Power BI for the Enterprise

All data projects come down to consumption. How do you get your historical, predictive, and prescriptive analytic solutions in the hands of your users? I have worked on a wide variety of projects where we have infused intelligence into applications, automated systems, and reporting. Many organizations require an internal analytics strategy that is centered around reporting, therefore, I would like the focus of this blog to address the number reason why enterprise reporting roll outs fail.

Report creators fail to provide a consumption layer that fits the desired use of the report. As an Azure consultant we will focus on Power BI rollouts and how it is important to understand the four types of Power BI users: The Developer, The Power User, The Data Query, and The Quick Answer. Please note that these types of users are not exclusive, as a single individual can fall into any number of these categories.

The Power BI Developer

This individual creates, manages, and provides knowledge transfer on the report. The developer will love drilling into and cross filtering the report to find new information because they know how to push Power BI to its limits and will include as much functionality in the report as possible by default. However, this individual is not necessarily the intended business owner or end user of the report.

A Power BI developer knows the product extremely well, and their main responsibility is to create and manage reports for business users to support the organization. These employees are not necessarily easy to find, as the analytical skillset is uncommon in the marketplace, making their time valuable. Therefore, the report developer must understand the type of end user they are delivering the report to. There is nothing more frustrating than a developer creating a report that is too complex for the user to use, and they simply discard the report after a few uses. End users discarding reports due to complexity is the biggest blocker when it comes to implementing an organizational analytics solution, and it can be avoided by the Power BI Developers.

The Power User

A Power User is someone who uses a report to make strategic decisions for the organization. They understand the product well enough to create a few simple reports if the data model is provided, and is able to understand the cross filter and drill down capabilities so that they can use the tool to answer new questions and discover insights.

From experience, these users are desired in an organization but are rarely found. It is difficult to find an individual how knows Power BI well enough to use all of its capabilities, but is not Power BI Developer. Therefore, most of the people who fall into this category are the ones who are actually creating the report as well.

As a developer, if you have a Power User consuming the report then include as many dynamic visualizations and capabilities as possible. The Power User loves finding insights and will spend great lengths of time understanding the data you provide.

The Data Query

The most common user of Power BI is the Excel user who says they want to learn Power BI but doesn’t put the effort in to understanding it. Therefore, they use Power BI as a query interface to export data into excel for them to do their own analysis. This is extremely common and is a great way to utilize Power BI. Organizations typically shake their head at an individual who uses Power BI as a data acquisition tool but I believe that getting data into the hands of users is the number one goal of an analytics strategy and this is a great way to provide specific data to users.

As a developer, if you have an individual simply querying for data then you should focus on providing simple data visualizations and lots of data tables. The visualizations will give them a quick look at trends but the tables will provide them all the information they need to complete their analysis.

The Quick Answer

Another common use of Power BI is to get the quick and high-level answers about a dataset. This individuals want to spend as little time as possible to get the information they want so that they can make intelligent decisions.

As a developer, you will need to know the exact questions this individual wants answered and create simple visuals that answer those questions. The visuals can be dynamic like bar charts and maps, but typically summary numbers are sufficient. These reports are typically provided in a dashboard using the Power BI Service.

Conclusion

Understanding your business users capabilities and needs for data consumption determines how successful your analytics deployment is. All the users described above are present in every organization and are crucial to the day to day business. Creating consumable data interfaces rests on the developer, so understand what people need and good luck!

3 Keys for Your Organization to Get the Most from AI

Organizations are constantly weighing the cost and benefit of investing in Artificial Intelligence (AI) solutions. Introducing advanced predictive analytics to a company can push them to the bleeding edge of innovation and past their competitors, however, the hurdle is often difficult to get over. But why?

First, understand how we define AI

The term AI can mean several different things; however, the most commonly used definition refers to the idea of intelligent machines, which is in slight contrast to the aspirational machine with human-level intelligence. There are endless ways to implement Artificial Intelligence, but the primary ways are via machine learning and deep learning.

Machine learning uses labeled historical data to train a model to understand patterns and make accurate predictions on new and unlabeled data points.

Deep learning is a subcategory of machine learning revolving around neural networks. While neural networks have been around for decades, they have truly exploded in research and use in the last five to ten years. Deep learning is used to solve problems that require a human such as image recognition, text/speech analytics, and decision making (i.e. game playing).

For the sake of this article, we will use AI as a synonym for machine learning and deep learning, even though AI in general may refer to software and hardware having human-level intelligence which is not achieved using current methodologies.

Let’s focus on 3 key areas to get the most from AI

At 10th Magnitude, our data intelligence community focuses on bringing analytics to solve our customers’ problems through data science, reporting, and big data pipeline projects.

Outside of developing solutions we encourage organizations to focus on the following cultural- and process-oriented areas to truly get the most out of their AI solutions.

Know Your Business Use Case(s) and Collaborate 

The majority of AI applications are powered by machine learning, which is used to solve a very specific problem using data.The first thing that I do with a customer who is new to machine learning is to understand and identify all of their business problems. These problems often turn into new use cases for machine learning or deep learning.

Since the use cases are derived from the business itself, the key to creating a successful solution is collaboration between the data science team and business stakeholders. Additionally, stakeholders are likely the individuals who will need to approve the completion or evaluate the success of the developed application

Therefore, understanding what is needed to solve the problem and then relating the problem back to the data is crucial. Keeping the stakeholder aware of the development cycle allows them to understand the challenges data scientists encounter when creating new predictive analytics workflows.

Additionally, this enables the organization to develop a data-driven culture. Involving business users in the development room gives non-technical folks insight into what is possible, allowing them to spot other areas where AI can be of use.

10^th Magnitude believes in the idea of data-driven design, where we use data to solve problems, power applications, and change the way an organization thinks about their business.

Don’t Stop After Development

Developing a machine learning solution is difficult. It is an iterative cycle where individuals go back and forth from the business to understand the problem, gather data, and train models. Developing these solutions takes time, however, once development is done you are only partially completed with the project.More often than not we see customers give up on a solution after the development portion because the model did not perform as well as hoped or the cost to put it all in production is simply too high. It takes a lot of work to move a solution from a development environment to a production one.For example, we recently worked with an organization to build and develop a model to detect anomalies for their different pieces of equipment. It took 2-3 weeks to develop the solution and an additional two weeks to set it up in production; we had to move the code to a production workload, build and release pipelines for two environments, model consumption, model monitoring, and more.

As data scientists, we often forget the difficulty and the amount of time it takes to move a solution to a production so that the organization is able to see the true benefit.

It is important to keep in mind that empowering an application or workflow with machine learning is about more than just the application. It also gives people the ability to see what is possible with the data.

Automation is Your Friend

Usually, data scientists are not familiar with automated build and release pipelines, but it is a skill that is quickly becoming a requisite in order to properly participate in the predictive analytics space. DevOps is the process and culture of delivering value to customers in a sustainable manner. As predictive insights grow organically within an organization, individuals need to be available to develop new solutions. and not maintaining existing ones. Automation is extremely useful in data science projects, specifically for: deploying changes to production with automated tests, retraining of existing model with new data, and monitoring the performance of the model.No data scientists should bring “right-click and deploy” predictive solutions to production; unfortunately, that happens more often than one would hope. Using Visual Studio Team Services (VSTS), we enable our customers to version control their code for team collaboration and set them up with automated build and release pipelines to train, test, and deploy their code.

As more data is collected, the solution will need to be retrained on a cadence to keep the model up to date so that it continues to make good predictions. While this task may seem like a trivial manual task, the time it takes a data scientist to update a model could be used to create new solutions or enhance old ones.

Often clients will only focus on surfacing results to their end users via reports, applications, or workflows; they forget that they need to build an interface to their solution for themselves.

Data scientists are responsible for maintaining the quality of a solution over time, therefore, the metadata gathered from testing the solution (success criteria, training time etc.) should be stored and visualized to understand the current and historical performance of a solution.

Conclusion

Developing machine learning and deep learning applications is far from easy. However, clients often struggle with the amount of effort it takes to create custom solutions, or they get so bogged down in technical details that they forget the why their business started on the path to AI in the first place.

So, what are the keys to successfully incorporate AI into your organization? To start, collaboration between the data science team and business stakeholders, understanding the data science process, and deploying solutions using DevOps. This process makes predictive analytics possible for data science teams of all sizes even as it changes the mindset of the organization as a whole.

If you’re ready to bring AI into your day-to-day, 10^thMagnitude has the solutions to incorporate it seamlessly and painlessly, ensuring that you get the benefits without missing a beat.