the application stack will be critical for organizations to realize the value of technologies like AI and LLMs. Accordingly, companies should take a step back and consider the following strategies to streamline their application stack and thus unlock more innovation.<\/p>\n

Evaluate New Tech Trends Based on Specific Use Cases<\/strong><\/h3>\n

And not just what\u2019s trending<\/strong><\/h4>\n

It can be tempting to jump on new technology trends as they arise: every company wants to be at the cutting edge of their space and to be able to do<\/em> more<\/em>. But rapidly adopting technologies without a solid use case\u2014or without having a plan in place\u2014can actually set an organization back if they\u2019re not ready to handle the additional complexity they create. As mentioned, many companies don\u2019t have the prerequisites in place to get the most from technologies like AI and LLMs, for example. The early adoption of these innovations will no doubt provide a significant competitive advantage, but only if done right. Business leaders and development teams need to first understand the use cases and problems they are trying to solve with tech like AI. And, according to Gartner analyst Frances Karamouzis<\/a>, \u201cOrganizations still struggle to connect the [AI] algorithms they are building to a business value proposition.\u201d<\/p>\n

Given this, businesses need to focus on two key factors when assessing whether or not to adopt a new technology: need and manageability. First and foremost, is there a true need for this technology in the tech stack? Only implement solutions for which you have a verifiable need: it\u2019s crucial to understand the strategic purpose the technology supports.<\/p>\n

It\u2019s also necessary for organizations to determine whether they have the capacity to properly operate and manage any new technologies they bring on. Important factors to consider include staffing, hiring and\/or upskilling, support contracts, and your organization\u2019s overall appetite for change. This ensures that you have the \u201cintellectual bandwidth\u201d to actually move the needle and see value from these technologies instead of implementing them hastily and then later playing catch up due to the added complexity they create.<\/p>\n

It\u2019s also important to mention that not all new tech necessarily adds complexity. For example, things like open source technology are a scalable, cost-effective, and customizable solution that can provide companies with more capabilities without adding complexity. Additionally, the emergence of distributed systems and large, containerized applications that can be broken into microservices that can then be scaled independently of each other allows teams to test and improve smaller data sets without disrupting entire systems.<\/p>\n

Adopt Extensible Technologies<\/strong><\/h3>\n

They may even already exist in your application stack!<\/strong><\/h4>\n

Another way to simplify the application stack is by adopting extensible technologies. Organizations should make it a priority to seek out solutions that continually add performance enhancements: This way, they can take advantage of innovative technologies as they hit the market without the need to add anything new to their application stack. In fact, solutions like this may even already exist in your application stack, just waiting to be leveraged to their full potential. That being said, staying in the know about new releases, updates, and features so that you\u2019re always getting the most out of the solutions you already have in place is paramount.<\/p>\n

And when vetting new technologies, organizations need to ensure that they have a track record of being relevant and sufficiently extensible: Do they make it easy to do things like add new data types and their associated operations? Does the technology easily integrate and support the larger “data ecosystem\u201d of the organization? How flexible is the technology in question? And, historically, is the solution known to be well-liked by its users? Look for rock-solid solutions that will stand the test of time and continually offer new features and capabilities. Choosing extensible technologies keeps you from feeling the need to constantly chase \u201cthe next best thing\u201d\u2014and that\u2019s a good thing\u2014because that can become very expensive and time consuming.<\/p>\n

An exciting new world of possibilities is on the horizon thanks to AI. But in order to harness the power of this technology\u2014and whatever others may emerge in the future\u2014organizations need to focus on simplifying their application stack. Being thoughtful about which tech trends you adopt, and opting for extensible technologies (some of which you may even already have), is the best way to do that.<\/p>\n","protected":false},"excerpt":{"rendered":"

Solutions Review\u2019s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise tech. In this feature, EDB‘s Chief Product Officer Chandler Hoisington offers key considerations for simplifying your application stack, to consider. The application stack has gotten complex. Despite app consolidation initiatives and budget constraints, the typical large enterprise still […]<\/p>\n","protected":false},"author":1022,"featured_media":6418,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[3],"tags":[1431,1423],"acf":[],"yoast_head":"\n2 Key Considerations for Simplifying Your Application Stack<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/solutionsreview.com\/data-management\/2-key-considerations-for-simplifying-your-application-stack\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"2 Key Considerations for Simplifying Your Application Stack\" \/>\n<meta property=\"og:description\" content=\"Solutions Review\u2019s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise tech. 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