BI Professionals always look forward to the Gartner Magic Quadrant for Business Intelligence & Analytics, since it’s one of the leading studies in the BI space. But how do you get the essentials from this 125-page report? And how independent and reliable is Gartner’s research really? What are the most important trends in Business Intelligence for 2023?

In about half an hour, our experts bring you up to speed on the most important findings of the new Gartner Magic Quadrant. We also discuss the most important trends for the coming year.

What will you learn in this webinar?

• The key takeaways from the Gartner Magic Quadrant for Business Intelligence & Analytics report
• Most interesting new insights for the coming year

Within Data Engineering, there are various technologies that you can use within your organization. But which technologies keep up with developments, and what are the latest trends in the Data Engineering space?

Every year, BARC writes a data management summary with various technology and reviews them on different aspects. We reviewed this report for you and will tell you all the interesting details. Fill out the form below and know the most interesting trends and technologies in the field of Data Engineering.

What will you learn in this webinar?

• The key takeaways from the BARC report
• Most interesting new insights

We have been working with the International Business Communication Standards (IBCS) for years. This standard unifies the language of reports and dashboards so that business communication becomes more effective. A study done by the Technische Universität München (TUM) shows that with correct use of IBCS®, analysis speed increases by 46% and decision accuracy increases by 61%.

But what is IBCS all about? And how can you implement these standards within your organization? In 20 minutes, our expert explains all the details of how IBCS can help your organisation.

In the report “Magic Quadrant for Enterprise Low-Code Application Platform,” Gartner explains the differences between various Low Code platforms. But how do you get the important information from this 125-page report? Luckily, you don’t have to do this yourself. In half an hour, our Low Code specialist will update you on the most important developments and trends for the upcoming year.

What will you learn in this webinar?

• The key takeaways from Gartner’s report: Magic Quadrant for Enterprise Low-Code Application Platform
• Most interesting new insights

Initial Setup

Before we dive into the nitty-gritty of coding with Databricks, there are a few important setup steps to take.

First, we created a repository on Azure DevOps, where we could easily track and assign tasks to team members, make comments on specific items, and link them to our Git commits. This helped us stay organized and focused on our project goals.

Next, we set up a new resource group on Azure with three resources: Azure Databricks Services, a blob storage account, and a key vault. Although we already have clean data in our Rockfeather Database (thanks to our meticulous data engineers), we wanted to keep our intermediate files separately in this resource group to ensure version control and maintain a clean workflow. Within our blob storage, we created containers to store our formatted historical actuals, exogenous features, and predictions.

Finally, we sketched out a high-level project architecture to get a bird’s-eye view of the project. This helped us align on our deliverables and encouraged discussion within the team about what a realistic outcome would look like. By taking these initial setup steps, we were able to hit the ground running with Databricks and tackle our forecasting process with confidence.

Project Architecture

Moving on to Databricks

Setting up Azure Databricks

Now we’re ready to dive into Databricks and its sleek interface. But before we start coding, there are a few more setup steps to take.

First, we want to link the Azure DevOps repo we set up earlier to Databricks. Kudos to Azure Databricks, the integration between these two tools is seamless! To link the repo, we simply go to User Settings > Git Integration and drop the repo link there. For more information, check out this link.

To keep our database and blob storage keys and passwords secure, we use the key vault to store our secrets, which we link to Databricks. You can read more about that here!

Lastly, we need to create a compute resource that will run our code. Unlike Azure Machine Learning, Databricks doesn’t have compute instances, only clusters. While this means it takes a minute to spin up the cluster, we don’t have to worry about forgetting to terminate it since it automatically does so after a pre-defined time period of inactivity! It’s also super easy to install libraries on our compute: just head over to the Libraries tab and click on “⛓️ Install new”!

From Database Query to Forecast

Now that our setup is complete, we move on to using Databricks notebooks for data loading, data engineering, and forecasting. We break this down into three sub-sections:

1. Query Data: We create a notebook which reads the formatted data from the blob storage containers we created earlier. We then transform this data and create data frames for further data engineering.
2. Preprocess Data: Here, we perform data cleaning, feature engineering, and data aggregation on our data frames to get them into a shape suitable for forecasting. This is also the place to apply transformations such as normalisation, scaling, and one-hot encoding to prepare our data for modelling.
3. Forecast: We use machine learning models such as Random Forest, Gradient Boosting, and Time Series models to forecast future values based on our prepared data. For our baseline model, we use the classic Exponential Smoothing model. We then store these predictions back in our blob storage for further analysis and visualisation.

By breaking down the process into these three sub-sections, we can work more efficiently and focus on specific tasks without getting overwhelmed. As shown in the Project Architecture above, each step is one notebook. Let’s have a closer look at each notebook.

Notebook 1: Query Data

The code we’ll be writing in Databricks is pretty much in standard notebook format, which is familiar territory to all data scientists. Our data engineers in the audience will also appreciate that we can write, for example, SQL code in the notebook. All we have to do is include the %sql magic command (link: https://docs.databricks.com/notebooks/notebooks-code.html) at the beginning of the cell, as shown below. We use this approach for our first notebook, where we query the data we need. In our case here, it’s past billable hours and the available hours of our lovely consultants. Once we have the data we need, aggregated to the right level, we save it in our blob storage for the next step.

Notebook 2: Preprocess data

In Notebook 2, we use pandas and numpy to preprocess our data and do our feature engineering. We need to make sure we have all the data on the features we’ve identified for the whole forecast period before we start training any models. For example, if we’re forecasting billable hours and using total available hours of our consultants as an exogenous feature, we need to have that data available for the entire forecast period. While this may seem obvious in this case, it’s an important step to take before we jump into any modelling! Once we’ve got our data formatted the way we want it, we save it back to blob storage and move on to the next notebook.

Notebook 3: Forecast

Here’s where the magic happens – we finally get to do some forecasting! We use the darts package to train multiple models and compete against our baseline Exponential Smoothing Model. We love this package because it’s super easy to use and makes backtesting a breeze. To evaluate model accuracy, we use the mean absolute percentage error (MAPE) – it’s a simple metric that’s easy to understand.

We try out different models like linear regression, random forest, and XGBoost and compare their performance against our baseline. Our baseline model had an MAPE of 44%, which isn’t great, but we’re not deterred. By adding in our single exogenous feature and leveraging our three ML models, we were able to decrease our MAPE to 12% – a huge improvement! And of course, we save our results back to blob storage for future reference.

With our forecasting pipeline up and running in Databricks, we can sit back and watch the predictions roll in. It’s amazing what you can do with a little data and a lot of creativity!

💡 Forecast backtesting is a method used to check how accurate a forecast is by comparing its predictions with what actually happened. This helps identify any errors or biases in the forecasting model, which can be used to improve future predictions. It’s a useful tool in many industries, such as finance or weather forecasting, and helps decision-makers make better-informed decisions.

Our thoughts on Databricks

We have got to give props to Databricks, it’s a tool that makes our lives easier. It’s like having a Swiss army knife in your pocket – it’s slim, versatile, and gets the job done. We love the collaboration feature – we can code with our team, and it’s like a real-time jam session. Plus, setting up and scheduling pipelines is as smooth as butter. The best part is the seamless integration with mlflow and PySpark – it’s like having your favorite sauce on your favorite dish. Let’s just say that Databricks has been a game-changer for us, and we’re excited to see what new features they’ll cook up in the future!

Next Steps

Although we’ve got a pipeline set up, our forecasting journey still has an exciting ride ahead. That’s always how it is with data science projects. The next step is generating maximum value from our forecasts.

We are discussing with our Data Viz team how best to integrate this forecast into our dashboards. Also, we’re scheduling meetings with our CFO to see how exactly we can make his job easier by, for example, introducing more exogenous features or reporting historical accuracy.

As a data-thinking organization, we’re committed to becoming more anti-fragile, and we treat our customers the same way. This means building resilience into our forecasting models to ensure they can withstand unexpected events and continue providing reliable forecasts. If you found this post inspiring and would like to know more, don’t hesitate to reach out!

Choosing a Data Science solution for your business is a difficult task. Our Data Science experts have looked into all the options and will be talking about the best tools and the newest trends in the Data Science & Machine Learning market. All in a short and sweet 30 minute webinar. They’ll also tell you everything you need to think about when picking a tool and what the pros and cons of the most commonly used tools are.

What will you learn in this webinar?

• The best Data Science and Machine Learning tools on the market
• The most interesting new trends in the market

A highly requested solution with undesirable side effects.

The first update we want to highlight is an update that allows small multiples to be turned off on the shared axis. A common complaint among Power BI users was “due to the shared axis, I can’t see smaller values properly anymore”. This update does make that possible, however how useful is this turning off the shared axis really?

Let’s see what happens when we turn off the shared axis.

As you can see, it is not very easy to tell which product group is the largest if the y-axes are not equal. In fact, at first glance, it all looks the same. Only when you look longer do you see that the axes are not equal.

We believe that you should always keep the axes equal, and so even if they are small values. After all, you are comparing different segments. But then how can you visualize small multiples, while also making it easy to read? It’s simpler than it sounds, take a look:

In this case, we recommend using Zebra BI, a plug-in for BI tools such as Power BI. By using this plug-in, the small multiples are placed in boxes of different sizes depending on the values. This allows users to properly compare the segments and thus draw the correct conclusion, as they are not misled by different axes. Sounds useful right?

An update that simplifies reading a report

A feature from the same update that does make Power BI dashboards more effective, is the addition of Dynamic Slicers. With Dynamic Slicers, you can use field parameters to dynamically change the measurements or dimensions analyzed within a report. But why is this so useful?

With Dynamic Slicers, you can help readers of a BI report explore and customize the report, so they can use the information that is useful for their analysis. As shown in the GIF above, a user can filter by Customers, Product Family, Product Group, or other slicers that you’ve set up in the report.

In addition, you can parameterize slicers further to support dynamic filtering scenarios. In the GIF below, you can see how it works. You can see that the value comes from the dynamic slicer and changes dynamically. This gives your users even more opportunities to interact with the dashboard.

How do we make sure Dashboards are not misleading?

Of course, Microsoft continuously tries to update and improve Power BI, but some updates can have unwanted effects. We have been using IBCS standards for years as our Power BI report guidelines, and we also believe that if these standards are applied properly, you’ll prevent unintended consequences. To test if our reports comply with these standards, we use ZebraBI‘s IBCS-proof plug-in while building dashboards in PowerBI. Want to learn more about how IBCS standards can help your organization?

Fill out the form below, and we would love to see you at the webinar on March 28, where one of our experts explain IBCS standards with and how it can help your organization. If you prefer a 1-on-1 conversation, one of our experts will be at a Zebra BI Booth during the PBI Userdays. Will you be visiting us on the 10^th and 11^th of March?

What are power apps?

To put it in Microsoft’s words: “Power Apps is a suite of apps, services, and connectors, as well as a data platform, that provides a rapid development environment to build custom apps for your business needs.”

Power Apps enables you (an accountant, engineer, chef, CEO) to build applications that solve your problems regardless of their size.

What is low-code?

Low-code is a way to build applications quickly in a “drag and drop” environment. Low-code enables anyone with a desire to build apps quickly and create something without having to understand coding languages like Java or C++.

On top of that, Low-code also enables you to connect to a variety of back-ends or third party services without having to manually connect to an API or other complex connection tools.

What can low-code offer me?

• Automation
• Re-usability
• Scalability
• Rapid development
• Cross-platform applications

What are the low-code apps within the Microsoft Stack?

Power Apps

The bread and butter of the low-code Power Platform. It enables you to create applications that display data and allow you to interact with your data directly. Power Apps enables you to build applications for both Mobile and web by using a drag and drop interface.

Power Automate

If you want to create automated processes running in the background, this is how you do it. If you want to send emails after new records are created, while at the same time notifying users, Power Automate does this with just a few clicks.

Power Virtual Agents

One of Microsoft’s newest offerings allows you to create chat bots using a low-code approach. Chat bots can be deployed internally on Teams or on your own website. They enable users or customers to find answers to problems without the need for a human-ran support centre.

Power BI

With Power BI, you get even more out of Business Intelligence. Create stunning visuals that provide deep insights into your data and financials. Use forecasting to better predict changes in upcoming time periods and adjust your business planning accordingly.

Power Platform use cases

Imagine you’re an accountant that has to modify data in excel based on the parameters that someone emails to you on a daily basis. Based on these emails, you want to have an overview of all of your data to be able to show to management during your monthly meetings and, on top of this, you want to be able to modify something within the data on a last minute basis if needed. The Power Platform and it’s low code tools can help you automate a large part of your workflow. Here is how:

• We’ll use Power Automate to scrape the values from the email and modify the excel sheet.
• Once the sheet is modified, we can create a dashboard to have a full overview using Power BI to present at your monthly meetings.
• And lastly, we use Power Apps to create a small app that you can modify your data from, if case last-minute modifications are needed.
• Lastly, we could create a Virtual Agent to enable colleagues or clients to request copies of documents from a certain case file using a specified password.

Benefits of Low-code

• Save time and money
• No need to hire a full stack developer
• Custom solutions for custom problems
• Easy to implement

Rockfeather & Low-code

Here at Rockfeather, we know that not everyone has the budget for their business ideas to become reality. That’s why we offer both trainings in low-code as well as building services. We empower everyone to become a citizen developer – this enables you to build things in your own time, with your own tools. If you get stuck or need help, you can be sure that Rockfeather will be there to lend a hand.

For the more advanced projects, we use all of the tools above and more to create fully fledged desktop and mobile applications that can be exported and deployed to your environment seamlessly.

Forecasting with Data Science can help your organization take the next step in data maturity. What would it mean to you as a financial person if your forecasts became much more accurate? Would your management team be helped by more insight into the impact of developments in these turbulent times? By using Artificial Intelligence (AI), you can create forecasts that can track an unprecedented number of parameters. Moreover, these forecasts can be redefined at any time, and you don’t need to purchase new tools for this.

In this webinar on Advanced Forecasting, we show you concrete examples on how AI can offer you new insights as a financial. We’re not only looking at the technology, but also the direct applicability.

What do you learn during the webinar?

• How Artificial Intelligence can make your forecast better
• How you as a financial person can benefit from using AI in your forecasts
• How to get started with free tooling

To whom is this interesting?

• Managers Reporting en (Group) Controllers
• Business & Financial Controllers
• IT-managers en Finance Managers

Interested?

Unfortunately, this webinar was held in Dutch and there are no English subtitles available. Are you still interested in the contents of this webinar? Fill out this form, and you can rewatch the webinar.