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10 steps to create an engaging responsive website.

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These days you can’t be done with merely publishing a website. You have to adhere to the criteria that would ensure more engagement on your responsive website. It’s important to understand that each website is up there on the web to attain a goal. And, only when your website is engaging and responsive could you attain those goals successfully. 

Well, websites are the forefront executives representing your business to millions of users. We have advanced into a digitally driven world. And, websites are a prominent part of this digitally acclaimed world. Millions of websites go live every day, and do you think each of them stands at the top radar fulfilling their business objective with websites? Of course, not. While the responsive website has opened ways to be Globally visible, it is equally competing to get visible, to get noticed.

So before we dive into the ways that would facilitate an engaging and responsive website, what exactly does that mean? 

An engaging responsive website would attract and retain the attention of the users compelling them to take the action you desire. After all, isn’t that the ultimate motive? Further, a responsive website would respond well to all the screen sizes, making it equally responsive across all devices. Said that responsive website design ensures flawless operation across all the devices.

Moving further, here is your 10 steps detailed guide towards create an engaging responsive website.

1 Stick to simple layouts.

No one likes clutters. Understand that the attention span of humans these days is lesser than a Goldfish. Hence, if you are going to spam your website like a bin, you would face a bounce rate as high as the visiting rate.

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Instead, stick to simple, clean, functional layouts. Before you set out to select the layout know clearly the purpose of your responsive website. And accordingly, choose the layouts that could define your purpose.

2 Make proficient use of White spaces.

Don’t be afraid of those white spaces. Don’t rush in to fill in every corner and part of your webpage. White spaces are quintessential in determining user experience over your responsive website. You see, clean spaces offer a refreshment, improves readability, and reduces clutter on your webpage.

Leave adequate room for white spaces knowing exactly what part of your webpage demands it.

3 Ensure easy navigation.

Consider your website as a physical store outlet. When you are in a superstore, there’s a clear demarcation of departments and the sub outlets within it, allowing you to navigate easily without any help. Well, doesn’t that make shopping a great experience for you? 

The same concept applies to your website. Your website should navigate the users within the website adequately, without confusing them. Don’t experiment with standard menu designs. Keep the architecture/ layout/ rendering of your website absolutely simple. It’s important that users feel at home while navigating through parts of your responsive website.

4 Use a color palette to your advantage. 

Understand the basics of color psychology and incorporate such colors that convey your brand image. Further, limit the basic color scheme of your website to 2 colors with an addition of accent color. Try rendering the colors of your logo within your website. This is to establish brand consistency for easy recognition amongst users.

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5 Scheme your text. 

Less the better. Don’t go on scribbling inessential details on your webpage. Scheme your text to the bare minimum with paragraphs no longer than 3- 4 statements. However, when you have no choice with text lengths, it’s important to divide the contents into sections. All in all, the contents of your website should be easily scannable. Additionally, ensure that you make use of easy-to-understand language and maintain a friendly tone. 

No user is interested to read paragraphs and paragraphs about something completely inconsequential.

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How to Conduct SQL Performance Tuning

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How to Conduct SQL Performance Tuning

The concept of SQL Server performance tuning is simple. To put it briefly, if your organization’s functionality involves a lot of powerful tools to handle a range of data, then this system will be the perfect choice for you. It is important to have a properly functioning system to handle data in your organization. For, in a case where the handling system is inefficient, your organization’s resources will be affected.

Other relevant aftermath includes slow performances and the loss of service. This is where the SQL queries come into play; it has the efficiency to smoothen your organization’s functionality when it involves handling data.

By SQL Server performance tuning, you are making the SQL query system more efficient. You can know the concept in depth by enrolling in SQL certification online. The following segment will explain more about the SQL tuning aspect.

SQL performance tuning: a brief understanding

By now, it would be clear that the SQL query system acts as an efficient data warehouse.

But, why does it require tuning? Imagine a general physical warehouse. A warehouse would consist of a variety of shelves, and a number of other elements/ requirements to hold your products properly. Imagine the same physical warehouse being unguarded, in addition to being unattended.

What happens now? All your products could be stolen and could be damaged to a great extent too. Now, imagine this another scenario. You are maintaining the warehouse properly; regularly maintaining the place and attending to small repairs now and then. This would eventually increase the lifetime of your warehouse.

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During the course of regular maintenance, you will also make sure that additional products can be added to your warehouse, for more monetary benefits. Likewise, SQL performance tuning gives you more or less the same metamorphic benefits. The better care you give towards the SQL performance tuning, the better the overall SQL performance be.

To simply put, the SQL server generally has a lot of processes as well as procedures; this is to enable efficient functioning of the system. The performance tuning aspect helps in optimizing maximum efficiency in general. Furthermore, the tuning of SQL ensures that MySQL, as well as SQL servers, get benefitted as well.

Importance of SQL performance tuning

  • Faster Retrieval

Be it a physical warehouse or SQL data warehouse, the primary aim to store anything is to retrieve it easily. Imagine if it takes hours to get the simplest item from your warehouse; you would be frustrated of course. To be honest, it would be more annoying to sit in front of your workplace device to see the “loading” message. For, you know that a simple delay can alter your day’s schedule to a great extent. To avoid such inconveniences, it is always important to tune the performance of SQL. You can know more about this by checking out SQL interview questions.

Another key point to note here is that, as per several pieces of research conducted across the globe, the speed of your organization’s systems decides your client’s purchasing mood. Thus, the slowness of your SQL performance can impact your client too.

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Overall, it is a required action to improve the data retrieval speed of your organization.

  • Avoiding coding loops

To put it in simple terms, the concept of coding loops is a set of repetitive instructions. These repetitive instructions continue to happen till the conditional goal/ destination is reached. For an instance, if you want to modify a particular data, the loop will happen only till the modification of data only.

The loops are effective only when it is properly functional. Improper coding loops have the capacity to damage your data. Thus, by tuning the SQL performance, the loop will happen only one time. Without the fine-tuning of the SQL performance, the loop will happen many times, which would have aftermath on your data in general. 

Best practices to follow to effectively tune the performance of SQL

  • Analyze the task time

The best way to find the health of your server system is by starting your investigation at the task response level. Give your SQL system a simple task to perform and note the time taken. Simultaneously, give a complex task to your system and again note the time. Now check if there is any unreasonable lag as such.

Currently, there are several third-party apps/ software are available that would consistently check the speed of your system and would immediately notify you if there is any suspicious lag.

It is always recommended to record such results and monitor the improvements/ derailments in general.

  • Examination is the key

As a part of an organization, you know that every set of data falls under a particular category/ sub-category. Having said that, since you would have to deal with a large chunk of data in general, it is always good to have relevant filters. If you do not have filters as of now, you should seriously consider implementing the same. This will help in fine-tuning the SQL performance to a great level. You can know more about this by checking out the video below:

SQL Certification Training | Learn SQL for Beginners | Intellipaat

Conclusion

Enhancing your SQL is important to keep your data safe and strong. It is also important to improve the overall performance of your database too.  By implementing the fine-tuning mechanisms on your database, you are set to have a high-functioning database in your organization.

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Author:
Neelabh Verma is a content writer at Intellipaat having 5+ industry experience in databas management

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Obstacles and Opportunities of Democratizing AI for Organizations

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Obstacles and Opportunities of Democratizing AI for Organizations


Enterprise deployment of artificial intelligence (AI) is positioned for tremendous growth.

Artificial intelligence is set to change the business world by improving predictive analytics, sales forecasting, customer needs, process automation and security systems.

Types_of_AI_Use_Cases.png

Source: Everest Group

IBM’s Global AI Adoption Index revealed that a third of those surveyed will be investing in AI skills and solutions over the next 12 months. 

More expansive use of AI democratizes AI, providing access to insights to more people – technologists and non-technologists alike. The latter group might include people in leadership, sales, finance, human resources and operations. This is where AI will shine, empowering business teams to make AI-driven decisions.

Imagine: business teams do not have to know how to code or be schooled in the intricacies of AI’s backend. Instead, they will use AI like you and I use a mobile phone for efficiency (if we’re running late, we merely send a text notifying the other person), access information faster (if we’re in the grocery store and need a recipe, we look it up), make better decisions (GPS gives us the fastest route).

Just as mobile technology works without us understanding complex circuitry, algorithms or software, the democratization of AI across enterprises will be integrated in much the same way.

So, what will hold AI back and how will AI help enterprise companies gain traction?

3 Obstacles and Opportunities Organizations Face by Implementing Artificial Intelligence

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Artificial intelligence deployment approach | Source: IBM

Obstacle #1: Data in disarray. Data that does not provide a complete picture and single version of truth because of data silos and various data formats within an organization.

Opportunity: Employing a data fabric. Using a data fabric to help organizations use data more effectively and get the right data to users regardless of where it is stored. One significant advantage of a data fabric is that data governance rules may be automatically set for compliance.

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Having one information structure to garner insights and analytics from, integrating security to protect sensitive data and establishing a framework for implementing trustworthy AI positions AI as part of the business strategy, not solely an IT strategy so that AI directly impacts business operations.

It all comes back to connecting data with business drivers and a data fabric helps accomplish this. It is what I call “point-to-point” thinking – knowing the business imperatives, business drivers, the different levels of raw data, who is consuming the data, who will have access to the data, and why the data is important in decision-making and then, the big payoff with AI, how it will elevate experiences: customer experience, workforce experience, supply chain experience, strategic partner experience, community experience. In “point-to-point” thinking we don’t hoard data, but share it – securely.

Obstacle #2: Varied skill levels. A lack of AI technical skills across the enterprise and a reliable, open platform to bring AI to more people.

Opportunity: Creating a bridge to AI for people within the enterprise. Palantir for IBM Cloud Pak for Data is one of the great innovations of our time because it doesn’t require coding skills. People in non-technical roles can go from raw data to data insights quickly using application templates (think of all the designs being produced with minimal design experience because of apps like Adobe Photoshop and Canva). This is truly the path to democratizing AI.

People can now use AI to make better decisions in real time and improve business outcomes. These teams include sales and marketing, manufacturing operations, campaign managers, branch managers, franchise operators, human resources, among others.

An example: a customer walks into their regional bank. The banking professional greets the customer, invites them to sit down and pulls up their profile. They see, not only account information, but a 360-degree view of the person sitting across from them. Through a data fabric, non-tabular visualizations gathered from previously siloed data originating from different systems provides an AI-infused perspective.

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This might include two algorithmically recommended customer offers inspired by marketing analyst data and intelligent customer segmentation and campaign propensity scoring powered by Watson models.

Going further, feedback from the customer can then be entered and that data influences future offers because it goes right back into IBM’s data and AI platform. IBM Cloud Pak for Data, which helps to simplify data management and protect sensitive data by establishing a framework for implementing trustworthy AI.

Obstacle #3: Solving for the wrong “x.” In hundreds of conversations I’ve had with enterprise leaders over the years about AI, one common failure I see not identifying the right problem or identifying use cases that will yield high return from AI.

Opportunity: Clearly articulating the problem to be solved. With AI, we are talking about a machine making reasonable conclusions based on data. Better defining the problem is akin to asking better questions.

Imagine the difference if you were in a store and asked someone if they sold products. The question is too vague to expect a meaningful answer. Ask where the tomatoes are and you get a clear answer. Both are valid questions, but one is more focused. That’s how defining the problem should be (this is not just for AI purposes; I devote a lot of space in my book, Ascend Your Startup, to defining the customer problem because I believe building the wrong solutions plagues many companies)

Key Takeaways

How Artificial Intelligence Is Revolutionizing the Advertising Industry

In an interview with famed Mount Everest climber George Mallory, a reporter asked him why he wanted to climb the formidable mountain. His answer: “Because it’s there.” AI is very much the same thing. It has obstacles, yet it has the allure of opportunity and of making measurable progress.

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Here are the big three takeaways for enterprise companies:

•  Use a data fabric. Information is powerful – and it exists! Don’t let siloed data and inconsistent data formats hold people back from making better decisions.

•  Give people what they need to succeed in their jobs. Tools such as low code/no code enable business users to rapidly leverage data and apply AI in their decision making. 

•  Go back to square one and define the problem. Solving for “x” without fully understanding “x” wastes precious time, causes unnecessary frustration and marginalizes the experience for everyone involved.

The Rise of AI

AI_in_Business_2022.png

Source: Statista

In a Forbes article on the topic of AI, author Manas Agrawal writes, “With rapid learning and adoption, AI is no longer a crystal ball technology but something that humans now interact with in nearly every sphere of life.”

In a very short time, we won’t be talking about AI adoption as people see it as part of doing business and part of making life more efficient. AI then will shift to being part of an enterprise’s business strategy, delivering value for non-technical people working in many different areas like customer experience, brand differentiation, HR, research and development, management and sales.

This is what the democratization of AI looks like at the crossroad of technology and humanity to improve outcomes for people leading successful enterprise businesses.



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5 Proven Tips to Implement Machine Learning the Right Way

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5 Proven Tips to Implement Machine Learning the Right Way


Businesses that realize the value of data and contemplate machine learning implementation are usually faced with similar challenges, that can be overcome by following a few simple guidelines.

Business leaders are increasingly becoming aware of data science and machine learning’s role in supporting and enhancing business growth. In an attempt to leverage the technology, businesses either jump into implementation without planning or are stuck in the planning phase for too long, both leading to suboptimal outcomes. As business and technology leaders involved in machine learning implementation, you should keep the following tips in mind to ensure that you are doing it the right way:

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1. Use the Right Data

Like any other analytical or logical application, the principle of Garbage in, Garbage Out holds true for machine learning. When it comes to machine learning implementation, although the algorithm is considered to be an important factor for driving success, the data that is fed to it is equally important, if not more. The quality and relevance of data used in machine learning helps extract highly valuable insights and sets the machine learning initiative in the right direction. Choosing the right variables to track and process through the algorithm requires asking the right questions and verifying the data quality.

2. Experiment with Algorithms

Successful machine learning implementation requires a combination of quality data and a robust algorithm. It may not be easy to get everything right with your algorithms in the first attempt, requiring you to make improvements based on trial and error. Investigating your algorithms to pick out behaviors that are desirable, as well as, the ones that are not, will enable you to modify algorithm parameters to achieve the ideal results. You should also realize that algorithms that track highly complex data, such as that associated with humans, need constant re-evaluation and re-programming to ensure sustained effectiveness.

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3. Collaborate with Research and Academic Institutions

One way to make up for lack of expertise in machine learning implementation is partnering with academic institutions that research and teach machine learning and analytics. This will enable organizations to make use of subsidized expertise while making progress using machine learning. An example of such collaboration between business and academia is the Prototype optimization model made by Wayne State University that saved $12 million for Ford Motor Company on the first use. As a long-term initiative, businesses should invest in data science labs, to promote such fruitful collaborations with academic institutions.

4. Train Employees in Machine Learning Implementation

Another way to compensate for inadequacy in know-how is to ‘upskill’ existing employees through training to make them proficient in data science applications. Employees for an upskill should be selected based on aptitude for work in question. The most suitable candidates to receive such a training are the ones with a high aptitude for math and statistics, and the ability to translate data into useful insights. Organising training seminars and workshops are among the most common ways to train employees in a chosen skill.

5. Hire Third-Party Expertise

As there is high demand for data science experts, businesses can consult third-party specialists to help them initiate and integrate machine learning into the business. Experienced third-party professionals can guide your business through planning and execution of pilot projects, and educate organizational personnel on data science and machine learning.

Keeping these tips in mind will enable you, as a CIO or technology leader, to keep your machine learning implementation on the right track. To make the most of any technology, the best way is to gain enough practical knowledge about it. In addition to the tips mentioned above, you should also acquaint yourself with some machine learning best practices.

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