Metadata plays a pivotal role in the world of data visualisation and further provides insights in the data-driven decision-making industry. Its importance is clearly evident in popular BI tools such as Tableau. Tableau uses metadata to improve data comprehension, analysis, and interpretation, empowering users to produce smart visualisations and make data-driven decisions. Metadata makes the data findable, accessible, and reusable.

Findable: Metadata facilitates the discovery of pertinent facts. Because it describes in detail what the text document is about, metadata also makes text documents easier to find.

Accessible: Metadata describes how data can be accessed, possibly including authentication and authorization, once a user locates the information they require.

Reusable: Researchers must understand a data set’s structure, the meanings of the language used, how it was acquired, and how it should be read or used in order to reuse it. For data to be repeated and/or merged in numerous contexts, it must be adequately described.

In Tableau, if we want to extract the metadata, there are two options:

Tableau Metadata API: Based on the metadata, all of the information on our Tableau-Online or Tableau-Server can be retrieved, including the workbooks, data sources, flows, and metrics. The GraphQL query language, which is executed by this metadata API, explains how to request and receive only the data about which you are curious.

Using TWB File Conversion: .TWB files are specialised XML files designed to communicate with data sources. We will handle this XML file in this walkthrough because Tableau workbooks contain all of the metadata for reports and dashboards.

Therefore, here we will be using the second option, that is, Using TWB file conversion into XML to view metadata. Further, we decide to have some experiments with the metadata of a twb file. We learned some intriguing things by doing this.

We discovered that by changing a few settings in this xml metadata file, we can change the dashboard visualisations and, in cases where they are missing, add a new dashboard sheet and produce a different visualisation. We can only achieve this by making changes to the metadata file and not even opening the default tableau file.

Architecture:

Step 1: Opening A Tableau File

We have a simple visualisation for a dataset. Below the graph, we have Gender on the X-axis and Total on the Y-axis, and we divided the graph using colour in the form of several Age groups.

Step 2: Converting Into an XML File

This file is in a.twb file; when converting this .twb file into an xml file, we will change the file type to a.txt file. This is like the metadata for the tableau file.

Step – 3: Experimenting With The Metadata:

Above is the unmodified.txt file. Experimenting with the metadata, for example: If we plan to change the colours of the AGE group for the visualisation. Below, we have made some modifications to the.txt file.

Step 4: Observations:

Once, we converted the edited file from .txt to.twb. We can observe that the bars of the graphs have changed colours.

The Difference:

Some More Experiments:

As our Book 1 contains only one dashboard sheet, we will try adding another sheet of visualisation in the form of a Pie Chart. Modifying the original sheet accordingly.

Saving this.txt file into a.twb file for verification.

We discovered how to extract tableau metadata, and if we are able to alter the main tableau file (.twb) by only making a few changes to the XML metadata file, we can add or modify numerous visualisations and sheets without modifying the main.twb file.

The post Synergizing Data Insights: Amplifying Tableau Dashboards through Metadata appeared first on Indium.

Data visualization is crucial for making educated decisions as the business sector relies more and more on data. Data’s rising volume and pace make it impossible to comprehend without abstraction or visual depiction. Furthermore, data that are non-statistical, such as organization processes or customer journeys, are difficult to interpret and repair without visualization.

Data Visualisation has therefore become crucial for businesses to make data more accessible, understandable, and usable in decision-making. Data visualization is the source of business intelligence.

Is data Visualization so important?

Data visualization is a powerful tool that uses statistical visuals, information graphics, charts, and other approaches to clearly and effectively show complex data. Facts visualization facilitates user comprehension and reasoning about facts and evidence by encoding numerical data with dots, lines, or bars. Tables are used to search specific measurements, while charts display data patterns or correlations for more Variables.

Thanks to the Internet and increasing modern technologies, transforming data into understandable images is now possible for everyone. One con is the inclination to prioritize convenience above quality. Transforming the spreadsheet cells into charts can provide merely passable or useless charts since it fails to convey the fundamental notion. As a result, before clicking and viewing data, it is critical to evaluate your aim and objectives.

Creating an Insightful and Profitable Visualization Strategy

To make effective charts, it takes more than just understanding visual grammar rules. It is crucial to understand when to use and how to handle the key and colours; relying solely on rules can result in a lack of strategy in the chart-making process, similar to planning a marketing campaign without a plan. Instead, effective chart-making demands acknowledging a sequence of tasks requiring varying degrees of plotting, resources, & expertise.

Analyzing the purpose of data or information is critical before generating a visualization. Is it conceptual? Is the visualization meant to make a statement or discovery? By answering these questions, you may identify the sources and gears required to build a successful visualization that meets your objectives. This method allows you to choose the most effective visualization style for conveying your message to your audience. As a result, good chart creation begins with careful planning and a clear knowledge of your visualization objectives.

Also Read:  Domo for Dummies: A Guide to Creating Powerful Data Visualizations with Domo

Tips and Tricks!

Here are some surprising yet effective Data Visualization techniques that experts have emphasized and accepted:

Source: merkle.com (Image for representation only)

Art of Omission

The skill of omission should be treasured. You should emphasize what is vital and exclude what isn’t. This will assist in avoiding clutter and allow your audience to focus on the important issues.

Colors should be chosen with Caution.

Colors may be used to highlight the information, while incorrect use can conceal it. Choose easy colors on the eyes and provide a clear contrast between different data points.

Eliminating  Gauges

Although speedometers and gauges have been widely used in dashboards, newer visualization techniques that take up less space are now available. It’s recommended to consider using an easier visualization method instead of gauges.

Begin at zero

To prevent misinterpretation and ensure correct understanding of the scale, it is recommended to always start the horizontal axis of a bar chart at zero.

Display the distinction

You may highlight the differences if you wish to compare the two series. This will assist your readers in comprehending the significant areas of comparison and emphasize the value of the facts.

Pies

Pie charts may be colorful and visually appealing but are not always the best choice for displaying data. It is important to evaluate the relevance of a pie chart to the data being presented and use it only when appropriate.

Highlight what is relevantly essential

Maintain a neutral dashboard and highlight just what is relevant, such as the present location or a critical metric. This will allow the audience to concentrate on the essential points and comprehend the value of the material.

Graphs from a different perspective

Consider using a horizontal bar graph when dealing with labels or hierarchy in your data. It is recommended to explore various types of charts and graphs to effectively highlight your information.

Here are some tools that can be used to implement the mentioned Data Visualization techniques:

Art of Omission:

a. Tableau – Allows users to selectively show or hide elements of a visualization.

b. Power BI – Offers various filters and slicers to customize and refine visualizations.

Colors should be chosen with Caution:

a. ColorBrewer – Provides color schemes that are colorblind-safe and printer-friendly.

b. Adobe Color – Allows users to create, save, and export color schemes.

Eliminating Gauges:

a. D3.js – A JavaScript library that can create custom visualizations and eliminate gauges.

b. Plotly – Offers various visualization types that can replace gauges, such as bullet charts.

Begin at zero:

a. Microsoft Excel – Allows users to manually set axis limits and customize the display of data.

b. ggplot2 – A popular R package that includes the ability to set axis limits and control the display of data.

Display the distinction:

a. QlikView – Offers various charts and tables to highlight the difference between data points.

b. Highcharts – Provides a wide range of customizable chart types to display distinctions.

Pies:

a. Google Charts – Provides a variety of pie chart customization options.

b. Chart.js – A JavaScript library that can create customizable pie charts.

Highlight what is relevantly essential:

a. Plotly – Provides a range of charts and tables that can be customized to highlight essential data points.

b. SAP Analytics Cloud – Offers features to highlight the important aspects of a visualization, such as conditional formatting and alerts.

Graphs from a different perspective:

a. Matplotlib – A popular Python library that provides a wide range of visualization types, including 3D graphs.

b. Vega-Lite – A declarative language for creating interactive visualizations, including custom perspectives.

Excel:

Excel is a widely used spreadsheet program that also offers basic data visualization capabilities. It can be used to create charts, graphs, and other visualizations, and can be a good option for simple visualizations or data exploration.

Wrapping Up

Being able to visualise data in the data-driven world of today is essential for making successful decisions. The ability to master data visualisation is a skill that is attainable through the use of a variety of tricks and tips, and it can significantly improve one’s capacity to comprehend and analyse complex data. Anyone can become a better analyst and improve their data visualisation skills by adhering to best practices like selecting the proper visualisations, structuring data in a meaningful way, and using colour and design effectively. With these strategies in mind, people and organisations can use data visualisation to generate insight, wise choices, and significant outcomes.

The post Mastering Data Visualization: Tips and Tricks to Effectively Analyze Information appeared first on Indium.

What is Power BI and .pbix files?

Power BI is a market leading business intelligence tool by Microsoft for Cleaning, Modifying and Visualizing raw data to come up with actionable insights. Power BI comes with its own data transformation engine called power query and a formula expression language called DAX (Data Analysis Expressions).

DAX gives power BI the ability to calculate new columns, dynamic measures, and tables inside Power Bi desktop.

By default, Power BI report files are saved with .pbix extension which is a renamed version of a ZIP file which contains multiple components, such as the visuals, report canvas, model metadata, and data.

What is Power BI .pbit file

.pbit is a template file created by Power Bi desktop which is also a renamed version of a ZIP file that contains all the Meta data for the Power BI report but doesn’t contain the data itself. Once we extract .pbit file we get a DataModelSchema file along with other files which contain all the Meta data of a Power BI desktop files.

Later in this blog we will be using these .pbit and DataModelSchema files to extract Power BI desktop Meta data.

What is the Meta data in a Power BI Desktop file

Regarding what you see in the Report View in a Power BI desktop, meta data is everything. You can think of all the information as meta data, including the name, source, expression, data type, calculated tables, calculated columns, calculated measures, relationships and lineage between the model’s various tables, hierarchies, parameters, etc.

We will mainly concentrate on extracting Calculated Measures, Calculated Columns, and Relationships in this blog.

Extraction of Meta data using Python

Python was used to process and extract the JSON from the.pbit file and DataModelSchema. We first converted JSON to a Python dictionary before extracting the necessary Meta data.

Below are the steps we will need to achieve the requirement:

1. Exporting .pbix file as .pbit file

There are two ways to save our power BI desktop file as .pbit file.

• Once we are in Power BI desktop, we have an option to save our file as power BI template(.pbit) file
• We can go to File–>Export–>Power BI Template and save the .pbit file at the desired directory.

2. Unzipping .pbit file to get DataModelSchema file

We can directly unzip the .pbit file using the 7z-Zip file manager or any other file manager. Once we Unzip the file, we will get a folder with the same name as that of the .pbit file. Inside the folder we will get the DataModelSchema file, we will have to change its extension to .txt for reading in python.

3. Reading .pbit and Data model schema file in python

We have an option to directly read the .pbit file in python using the dax_extract library. Second option to read the text file in python and using the JSON module convert it into a Python dictionary. Code can be found in the GitHub repository link given at the end of this file.

4. Extracting Measures from the dictionary

The dictionary that we get consists details of all the tables as separate lists, Individuals tables have details related to the columns and measures belonging to that table, we can loop on each table one by one and get details of columns, Measures etc. Below is an example of the Python code can be found in the GitHub Repository link given at the end of this file.

5. Extracting calculated columns from the Dictionary

Like how we extracted the measures we can loop on each table and get details of all the calculated columns. Below is the sample output of the Python code can be found in the GitHub Repository link given at the end of this file.

Also Read:  Certainty in streaming real-time ETL

6. Extracting relationships from the dictionary

Data for relationships is available in the model key of the data dictionary and can be easily extracted. Below is the sample output of the Python code can be found in the GitHub Repository link given at the end of this file. 

7. Saving Extracted data as an Excel file

All the extracted data can be saved in empty lists and these lists can be used to derive a Pandas data frame. This Pandas data frame can be exported as Excel and easily used for reference and validation purposes in a complex model. Below snapshot gives an idea of how this can be done.

Conclusion

In this blog we learnt about extracting metadata from .pbit and DataModelSchema file. We have created a Python script that allows users to enter the file location of .pbit and DataModelSchema file and then metadata extraction along with excel generation can be automated. The code can be found on the below GitHub also sample excel files can be downloaded from below GitHub link. Hope this is helpful and will see you soon with another interesting topic.

The post Power BI Meta Data extraction using Python appeared first on Indium.

In addition, data is increasingly being used to power artificial intelligence (AI) and machine learning (ML) systems, which are driving innovation and transforming businesses across various industries. AI and ML systems require large amounts of high-quality data to train models, make predictions, and automate processes. As such, companies are investing heavily in data infrastructure and analytics capabilities to harness the power of data.

Data is also a highly valuable resource because it is not finite, meaning that it can be generated, shared, and reused without diminishing its value. This creates a virtuous cycle where the more data that is generated and analyzed, the more insights can be gained, leading to better decision-making, increased innovation, and new opportunities for growth. Thus, data has become a critical asset for businesses and governments alike, driving economic growth and shaping the digital landscape of the 21st century.

There are various data storage methods in data science, each with its own strengths and weaknesses. Some of the most common data storage methods include:

• Relational databases: Relational databases are the most common method of storing structured data. They are based on the relational model, which organizes data into tables with rows and columns. Relational databases use SQL (Structured Query Language) for data retrieval and manipulation and are widely used in businesses and organizations of all sizes.

• NoSQL databases: NoSQL databases are a family of databases that do not use the traditional relational model. Instead, they use other data models such as document, key-value, or graph-based models. NoSQL databases are ideal for storing unstructured or semi-structured data and are used in big data applications where scalability and flexibility are key.

• Data warehouses: Data warehouses are specialized databases that are designed to support business intelligence and analytics applications. They are optimized for querying and analyzing large volumes of data and typically store data from multiple sources in a structured format.

• Data lakes: Data lakes are a newer type of data storage method that is designed to store large volumes of raw, unstructured data. Data lakes can store a wide range of data types, from structured data to unstructured data such as text, images, and videos. They are often used in big data and machine learning applications.

• Cloud-based storage: Cloud-based storage solutions, such as Amazon S3, Microsoft Azure, or Google Cloud Storage, offer scalable, secure, and cost-effective options for storing data. They are especially useful for businesses that need to store and access large volumes of data or have distributed teams that need access to the data.

To learn more about : How AI and ML models are assisting the retail sector in reimagining the consumer experience.

Data collection is an essential component of data science and there are various techniques used to collect data. Some of the most common data collection techniques include:

• Surveys: Surveys involve collecting information from a sample of individuals through questionnaires or interviews. Surveys are useful for collecting large amounts of data quickly and can provide valuable insights into customer preferences, behavior, and opinions.

• Experiments: Experiments involve manipulating one or more variables to measure the impact on the outcome. Experiments are useful for testing hypotheses and determining causality.

• Observations: Observations involve collecting data by watching and recording behaviors, actions, or events. Observations can be useful for studying natural behavior in real-world settings.

• Interviews: Interviews involve collecting data through one-on-one conversations with individuals. Interviews can provide in-depth insights into attitudes, beliefs, and motivations.

• Focus groups: Focus groups involve collecting data from a group of individuals who participate in a discussion led by a moderator. Focus groups can provide valuable insights into customer preferences and opinions.

• Social media monitoring: Social media monitoring involves collecting data from social media platforms such as Twitter, Facebook, or LinkedIn. Social media monitoring can provide insights into customer sentiment and preferences.

• Web scraping: Web scraping involves collecting data from websites by extracting information from HTML pages. Web scraping can be useful for collecting large amounts of data quickly.

Data analysis is an essential part of data science and there are various techniques used to analyze data. Some of the top data analysis techniques in data science include:

• Descriptive statistics: Descriptive statistics involve summarizing and describing data using measures such as mean, median, mode, variance, and standard deviation. Descriptive statistics provide a basic understanding of the data and can help identify patterns or trends.

• Inferential statistics: Inferential statistics involve making inferences about a population based on a sample of data. Inferential statistics can be used to test hypotheses, estimate parameters, and make predictions.

• Data visualization: Making charts, graphs, and other visual representations of data to better understand patterns and relationships is known as data visualization. Data visualization is helpful for expressing complex information and spotting trends or patterns that might not be immediately apparent from the data.

• Machine learning: Machine learning involves using algorithms to learn patterns in data and make predictions or decisions based on those patterns. Machine learning is useful for applications such as image recognition, natural language processing, and recommendation systems.

• Text analytics: Text analytics involves analyzing unstructured data such as text to identify patterns, sentiment, and topics. Text analytics is useful for applications such as customer feedback analysis, social media monitoring, and content analysis.

• Time series analysis: Time series analysis involves analyzing data over time to identify trends, seasonality, and cycles. Time series analysis is useful for applications such as forecasting, trend analysis, and anomaly detection.

Use Cases

To illustrate the importance of data in AI and ML, let’s consider a few use cases:

• Predictive Maintenance: In manufacturing, AI and ML can be used to predict when machines are likely to fail, enabling organizations to perform maintenance before a breakdown occurs. To achieve this, the algorithms require vast amounts of data from sensors and other sources to learn patterns that indicate when maintenance is necessary.
• Fraud Detection: AI and ML can also be used to detect fraud in financial transactions. This requires large amounts of data on past transactions to train algorithms to identify patterns that indicate fraudulent behavior.
• Personalization: In e-commerce, AI and ML can be used to personalize recommendations and marketing messages to individual customers. This requires data on past purchases, browsing history, and other customer behaviors to train algorithms to make accurate predictions.

Real-Time Analysis

To achieve optimal results in AI and ML applications, data must be analyzed in real-time. This means that organizations must have the infrastructure and tools necessary to process large volumes of data quickly and accurately. Real-time analysis also requires the ability to detect and respond to anomalies or unexpected events, which can impact the accuracy of the algorithms.

Wrapping Up

In conclusion, data is an essential component of artificial intelligence (AI) and machine learning (ML) applications. Collecting, storing, and analyzing data effectively is crucial to maximizing the performance of AI and ML systems and obtaining optimal results. Data visualization, machine learning, time series analysis, and other data analysis techniques can be used to gain valuable insights from data and make data-driven decisions.

The post Maximizing AI and ML Performance: A Guide to Effective Data Collection, Storage, and Analysis appeared first on Indium.

This is one of the key ways where businesses can leverage real-time data to deliver a key business outcome.

Gartner predicts that by 2022, nearly a half of new business systems will embed continuous intelligence to leverage real-time context data for informed decision making. Using Industry 4.0 technologies such as augmented analytics, ML, event stream processing, business rule management and optimization, businesses can process real-time analytics to process current and historical data for continuous intelligence to respond to events in a timely manner.

Real-time data can empower businesses in many ways, enabling them to accelerate growth, prevent security breaches and respond to customer needs quickly to improve business outcomes.

This is one of the growth drivers for global real-time analytics, which is expected to grow at a Compound Annual Growth Rate (CAGR) of 25.2% from USD 12.5 billion in 2020 to USD 38.6 billion by 2025. Enabling accurately forecasting for faster decision making is another growth driver.

The Benefits of Real-Time Data

In a dynamic world with ever-changing customer needs and newer technologies empowering businesses to compete in global markets, the traditional forecasting and customer service methodologies are proving inadequate. Businesses also need efficiency to lower operational costs and improve their profitability.

From improving employee productivity to an oversight of the entire supply chain and distribution networks are also becoming important to derive higher values. Real data analytics can enable businesses to experience multifold benefits including:

Faster, Data-Backed Decision Making

Decision making by far has relied on gut feel derived from past performance and experience and has been a hit-or-miss affair. Decision-makers can now process and analyze real-time data to assess the impact of factors influencing their business dynamically and not only create a long-term strategy but also make mid-course corrections where needed.

Operational Efficiency

Improving productivity by resource optimization, reducing downtimes and timely maintenance of systems can also help reduce operational costs. Real-time data from across the enterprise can provide insights that can help plan operations and the working structure better. It can also help reduce costs of repair and maintenance, thereby improving the bottom line.

Enhance Customer Delight

Businesses that can solve their customers’ problems dynamically will be able to win greater loyalty. In these times of social media where the word can spread really fast, customer delight has assumed magnificent proportions due to the widespread impact it can have. Constant improvement in customer service, prompt response to customer queries and complaints, and assessing and anticipating customer needs have become crucial for survival and growth.

With real-time data, businesses can catch the pulse of their customers and be ever-ready to cater to them meaningfully. Pricing and competitor information can help businesses know how to position themselves and accordingly define their marketing strategies.

Reduce Breaches, Frauds, and Performance Issues

As the Amazon example shows, real-time data can enable businesses to detect fraud early and prevent their occurrences. Similarly, potential network breaches can be avoided and other errors corrected in a timely manner. Improving website performance and identifying bugs and issues for app developers can help with enhancing performance and customer retention.

Become More Agile and Responsive

Be it creating marketing promotions, preventing operational glitches, improving supply chain management, delivering goods on time, responding to customer complaints meaningfully and promptly, real-time data can provide the required insights. What’s more, it is possible to be proactive, which can help tap opportunities and prevent interruptions, thereby enhancing the revenue and the market value of the company. Real-time data helps identify and predict problems, which ensures smooth functioning of the company with lesser downtimes.

To be able to benefit from real-data, businesses need the right technologies and solutions to store, standardize, access and process. They need tools that enable business users to visualize trends and arrive at decisions without depending on IT.

Indium Software, a cutting edge software solution provider, has experience in Big Data, data analytics solutions, data management, data science, IoT, and AI/ML. Its technology team is complemented by domain experts who have worked across different industries and understand the challenges and requirements to break free and accelerate growth.

Indium also works with the latest technologies such as Hadoop, AWS, Striim, Mendix, and other such cutting edge technologies and can create bespoke solutions to enable businesses to tap real-time data to meet their business goals.

To find out more about our real-time data streaming and data analytics capabilities on real time data, visit: https://www.indiumsoftware.com/striim/

If you would like to find out more, contact us now: https://www.indiumsoftware.com/inquire-now/

The post Taking Full Advantage of Your Real-time Data to Deliver Business Outcomes appeared first on Indium.

Some of the key drivers will be the increasing disposable income that has led to an improving purchasing power of people. The convenience of ordering products online on e-commerce platforms and retail stores will further stimulate market growth.

To meet this growing demand and understand its customers better, e-commerce businesses are increasingly investing in advanced business intelligence and analysis tools. This can provide insights into which products are moving fast, in which markets and how to improve their operations to service the customers better, maximize profits and gain a competitive edge.

3 Focus Areas

E-commerce analytics falls into three main areas:

• Data Visualization and Descriptive Analytics: Dashboards created using historical data of customer behaviour and sales records provide snapshots of all key metrics for improved decision making
• Predictive Analytics: Using churn prediction, market-basket analysis and the like, e-commerce marketplaces can predict the demand for products and design promotions to cross-sell and upsell for improving sales and customer engagement
• Cognitive Analytics: Video and images are analysed for product classification based on predefined parameters to quickly upload new products and avoid errors and time delays associated with manual intervention

Challenges and Benefits

For e-commerce platforms and online stores of retail outlets, an understanding of which products are moving, where their customers are coming from and what their customers are saying are very important.

When a product is performing well, they can boost it further by creating suitable marketing collaterals and also pair it with likely related products to increase the overall sales and growth.

A product which is not performing well will need equal efforts to promote and special offers and discounts to increase its visibility can be designed to improve its sales.

Based on geographies, retail businesses can also plan their campaigns for their stores in those locations and step up promotions for those geographies where they have a presence but not as many footfalls.

Machine learning and artificial intelligence can be used for cross-selling and upselling of related products. For example, when someone is purchasing a mobile, relevant accessories can be displayed to encourage customers to purchase a mobile case or headphones, and so on. When a customer purchases a particular model, they can be tempted with a higher model with better and more features.

Analytics can also be used to understand conversion rates from footfall to sales and the insights used to improve the conversions. Reviews, both positive and negative, are a storehouse of information on what works and what doesn’t.

Negative Review Analytics helps to build the product line with quality to meet customer expectations. Sentiment analysis allows the e-commerce players to build on their strengths, rectify their weaknesses and retain the unsatisfied customer.

For instance, in an e-commerce site, a particular bag was very popular but soon, negative feedback started pouring in. On analysis, it was discovered that the bag was still good but a flap that was added as a design element was made of a different material that did not last long as expected. This is valuable input for the e-commerce marketplace as well as the manufacturers to improve.

Competitor analysis can also be used to devise marketing and, more importantly, pricing strategies to improve the edge over business rivals. Marketplaces and FMCG can especially benefit from this.

Use Cases

Indium used sentiment analysis for a sports retailer where the reviews were analysed to understand customer perception and feedback of the products. Indium’s proprietary data extraction tool, Tex.Ai enabled extracting key phrases to gain insights on customer views. This helped the sports retailer improve on its design and customer service.

For an e-commerce aggregator, Indium used teX.Ai to automate product classification.

Chats with customers, either on chatbot or by a customer executive over the phone can be another rich source of insight into customer satisfaction levels. Using data extraction, the discussion can be analysed for what the customer needs, how it was responded to and if it had been concluded to satisfactorily. This is crucial in building customer loyalty and training the executives and the chatbots to ensure there is a closure.

Analytics can also be used for resource optimisation to reduce the waiting time of customers trying to reach a representative.

Indium Advantage

Indium Software, in its more than two decades of existence, has been providing holistic solutions on cutting edge technologies. It has carefully built a team that is a judicious mix of domain and technology experts.

Our e-commerce team can set up and run a marketplace from the ground up using the latest technologies including in-build analytics. It can also build solutions for analytics on existing platforms using machine learning and artificial intelligence. Strong solution architects, subject matter experts and expertise in analytics make Indium an ideal partner for e-commerce platforms and retail brands seeking to leverage the World Wide Web.

The post Analytics in E-commerce and Indium’s Expertise appeared first on Indium.

This allows them to grasp difficult concepts easily and also in the identification of new patterns.

The interactive technologies available today further help to drill down into graphs and charts for in depth detail, in turn interactively changing the data you view and how it’s processed.

The post A Comparison Of The Best Data Visualization Tools Today (Infographic) appeared first on Indium.