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Why Web Optimization is a Must for Businesses?

Have you ever come across any website which takes a lot of time and effort to load? Would you like to go to that site again? Well, the answer is of course no. This is where web optimization comes into the picture. It helps in improving the user experience (UX) and performance of the website. And if the website performance is good, your audience would want to revisit your website, and also, your website will be ranked higher. 

According to a report by First Page Sage, the top 3 organic search results receive more than two-thirds (68.7%) of all clicks on the Google Search page.

In this digital era, search engines play a major role in deciding your website’s rank. Impressing search engines has become highly important if you want to rank your website at the top of the search engine result page. 

In this article, we’ll explain what is web optimization, why is it a must for businesses and how can they do it effectively.

What is web optimization – A bird’s eye view: 

‘Optimization’ is a word referring to improving performance and achieving a business goal using a strategic approach. The objective is to enhance the user experience, and engage more users, to improve traffic by improving SEO. 

There are many ways to optimize your website, forming the strategy depends on factors like what goals you want to achieve doing this, what aspects you want to improve in your website, whether there are any specific requirements from clients, whether it is a new innovative feature, etc.

Usually, developers follow strategies like A/B testing, inputs from clients, and results from user experience surveys, etc., to narrow down what we want to achieve. Then they select a strategy that will be most suitable to achieve the goal keeping in mind other factors also – time, budget, resources they have, etc.

Need for web optimization

How does a brand survive and grow? Why is web optimization essential for it? The answer is simple, your users interact with the brand’s website or app more than any sales or marketing, or support person. Designing and optimizing your site to create a first-rate user experience (UX) for your users becomes highly important. Optimization happens on many levels in an organization – process, budget, technical, etc. but this blog post will discuss web optimization on a technical aspect.

We have a lot of problem statements that we face in day-to-day life when accessing business websites. The need for web optimization arises due to 2 main reasons:

  1. UI / Performance related issues 
  2. SEO-related issues

Poorly optimized websites give poor user experience and the repeat visits for these sites are comparatively very low as this requires more time and effort from users. Poorly optimized websites in terms of UI/UX and performance may lead to problems like

  • Slow website loading/user interaction
  • Disturbed/unhappy viewing experience
  • Poor organization of content
  • Lagging in trend with competitors
  • Slow website loading/user interaction

 Performance issues such as poorly done Javascript, render-blocking resources, etc., slow down the website considerably on the following aspects – Time to load the site, 

Time to interact (getting inputs and processing), Content display, etc., which may eat the user’s time and test their patience and drive them frustrated.

  • Disturbed/unhappy viewing experience

Sites with several ads, unnecessary pop-ups, unskippable ads, clickbait ads that lead to another page, etc., which overlap the content may divert the user’s attention and increase the complexity of using the site. Pirated movie sites, poorly done shopping sites, and social media sites fall into this category as the best examples. It also increases the user’s time and effort when the user needs to find out where the ad is playing from and close the ad and related pages that might open. Most of the product surveys and reviews from these sites get a negative score from users for viewing experience and the user satisfaction/user retention score is very less.

  • Poor organization of content

Sites that need more content like policy pages, terms, and conditions, and where information needs to be divided into several tabs and categories, dropdowns, etc., need to be crafted very carefully and strategically. Else, it results in a lot of cluttering, and the user ends up spending more time and effort in identifying which is where resulting in a poor accessibility score.

The best example is service sites or information-based sites where users need to reach out to support or read the policy pages. If the support icon is not shown on the main screen, the user needs to scroll to the bottommost section and search which may frustrate the user before even reaching out to the helpline. Also, if policy pages are not transparently marked in a visible dropdown or appear to be hidden somewhere, then it makes the user question the credibility of the brand and reduces trust.

  • Lagging in trend with competitors

Business sites that highly depend on user traffic (number of clicks, downloads, subscriptions, inquiries) for their revenue need to do a careful analysis with competitor sites and keep a tab on user traffic data, user patterns, and website feedback. They can do so by conducting product surveys with a strategically designed questionnaire and keep improving in multiple iterations to achieve a good score on the number of users, reviews and user acquisition, user retention, and be the number one choice of users in the space, good lead generation, etc. 

Good web optimization which is carefully designed on the above aspects plays a vital role in achieving a high score.

SEO-related issues affect key aspects of business like user traffic, ease of accessibility, etc. 

  • The user traffic and visibility of the site

 Poor choice of keywords in the domain name, and links may hamper the visibility of the site in the search result. Even if a website is designed has a great design, it should come in the first few listings once the user searches for a keyword. Generally, showing up within the first 20 Google suggestions is considered to achieve the best user traffic.

  • Accessibility-related issues:

We’ll understand this with the help of an example. Let’s say there’s a shopping site where there’s a specific interval when the user traffic is anticipated much more than usual and we have a risk of website loading speed. Factors like options and dropdowns, the crawlability of links, etc are very crucial in providing a good user experience for everyone. Adding picture tags, checking link crawl-ability, ARIA and href tags check, etc will help us get a good accessibility score and improve user experience.

Tools to check website score and optimization suggestions: 

Here is a list of tools that we find useful with their unique aspects which helped us to derive insights and optimize our projects.:

  1. GTmetrix– Slow to respond, provides almost the same data as PageSpeedInsight
  2. PageSpeedInsights -Detailed metrics score plus compare option, display charts.
  3. Lighthouse– Detailed insights useful for analyzing Speed Index metrics
  4. ScreamingFrog- Detailed insights on technical aspects like coding suggestions
  5. Google Chrome– Specific for testing chrome related extensions and future versions in pre-release or development stages
  6. Yellowlab tools-Detailed technical scores with color coding (Red, yellow, green), number of codes in every category (CSS, HTML, JS)

Wrapping Up:

Building a brand is directly related to user experience. The search engine algorithm keeps on changing and along with it, the user behavior and expectations. With so much competition in the industry, users have a wide range of choices to select from. Optimizing your website can help you grow your traffic and customer base and directly impact customer engagement. Attracting users is just the tip of the iceberg, user retention is where a business lays its foundation for growth as a brand. 

Hope you found this article interesting. 

We’ll further elaborate on the SEO techniques in the next series of this article. 

Till then, Check out our latest case study:

Unlocking the Power of Web Optimization

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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