Category Archives: SAP

SAP BW/4HANA – The Value of Agility

Thank you Neil McGovern, SAP Senior Director of Marketing for this article.

On August 31st, 2016 we released SAP BW/4HANA. A great deal of ink and pixels has been spilled outlining its capabilities, but I’d like to look at one of the key reasons we built this new product.
As we stated in our earlier post the Forrester research we sponsored showed a correlation between business agility and revenue growth.
In SAP BW/4HANA the option to deploy on SAP HEC and Amazon AWS (plus others to follow) in addition to on-premise, has given customers the ability to start a new BW/4HANA instance in under an hour.
BW/4HANA users also will have the advantage of enhanced modeling coupled with a dramatically simplified set of data warehousing objects and simplified governance that will enable agile data warehousing development, delivering better real-time business insights faster, and at less cost, than before. Business applications can either leverage “pre-built” data warehousing or “SQL-based” development environments that delivers faster go-to-market and lets applications work with other SQL-based solutions.
Agility is key for BW/4HANA. We have improved agility in three ways:
SIMPLICITY
• The number of data objects is reduced –eliminates data redundancies, increases consistency, and results in a smaller footprint
MODERN INTERFACE
• Administration efforts to maintain data objects and error-prone data flows are also reduced
• Customers can build their own HANA models on top of the BW/4 HANA models
• User interface will improve for administrators, as next generation HANA Studio and Browser front ends will be used instead of SAP GUI front ends
OPENNESS
• Customers see great benefit in the possibility to expose BW/4 HANA models as native HANA views
• Customers will be able to use HANA in “BW” mode and in native “SQL” mode or a combination of the two
One of our first BW/4HANA customers is Fairfax Media. Fairfax are an experienced BW customer who had business challenges that fit BW/4HANA new capabilities. With BW/4HANA the project took half the time anticipated, three months instead of six. The flexibility and simplicity of the new object set and development environment, plus the ability to develop in the Cloud were key to this success. The resulting system was 10 times faster for end users and allowed cost savings to be identified in their expenses.

BW/4HANA, HANA Cloud Integration, and S/4HANA Finance are areas where Amick Brown can help your company succeed. AmickBrown.com

Learn more about BW/4HANA at sap.com/bw4hana

 

5 Ways to Drive Value with BI Proof of Concepts

by Kaan Turnali, Global Senior Director, Enterprise Analytics

Designers in a meeting --- Image by © Laura Doss/CorbisProof of concepts (POC) specifically designed for business intelligence (BI) projects can be invaluable because they can help to mitigate or eliminate the risks associated with requirements whether we’re working with a new BI technology, asset, or data source.

POCs (sometimes referred to as proof of principle) may be presented with slightly varying interpretations in different areas of business and technology. However, a BI POC attempts to validate a proposed solution that may cover one or more layers of the BI spectrum through a demonstration with a small number of users.

There are many reasons why a BI POC may be needed, and they may come in different shapes and sizes. Some focus on the end user; others may deal with data or the ETL process. BI POCs can be small, quick, and even incomplete. Or they can be involved, measured, and lengthy. Some are initiated ad-hoc and executed informally while others may require a process as strict as a full-scale project and the same level of funding as a formal engagement.

Here are five ways to drive value with your BI POCs.

1. Focus More on the Value and Less on the Mechanics

You can’t lose the sight of the big picture—it doesn’t matter how simple the BI requirement may appear or how informal the process may be that you’re asked to follow. Often BI teams concentrate on the technical details (a necessary step), but you need to go beyond just the mechanics and think about the value. Sometimes, a technical solution alone may not be adequate because technology is only half of the solution. And BI is no different.

2. Identify All of the BI Layers in Question

In a typical BI project, there are usually several layers involved: data, ETL, reports, access, and so on. Depending on the size and/or scope of your BI project, identifying the correct BI layers that need validation becomes critical. For example, you may be looking at a report design, but you can’t simply ignore the underlying data source or required data transformation rules.

3. Cheat on Sample Data, but Not on the Logic

Time is an extremely scarce resource in business, and POCs are often executed at a higher velocity. As you manage the process, it’s completely acceptable to cut corners such as hard coding a value in a report instead of fully defining the formula or building an integrated process to calculate it. But if you cheat, you should always cheat on time and not on concept.

4. Define and Manage the Scope

No matter how informal your BI POC may be, you need to define and maintain a POC scope. Open-ended or prolonged efforts result in waste. And BI POCs are not immune to this virus. It may not require intricate project- or change-management processes, but you still need to have a plan and execute around that plan.

5. The Right Talent Matters

Identifying the right talent with the right background is critical to your BI POCs success. It goes without saying that subject matter expertise around BI as well as areas related to business content and processes is a prerequisite. However, equally important are the soft skills, starting with critical thinking.

Bottom Line

If our goal is to enable faster, better-informed decisions, technical know-how alone won’t guarantee successful outcomes, because a POC is only as good as its assumptions and the BI team that’s executing it.

It all starts and ends with leadership that can pave the way for executing a BI vision where technology becomes a conduit to delivering business growth and profitability through the talent and passion of our teams.

What other ways do you see that can drive value with BI POCs?

AmickBrown.com

 

What if Beethoven and Mozart Invented Their Own Notation System

sheet_music_violinTo appreciate how semantic notation can impact your business, take a step back for a moment and imagine if every composer from Mozart to Beethoven used a different notation system. How would conductors and musicians interpret the music in a moment without standardized notation? What if engineers didn’t have a standardized notation system? Most likely they wouldn’t be able to communicate vast amounts of information clearly and quickly.

Yet in business, there is an overabundance of ways to layout out corporate reports and dashboards. And even within a single company, you will find forecast data or averages defined and displayed differently. But with pattern recognition, you can immediately understand the context of that information. This is the essence of a standard notation system, which brings clearer, data-driven insights and faster visualization turnaround.

Communicate Vast Amounts of Data-Driven Insights with Clearer, More Aligned Messages

Executives can  digest and act on visual data faster when it is always laid out the same: forecast, averages, historical and all metrics always looks the same and are in the same layout. People learn quickly to recognize patterns, and this is helpful to interpreting volumes of data. Critical to good business decision-making is the ability to portray very dense amounts of information, while maintaining clarity. This is vital when extrapolating multiple metrics for data to understand how it relates to the business.

A visualization that shows a percentage breakdown of revenue into products is, in itself, not very useful. To act and make better decisions, you need to understand how revenue has changed over time and to compare it to other product lines, the budget, profit margin, and market share. Also, executives spend less time trying to align the data into one version of the truth when all metrics are calculated and portrayed using the same standards across all business units. Having a standard notation system in your business can help you foster data-driven culture and alignment for better decision making. 

Faster and Improved Visualization of Analysis and Insights

Although content creators spend less time inventing their own system and layout, they still follow guidelines. In speaking with people who have adopted international business communications standards (IBCS), they found, for example, that the average time to create dashboard dropped three-fold. Through standard notation systems, they shortened implementation times and improved the outcome of their analytics investments.

Don’t Start from Scratch – These Are Best Practices

One of the best-developed, semantic notation systems – which was only chosen by the SAP Executive Board back in 2011 – is based on an open source project called International Business Communications Standards. Anyone can join the association and benefit from years of thought leadership and best practices developed over decades. Plus, the community is included in the evolution of these standards.

Register for the Standards Course

OpenSAP allows you to learn anywhere, anytime and on any device with free courses open to the public.

This blog orignially appeared on the  D!gitalist Magazine by SAP and SAP Business Objects Analytics blog has been republished with permission.

 

Dresner’s Advanced and Predictive Analytics Study Ranks SAP #1 for Second Time in a Row

By  Chandran Saravana,  Senior Director Predictive Analytics Product Marketing

For the second year in a row SAP has received the number one ranking in the Wisdom of Crowds 2016 Advanced and Predictive Analytics Market Study by Dresner Advisory Services. The Dresner study reached over 3000 organizations and vendors’ customer communities and 20+ industry verticals with an organization size ranging from 100 to 10,000+.

Study findings include:

  • Organizations view advanced/predictive analytics as building on existing business intelligence efforts.
  • Over 90% agree about the importance and value of advanced and predictive analytics.
  • Statisticians/data scientists, business intelligence experts, and business analysts are the greatest adopters of advanced and predictive analytics.
  • Regression models, clustering, textbook statistical functions, and geospatial analysis are the most important analytic user features/functions.
  • Usability features addressing sophisticated advanced/predictive analytic users are almost uniformly important today and over time, led by easy iteration, advanced analytic support, and model iteration.
  • In-memory analytics and in-database analytics are the most important scalability requirements to respondents, followed distantly by Hadoop and MPP architecture.

I find it interesting that the Dresner study finds “Hybrid roles are also evident” and confirms SAP’s customer organization usage of predictive analytics. The research study looked at core advanced and predictive features, data preparation, usability, scalability, and integration as key criteria to rank the vendors.  Though usability criteria looked at many things, I would like to highlight one key one—“Support for easy iteration”—that ranked as most important.

In the scalability criteria, “In-memory analytics” ranked as most important one followed by “In-database analytics” and “In-Hadoop analytics (on file system).”

Read the Complete Dresner Report

You can find lots more in the 92-page Dresner Wisdom of the Crowds report. I invite you to take a look.

AmickBrown.com

I Think We Need a GRC Tool. Where Do We Start?

 by Thomas Frenehard,                                               SAP GRC Solution Management

compassI’m not going to say that I have this question every Monday morning, but it does pop up rather more often than I would have expected. This is especially the case when risk, compliance or audit departments ask their IT counterparts to send them a list of suitable governance, risk and compliance (GRC) vendors but fail to really explain what they need.In essence, the request to their mind is pretty simple—just get me the list and rankings published by “[…] and […]” ( fill-in-the- blank spaces with your favourite analyst companies). That should do the trick.

This is usually what triggers the question above, with the IT department reaching out asking for a discussion on what is a GRC solution to ensure that they only source relevant options to present to their internal clients.

As mentioned by my colleague Jan Gardiner a few weeks ago (GRC ≠ Access Authorization Management), for us at SAP, the GRC portfolio is a combination of more than 10 solutions.

As a result, before even going further into any discussion, my answer is always the same, ”Well, what do you need to do?” I could spend hours explaining and illustrating the benefits of a full internal control solution, but if the original request comes from the audit team who is looking for a tool to help them support their risk-based auditing process, I’m not sure it’ll be of much use.

What’s the Requirement?

So first things first—define the need. Easier said than done of course, but it will be the foundation for everything. In case the requiring team doesn’t have a predefined idea of what exactly it is they need in terms of detailed requirements, you can always reach internally and see what is already available and being used (tools, spreadsheets, shared drives,).

Even if you then decide that none are the right option, this will still give you a good idea of what people are using today and for what purpose. And if you push your investigation further and interview the key users, they might even tell you what they currently lack. This is essential as it may lead to needs or pain points that are beyond the ones initially expressed.

Prepare for Today but Plan for Tomorrow

Now that you know the requirements, define where you are today and where you want to be tomorrow (or the day after). And keep in mind that a tool will never solve all your problems at once but it can bring new ones if expectations aren’t managed properly.

Using the information you collected above, work on a roadmap—what would be the first features needed today to facilitate work life, and what is,at the moment,  a “nice to have” but that you know will be important in the future.

With this in mind, start prioritizing so that the tool selected will be able to answer the immediate requirements, but also accompany the company as it evolves.

Leave Tabula Rasa to Aristotle!

Your company already has a wealth of risk registers, control libraries, audit repositories, and so on.

This is the “GRC memory” of your company and you certainly don’t want to get rid of it.

Collect as much as you can and then work with the business owners to review the data—define what should be carried forward, what is redundant and can be let go, and so forth.

And for what you decide is worth keeping, ensure that it’s complete and well documented. This way, not only will you embark on a new tool, but you will also have secured the consistency of the imported data. No need to have a Formula 1 car if you don’t have the right fuel, right?

Of course, I have over simplified the process, but for  a short blog that was my intent. But I hope this has still given you some food for thought for the next time your business owners call saying, “We need a GRC tool, what do you suggest?”

I look forward to reading your thoughts and comments either on this blog or on Twitter @TFrenehard

 

The Self-Service BI Application Dinner: Restaurant Guests and Home Cooks

chef_prepares_dishIn a recent thread on social media, there was an interesting discussion about just “how self-service-like” today’s self-service analytics components really are. Some of the thread contributors doubted whether self-service BI was really something one could hand over to a business end-user. They are concerned whether self-service really can exist in the day-to-day life of an end user.  “Isn’t there always some ICT intervention needed?”someone asked. It’s an interesting discussion that hasn’t a black and white answer. So let’s take a closer look with the help of a restaurant analogy.

The doubters in the social media thread were talking about self-service for data analysts. But there is a small, but strict, difference between self-service for the end user or consumers, and self-service for data analysts. To explain this, I’ll  need to use the analogy of an analytics dinner, and consider the differences between the home cook and the restaurant guest.

The BI Restaurant Guest

Our guests “equal” the business end users of analytics. A dinner can be seen as a collection of analytical insights. The insights are thoroughly selected as our guests pick either from a menu—and ordering à la carte—or they go to the buffet and pick the things presented to them already ready for consumption. Ordering à la carte refers to end users opening specific dashboards, reports, or storyboards from the business analytics portal.

The BI restaurant guest’s workflow is:

  • Screen the menu and roughly select the type and amount of items they want. Our analytics end user chooses whether he/she needs financial info or logistic info, and what kind of detail-level is needed.
  • Next our guest chooses a specific item from the menu. In analytics terms, the user decides which reports, dashboard and/or storyboards he/she needs to get the insights required. Our user also decides on prompts or variables needed to get the specific scope of the insights.
  • When dinner is served our guest just enjoys what he/she asked for, leaving leftovers if feeling like it.

buffet_dinner_tableThe BI restaurant buffet guest’s workflow is similar, with the difference being that adding special requests (like steak well done) is not possible. However, the buffet allows the guest to digest multiple small plates according to their individual needs, just like an analytical end user could consume reports and dashboards in random order.

Our guest will typically be a user of existing SAP BusinessObjects Design Studio applications or SAP BusinessObjects Cloud storyboards. I have stipulated how they work in this article.

The BI Analytics Home Cook

Our next ‘flavor’ of a self-service user is the home cook that has to cook for him/herself. This user is more like a data analyst. Somebody who may not have a clear view on what kind of insight is needed, or requires insight on non-corporate data that is not explored on a regularly basis.

Here the workflow differs. Imagine the workflow of the TV cooks we all see on tele every single day; it is the exact same workflow as our self-service end user.

1.      Our home cook opens up the fridge and explores the ingredients needed; think of the data analysts that accesses the data sources he/she requires to start exploring data.

2.      Next our home cook starts cleaning, cutting, seasoning, mixing and combining his/her ingredients. Only those pieces of the ingredients that are needed for the meal are used. This is where our data analyst starts filtering, enriching (hierarchies, formulas), blending (combining data sources) and cleaning his data.

3.      When this is all done, we typically see the home cook putting his selected ingredient-mix in the pot on the stove. This is where the data analysts starts creating the visualizations, graphs, and maps and combines them to a final storyboard which might be shared with others later on.

4.      Our home cook makes quite an important decision in the last step; either they serve the plate to their guests (his colleagues or management), or the final meal is just put on a buffet for guests/users to consume.

The Final Analysis

So in the end I believe self-service always needs to be seen in the context of the type of end user. Do we talk about a guest in our restaurant who wants to digest analytics, play with the data to any extent and conclude on the fly, or do we talk about a home cook who needs to create the insights from scratch?

In terms of the guest, self-service BI 100% exists today in the sense that they can use applications and reports and do anything (!) with the data as longs as this data is part of the menu. For  home cooks, there is a bit more work to be done—they need to open the fridge and make choices. Maybe some of the ingredients are not in, and our cook needs to go to the shop to buy them. Also, the personal touch given to the meal is fully on the creativity and capability of our cook.

Oh, and You Mr. Restaurant-Owner, What Do You Think?

If you happen to be the restaurant owner—BICC or ICT manager—of course you decide on the quality of the overall meals presented by managing ingredients and menus, but you also monitor the experience your guests go through. We might call this governance and organization. Even in self-service environments, the restaurant owner is key to the success of the restaurant. If you fail, your guests will go somewhere else.

This blog is excerpted from Iver van de Zand’s article, “How ‘Self-Service Like’ Are BI Applications Really? Buffet or a la Carte.” Read the complete article at the Iver van de Zand blog.

In the New Digital Economy, Everything Can Be Digitized and Tracked : Now What?

Woman Buying ClothesWelcome to a world where digital reigns supreme. Remember when the Internet was more of a ‘push’ network? Today, it underpins how most people and businesses conduct transactions – providing peer-to-peer connections where every single interaction can be tracked.

Enterprises are still not taking full advantage. With hundreds of millions of people connected, it’s possible for them to connect their suppliers with their customers and their payment systems, and reach the holy grail of seamlessly engaging in commerce, where a transaction can be tracked from purchase, to order received, to manufacturing, through to shipment— all in real time. It’s clear that end-to-end digitization delivers enormous potential, but it has yet to be fully tapped by most companies.

In the latest #askSAP Analytics Innovations Community Webcast, Reimagine Predictive Analytics for the Digital Enterprise, attendees were given an introduction to SAP BusinessObjects Predictive Analytics, along with some key use cases. The presentation covered native in-memory predictive analytics, deploying predictive analytics on Big Data, and how to bring predictive insight to Business Intelligence (BI).

The live, interactive call was moderated by  SAP Mentor  Greg Myers and featured expert speakers Ashish Morzaria, Global GTM Director, Advanced Analytics, and Richard Mooney, Lead Product Manager for Advanced Analytics.

The speakers noted that companies used to become leaders in their industries by establishing an unbeatable brand or by having a supply chain that was more efficient than anyone else’s. While this is still relevant in the digital economy, companies now have to think about how they can turn this new digital economy to their advantage. One of the keys is turning the digital economy’s key driver —the data— to their advantage.

Companies embracing digital transformation are outperforming those who aren’t. With predictive analytics, these companies can use historical data to predict behaviors or outcomes, answer “what-if” questions, and ensure employees have what they need to make optimized decisions. They can fully leverage customer relationships with better insight, and make meaningful sense of Big Data.

One big question delved into during the call: How can companies personalize each interaction across all channels and turn each one into an advantage? The answer: By getting a complete digital picture of their customers and applying predictive analytics to sharpen their marketing focus, optimize their spend, redefine key marketing activities, and offer product recommendations tailored to customers across different channels.

Real-World Customer Stories

The call also focused on some real-world examples of customers achieving value by using and embedding predictive analytics in their decisions and operations, including Cox Cable, Monext, M-Bank, and Mobilink.

These companies have been able to improve performance across thousands of processes and decisions, and also create new products, services, and business models. They’ve squeezed more efficiencies and margins from their production assets, processes, networks, and people.

One key takeaway is the importance of using algorithms, as they provide insights that can make a business process more profitable or competitive, and spotlight new ways of doing business and new opportunities for growth.

The speakers also presented a very detailed customer case study on Harris Logic. The company is using SAP BusinessObjects Predictive Analytics for automated analytics and rapid prototyping of their models. They execute models into SAP HANA for real-time predictions using a native, logistical regression model. This approach is allowing for the identification of key predictors that more heavily influence a behavioral health outcome.

Learn More

Lots of food for thought. See what questions people were asking during the webcast and get all of the answers here. Check out the complete presentation, and continue to post your questions and watch for dates for our upcoming webcast in the series via Twitter using #askSAP.

AmickBrown.com

10 Data Visualizations You Need to Know Now

word cloud predictive dataNo one likes reading through pages or slides of stats and research, least of all your clients. Data visualizations can help simplify this information not only for them but you too! These ten different data visualizations will help you present a wide range of data in a visually impactful way.

1.Pie Charts and Bar Graphs—The Usual Suspects for Proportion and Trends

New to data visualization tools? Start with the traditional pie chart and bar graph. Though these may be simple visual representations, don’t underestimate their ability to present data. Pie charts are good tools in helping you visualize market share and product popularity, while bar graphs are often used to compare sales revenue over the years or in different regions. Because they are familiar to most people, they don’t need much explanation—the visual data speaks for itself!

2.Bubble Chart—Displaying Three Variables in One Diagram

When you have data with three variables, pie charts and bar graphs (which can only represent two variables at the most) won’t cut it. Try bubble charts, which are generally a series of circles or “bubbles” on a simple X-Yaxis graph. In this type of chart, the size of the circles represents the third variable, usually size and quantity.

For example, if you need to present data on the quantity of units sold, the revenue generated, and the cost of producing the units, use a bubble chart.  Bubble charts immediately capture the relationship between the three variables and, like line graphs, can help you identify outliers quickly. They’re also relatively easy to understand.

3.Radar Chart—Displaying Multiple Variables in One Diagram

For more than three variables in a data set, move on to the radar chart. The radar chart is a two-dimensional chart shaped like a polygon with three or more variables represented as axes that start from the same point.

Radar charts are useful for plotting customer satisfaction data and performance metrics. Primarily a presentation tool, they are best used for highlighting outliers and commonalities, as radar charts are able to simplify multivariate data sets.

4.Timelines—Condensing Historical Data

Timelines are useful in depicting chronological data. For example, you can use it to chart company milestones, like product launches, over the years.

Forget the black and white timelines in your history textbooks with few dates and events charted. With simple tools online, you can add color and even images to your timeline to accentuate particular milestones and other significant events. These additions not only make your timeline more visually appealing, but easier to process too!

5.Arc Diagrams—Plotting Relationships and Pairings

The arc diagram utilizes a straight line and a series of semicircles to plot the relationships between variables (represented by nodes on the straight line), and helps you to visualize patterns in a given data set.

Commonly used to portray complex data, the number of semicircles within the arc diagram depends on the number of connections between the variables. Arc diagrams are often used to chart the relationship between products and their components, social media mentions, and brands and their marketing strategies. The diagram can itself be complex, so play around with line width and color to make it clearer.

6.Heat Map—For Distributions and Frequency in Data

First used to depict financial market information, the heat map has nothing to do with heat but does display data “intensity” and size through color. Usually utilizing a simple matrix, the 2D area is shaded with different colors representing different data values.

Heat maps are not only used to show financial information, but web page frequency, sales numbers and company productivity as well. If you’ve honed your data viz skills well enough, you can even create a heat map to depict real time changes in sales, the financial market, and site engagement!

7.Chloropleth and Dot Distributions Maps—For Demographic and Spatial Distributions

Like heat maps, chloropleths and dot distribution maps use color (or dots) to show differences in data distribution. However, they differ from heat maps because they’re specific to geographical boundaries. Chloropleths and dot distribution maps are particularly useful for businesses that operate regionally or want to expand to cover more markets, as it can help present the sales, popularity, or potential need of a product to the client in compelling visual language.

8.Time Series—Presenting Measurements over Time Periods

This looks something like a line graph, except that the x-axis only charts time, whether in years, days, or even hours. A time series is useful for charting changes in sales and webpage traffic. Trends, overlaps, and fluctuations can be spotted easily with this visualization.

As this is a precise graph, the time series graph is not only good for presentations (you’ll find many tools to help you create colorful and even dynamic time series online), it’s useful for your own records as well. Professionals both in business and scientific studies typically make use of time series to analyze complex data.

9.Word Clouds—Breaking Down Text and Conversations

It may look like a big jumble of words, but a quick explanation makes this a strong data visualization tool. Word clouds use text data to depict word frequency. In an analysis of social media mentions, instead of simply saying “exciting” has been used x number of times while “boring” has been used y number of times, the word that is used most frequently appears the largest, and the word that hardly appears would be in the smallest font.

Word clouds are frequently used in breaking down qualitative data sets like conversations and surveys, especially for sales and branding firms.

10.Infographics—Visualizing Facts, Instructions and General Information

Infographics are the most visually appealing visualization on this list, but also require the most effort and creativity. Infographics are a series of images and text or numbers that tell a story with the data. They simplify the instructions of complex processes, and make statistical information easily digestible. For marketers, infographics are a popular form of visual content and storytelling.

Get more information on building charts, graphs and visualization types.

– See more at: http://blog-sap.com/analytics/2016/07/11/10-data-visualizations-you-need-to-know-now/#sthash.UXqH0lkE.dpuf

Part 1: Winning your End Users – SAP BusinessObjects Design Studio or SAP BusinessObjects Lumira or …

 by Iver van de Zand,  Guest Blogger

 

confused_lost_man_cartoonBeing part of one of the leading software companies is great and brings advantages and (sometimes) disadvantages. A key element I like so much in my work is that with my company I can be part of large—or even huge—scaled analytics journeys with customers and BI competence centers who need to serve thousands and thousands of users. In today’s digital economy, they all struggle similar challenges. Let us focus on the business users and reflect their biggest requirements for analytics, and how this often brings us to the self-service dilemma.

Enterprise End Users Require at Least:

  • Self-service capabilities: Business users require a great deal of autonomy in their analytics work. They want to easily create, deploy and share their business analytics content themselves without being too reliant on their ICT or BI Competence Centers. The data analysts among them even require access to non-corporate data in order to blend this with the corporate data and search for new insights.
  • Agility and Flexibility: It’s almost become a magical word, ‘agility’ is what I hear every user talking about. Users nowadays require full-flavor flexibility when using analytics. It means easy accessible on any device, the ability to change graph types on the fly. It also means being able to swap measures and attributes at any place in the analytics dashboard, storyboard, or report. Users also require drill-anywhere capabilities and a definite must-have is to drill to the transactional level if applicable. The agility requirements for tooling are based on what the business decision makers’ need to have towards process or market fluctuations and their customer needs
  • financial dashboardOnline or real-time information, yet still highly performant. As you already expected, all the users I met want the data to be accessible in real-time and—ideally—also online. I understand that need; driven by this agility, users absolutely need to have the latest data to respond to any fluctuation in process or market.
  • Consistency in metrics and metadata: Though this should be a no-brainer, users frequently mention that they’ve had negative experiences in the past with consistency in metrics and metadata. In any type of business analytics applications (reports, storyboard, workspaces or dashboards) they expect consistency in metrics, the use of definitions, hierarchies, prompts variables or other metadata-related content. End of the line!
  • Governed: Oh yes, end users do have concerns about governance. Though everybody always wants to have access to anything, deep in their hearts they all understand authorizations and security are top notch subjects and need to be treated with ultimate care. Another one here is SSO (Single Sign On)—would you like to logon and enter your credentials 75 times per day? Nah, don’t think so, so SSO is a must-have.
  • Visually appealing: Basically, I’m talking about the user experience here. Since analytics are widely spread—often also to my customer’s customers—they need to be visually appealing to attract the attention. This element of visually-appealing analytics is more complex than you might think. The visualizations need to have the creativity, effect, and structure to exactly communicate the message that “needs to be communicated.”(This subject is worthy of a few articles already.)

The Self-Service Dilemma – SAP BusinessObjects Lumira or SAP BusinessObjects Design Studio?

train_schedules_infographicSo, here we are with the large enterprise using SAP BusinessObjects Business Intelligence suite and users are looking for self-service and agility. Typically now the self-service dilemma starts: users, architects, and IT leaders are all very well informed these days, and consider SAP BusinessObjects Lumira as the ultimate tool to provide for every end user.

And they have a point, considering end users get full flexibility and self-service capabilities while the learning curve is extremely low. It brings powerful visualization capabilities and people can easily blend their data with other – i.e. external – data. (See a detailed component selection tool.)

But  I tend to challenge their considerations, especially if SAP Business Warehouse and/or SAP HANA are involved. They forget about SAP BusinessObjects Design Studio for enterprise dash boarding, and I don’t know why. Apparently, they still believe SAP BusinessObjects Design Studio is a developer tool and that is permanently incorrect.

In a lot of cases, SAP BusinessObjects Design Studio can cover all the end-user needs mentioned above, and it does this in a remarkably powerful way. SAP BusinessObjects Lumira really comes in for data analysts. It is a matter of clearly choosing the best-suited BI component to sort out the self-service dilemma enterprises might have.

I’ll go into detail on how to choose in my blog post next week. Stay tuned!

AmickBrown.com

Integrate Non-SAP Data into Planning

Contributed by Gunnar Steindorsson, Allevo US

Earlier we took a look at how effective cost planning in SAP isn’t always easy – but how it can be with Allevo.

Let’s take a deeper look at one challenge specifically that Allevo accomplishes with ease:  integrating data from systems or processes outside of SAP.

As you know, it’s not easy to bring data into SAP from other systems – say production or sales plans – and integrate these into your planning process. Allevo’s innovative Satellite Technology was designed to do just that however.

With Allevo, data integration with external systems occurs automatically and in real-time. Best of all, once your planning is complete you can save the results in SAP along with your assumptions, comments and other metadata for later retrieval and reference.

Allevo integrated non sap data chart

Better yet, with Allevo you work in your familiar, easy-to-use Excel environment, using your own templates, all while fully integrated with SAP.

So, if you’re tired of not having real-time access to your vital planning data, frustrated by cumbersome workarounds, and annoyed with working in a system that doesn’t support the way you want to work, Allevo is your solid answer.

Contact info is below.

amickbrown.com.allevo