data

In the world of machine learning, evaluating the performance of a model is just as important as building the model itself. One of the most fundamental tools for this purpose is the confusion matrix. This powerful yet simple concept helps data scientists and machine learning practitioners assess the accuracy of classification algorithms, providing insights into how well a model is performing in predicting various classes.

In this blog, we will explore the concept of a confusion matrix using a spam email example. We highlight the 4 key metrics you must understand and work on while working with a confusion matrix.

 

 

What is a Confusion Matrix?

A confusion matrix is a table that is used to describe the performance of a classification model. It compares the actual target values with those predicted by the model. This comparison is done across all classes in the dataset, giving a detailed breakdown of how well the model is performing. 

Here’s a simple layout of a confusion matrix for a binary classification problem:

In a binary classification problem, the confusion matrix consists of four key components: 

1. True Positive (TP): The number of instances where the model correctly predicted the positive class. 
2. False Positive (FP): The number of instances where the model incorrectly predicted the positive class when it was actually negative. Also known as Type I error. 
3. False Negative (FN): The number of instances where the model incorrectly predicted the negative class when it was actually positive. Also known as Type II error. 
4. True Negative (TN): The number of instances where the model correctly predicted the negative class.

Why is the Confusion Matrix Important?

The confusion matrix provides a more nuanced view of a model’s performance than a single accuracy score. It allows you to see not just how many predictions were correct, but also where the model is making errors, and what kind of errors are occurring. This information is critical for improving model performance, especially in cases where certain types of errors are more costly than others. 

For example, in medical diagnosis, a false negative (where the model fails to identify a disease) could be far more serious than a false positive. In such cases, the confusion matrix helps in understanding these errors and guiding the development of models that minimize the most critical types of errors.

 

Also learn about the Random Forest Algorithm and its uses in ML

 

Scenario: Email Spam Classification

Suppose you have built a machine learning model to classify emails as either “Spam” or “Not Spam.” You test your model on a dataset of 100 emails, and the actual and predicted classifications are compared. Here’s how the results could break down: 

• Total emails: 100 
• Actual Spam emails: 40 
• Actual Not Spam emails: 60

After running your model, the results are as follows: 

• Correctly predicted Spam emails (True Positives, TP): 35
• Incorrectly predicted Spam emails (False Positives, FP): 10

• Incorrectly predicted Not Spam emails (False Negatives, FN): 5
• Correctly predicted Not Spam emails (True Negatives, TN): 50

Understanding 4 Key Metrics Derived from the Confusion Matrix

The confusion matrix serves as the foundation for several important metrics that are used to evaluate the performance of a classification model. These include:

1. Accuracy

• Formula for Accuracy in a Confusion Matrix:

Explanation: Accuracy measures the overall correctness of the model by dividing the sum of true positives and true negatives by the total number of predictions.

• Calculation for accuracy in the given confusion matrix:

This equates to = 0.85 (or 85%). It means that the model correctly predicted 85% of the emails.

2. Precision

• Formula for Precision in a Confusion Matrix:

Explanation: Precision (also known as positive predictive value) is the ratio of correctly predicted positive observations to the total predicted positives.

It answers the question: Of all the positive predictions, how many were actually correct?

• Calculation for precision of the given confusion matrix

It equates to ≈ 0.78 (or 78%) which highlights that of all the emails predicted as Spam, 78% were actually Spam.

 

 

3. Recall (Sensitivity or True Positive Rate)

• Formula for Recall in a Confusion Matrix

Explanation: Recall measures the model’s ability to correctly identify all positive instances. It answers the question: Of all the actual positives, how many did the model correctly predict?

• Calculation for recall in the given confusion matrix

It equates to = 0.875 (or 87.5%), highlighting that the model correctly identified 87.5% of the actual Spam emails.

4. F1 Score

• F1 Score Formula:

Explanation: The F1 score is the harmonic mean of precision and recall. It is especially useful when the class distribution is imbalanced, as it balances the two metrics.

• F1 Calculation:

This calculation equates to ≈ 0.82 (or 82%). It indicates that the F1 score balances Precision and Recall, providing a single metric for performance.

 

Understand the basics of Binomial Distribution and its importance in ML

 

Interpreting the Key Metrics

• High Recall: The model is good at identifying actual Spam emails (high Recall of 87.5%). 

• Moderate Precision: However, it also incorrectly labels some Not Spam emails as Spam (Precision of 78%). 

• Balanced Accuracy: The overall accuracy is 85%, meaning the model performs well, but there is room for improvement in reducing false positives and false negatives. 

• Solid F1 Score: The F1 Score of 82% reflects a good balance between Precision and Recall, meaning the model is reasonably effective at identifying true positives without generating too many false positives. This balanced metric is particularly valuable in evaluating the model’s performance in situations where both false positives and false negatives are important.

 

 

Conclusion

The confusion matrix is an indispensable tool in the evaluation of classification models. By breaking down the performance into detailed components, it provides a deeper understanding of how well the model is performing, highlighting both strengths and weaknesses. Whether you are a beginner or an experienced data scientist, mastering the confusion matrix is essential for building effective and reliable machine learning models.

In today’s dynamic digital world, handling vast amounts of data across the organization is challenging. It takes a lot of time and effort to set up different resources for each task and duplicate data repeatedly. Picture a world where you don’t have to juggle multiple copies of data or struggle with integration issues.

Microsoft Fabric makes this possible by introducing a unified approach to data management. Microsoft Fabric aims to reduce unnecessary data replication, centralize storage, and create a unified environment with its unique data fabric method. 

What is Microsoft Fabric?

Microsoft Fabric is a cutting-edge analytics platform that helps data experts and companies work together on data projects. It is based on a SaaS model that provides a unified platform for all tasks like ingesting, storing, processing, analyzing, and monitoring data.

With this full-fledged solution, you don’t have to spend all your time and effort combining different services or duplicating data.

 

 

Fabric features a lake-centric architecture, with a central repository known as OneLake. OneLake, being built on Azure Data Lake Storage (ADLS), supports various data formats, including Delta, Parquet, CSV, and JSON. OneLake offers a unified data environment for each of Microsoft Fabric’s experiences.

These experiences facilitate professionals from ingesting data from different sources into a unified environment and pipelining the ingestion, transformation, and processing of data to developing predictive models and analyzing the data by visualization in interactive BI reports.  

Microsoft Fabric’s experiences include: 

• Synapse Data Engineering 
• Synapse Data Warehouse 
• Synapse Data Science 
• Synapse Real-Time Intelligence 
• Data Factory 
• Data Activator  
• Power BI

 

 

Exploring Microsoft Fabric Components: Sales Use Case

Microsoft Fabric offers a set of analytics components that are designed to perform specific tasks and work together seamlessly. Let’s explore each of these components and its application in the sales domain: 

Synapse Data Engineering:

Synapse Data Engineering provides a powerful Spark platform designed for large-scale data transformations through Lakehouse.

In the sales use case, it facilitates the creation of automated data pipelines that handle data ingestion and transformation, ensuring that sales data is consistently updated and ready for analysis without manual intervention.

Synapse Data Warehouse:

Synapse Data Warehouse represents the next generation of data warehousing, supporting an open data format. The data is stored in Parquet format and published as Delta Lake Logs, supporting ACID transactions and enabling interoperability across Microsoft Fabric workloads.

In the sales context, this ensures that sales data remains consistent, accurate, and easily accessible for analysis and reporting. 

Synapse Data Science:

Synapse Data Science empowers data scientists to work directly with secured and governed sales data prepared by engineering teams, allowing for the efficient development of predictive models.

By forecasting sales performance, businesses can identify anomalies or trends, which are crucial for directing future sales strategies and making informed decisions.

 

 

Synapse Real-Time Intelligence:

Real-Time Intelligence in Synapse provides a robust solution to gain insights and visualize event-driven scenarios and streaming data logs. In the sales domain, this enables real-time monitoring of live sales activities, offering immediate insights into performance and rapid response to emerging trends or issues.  

Data Factory:

Data Factory enhances the data integration experience by offering support for over 200 native connectors to both on-premises and cloud data sources.

For the sales use case, this means professionals can create pipelines that automate the process of data ingestion, and transformation, ensuring that sales data is always updated and ready for analysis.  

Data Activator:

Data Activator is a no-code experience in Microsoft Fabric that enables users to automatically perform actions on changing data on the detection of specific patterns or conditions.

In the sales context, this helps monitor sales data in Power BI reports and trigger alerts or actions based on real-time changes, ensuring that sales teams can respond quickly to critical events. 

Power BI:

Power BI, integrated within Microsoft Fabric, is a leading Business Intelligence tool that facilitates advanced data visualization and reporting.

For sales teams, it offers interactive dashboards that display key metrics, trends, and performance indicators. This enables a deep analysis of sales data, helping to identify what drives demand and what affects sales performance.

 

Learn how to use Power BI for data exploration and visualization

 

Hands-on Practice on Microsoft Fabric:

Let’s get started with sales data analysis by leveraging the power of Microsoft Fabric: 

1. Sample Data

The dataset utilized for this example is the sample sales data (sales.csv). 

2. Create Workspace

To work with data in Fabric, first create a workspace with the Fabric trial enabled. 

• On the home page, select Synapse Data Engineering.
• In the menu bar on the left, select Workspaces.
• Create a new workspace with any name and select a licensing mode. When a new workspace opens, it should be empty.

 

 

3. Create Lakehouse

Now, let’s create a lakehouse to store the data.

• In the bottom left corner select Synapse Data Engineering and create a new Lakehouse with any name.

 

 

• On the Lake View tab in the pane on the left, create a new subfolder.

 

 

4. Create Pipeline

To ingest data, we’ll make use of a Copy Data activity in a pipeline. This will enable us to extract the data from a source and copy it to a file in the already-created lakehouse. 

• On the Home page of Lakehouse, select Get Data and then select New Data Pipeline to create a new data pipeline named Ingest Sales Data. 
• The Copy Data wizard will open automatically, if not select Copy Data > Use Copy Assistant in the pipeline editor page. 
• In the Copy Data wizard, on the Choose a data source page select HTTP in the New sources section.  
• Enter the settings in the connect to data source pane as shown:

 

 

• Click Next. Then on the next page select Request method as GET and leave other fields blank. Select Next. 

 

 

• When the pipeline starts to run, its status can be monitored in the Output pane. 
• Now, in the created Lakehouse check if the sales.csv file has been copied. 

5. Create Notebook

On the Home page for your lakehouse, in the Open Notebook menu, select New Notebook. 

• In the notebook, configure one of the cells as a Toggle parameter cell and declare a variable for the table name.

 

 

• Select Data Wrangler in the notebook ribbon, and then select the data frame that we just created using the data file from the copy data pipeline. Here, we changed the data types of columns and dealt with missing values.  

Data Wrangler generates a descriptive overview of the data frame, allowing you to transform, and process your sales data as required. It is a great tool especially when performing data preprocessing for data science tasks.

 

 

• Now, we can save the data as delta tables to use later for sales analytics. Delta tables are schema abstractions for data files that are stored in Delta format.  

 

 

• Let’s use SQL operations on this delta table to see if the table is stored. 

 

 

 

6. Run and Schedule Pipeline

Go to the already created pipeline page, add Notebook Activity to the completion of the copy data pipeline, and follow these configurations. So, the table_name parameter will override the default value of the table_name variable in the parameters cell of the notebook.

 

 

In the Notebook, select the notebook you just created. 

7. Schedule and Monitor Pipeline

Now, we can schedule the pipeline.  

• On the Home tab of the pipeline editor window, select Schedule and enter the scheduling requirements.

 

 

• To keep track of pipeline runs, add the Office Outlook activity after the pipeline.  
• In the settings of activity, authenticate with the sender account (use your account in ‘To’). 
• For the Subject and Body, select the Add dynamic content option to display the pipeline expression builder canvas and add the expressions as follows. (select your activity name in ‘activity ()’)

 

 

8. Use Data from Pipeline in PowerBI

• In the lakehouse, click on the delta table just created by the pipeline and create a New Semantic Model.

 

 

• As the model is created, the model view opens click on Create New Report.

 

 

• This opens another tab of PowerBI, where you can visualize the sales data and create interactive dashboards.

 

 

Choose a visual of interest. Right-click it and select Set Alert. Set Alert button in the Power BI toolbar can also be used.  

• Next, define trigger conditions to create a trigger in the following way:

 

 

This way, sales professionals can seamlessly use their data across the platform by transforming and storing it in the appropriate format. They can perform analysis, make informed decisions, and set up triggers, allowing them to monitor sales performance and react quickly to any uncertainty.

 

 

Conclusion

In conclusion, Microsoft Fabric as a revolutionary all-in-one analytics platform simplifies data management for enterprises. Providing a unified environment eliminates the complexities of handling multiple services just by being a haven where data moves in and out all within the same environment for ease of ingestion, processing, or analysis.

With Microsoft Fabric, businesses can streamline data workflows, from data ingestion to real-time analytics, and can respond quickly to market dynamics.

 

Want to learn more about Microsoft Fabric? Join our crash course today for a comprehensive understanding!

In today’s data-driven world, businesses are constantly collecting and analyzing vast amounts of information to gain insights and make informed decisions. However, traditional methods of data analysis are often insufficient to fully capture the complexity of modern data sets. This is where graph analytics comes in.

One might say that the difference between data and graph analytics is like a movie script and a movie itself – but that is not entirely accurate. It can be compared to a movie that tells a story, while analytics is akin to the script that guides the movie’s plot. In contrast, data itself can be likened to a jumbled set of words, much like an incomplete puzzle that traditional methods cannot piece together.

What is graph analytics?

Enter graph analytics – the ultimate tool for uncovering hidden connections and patterns in your data.  

Have you ever wondered how to make sense of the overwhelming amount of data that surrounds us? It is a game-changing tool/technology that allows us to uncover patterns and connections in data that traditional methods can’t reveal. It is a way of analyzing data that is organized in a graph structure, where data is represented as nodes (vertices), and the relationships between them are represented as edges.

How graph analytics are better for handling complex data sets?

And let’s not forget, it is also great at handling large and complex data sets. It’s like having a supercomputer at your fingertips. Imagine trying to analyze a social network with traditional methods, it would be like trying to count the stars in the sky with your bare eyes. But with graph analytics, it’s like having a telescope to zoom in on the stars. 

Furthermore, graph analytics also provides a valuable addition to current machine-learning approaches. By adding graph-based features to a machine learning model, data scientists can achieve even better performance, which is a great way to leverage graph analytics for data science professionals. 

Explanation of graph structure in data representation

It is a powerful tool for data representation and analysis. It allows data to be represented as a network of nodes and edges, also known as a graph. The nodes in the graph represent entities or objects, while the edges represent the relationships or connections between them. This structure makes it easier to visualize and understand complex relationships between data points.

Comparison to traditional methods of data analysis

Without graph analytics, a data scientist’s life would be like trying to solve a jigsaw puzzle with missing pieces. Sure, you can still see the big picture, but it’s not quite complete.

Traditional methods such as statistical analysis and machine learning can only get you so far in uncovering the hidden insights in your data. It’s like trying to put together a puzzle with only half the pieces but with graph analytics, it’s like finding the missing pieces to the puzzle. It allows you to see the connections and patterns in your data that you never knew existed. 

Insights from industry experts on real-world applications

In our webinar, “Introduction to Graph Analytics,” attendees learned from industry experts Griffin Marge and Scott Heath as they shared insights on the power of graph analytics and discovered how one can begin to leverage it in their own work.

 

Summing it all up, this talk will help you in understanding how graph analytics is being used today by some of the world’s most innovative organizations. So, don’t miss out on this opportunity to expand your data analysis skills and gain a competitive edge.

Conclusion

All in all, graph analytics is a powerful tool for unlocking insights in large and complex data sets that traditional methods of data analysis cannot fully capture. By representing data as a graph structure with nodes and edges, graph analytics allows for a more comprehensive understanding of relationships between data points. If you want to expand your data analysis skills and stay ahead of the curve, graph analytics is a must-have tool in your arsenal.

 

Written by: Hamza Mannan Samad

Designers don’t need to use data-driven decision-making, right? Here are 5 common design problems you can solve with the data science basics.

What are the common design problems we face every day?

Design is a busy job. You have to balance both artistic and technical skills and meet the needs of bosses and clients who might not know what they want until they ask you to change it. You have to think about the big picture, the story, and the brand, while also being the person who spots when something is misaligned by a hair’s width.

The ‘real’ artists think you sold out, and your parents wish you had just majored in business. When you’re juggling all of this, you might think to yourself, “at least I don’t have to be a numbers person,” and you avoid complicated topics like data analytics at all costs.

If you find yourself thinking along these lines, this article is for you. Here are a few common problems you might encounter as a designer, and how some of the basic approaches of data science can be used to solve them. It might actually take a few things off your plate.

1. The person I’m designing for has no idea what they want

If you have any experience with designing for other people, you know exactly what this really means. You might be asked to make something vague such as “a flyer that says who we are to potential customers and has a lot of photos in it.” A dozen or so drafts later, you have figured out plenty of things they don’t like and are no closer to a final product.

What you need to look for are the company’s needs. Not just the needs they say they have; ask them for the data. The company might already be keeping their own metrics, so ask what numbers most are concerning to them, and what goals they have for improvement. If they say they don’t have any data like that – FALSE!

Every organization has some kind of data, even if you have to be the one to put it together. It might not even be in the most obvious of places like an Excel file. Go through the customer emails, conversations, chats, and your CRM, and make a note of what the most usual questions are, who asks them, and when they get sent in. You just made your own metrics, buddy!

Now that you have the data, gear your design solutions to improve those key metrics. This time when you design the flyer, put the answers to the most frequent questions at the top of the visual hierarchy. Maybe you don’t need a ton of photos but select one great photo that had the highest engagement on their Instagram. No matter how picky a client is, there’s no disagreeing with good data.

2. I have too much content and I don’t know how to organize it

This problem is especially popular in digital design. Whether it’s an app, an email, or an entire website, you have a lot of elements to deal with, and need to figure out how to navigate the audience through all of it. For those of you who are unaware, this is the basic concept of UX, short for ‘User Experience.’

The dangerous trap people fall into is asking for opinions about UX. You can ask 5 people or 500 and you’re always going to end up with the same conclusion: people want to see everything, all at once, but they want it to be simple, easy to navigate and uncrowded.

The perfect UX is basically impossible, which is why you instead need to focus on getting the most important aspects and prioritizing them. While people’s opinions claim to prioritize everything, their actual behavior when searching for what they want is much more telling.

Capturing this behavior is easy with web analytics tools. There are plenty of apps like Google Analytics to track the big picture parts of your website, but for the finer details of a single web page design, there are tools like Hotjar. You can track how each user (with cookies enabled) travels through your site, such as how far they scroll and what elements they click on.

If users keep leaving the page without getting to the checkout, you can find out where they are when they decide to leave, and what calls to action are being overlooked.

When you really get the hang of it, UX will transform from a guessing game about making buttons “obvious” and instead you will understand your site as a series of pathways through hierarchies of story elements. As an added bonus, you can apply this same knowledge to your print media and make uncrowded brochures and advertisements too!

3. I’m losing my mind to a handful of arbitrary choices

Should the dress be pink, or blue? Unfortunately, not all of us can be Disney princesses with magic wands to change constantly back and forth between colors. Unless, of course, you are a web designer from the 90’s, and in that case, those rainbow shifting gifs on your website are wicked gnarly, dude.

For the rest of us, we have to make some tough calls about design elements. Even if you’re used to making these decisions, you might be working with other people who are divided over their own ideas and have no clue who to side with. (Little known fact about designers: we don’t have opinions on absolutely everything.)

This is where a simple concept called “A/B testing” comes in handy. It requires some coding knowledge to pull it off yourself or you can ask your web developer to install the tracking pixel, but some digital marketing tools have built-in A/B testing features. (You can learn more about A/B testing in Data Science Dojo’s comprehensive bootcamps cough cough)

Other than the technical aspect, it’s beautifully simple. You take a single design element, and narrow it down to two options, with a shared ultimate goal you want that element to contribute to. Half your audience will see the pink dress, and half will see the blue, and the data will show you not only which dress was liked by the princesses, but exactly how much more they liked it. Just like magic.

4. I’m working with someone who is using Comic Sans, Papyrus, or (insert taboo here) unironically

This is such a common problem, so well understood that the inside jokes about it between designer’s risk flipping all the way around the scale into a genuine appreciation of bad design elements. But what do you do when you have a person who sincerely asks you what’s wrong with using the same font Avatar used in their logo?

The solution to this is kind of dirty and cheap from the data science perspective, but I’m including it because it follows the basic principle of evidence > intuition. There is no way to really explain a design faux-pas because it comes from experience. However, sometimes when experience can’t be described, it can be quantified.

Ask this person to look up the top competitors in their sector. Then ask them to find similar businesses using this design element you’re concerned about. How do these organizations compare? How many followers do they have on social media? When was the last time they updated something? How many reviews do they have?

If the results genuinely show that Papyrus is the secret ingredient to a successful brand, then wow, time to rethink that style guide.

5. How can I prove that my designs are “good”?

Unless you have skipped to the end of this article, you already know the solution to this one. No matter what kind of design you do, it’s meant to fulfill a goal. And where do data scientists get goals? Metrics! Some good metrics for UX that you might want to consider when designing a website, email, or ad campaign are click-through-rate (CTR), session time, page views, page load, bounce rate, conversions, and return visits.

This article has already covered a few basic strategies to get design related metrics. Even if the person you’re working for doesn’t have the issues described above (or maybe you’re working for yourself) it’s a great idea to look at metrics before and after your design hits the presses.

If the data doesn’t shift how, you want it to, that’s a learning experience. You might even do some more digging to find data that can tell you where the problem came from, if it was a detail in your design or a flaw in getting it delivered to the audience.

When you do see positive trends, congrats! You helped further your organization’s goals and validated your design skills. Attaching tangible metrics to your work is a great support to getting more jobs and pay raises, so you don’t have to eat ramen noodles forever.

If nothing else, it’s a great way to prove that you didn’t need to major in accounting to work with fancy numbers, dad.

 

Written by Julia Grosvenor

Data democratization is a complex concept. The concept, in any organization, rests on four major pillars: data, tools, training, and people.

Data democratization allows end-users to assess data in a digital format without requiring help (typically from IT).  The culture of a company and how its employees think are driven by people who are quite passionate about data.

Today, Data democratization can be a game-changer because it makes it easier, faster, and simpler for employees to access the insights they require. Data democratization safeguards the company from becoming a top-down organization where the highest-paid person’s opinion wins. Users are given more ownership and greater responsibility with data democratization and need no longer be driven by hunches or assumptions. Let us see how this happens.

Pillars of data democratization

1. Data

A considerable percentage of data exists in silos and is spread across the enterprise.  It could be stored in flat files accessed by Microsoft SQL Server; it could be saved in folders on an employee’s hard drives, or it could be stored at (and shared by) partner companies. As you’d expect, this is not conducive to viewing the “big picture.” Enterprises have created cloud-based data warehouses to tear down the silos. For data analytics, warehouses serve as a solitary, consolidated source of truth.

2. Tools and training for data democratization

Data democratization can be empowering to users, but only if the data is properly used.  To make sure data democratization doesn’t lead to misinterpreting data.  After training (typically by IT), users may reinforce that training by creating or joining mailing lists or chat rooms; they may even ensure that beginners and experts physically sit next to each other.

As companies identify which business users need to explore the data more deeply and freely on their own, they must also understand the different levels of user needs when it comes to data. Instead of limiting the analytics by offering just summarized or just raw data to all users, a multi-tiered approach is essential to provide the right depth of data to a user’s analytical skills and needs.

A first tier might provide only dashboards and static reports, and the second tier might add interactive, dynamic dashboards where the users can drill down to additional insights.

The third tier could include guided analysis that a senior analyst prepares for an individual user or a group of business users to work in a safe and rich environment in which technical users can follow the analysis process through annotations and explanations.

The fourth and final tier provides access to a visual data discovery tool so business users can visually explore a broad set of data (perhaps through a simple, familiar tool such as Excel) instead of using less intuitive means such as data tables and SQL queries. An enterprise will need to ensure that the more data a user can access, the greater their understanding of that data must be to avoid data misuse or misunderstanding.

3. People

Expertise in data analytics is strongly associated with open, persistent, positive, and inquisitive people. Enterprises must ensure that such enquiring minds are regularly challenged and involved.  Employees need to be motivated and engaged to think, play with data, and ask questions. Engage them with regular seminars on key concepts, tools, and modern technologies.

4. Challenges faced while implementing data democratization

The main challenge enterprises face in their move to data democratization is that data teams are struggling to keep up with the rising hunger for data throughout the organization.  More data demands a more complex analysis. For many organizations, moving to data democratization may require more resources than they have.

This problem can be addressed by self-service analytics. It enables everyone in the company to become a data analyst.  In many cases, users need only a dashboard that provides real-time data; others need that data available in analysis tools. Putting the technology in place is not just enough to make it work. The training that is essential for the staff must be offered to bring real value. Thus, data democratization is made highly efficient by self-service analytics.

Conclusion

To enhance data democratization in your enterprise, you must keep in mind that this is a slow process in which small wins are brought via incremental transformations in a culture that drives the next culture change. Today, more organizations are trying to provide access to data to all their staff via data democratization and this, in turn, is helping them enhance the job performance and overall health of the organization.

Learn more about data science at our Data Science Bootcamp.