Wifi Analytics will provide your business with multiple advantages. However, it is essential to understand some basic elements. First, today’s consumers value free wifi at the companies they visit. Second, they estimate the Internet of Things (IoT) readiness to facilitate the processes that involve them as customers. Having a tool that provides what they need while obtaining valuable information is the best strategy to achieve the desired profitability. Continue reading!

What Is The Usefulness Of Wifi Analytics For Your Business

The commercial dynamics have been configured to consolidate an offer built from the data obtained from the consumers themselves. Profitability no longer depends on the number of assets but information management. In this sense, wifi Analytics is the tool with the most significant strategic value to increase sales because it helps you understand the customer and meet their expectations. Let’s look at the most fundamental reasons why it is essential to have this technology.

It Helps Define Audience Segmentation

With wifi Analytics, you have the opportunity to make a realistic profile of ideal clients and get to know the target audience. Segmentation provides the ability to offer products and services to those genuinely interested. Understanding customers’ purchasing behavior allows us to identify which are the most attractive areas and which are not. Therefore, your business plans will be able to anticipate needs and adapt offers.

Get Reports In Real-Time

One of the reasons why the wifi Analytics platform is considered an indispensable tool is its ability to generate reports with accurate information in real-time. This document shows how much customers spend in a particular area, purchase time, and other specific patterns.

Also Read: Discover How WiFi Marketing Works

Achieve Higher Profitability At A Low Cost

The installation of the wifi Analytics platform does not require a significant investment. However, when weighed against all the benefits, your business will realize that the start-up costs outweigh. This tool can personalize marketing strategies and make digital campaigns much more effective. It guarantees the successful compilation in a database with helpful information for business adaptation plans to the commercial expectations of the most demanding clients.

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5 Data Analysis Trends

Data analysis has proven to be one of the best methods for decision making, cost reduction and increased sales for many businesses. These will be the five trends for 2022: 

• Big Data will reach consumers. The democratization of data will mean that, throughout this year, Big Data will reach consumers, giving them access to the personal information that companies collect about them. In this way, they will begin to decide how this information is used and earn money from it. Users will demand greater participation in the decision-making that businesses carry out based on their data, altering the power relationship between companies and consumers.
• Predictive analytics will start to be used in the personal realm. Until now, large companies used predictive analysis to perform statistical analysis. However, beginning in 2022, anyone with a smartphone or tablet will download applications of this type to take advantage of all the advantages this science and technology offers. The use of remote servers, by companies, through cloud computing is the reason why predictive analytics will be portable and accessible to everyone this year.
• Artificial Intelligence will rely on the ‘crowd’. This year, we can say that Artificial Intelligence will find a great ally in ‘the crowd’ as it will allow companies to take advantage of the collective knowledge they collect about users, customers, and consumers to detect patterns that would otherwise be impossible to find. According to Gartner’s definition, “Collective Artificial Intelligence is the aggregation of data assets contributed by individuals or organizations to create high-quality data assets for training machine learning algorithms.”. Or what is the same, it is the process by which information is collected from individuals who use their own devices or applications to capture events in real-time, which are then shared with other contributors.
• Data management will increase its complexity. Data will continue to be essential for companies, so to face the challenge of collecting and analyzing a greater volume of information, they will have to look for solutions that allow complex data management. In 2022 we will see how Artificial Intelligence is added to data management tools that help discover ideas in the information collected with limited human intervention. Machine learning in these types of management tools will positively impact data manipulation tasks, such as cleaning email addresses from text or detecting events in social media posts.
• IoT will enter homes. In 2022, there will be 5 billion connected things in the house, compared to 10 million connected devices in the year 2000. This 22-fold increase in connected home devices will impact consumers’ way of life who, for example, already see how their smart TVs collect viewing habits and search histories to serve them targeted ads. Home appliances that clean themselves, applications that connect with service providers, security alerts. These are just some of the things that will change our daily lives. This will make companies have a greater volume of data that will be used to offer customers and consumers unique experiences.

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The Top Benefits of Using an Effective Data Strategy to Organize and Streamline Your Business

First, what is a data strategy? To best organize your business and streamline processes that can typically take manpower and costly resources, you can use this strategy to increase productivity, efficiency, and the workload of your company.

A data strategy is a method for businesses to organize their data, storing their data, and collecting important information that lets them best utilize their business. By coming up with various channels on how to best integrate their business processes into one streamlined method, they can avoid costly resources being used daily. Using a data strategy can help companies facilitate quicker business decision-making, identifying weak areas in their company, lessening the risk and security failures, strengthening their security, and facilitating faster data transfer.

Helps Your Business Make Better Decisions

One of the main benefits of using a data strategy to help streamline your business processes is to help your company make smarter and faster decisions. By providing your management staff with better information and the ability to analyze your data, you can help those in charge of your company make more informed decisions regarding their data and information.

By using real-time metrics and real-time data to see what is working, what is not working, and where your business is lacking, companies can find out how to best alter their daily processes to become more effective in the long run.

Understand Business Risks

The second benefit of using a data strategy to help streamline your business is to help understand, fix, and mitigate the business risk that can be associated with daily processes or data breaches. Using data analysis to see the risks that your business is facing can help companies come up with new plans and strategies to audit their company, minimize risk, and manage any current security situations.

Figuring out The Anticipated Value

The last benefit of using a data strategy to help organize your business is the ability to figure out how much you may gain from a financial transaction. Since data analysis is one of the best processes when it comes to figuring out how much you might make from a transaction, businesses that have a great level of insight into their potential earnings have a greater ability to see their current strengths, weaknesses, and what to improve on their business.

By figuring out how much you can make from a transaction, businesses can then see if there are any discrepancies in their accounting process or if they should adjust their pricing structure.

Conclusion

As you can see, there are numerous benefits of using a data strategy for your business to be more organized and streamline each process. A data strategy allows businesses to anticipate how much they will learn from a transaction, reduce security risk, and help your management staff make better decisions.

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Identify Demographics

It is difficult to run a successful business without knowing who exactly you are best off marketing to. If you are not sure about your most reliable demographic, you can use analytics to work this out. The most obvious way to do this is through sales data—where in the world are you selling most of your products/services to? Which gender is buying your products/services more? The answers to these questions can often be found in online orders, although other information such as the age of your customers may be harder to find this way.

Outside of sales, you can use data such as website hits and social media engagement (sites like Facebook and YouTube even have inbuilt analytics tools) to paint a picture of the demographic you are having the most success with.

Measure Success of Advertisements, Posts, Promotional Offers, and More

In simple terms, analytics tools will tell you numerically how popular each online ad, social media post, or special offer has been. However, knowing how to use this information is not as simple as it sounds. Be aware that online response doesn’t always translate to custom, and a social media post getting lots of engagement doesn’t necessarily lead to an increase in conversion rates.

If your digital marketing strategy is bringing in hits and traffic, but your sales aren’t increasing, there may be an issue with your website that is putting people off becoming customers. This is one of the ways in which you can work out if it is worth investing in the services of a website designer or not.

Adapt Your Digital Marketing Strategy to Reflect Your Findings

Finding the data you want is the easy part—knowing what to do with it and how to adapt it to your business is more difficult. Should you double down on marketing to your current demographic or try a new marketing strategy to shift focus to a demographic you haven’t really connected with yet? The way that you use this information is up to you.

Also Read: How to Check if a Phone Number is Real

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Data governs both the organization of companies and their activity. They are also of different natures:

• Structure Data: These define the organization, whether it is the structure of the company’s services, information on employees, or an accounting organization.
• Product Data: This data concerns the commercial and technical characteristics of the products and the manufacturing and sales information.
• Third-Party Data: It concerns customers, prospects, suppliers, and business partners, as well as marketing sites.
• Resource Data: This is information relating to equipment, service providers, sites, and any other tool is allowing the implementation of the activity.

This wealth of data makes their management (called Master Data Management) a strategic subject for IS. All business processes are based on their proper implementation. Operations that must be fed quickly, with a high degree of confidence in the compliance and correctness of the data.

Suppose the final objective of Master Data Management is to circulate reliable, available, and exhaustive data throughout the IS. In that case, it is necessary to address a certain number of issues to achieve this objective:

• Fluid sharing of reference data between all IS applications
• The best data entry, without errors or duplicates
• Improving the user/employee experience
• The compliance with regulations such as RGPD
• Limitation of maintenance operations and other time-consuming tasks

The weight of each of these issues on data architecture differs from one company to another. Therefore, it calls for a personalized organization, which will take into account business needs and guarantee the best possible circulation of data on a technical level.

Also Read: How To Build Big Data Applications With A Low-Code Platform

What Architectures Exist Around MDM?

The architecture chosen to exchange reference data across the entire IS has a strong impact on the organization. It conditions the resources used and the speed of information processing. Each type of architecture will correspond to a particular mode of consultation of the data.

Among the most common architectures, there are four main solutions:

Centralized Architecture

This first type of architecture is organized around a central tool, the MDM software which will collect and unify information. The software tools dedicated to MDM are based on deduplication, data tracing, and monitoring functions throughout the life cycle. As soon as it is collected, the data is structured according to the company’s business rules, thus establishing a unique version of the data.

This is how it will provide the greatest added value and the greatest possible efficiency to the processes.MDM makes it possible to establish precise control over the entire data lifecycle. With regulations and data integrity taking precedence in many industries, this control is often the predominant criterion for successful architecture.

Distributed or Distributed Architecture

This architecture is based on the current concept of microservices: classified into a certain number of services, business applications themselves take charge of the data that concerns them. With this model, the reference data is therefore delocalized for each application and not centralized. Any other instance wishing to consult the data will have to retrieve it from the master application. This architecture makes it possible to guarantee true business data integrity. These do not undergo standardization, as would be the case with a single tool. 

Their quality is better preserved. However, the distributed model creates a complex organization, which requires knowing the location of the data well and keeping the system synchronized in the event of modification thereof. The distributed architecture also raises an important application availability challenge. A significant part of the process then depends on the performance of the information transfer.

Virtual Architecture

This model attempts to reconcile centralized and distributed architectures. The data, still managed individually by the applications, is easier to locate: a virtual repository acts as an information mediator, communicating the location of the data to the applications that consult it. Here again, the question of the availability and the freshness of the data is complex. The virtual model creates opacity as to which applications manage the data.

Also Read: DNA Inspired Encryption To Protect Data In The Cloud

How To Choose The Right Architecture For Your Reference Data?

The management of master data is a real fundamental subject: it calls for a strategic reflection around how the company’s processes, the main users of the data, are organized.

• On the one hand, data is shared by so-called vertical applications ( ERP, PLM, WMS, PIM, etc.), which enrich it throughout the life cycle of products or services.
• But cross-functional processes and the applications that employ them (BI, project portfolio management, etc.) are also important in making data more useful and breaking down barriers.
• Finally, the regulatory framework and the sensitivity of certain data play a strong role in the chosen architecture.
• For the company, it is, therefore, necessary to organize the reference data according to its priorities after having identified them.

Reference data must now support the customer-centric strategy of companies: customer data must be quickly collected and cross-checked to offer a high-performance user experience. But the need for quality and traceability continues to prevail, especially in highly regulated sectors. To make the right choice, it is necessary to identify the purpose of the data and their level of sensitivity and anticipate the volumes of data and the applications that will use it.

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Data engineers can publish a semantic business model on the data, and data analysts can extend that model with their own data sets. Data analysts can also explore data with rich interactive visualizations and share it with their coworkers. In contrast, data engineers can implement a secure deployment of their analytics solutions across the enterprise. And finally, regardless of how the solution has been built and implemented, the business user can obtain valuable information using various experiences, such as the Oracle web application, mobile applications, custom applications, productivity tools such as Slack and Excel.

The Latest Innovations in Oracle Analytics Cloud. Democratizing access to data analytics has never been more important to help organizations remain competitive and grow in an ever-changing business environment. Oracle is empowering its customers with AI-powered capabilities throughout the analytics process, from data preparation and predictive modeling to visualizations, automated data flows, and insights. With these new capabilities, Oracle Analytics accelerates the journey from data to information and action, helping organizations extract much greater value from it, achieve results faster, and improve productivity. Key new capabilities of the platform include:

• New User Experience: The “Redwood” user experience and design system, a new standard in all Oracle products, offers visually compelling data experiences for all users.
• Explainable In Machine Learning: Business users and citizen data scientists can now access all the details about how a machine learning model, built in the Autonomous Data Warehouse, has calculated its predictions. They can also view and interact with all predictability results in Oracle Analytics Cloud and expand them to gain insight into the factors that influence them quickly.
• Smart Data Preparation: This patented data profiling engine samples and scans data sources to identify data quality issues and misuse of sensitive data and provide troubleshooting recommendations and even provide suggestions on how to improve the data. Oracle Analytics Cloud contains a built-in, extensible knowledge base with domain-specific content.
• Built-in Text Analytics: With the latest update, users can access built-in text analytics features, such as token mining and sentiment analysis. Additionally, support for other unstructured data formats, such as documents and images, will also be incorporated, taking advantage of the new AI services in Oracle Cloud Infrastructure (OCI).
• Association Analytics And Shopping Cart Analytics: With Association Analytics, users can determine the probability of matching items within the same collection. This type of analysis is invaluable for direct marketing, sales promotions, and uncovering business trends across multiple industries. Shopping cart analysis can also be used effectively for store design, coupon offers, and cross-selling.
• Custom Reference Insights: This update also enables organizations to use their business-specific data as automatic recommendations for users to enrich their data sets. This is a very useful function because it eliminates the need to duplicate data sets often used in an organization but are typically stored in separate data structures. It also speeds up data merging automatically without the need to write code.
• Graph Analytics: Graph analytics is a set of techniques that allows users to explore the relationships between various entities, such as people or transactions. The application of this functionality is very useful in multiple areas, from marketing and social networks to security and compliance.
• Custom Map Analytics: Map analysis gives users the ability to apply map layers and backgrounds to enhance data visualizations. For example, the user can analyze data on a population map or overlay bicycle or bus routes to optimize coverage in a given area. Users can now take advantage of maps hosted on a web server as a dynamic background, using the Web Map Service (WMS) protocol and XYZ tile layers. This allows them to take advantage of the integration of maps containing information that they may not have access to in their company and present it spatially in a simple way with their business data.
•  New Mobile App: The new Oracle Analytics mobile application allows users to find information quickly and easily, all with a consistent user experience in Oracle Analytics Cloud and the application. This enables users to interact with data visualizations, explore dashboards, and share information with teams to collaborate better. Users also can hear natural language-generated audio narratives of the highlights of different reports, dashboards, and visualizations.

Also Read: How To Build Big Data Applications With A Low-Code Platform

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Safety first

Companies work with sensitive data every day. No matter whether it is your customers’ contact information or new product developments that may not yet be patented. Data and knowledge are important assets and must be well secured accordingly. Companies fear that their data in the cloud is not sufficiently protected. 30% fear unauthorized access to sensitive data. Fear of data loss follows with 20%, closely followed with 19% who fear that important IT know-how will be lost. More than 50% of companies also say they find it more difficult to protect data in the cloud. Opposed to that that almost 70% of the companies had no security-related incidents with the cloud and around 10% only had suspicions about security gaps. This feeling of insecurity among companies not only means that many have so far refrained from doing so, they do not entrust all data to cloud services.

In principle, companies are increasingly open to the topic, but it is used much less frequently. Just under 20% entrust sensitive data to a cloud service. If information is stored in a cloud, it is mostly information that is not business-critical. If there is a loss or data theft, the damage to a company is relatively minor.

Also Read: Cloud Security Acquires More Relevance In 2020

leaders so far

If you look at the ranking of companies by country, Finland is at the top with almost 70%. Denmark and Sweden follow with around 50% of companies using cloud services. With just under 20% usage, Germany is far behind in comparison. But cloud computing is also carried out in Germany. There are around 3.4 million companies in Germany, 65% of which use at least cloud computing. The possibilities of which services can be used are diverse. So far, Amazon has been one of the largest providers of cloud infrastructure. This is in direct contrast to the customer request because many companies want their provider to be based at least in the EU. This would also increase the feeling of security because companies want data security to be guaranteed.

There is a lot to be said for the cloud

However, there are many reasons to choose cloud computing. This includes, for example, the possibility of a home office. So far, only a good 20% of workers have the opportunity to work at home. For companies, however, an expansion would mean that it is easier to get qualified personnel because they can address people in a much larger area. They would also be able to save on infrastructure costs themselves since significantly smaller office space would be required. In general, cloud computing would save them a lot of costs because they would receive customized solutions and would only have to pay for what they use.

Also Read: What Is Multi-Cloud And How It Benefits Companies

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Step 1. Choose metrics and KPIs to monitor and predict

First, you need to come up with the question regarding the workforce that directly relate to the goals that the organisation is looking forward to achieving. When looking to introduce custom KPI (key performance indicators), you need to be clear and describe them in details:● What does it express?
● How will it be calculated
● How to represent it i.e. text or numeric
● What frequency should the measure be taken
● Which data source will be used to retrieve the indicator?

Step 2. Define data sources

Once the indicators are clear, it’s time to document the source from where the data will be pulled:
● ERP HR module
● Applicant tracking systems
● Employee scheduling software
● Talent management software
● Training systems
● Excel or Google sheets if required
● Skill testing solutions

Step 3. Decide on a tool: off-the-shelf or custom

• Once you have decided the sources for the information, the next step is to choose whether you build a custom tool or take one from the vendors.
• Off-the-shelf: There are a lot of great vendors available on the market that provide high-quality HR software like Darwinbox, which contain multiple features like recruitment management, onboarding, performance management, payroll management and much more.
• Custom Analytics Tool: If you are looking to develop a custom analytics tool based on your requirements you would need to hire a vendor or a consultant or assign the task to your own developers.

Step 4. Gather a team

If you are going to build a custom HR management tool with analytics then you’ll need to get a team to go ahead. Here are the list of teams you require for this project:
● HR manager
● Data engineer
● Data warehouse developer
● Database administrator
● Data analyst
● ETL developer
● Data scientist
● Front-end developer
● UX designer
● UI designer

Step 5. Set up data infrastructure: ETL and a data warehouse

The next step is setting up data infrastructure that’ll be used to extract data from multiple systems and transfer them into a single storage unit, for the easy analysis process. This includes a data integration tool & a data warehouse.

Tools for data integration

Data integration tools are used to transfer data from the first systems to data warehouses. It contains a process known as ETL process that includes three main phases:
● Extract
● Transform
● Load
These warehouses are designed to store and analyse a large amount of structured data.

Step 6. Build a predictive model

The next step is to develop a model that will help the human resource team get the most accurate predictions for a question. If there is not enough data present in the warehouse, the data scientists will start collecting additional data. Once the data is prepared, the specialists will start model training. These models are then tested for their accuracy and one the best model is determined; it is deployed inside the software.

Step 7. Develop UI of a solution

The next step is to develop a UI so that business users can use the software without the need for data science background. With a good UI, the users will be able to generate custom reports with ease. Here are the features that should be present in the UI:
● Static & Dynamic dashboards
● Multiple types of Charts
● Ease of Report creation
● Sharable dashboards
● Ability to upload custom visuals

Step 8. Train employees on how to use the system

The next and last step is training the users or employees on how to use the HR management software. They should understand every function available in the software.

Choose your approach

The most important thing to understand here is great employee experience leads to better business outcomes. There are a lot of tools that are available in the market that can help managers solve their specific problems like employee engagement management or employee recruitment etc.
Meanwhile, developing custom HCM Software is also one of the common practices among business. The benefit of such am approach is getting a higher level of data security, and the tools will be specifically built for your organisation’s requirements.

Also Read: Why Do Companies Consider Adopting The Blockchain?

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