5 Tips for Creating and Designing Digital Boardrooms

Authors:
Nicole Aragon, Consulting Intern

Treeank Patnaik, Consulting Intern
JS Irick, Director of Data Science and Artificial Intelligence

When highly informed business decisions are needed, executives and key decision-makers are often faced with the complexity of having to refer to separate reporting solutions which often present isolated and static business data. 

To help organizations overcome this challenge, SAP has brought to market the SAP Digital Boardroom. A one-of-a-kind solution that harmonizes the view of an organization’s operations across all lines of business. The Digital Boardroom is an easy-to-consume, 3 touch screen touch setup that provides the means for more efficient and collaborative decision-making.

Image Source: SAP

Once an organization decides to embark on a Digital Boardroom initiative, it is essential that each boardroom is designed to be flexible, provide a collaborative reporting and analysis environment, is capable of handling digressions, and provides detail for a variety of job roles and goals. Here are 5 tips from TruQua on how to build and design a Digital Boardroom that will meet the needs of your organization.

Tip #1: Revisit Previous Meetings 
When debuting any new technology, it is critical to not demonstrate any gaps when compared to existing processes. Either through interviewing an attendee, or by reviewing the published minutes from the meeting, get an understanding of the content covered in previous meetings. Look for details in three key areas.

  • What information was presented, and at what level?
  • What were the follow up action items from an analysis perspective and can these action items be achieved quickly in your digital boardroom? 
  • What opportunities exist to bring in non-financial data sources? Were details from operations, HR, R&D, customer relations or production a major part of the meeting, and could they be integrated into the digital boardroom to provide a more holistic view of the organization?

Tip #2: Determine “Analysis Paths” 
Based on the presented information in the previous meeting, what were the follow up questions asked?  These questions can fit into one of three groups:

  • Drill down based questions such as, “How did each product line perform?”.
  • Filtering based questions such as “How are doing in South America?”
  • What-if analysis/simulation such as “What if we increase marketing spend?”.

Typically, you can theme pages in your Digital Boardroom to encompass these question areas. Depending on the data and dimensions available, you can create charts and graphics to view overall performance for products and sales channels or do an analysis strictly on operational reporting.  

By using the Linked Analysis function, drilling down in a chart will update all elements of the page to show a more granular and focused view of performance. Similarly, choosing the right filters can offer new perspectives and effectively handle potential questions in each of these areas. 

Shown below is a Digital Boardroom built to track a company’s financials across channels and products considering different expenses and revenue. As you can see, by drilling down on Product Type 3 in the Cost of Revenue Pie Chart, the rest of the page updates to display the same overall analysis for Product Type 3 and performance for the products within that product type.

Tip #3: Create a Filtering and Navigation Strategy

Based on the analysis paths determined, create a filtering strategy for your digital boardroom. Should filtering be done at the boardroom, page or chart level? If filtering is to be done at the boardroom or page level, use Linked Analysis to trigger the proper overall filtering when an individual chart is filtered. Similarly, make a strategy for navigation across pages.

In a lot of instances, adding page filters will be the best strategy for your digital board room. If you have a page in your digital boardroom already dedicated to analyzing channel and product performance, it wouldn’t be efficient to have a filter for channel or product at the boardroom level even if there are other parts of the boardroom that could be filtered by these dimensions. If we have a page dedicated to operational reporting, filtering by channel and products could bring valuable insights whereas in a page dedicated to product and channel performance, other things may bring better analysis such as filtering by region or customers. 

Adding filters on the Digital Boardroom level is usually most fitting when performing analyses on different and unrelated versions of data. An example of this could be when working with different customers or comparing performance across different years.  

Tip #4:  Champion Training 

Prepare training specific to your Digital Boardroom for the person who will be “driving” the meeting. The most effective training will leverage the conclusions from reviewing the previous meeting in order to train on the exact questions generated during the previous meeting. This not only ensures that the digital boardroom created will hit the mark, it also allows the presenter to build confidence using the tool. It may be helpful to have this person practice teaching the tool to less technical colleagues to pinpoint which areas are harder to understand and will need to be elaborated on more.

Allow the champion to become comfortable with the filtering and navigation strategy. They should be capable of understanding how to use a relatively data dense screen such as the one below and drive to the necessary data the attendees need.

Tip #5: Presentation and Review

After a successful meeting, it is valuable to review what worked well, and which aspects of the Digital Boardroom can be leveraged to enable self-service analytics across the organization. Charts, visualizations and scenario tools can be broken out into distinct stories or integrated into existing analysis processes. 

Conclusion

Digital Boardrooms are custom solutions to complex business problems used for reporting and analysis. There is no uniform template or guide for exactly how a Digital Boardroom should be built or what exactly it should look like but following these 5 tips can ensure your Digital Boardroom is useful and effective.

About our authors:

Nicole Aragon

Nicole Aragon

Consulting Intern

Nicole Aragon is a Senior at the University of Texas at Austin, where she studies Management Information Systems in the McCombs School of Business. She plans on continuing her experience working in data science after college.

Treeank Patnaik

Treeank Patnaik

Consulting Intern

Treeank Patnaik is a Consulting Intern at TruQua, working on improving business intelligence by using advanced analytics and machine learning software. He is currently pursuing a degree in Mechanical Engineering at the University of Texas at Austin, along with certificates in Applied Statistical Modeling and Foundations of Business Administration. 

JS Irick

JS Irick

Director of Data Science and Artificial Intelligence

JS Irick has the best job in the world; working with a talented team to solve the toughest business challenges.  JS is an internationally recognized speaker on the topics of Machine Learning, SAP Planning, SAP S/4HANA and Software development. As the Director of Data Science and Artificial Intelligence at TruQua, JS has built best practices for SAP implementations in the areas of SAP HANA, SAP S/4HANA reporting, and SAP S/4HANA customization.     

For more information on the SAP Digital Boardroom and how it can help streamline the way in which your organization views, analyzes, and presents its business data to key decision makers, contact TruQua using the form below. 

 

Engaging Experiential Data (x-data) in Financial Planning

Authors:
Nicole Aragon, Consulting Intern

Treeank Patnaik, Consulting Intern
JS Irick, Director of Data Science and Artificial Intelligence

Bill McDermott’s SAPPHIRE keynote focused on the Experience Economy – how businesses must enrich their financial and operational processes with customer feedback. To quote McDermott, “Experience is now the organizing principle of the global economy”. This blog will examine how experiential data or (X-Data), can be combined with operational data or (O-Data) and how SAP Analytics Cloud, in turn, supports the concurrent analysis of multiple data sources (“Data Blending”) to create more powerful analytic applications for the Experience Economy.

To show the power of blending X-Data and O-Data, we will review data from the 10 highest grossing movies of the summer. Our operational data set includes box office performance, ticket sales, and production cost data for each of the films.  (“Information courtesy of Box Office Mojo. Used with permission.” – see more great movie analysis at boxofficemojo.com). For our experiential data set, social media postings for each movie are analyzed based on data collected from Twitter (i.e.- keywords, hashtags, and the Twitter handles). Utilizing a Machine Learning technique called Natural Language Processing (NLP), we’re able to understand the emotion present in each tweet (this is also called sentiment analysis) and by combining the NLP analysis and location data, we can then begin to understand how each market is reacting to the various films.

To account for the informal language used on Twitter, the NLP library used in this analysis weighs capitalization, punctuation, and emojis. Including these features in the analysis of sentiment allows it to be as accurate as possible.

 

Data Blending

SAP Analytics Cloud (SAC) allows any number of local or remote data sources to be present in a given story, allowing analysts to avoid the data silos which are often present in legacy reporting tools.  SAC can join separate models together based on their corresponding dimensions in a process referred to as, Data Blending. This process makes it possible to draw conclusions from one model in relation to another and to identify patterns that can be used to establish a correlation between two sets of data. By linking the sentiment analysis model to the box office performance model, it is possible to analyze how the financial performance of a movie is influenced by the customer experience.

SAC makes it incredibly simple to couple models based on similar dimensions between them. For instance, we were able to link our Twitter and box office data models by their dimensions related to movie titles and dates.

By linking these two models and blending their data, visualizations can be created in SAC that displays information from both simultaneously. This allows us to discover connections on a larger scale, and focus on specific elements within each model. In the visualization below, the financial metrics of approximate ticket sales and studio revenue are contextualized and presented alongside a graphic related to the volume of posts made on Twitter and their overall sentiments.  This uniform presentation makes it possible to then observe our X-Data and O-Data for individual movies and examine the relationship between their financial and social media metrics. For instance, the first image below shows data for the movie Booksmart, while the second shows the same data, but for the movie Brightburn.

Visualization displaying box office and social media data for the movie Booksmart.

Visualization displaying box office and social media data for the movie Brightburn.

 

Reporting

Combining these two streams of data with the modeling capabilities of SAC allows for the creation of robust visualizations, which can be used to more effectively examine relationships within the data and provide context for key business problems. These visualizations provide an avenue for furthering the insights generated by the models created in SAC and offer a means of integrating predictive analytics technology with already existing data.

 

Certain functionalities allow for the creation of charts showing financial measures, such as unit sales, and measure them against Twitter sentimentality. This allows for the analysis of patterns that may predict the performance of a product. For instance, in the case of the movie Pokémon Detective Pikachu, a high volume of tweets that were predominantly positive were immediately followed by a spike in ticket sales.

By mapping the number of Tweets posted that associated with specific films across days in the month of June, we can see how overall Twitter activity related to each movie varies as days go by. As demonstrated, this visual can display this data for the entire month of June but can be effortlessly adjusted to do so by day in order to offer more detailed insights. The visual language capabilities of SAC allow for this relationship to be expressed in a distribution chart that not only utilizes engaging aesthetic elements but takes advantage of these elements to communicate information directly and effectively.

Modeling in SAC also allows for geo-enrichment, after which data can be presented in dynamic map visuals such as the one above. This map contains two layers of information related to our X-Data and O-Data. The size of each bubble indicates how many tweets related to our set of movies were posted in each country, while the shading of each country indicates the average positivity of these tweets in that region. The visualization separates the data in a way that makes it easy to measure activity by region and displays it in a coherent and interesting manner. In the same way that other charts can be filtered to highlight relevant data, these maps can be altered to focus on specific geographical areas or movies.

Map filtered to show global social media activity related to only the Pokémon Detective Pikachu movie.

 

Conclusion

Within this blog, we have examined some of the powerful ways in which experiential data can be used to better understand operational performance. Using SAP Analytics Cloud, we can visualize these insights and easily follow trends over time as well as compare them to different metrics, driving “Experiential KPIs”. Collaborative Enterprise Planning with SAC replaces data and process silos with seamless collaboration to present and analyze data from different angles. The visualizations presented to allow for greater discovery of patterns and causation that may have been previously hidden in large spreadsheets, divided among separate department and buried in vast amounts of data. With the facilitated discovery, drawing insight becomes less of a challenge and easier to transform into planning and solutions. The experiential economy is won and lost on data insights, and the analysis shown today are just some of the ways to achieve those insights.

This blog was created by TruQua’s Summer 2019 Interns, Nicole Aragon, and Treaank Patnaik. Stay tuned for our upcoming “Movie Watch” blogs, where we will continue to analyze social media trends for this summer’s hottest films.

About our authors:

Nicole Aragon

Nicole Aragon

Consulting Intern

Nicole Aragon is a Senior at the University of Texas at Austin, where she studies Management Information Systems in the McCombs School of Business. She plans on continuing her experience working in data science after college.

Treeank Patnaik

Treeank Patnaik

Consulting Intern

Treeank Patnaik is a Consulting Intern at TruQua, working on improving business intelligence by using advanced analytics and machine learning software. He is currently pursuing a degree in Mechanical Engineering at the University of Texas at Austin, along with certificates in Applied Statistical Modeling and Foundations of Business Administration. 

JS Irick

JS Irick

Director of Data Science and Artificial Intelligence

JS Irick has the best job in the world; working with a talented team to solve the toughest business challenges.  JS is an internationally recognized speaker on the topics of Machine Learning, SAP Planning, SAP S/4HANA and Software development. As the Director of Data Science and Artificial Intelligence at TruQua, JS has built best practices for SAP implementations in the areas of SAP HANA, SAP S/4HANA reporting, and SAP S/4HANA customization.     

Editor’s Note

TruQua is currently hosting a closed beta for the Social Media analytics tools described in this article.  If you are interested in joining the beta program, please contact js.irick@truqua.com

 

Movie Sources

Aladdin, Directed by Guy Ritchie. https://www.imdb.com/title/tt6139732/

Brightburn, directed by David Yarovesky. https://www.imdb.com/title/tt7752126/

Booksmart, Directed by Olivia Wilde. https://www.imdb.com/title/tt1489887

Pokémon Detective Pikachu, Directed by Rob Letterman. https://www.imdb.com/title/tt5884052

For more information on TruQua’s services and offerings, visit us online at www.truqua.com. To be notified of future blog posts and be included on our email list, please complete the form below.

 

Webinar: Adding SAP Analytics Cloud to On-Premise- When does it make sense and how to maximize its value

Webinar: Adding SAP Analytics Cloud to On-Premise- When does it make sense and how to maximize its value

Tuesday, May 21, 2019
10:00 am PDT/1:00 pm EDT

Overview

Join TruQua’s Director of Software Development, JS Irick, for an up-close look at the latest benefits and capabilities of SAP Analytics Cloud for extending on-premise planning, reporting, and analysis processes to the cloud.

During this 60-minute webinar attendees will:

  • Understand the 6 scenarios in which extending your current planning process with SAP Analytics Cloud might be right for you
  • Get actionable advice on how to leverage SAP Analytics Cloud to maximize planning improvements and value to the business (and how to measure its impact!)
  • Walk through a real-world example showing how to avoid common challenges when onboarding a new Business Unit via SAP Analytics Cloud
  • Learn how to boost your predictive capabilities and accuracy through the power of SAP Analytics Cloud’s driver-based planning and what-if analysis

 

Register Here: https://info.sapdigital.com/2019-05-21-add-sac-on-premise.html

New Perspectives Blog Series: Maximizing Enterprise Collaboration with SAP

Welcome to TruQua’s New Perspectives blog series.  This blog series provides a platform for our consultants to share their perspectives, insights, and experiences on emerging technologies such as SAP HANA and SAP Analytics Cloud (SAC). This week we are going to be speaking with Andrew Xue.  Andrew is a Consultant at TruQua, specializing in cutting-edge cloud analytic and planning technologies.  Today Andrew and I are going to discuss some of the key trends we are seeing at customers as it relates to Collaborative Enterprise Planning and the latest capabilities in SAP Analytics Cloud.

Perspective 1 – Creating organizational alignment with Collaborative Enterprise Planning

JS Irick:  I find Collaborative Enterprise Planning to be a really effective concept since customers intuitively understand what it means and are excited by the possibilities. In addition to SAP’s talk tracks of organizational alignment and breaking down silos, SAP Analytics Cloud allows stronger collaboration within teams, thanks to technologies like shared Private Versions and in-app chat.  Andrew, can you talk a little bit about the collaboration you are seeing on implementations both from a macro (organizational) and micro (team) perspective?

Andrew Xue: The two you mentioned, Private Versions and in-app chat, are just scratching the surface of how collaborative SAP has made SAP Analytics Cloud. With Private Versions, users can not only draft what changes they want to see without impacting real data but also utilize Private Versions to create instantaneous backups of real data as an unofficial checkpoint. With a Private Version, users may share them with other users or teams so that multiple people can collaborate on that information. Definitely beats sending out ten Excel spreadsheets at once!

Picture: An instance of sharing a private Budget version with Read and Write access to other users or teams

I also find in-app chat to be one of the most interesting features of the tool because it is the backbone of connecting users to each other, and then to their data. First, are stories: at a client, we created many stories that were designed as templates, such that users could not change those stories, but could save a copy as a starting point, or use it as inspiration. Once users were more familiar with story-building (graphs, buttons, tables, more), they could create as many as they wanted in their own Private Folders, such as one-off reports for checking on some data, drafting new reports, or testing a feature for potential new value. This is where it gets interesting because users could then share their private stories with their own reports built out to their superiors or teammates. With everyone the story is shared with, read-only or edit permissions can be set as well! Instead of endless back-and-forth emails, people can simply change a chart, and make a note of it in the team group chat! So far, customers have used this tool to both ask me how a certain feature works (and how it helps them), and then proceed to ask me when I would like to grab lunch.

Picture: From the chat menu, sharing is as easy as linking the story for anyone in the conversation to access

JS Irick: You’ve touched on a really interesting point when you mentioned both Private Versions and Private Folders. Let’s be real – Finance organizations are stocked with really smart people; if someone doesn’t have the tools needed to do their jobs, they’ll build it themselves. For a long time, organizations have tried to prevent “Shadow IT” by locking down the ability to save different versions or create untracked reports. Now SAP Analytics Cloud actually encourages good behavior by providing these capabilities in a centralized, organized fashion. It’s a great sign for the future that new features are designed for how customers use the tool.

 Andrew Xue: Of course, the people I have worked with are all extremely smart and very qualified, holding CPAs, MBAs, and more. Once taught how to use SAP Analytics Cloud, they’ve built tools beyond my imagination. Avoiding a “Shadow IT” or simply not having to go through IT for very small changes that don’t impact real data leads to eliminating so many inefficiencies and frees up so much valuable time for people to do more meaningful activities. But this isn’t a tool that gives free rein to all that use it, because SAC also gives IT the ability to enact specific governance via SAC Security in roles & teams, data locking, and more.

People are accountable for their private folders of what items they create, how they organize it, and who they share their creations too. This way, the public folders are saved for all reports, input templates, and more, instead of several drafts cluttering up.

 

Perspective 2 – Evolving business processes utilizing Native Analytics within Planning

JS Irick: The development path for SAP Analytics Cloud says a lot about the tool’s strength and SAP’s overall analytics strategy.  SAC started as Cloud for Planning (C4P), but the analytic components were so strong that the product became the go-to analytics tool for SAP as a whole. Planning applications have always had a begrudging tie to Microsoft Excel due to its calculation and display capabilities.  Now we’re planning within an Analytics and Visualization tool, which is a monumental shift. How are you seeing customers take advantage of these powerful capabilities, and how are they evolving business processes?

Andrew Xue: The largest shift as you said here is from Excel to SAC. From what I’ve seen in a certain costing process that my current client conducts, the task used to take 4, 12-hour days of everyone’s work. Now with the centralized collaborative tool in SAC we’ve created, the task takes a mere few hours, leaving so much more time for the users to conduct valuable analysis instead of checking if their formulas in Excel dragged down correctly, something that they simply had almost no time to do previously! Also, what the team finds to be so incredibly useful is how quickly they can run different scenarios and what-ifs, such as, how does changing a certain rate assumption affect total costs for a specific sector? Before while using Excel, if they even had time to, it would take a day or more just to edit multiple spreadsheets, confirm, run, and compare the changes. Now, that scenario can be done in about half an hour. In just one day they could be running tens of scenarios!

 

Along with that, the client team has access to so much more historical data at the tips of their fingers. They can compare past assumptions, run year-over-year analysis on a whim, and so much more. It previously took around 10 minutes just to get all the necessary Excel files open to building a chart, let alone compiling the data. As the team became more familiar with SAC, they also became more creative, making more than just tables and input templates. I have seen them make waterfalls, dynamic pie charts, and even utilized some of the instantaneous Smart Insights on their data! It has been wonderful seeing them realize how powerful this tool’s capabilities are.

JS Irick: The analysis tools and visualizations you’re creating for your customers are truly impressive. In particular, I loved seeing your profitability reports, that are blending the advanced analytic views, what-if analysis, and drill down to the customer/SKU level details. I’m curious though, how are you helping your customers get a handle on this incredibly versatile tool? Even at the most basic level, we’re going from a spreadsheet (which is easy to understand) to a story, which we could think of as multiple dashboards in the previous generation. So, we need to cover dashboard arrangement/design, dozens of new data visualizations, new data features such as Value Driver Trees and Data Actions. This isn’t just moving someone from PowerPivot to Analysis for Office, this is a monumental shift.  Can you share a bit of your training strategy with our readers?

Andrew Xue: By all means in any implementation and move to a new tool, there is going to be a lot to learn. From what I have seen, the most successful strategy is changing the customer’s high-level process as little as possible; no change is preferred. How we help them pick up this new tool is by reviewing their process first at a high level in the new tool, and then at a more detailed level in segments, starting from where the data is, all the way through creating their first reports.

What we have found especially useful is parallel pathing. Earlier when I mentioned a time reduction from several days to a few hours, this was not negatively impacted parallel pathing. In fact, running a process in conjunction only takes a little 3-minute detour every now and then to show them what their work looks like in the new tool now. Then, once they are fully familiar with their responsibilities in the new tool, we help them run another process, only this time we help them with our hands off.

Keys to a Successful Training Strategy with SAP Analytics Cloud

Perspective 3 – Starting a successful SAP Analytics Cloud implementation

JS Irick: TruQua recently presented at SAPInsider Financials 2019 on leveraging the SAC/BPC hybrid scenario.  One of the key points we focused on was the fact that performing a “Lift and Shift” (technical migration of your planning solution without making any modifications) ignores many of the features that make SAC such a dynamic environment. Since you’ve successfully worked with customers from both a Greenfield (first planning implementation) and Brownfield (improving an existing implementation), I would love to get your perspective on how customers can set themselves up for success in SAP Analytics Cloud.

Andrew Xue: I believe SAP Analytics Cloud is the perfect segment for customers seeking to make a change but aren’t too comfortable with a massive alteration. In many scenarios across every industry now, a significant portion (in some cases, all!) of business processes are on Excel. SAC gives users that platform to transition to while still retaining some Excel familiarity, so we’re not throwing them in the deep end at all. We can’t force customers to drop all they know from an old business process just to use a new tool that people tell them is better. And on top of that, to balance their responsibilities while completing weeks of training, it’s illogical. For customers with existing implementations, SAC is quite a different tool that serves to only improve.

In both the Greenfield and Brownfield scenarios, customers on SAC were able to continue their current business processes, while also picking up new features one at a time by simply being in the SAC. This wonderful tool helps the users themselves figure out what was inefficient about their old methods while also giving them new tools to take everything they already do to the next level. Success for the customer here is being willing to welcome this new tool and understand that the first steps are not big at all. Then, by using this tool more in their day-to-day work, customers develop better and faster approaches for gaining more valuable insight into their processes. With more experience in the tool, finding more features to augment their work, and easily adding valuable insights into their reports, customers are enabled to maximize the SAC’s value. The beauty here with SAC is that people who use the tool are able to transition away from traditional methods at whatever pace they feel comfortable.

JS Irick:  So, having been through multiple successful implementations, what are the areas where you are seeing maximum value?  Have there been any surprise wins that may inform the planning roadmap for organizations looking to improve their processes.

Andrew Xue: What I have been mentioning earlier leads up to this: the highest impact and biggest value-adds are shifts from heavily reliant Excel processes. This can be generalized to processes where the data is not centralized, and where changes are tracked through copies named v2, and so on. I agree with that combining different data streams is a surprise success. As I had mentioned earlier, one difficulty many clients face is comparing past data, as the very act of compiling could take hours. As you said, multi-source reports are a nice corollary of the implementation, which is exactly what we have seen. As the client got comfortable transitioning to the new tool and went live, they realized they could pull data from so many more sources, adding to the vast amount of data they already had at their fingertips. Whether this is combining multiple Excel documents, pulling from HANA or other sources, SAC centralizes it all for you to for undiscovered valuable new opportunities with the tool.

I wanted to add to that by saying, trusting the system to run the calculations for you is another surprise success. Users utilize the features in SAC such as Data Actions, Allocations, and more, running those calculations in the tool allow users to fully utilize the cloud resources and functionality. Using these calculation features are where a large portion of the time-saving comes from letting the system work.

JS Irick: Thanks so much for taking the time to speak with me today Andrew. With such an exciting, rapidly developing tool, I really enjoyed taking a step back and looking at the ways customers are generating real business value in SAC.

About our contributors:

JS Irick

JS Irick

Director of Data Science and Artificial Intelligence

JS Irick has the best job in the world; working with a talented team to solve the toughest business challenges.  JS is an internationally recognized speaker on the topics of Machine Learning, SAP Planning, SAP S/4HANA and Software development. As the Director of Data Science and Artificial Intelligence at TruQua, JS has built best practices for SAP implementations in the areas of SAP HANA, SAP S/4HANA reporting, and SAP S/4HANA customization.

Andrew Xue

Andrew Xue

SAP Consultant and Software Developer

Andrew Xue is an SAP Consultant and Software Developer at TruQua who specializes in cutting-edge cloud analytic and services technologies. Most recently, Andrew has been building and implementing cloud applications for financial planning, global budgeting, and executive reporting. He has expertise in an array of SAP enterprise collaborative planning technologies including SAP Analytics Cloud, SAP Business Planning and Consolidation and the SAP Digital Boardroom.

Thank you for reading this installment in our New Perspectives blog series. Stay tuned for our next post where we will be talking to Daniel Settanni on the role of Strategic AI in Finance.

For more information on TruQua’s services and offerings, visit us online at www.truqua.com. To be notified of future blog posts and be included on our email list, please complete the form below.  

 

 

Mapping Planned Data from SAP Analytics Cloud to SAP S/4HANA: A Short Tutorial

SAP Analytics Cloud (SAC) combines the flexibility of the cloud with the ability to run new deep-level analytics allowing greater insights into critical business processes. A powerful and agile tool for planning, analytics, and predictive insights, SAP Analytics Cloud enables full-fledged collaboration from anywhere in the world and provides real-time connectivity to on-premise systems such as SAP S/4HANA to removing traditional barriers and accelerating critical business processes.

This blog will walk through a Revenue and Cost Planning example and illustrate how to export and map that planning data from SAC into S/4HANA.

 

Planning in SAP Analytics Cloud: A Revenue and Cost Planning Example

With the release of SAP Analytics Cloud, enterprise planning has never been easier or more efficient.  With a wide array of planning capabilities and features coupled with the ability to separate hypothetical business scenarios from real data, companies are able to drive better business outcomes with collaborative enterprise planning while also gaining full alignment across all areas of the business.

In this example, you’ll see there are two planning-enabled tables located within a story in SAP Analytics Cloud. The charts on the right focus on gross margin by product and total revenue by product, which will update automatically as entries in the tables are adjusted. These entries can be made at a summarized node level by cost center groups, material hierarchies, or planned at the base level, by SKU or customer.

Source: TruQua Internal Systems

 

When changing the data, a draft version is created for the end-user to run their what-if planning scenarios on. This means that data changes made by the user will not be implemented until the user confirms and publishes the data. When publishing, users are provided the option to either confirm all their changes made into the model or revert to the original data. SAP Analytics Cloud is also able to highlight any data changes made to keep track of any modifications.

 

Source: TruQua Internal Systems

 
Exporting and Mapping Planned Data from SAP Analytics Cloud into SAP S/4HANA

Now that our we’ve run our what-if scenarios and published the data, we want to export that data into SAP S/4HANA. Thanks to the tight integration between SAC and SAP S/4HANA, we can write the new plan data directly to the S/4HANA planning table (ACDOCP).

 

Source: TruQua Internal Systems

 

Utilizing the easy-to-use mapping services from the SAP Analytics Cloud model, we can select what fields to map SAP Analytics Cloud dimensions to then assign those dimensions to S/4HANA fields. This is a one-time process to define or update the mappings and can be scheduled per implementation requirements.

Source: TruQua Internal Systems

 

Another point worth noting, is that within the SAP Analytics Cloud Dimension to S/4HANA field mapping process, users have the ability to create filters on their planned data as well. Meaning they be selective in the data that they upload, such as only uploading a certain period / year, or only a single cost center node. In the Versions and Categories, users may map one Analytics Cloud version to multiple S/4HANA Categories. In this example we see PLN version mapped on SAP Analytics Cloud to PLN, PLN01, and PLN02 in SAP S/4HANA.

Source: TruQua Internal Systems

 

Once the data has been successfully exported to S/4HANA, a quick ACDOCP query verifies our results.

Source: TruQua Internal Systems

 

SAP provides a wide variety of out-of-the-box sample content to inspire users on the endless creative possibilities found in SAP Analytics Cloud. With SAC’s connection possibilities (such as S/4HANA, BPC, BW, and more), systems integration previously limited to heavy IT involvement is within reach. For organizations looking to detach from a decentralized planning process, and reach collaborative enterprise planning, SAP Analytics Cloud is a perfect fit.

[This post is based on SAP Analytics Cloud version 2019.5.1 and S/4HANA 1809 SP01.]

Stay tuned for our next blog where we show you how this data can be consumed in Group Reporting and used for consolidation purposes. For more information on SAP Analytics Cloud, S/4HANA, Group Reporting, and how they can take your business intelligence to the next level, please fill out the form below.

 

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