Getting employees with the right skills mix to fill a vacancy is a long and tedious one, overwhelming most human resource practitioners.  As a result, 74% of employers have hired the wrong person to fill a position at one time or another according to a 2017 survey.

“Engaging the wrong hire can have severe consequences for companies, which include loss of time and funds during the hiring and onboarding processes.” 

The good news is that businesses can do something to avoid such incidents and repercussions. One solution is utilizing data analytics for talent acquisition. 

Data analytics is fueling all kinds of business decisions, from product development and marketing to sales and even team member recruitment. For instance, if your company wants to hire a sales representative, data analytics allows you to conduct sales assessment on potential candidates to predict their performance on the job. 

Defining Analytics For Talent Acquisition

Analytics for talent acquisition involves utilizing data to determine your company’s recruitment strategies and processes. Companies with skills gaps want to attract and hire the best talents. The process of getting the right person often entails identifying professionals who have the organization’s strategic objectives in mind.  

Human resource departments use analytics to gather insights from the staff and the company data. These insights help them understand: 

• The key aspects that contribute to team member success 
• Strategies for identifying suitable candidates  
• Elements that make the organization attractive to future employees 

This information is leveraged to attract, identify, and hire the right talents to fill the skills gaps in the company.

5 Things To Know About Analytics For Talent Acquisition 

If your company plans to integrate analytics into talent acquisition, here are five things you need to know: 

• Automation Takes Over The Manual Process  

To get the most out of talent acquisition analytics, you need to know that in talent acquisition analytics, automation is applied in most recruitment processes. If you’re a human resource practitioner, you must realize that you have little time to organize information relating to the recruitment and hiring processes.  

When done manually, most tasks in the recruitment process can take numerous hours. The use of data analytics to acquire talent allows you to automate time-consuming recruitment tasks, including candidate screening, tracking applications, and scheduling interviews.  

• Employees Participate In Recruitment 

The thing you need to know about talent acquisition analytics is that it creates opportunities for employees to participate in the recruitment process. Employees participate in the screening and selection processes. To facilitate their participation, HR explains what the company is doing and why to the teams participating in the process, allowing the company to leverage the collaborative recruitment model. 

Traditionally, a top-down approach was used to make recruitment and hiring decision, but this has changed significantly in recent years. Talent acquisition is replacing this with a collaborative model wherein human resource teams and other departments participate in recruitment. This allows HR teams to work with other employees, often those holding positions below and above the candidates, to make recruitment decisions.  

The collaboration builds trust among employees and enhances transparency in the recruitment process.  

• Talent Acquisition KPIs Are Customizable 

When it comes to determining recruitment efficiency, talent acquisition analytics allows companies to track recruitment processes using key performance indicators. Companies can use data analytics to track broad metrics, like market trends and workforce demographics. Furthermore, companies can apply analytics to inform specific recruitment decisions, including: 

• Determining recruitment issues to prioritize during recruitment. These may include vacancies, the candidates to focus on, and how much resources to spend on them.  
• Objectives of talent acquisition analytics. These could include the need to fast-track the hiring process, cut down on hiring costs, increase the quality of hires, and improve candidate experience. 
• Characteristics of candidates may include general and job-related factors that applicants must be able to meet while on the job. This increases the quality, makes the process more efficient, solves problems for recruitment teams, and supports the organizational culture. 
• Screening process, including the procedure of rating and selecting the most suitable candidates.

With these metrics, human resources managers can evaluate the strengths of recruitment teams and establish efficiency in the hiring process. Companies may build a dashboard software to identify recruitment trends, find correlations, and gather valuable insights to inform future recruitments.  

If you choose to invest in a dashboard, opt for talent acquisition analytics software with built-in recruitment analytic solutions. This will allow your company to make better, more informed decisions.  

• Data Insights Ease Talent Identification

Using data analytics to monitor the hiring process helps you effectively manage your application pipeline. It also enables you to determine which platforms are suitable for sourcing potential employees. Also, you can use analytics to determine whether the existing strategy you have for talent acquisition is working well or not.  

The insights you get from the data you collect throughout the recruitment process make this faster, saving you time and other resources. Better, still, you can create, update, and even revise team member personas, job offers, and application procedures over time, based on data insights.  

Final Thoughts  

Employees are the most important resource in any company. Without their knowledge, experience, and natural talents, it’s virtually impossible for companies to achieve their revenue and growth goals.

This explains why companies are always searching for talented people to join their high-performing, growth-oriented, and highly focused teams.  But, with so much to do, HR practitioners are often overwhelmed by the tedious activities that characterize the recruitment process. The tasks should be completed before hiring decisions are made.  

Analytics for talent acquisition comes in handy, facilitating automation of time-consuming recruitment activities, candidate screening, tracking job applications, and scheduling interviews. With the milestones in data science, these processes can be accelerated efficiently and companies are empowered to identify highly talented candidates through performance predictions.

Also, Read Reasons Why You Should Use Analytics In Your Business

The post Analytics For Talent Acquisition: 4 Things To Know appeared first on AiiotTalk - Artificial Intelligence | Robotics | Technology.

In simple words, we can define artificial intelligence as the tech or algorithms that could learn things autonomously and advanced tech can even have the ability to think. Here, we are going to discuss how Artificial Intelligence plays a very vital role in digital marketing.

AI in Digital Marketing

AI is very significant for digital marketing. And the reason can be explained in a single word – Data. Unlike traditional advertising, digital marketing offers you the chance to get in contact with the interested target market by collecting data about the. For instance, if you have searched for any vape device then you might see the ads of vape pens and vape e-liquids like candy king e juice on every webpage you visit. Sounds familiar, right?

To be precise, so many data about users can be collected on the Internet that no one could ever access them. And that’s where AI and process automation comes into play.

“Of course, computers are much faster than humans when it comes to analyzing data. But first, you have to teach them exactly how to use that particular data.” 

AI can be used in various ways in marketing. For example, an AI can answer the most frequently asked questions about a product. For this purpose, a chatbot could be trained with the questions and answers that are asked most by customers, then it can analyze the “real” questions of the customers and either give a suitable answer or refer to customer service. This improves the customer’s user experience and can thus contribute to the loyalty to the brand or the company.

The automated booking of advertising space by an AI goes in a completely different direction. This analyzes the respective site, including the context and the semantic environment, and then decides whether the respective advertising space on the page is suitable for its own advertisement or not. User data could also be included, such as typical times when most of the previous customers were online.

Another option is the large-scale analysis of user data in order to find out which factors have made a decisive contribution to the success of an advertising campaign. So far, this was already possible, but with AI, the whole thing is much faster and can also be carried out more deeply and comprehensively.

If you pay attention to the progression and usage, you may notice that AI is not just an exaggerated term. It has proved on many levels that the future really can be better with it. It has many benefits and due to its extensive array of likely usage, an online business can use AI very effectively.

“With the rise of online businesses and the rising number of online customers, digital marketing is a must. Any user would search for a product review even before buying from a conventional shop.” 

And, this activity on the internet collects the data and AI can be used effectively to use that data into diverting the customers to your business. But don’t get confused that AI can do all the things and like the genie of the lamp as one should have a clear vision of their target and before using this modern technology, you have to teach it how it ought to act. That is efficient in ways to make optimum use of AI.

Also, Read The Era of Digital Marketing Revolution

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Knoldus states that Apache Druid is a high-performance columnar data storage system that can be used to execute real-time analytics on a massive data set. 

Druid has been utilized by large companies such as Airbnb and Netflix because it scales well. Companies can easily manage real-time data input, update their database, and create output in the blink of an eye. 

As someone new to Apache Druid Architecture, understanding the basic building blocks is the first step in utilizing this analytics database. As such, this article hopes to offer insight into what Apache Druid does differently, and how it does it.

How Does Apache Druid Work?

As Towards Data Science reports, Druid is the intersection of OLAP databases, Timeseries databases, and search systems. At its heart, Druid utilizes different types of nodes, and each one is designed to handle a specific part of the system’s processing. 

The cluster of nodes forms the druid system. These nodes may exist on a single machine or they can be distributed throughout dozens or even hundreds of linked systems. Druid has four distinct types of notes that work together:

• Real-time Nodes: These perform the functions of real-time querying and storing the results in a columnar fashion on a buffer, enabling fast results for the query.
• Historical Nodes: These utilize the information produced by the real-time nodes, serving them for other processes. They aren’t aware of other nodes, and so a failure in a single node doesn’t impact the others.
• Broker Nodes: These nodes handle sending queries to historical and real-time nodes. They are also responsible for aggregating the data that historical and real-time nodes produce.
• Coordinator Nodes: These interact specifically with historical nodes, informing them of when data is outdated and when they need to reload from the source. They can also serve as redundant backup points.

Why is Druid so Good at Real-time Analytics?

Druid is very fast in how it delivers results to users. Druid’s processing speed was benchmarked by Xavier Léauté and promoted by Apache, showing how it outperformed SQL queries significantly on a large data set. The reason Druid is so good for real-time analytics boils down to a few key elements of the system.

Column-Oriented Data Storage

Data stored in columns is usually more efficient for compression ratios. Additionally, Druid doesn’t need to load all the columns to perform a query. At any point in time, a query will only need to access a single column of information, shortening the load time. String columns are even further compressed using LZF compression techniques.

Estimation of Cardinality

Cardinality estimates are a problem with larger data sets. Druid overcomes this by using the HyperLogLog algorithm. As stated by Flajolet et al. in the Conference on Analysis of Algorithms in 2007, the algorithm produces an estimation for the cardinality with approximately 97% of accuracy. 

The functionality offered by the HyperLogLog algorithm means that sites can get a real-time estimate for the number of users that are online at any point in time.

Caching and Load Balancing

Broker nodes maintain a pre-segment cache of previously executed queries which they can return when needed, once the data set doesn’t change. Historical and real-time nodes also perform caching to increase the speed of scans. 

Coordinator nodes work to distribute segments among historical nodes, so there isn’t an imbalance. Multiple historic nodes can then serve several queries instead of burdening a single node with all the processing.

Partitioning Based on Time

Timestamps are a significant part of Druid’s performance efficiency. The system can sort data according to timestamps, and so older data can be broken down into months, weeks, and days. Timestamps sorting further enables the system to speed up its writing to disk since it can safely ignore older data as a non-priority. Partitioning can also aid in replicating and distributing segments more efficiently.

Unnecessary Scanning Avoided

By having an indexed list of the locations that a particular value occurs, Druid can limit its selection to only the records in which the value is present. The use of this reference table makes accessing the data within the system much more efficient. 

Unlike a relational database, there is no need to search through every single row and column to locate the search term. Druid can access the locations directly and pull the data without having to search through redundant records unnecessarily.

When Should a Business Use Druid?

Apache Druid is an excellent distributed data store for real-time analytics, but there are situations where relational databases may be more useful. Searching through a data set using a primary key to update a record is better with traditional database systems. Druid doesn’t handle real-time updating, as those jobs happen in the background batches.

If speed isn’t a concern in your business analytics,  Apache Druid might not be a proper fit for you. Druid is a solution to the problem of real-time data analytics and processing massive stores of data quickly and efficiently. 

The system can scale with the growth of the business seamlessly, meaning that there is less need for upgrading those systems. 

The post A Beginner’s Guide to Apache Druid appeared first on AiiotTalk - Artificial Intelligence | Robotics | Technology.

Whatever may be the case, the importance of data cannot be understated. 

But sometimes, the data can be too complex to understand. This is why data visualization is essential. It’s the key factor that can help someone analyze vast amounts of information and make decisions based on data-generated insights.

There is something more important than data visualization as well – dashboards! So, what exactly does a dashboard do? Dashboards are a great way to pin and organize your data with different sections, graphs, maps, datasets previews, forms, etc. 

The only thing that you will need to take care of is to display information that is useful. However, dashboards can still fail. Though many factors can contribute to its failure, dashboard design mistakes are the most common ones. Such mistakes can be identified with the following:

• What data needs to be incorporated or excluded from the dashboard?
• How is the data filtered to focus on specific information from large datasets?
• How relevant and updated is the data?
• What sort of graphs are used to convey different types of information?
• What contextual data is displayed or missing?

Having mentioned that, the following post describes how your dashboard design can be on point and bring forward opportunities to present data in the most creative way possible.

• The dashboard should target a specific purpose

The dashboard should be designed to serve a particular purpose. There are different well-known approaches to categorize the dashboard into analytical, strategic, operational, tactical, etc. each based on a particular purpose. 

Generally, they fall into two broad structures – operational and analytical. 

Operational dashboard works best for users involved in time-sensitive tasks. In addition to imparting crucial information, it offers a clear view of the data deviations as well as their current resources and status. The operational dashboard also acts as a digital control room to view support actions and gain proficiency in their tasks.

Analytical dashboards offer the overall information at a glance to help users analyze the data and make appropriate decisions.

Depending on specific user roles, you need to decide which dashboard will be suitable. Usually, low-tier managers use an operational dashboard, and higher management will need an analytical dashboard.

• Focus on selecting the proper representation for the data

Charts are an important aspect of dashboards. As the dashboard presents multiple types of data, including static and dynamic ones, representing them can be quite a tough task.

You need to be careful while selecting the chart type as it could confuse the users and result in the misinterpretation of data. Therefore, it’s recommended to have a look at the internal documents to have a basic idea.

Scatter charts, bubble charts, and network diagrams are some graph types that can help users understand the relationship in data.

Column overlap charts, circular area charts, and line charts are probably the most used graph types to make it easier to compare values.

Pie and Donut charts should seldom be used owing to their inefficiency to convey proper information. Such charts usually include similar values and many components, making the data hard to read.

Distribution charts work quite well to help one understand outliers, the typical tendency, and the scope of data in your values.

However, there are some chart types that you should avoid using like gauges, 3D charts, and over-styled charts. They are tough to create and also distract the viewer from the important parts or data. You can consider the following questions to decide which representation type will go well with your requirements.

• How many variables are you planning to put in a single chart?
• How do you intend to display values among items or groups?
• How many data points do you think will each variable require? 

• Maintaining consistency 

The major purpose that a dashboard should serve is to help the user comprehend the message by looking at it. Therefore, you need to make sure that every small thing is taken care of. 

You should also focus on using a clear framework to ensure consistency of data. It will be a lot easier to use the tools if you follow similar naming conventions with your data.

• Choosing the right layout and flow

Grids and modules can prove to be very effective in ensuring alignment and consistency as well as to create a basic structure for your design. You can position your design elements on the invisible lines that the grids offer.

Thus, the elements are clamped together in the overall system, leaving you with a rational composition. That’s critical for a great dashboard design as that helps organize huge volumes of data seamlessly.

Consider the points below to decide which information needs to go where:

• As we tend to give more of our attention to the top left corner of the screen, it’s advisable to put the vital information arranged from left to right. Also, that is how we generally read information.
• If a group of the information displayed at one point is based on the information from another point, you need to develop a layout that has a continuous flow. This enables users to scan through the information without going back and forth.

• Adding widgets to the dashboard design

After you’re done with defining the grid, you can move on to the next process of working with widgets. Widgets are applications that help with tasks like holding the info, charts, and controls. Here, cards can be of great help.

Cards can be manipulated almost infinitely. They act as content containers and can organize information into meaningful sections, making it an excellent choice for responsive design. Also, the data in cards are easily digestible for users as they can easily access the data that interests them.

Another major characteristic of cards is the consistency they offer in the layout of controls and data inside. One can simply assign the top left corner for putting in the name, align view controls to the top right corner, and use the rest for adding the content. 

Working with the interface becomes a piece of cake when there’s a consistency in the structure of everything. Furthermore, it’s also beneficial for improving the overall scalability of all your designs.

• Paying attention to white spaces is important

You need to have the right space between the design elements. It’s also known as white space or negative space. It can be a texture, pattern, or merely the space between typography glyphs.

Readers might not be completely aware of the importance of the white space, but designers invest much attention and efforts in it. The use of white spaces is vital in creating a good design and ensuring elegance and quality user experience. But some clients and managers think of it as a space that’s wasted.

They feel that it could deliver a better use if it could hold more information or visual elements. On the contrary, it can make data very hard to read and comprehend if the white space is not balanced. Therefore, paying attention to adding appropriate white spaces is as important as any other typography element.

Some factors that you need to consider before deciding the amount of white space to be used are content, design, user research, branding message, etc.

• Data hiding and too much dependence on interactions don’t go very well with dashboard designing

A dashboard is meant to show information in a way that makes it easy to analyze and understand. If one still has to scroll through the bits of information or deal with too many interactions, the whole purpose of using a dashboard fades away.

Designers frequently tend to design long scrollable dashboards, and that’s a grave mistake. The information to be displayed is always enormous. So, the designers position the data one under the other to avoid overwhelming the user.

As a result, only the information over the screen fold is visible, and the rest doesn’t get much attention. There’s no point in doing that.

The solution to this issue is prioritization. You need to conduct thorough research to identify the core information and place it above the screen fold so that it is visible. You don’t have to display every bit of the data, but you can instead summarize and present the key info. You also have the option to add extra interactions to fit more content without overwhelming the user.

Interactions make it easier to surface secondary information, but you shouldn’t view them as the primary way to design the dashboard. 

A perfectly informative dashboard can be a tough thing to achieve and trying to do that could lead to extreme cases. It’s important to understand that users will be overwhelmed if they have to keep track of multiple things. 

Therefore, it’s advisable to use not more than 5-7 different widgets to create a view. Anything more than that will make it difficult for the user to focus and get a clear overview of the information.

• Personalization and customization

Users wishing to see content that is of value to them or caters to their individual needs. You can employ personalization and customization techniques to ensure that your dashboard shows data that matters to the users. 

Personalization is done by setting the system to identify users and presenting them with the content, experience, or functionality that is relevant to their role. It’s a good thing to enable the user to do their customization after the dashboard’s view is personalized. The system also offers them the ability to make certain changes to the layout, system functionality, or content to suit their needs.

•  Integrating data tables or lists

Whether you’re designing a system for energy management, data researchers, or for traders, people will expect tables. As you have to deal with hundreds of data chunks and their complex interactions, relying on a data table is the best way to go about it.

For instance, if you have a list of clients and their IDs, contacts, status, last activity, etc., a data table will do a great job of displaying the information comprehensively. It offers other benefits as well, such as easy scalability, better utilization of space, comfort to work with grids, and easy development.

Source: User administration dashboard

• It’s better to keep designing in the end

Last but not least, designing an admin dashboard template is exciting. Designers often do it before anything else, but it’s better if it’s done at the end.

As a dashboard is the summary of the whole process that displays key information from several areas of the application, designing it, at last, seems like a more practical approach. Let’s say you have designed your dashboard, and now, you will work on other pages. In this case, every time you make a change in your applications, you will have to go back and update your dashboard. Also, after you’re done with designing most of the views, you will have several components to work on when completing your dashboard design.

Conclusion

Whatever you do, make sure that your dashboard looks fantastic. The dashboard needs to look good as well as prove beneficial for your users and their business needs. 

Designing a great dashboard starts with identifying the users’ needs and ends with offering a solution to every area of concern. It delivers a more profound comprehension than a simple report and tells stories that can transform your business. 

It might take a while to get it right, but you need to persist. Eventually, you will nail it.

Author

Sunil Joshi is an avid designer cum developer who is passionate about solving complex UX challenges across digital businesses. He is a trendsetter in the field of data visualization and dashboard design and has been lauded for his clean and minimalist design aesthetic. He co-founded WrapPixel, design and react dashboard template marketplace in 2016 with an aim to bring great design and clean code within easy reach of everyone.

The post 10 Unspoken Rules of Dashboard Design Make Data Visualization Suck Less appeared first on AiiotTalk - Artificial Intelligence | Robotics | Technology.