iver van de zand Archives - Amick Brown

SAP Digital Boardroom Improves Public Transport Insights On Repairs, Delays, and More

Sheri Tate — Fri, 31 Mar 2017 09:47:38 +0000

Iver Van de Zand, Global SAP Analytics Hub Director

The public transport industry is a heavy innovator. Trains and railways are equipped with IoT sensors to measure performance, delays, and incidents. Bus, subway, and train repair, and maintenance costs are intensely watched due to budget pressures. In the meantime, passengers are more critical and demand immediate action whenever downtimes or delays occur.

In various interactions with public transport industry customers, I’ve noticed they are generally well informed regarding delays, have good insights on maintenance and repair, maintain remote visibility on infrastructure incidents, and are equipped with rolling budgeting models. The thing is that these insights tend to be isolated. None of the customers I spoke to has integrated and consolidated insights on them all, so they are like “blind men in the wood” when I ask which unexpected repair incidents affect which train delays.

Challenges

The major areas of interest for public transport companies are:

Incidents management: including both infrastructure incidents and incidents in the actual train, bus, subway, and so on.

Repair costs and maintenance: focusing on supplier management, unplanned downtime, and cost reduction.

Delays management: tied to narrowing the gaps between planned and actual arrival/departure times, rankings, and geospatial insights.

As I said above, today’s key challenge for the industry is not measuring those individual subject areas, rather it’s in consolidating and interrelating them. The key is the ability to understand how an issue in one subject area affects an issue in another one, and to what extent.

More Challenges

Are we done with challenges? No, we aren’t. Today’s competitive public transport market requires more than just consolidating insights. It also requires agility, meaning analysts must be able to consolidate insights in real time, to continuously compare their actuals against budgets and forecasts, and even to be able to adjust and simulate them while analyzing. We call this the closed-loop portfolio of monitoring, budgeting, and forecasting. And last, they need instant insights from massive data volumes; remember, they all use IoT devices – and we know what they generate.

So in summary, insights must have:

Real-time reporting

Predictive-forecast capability

The ability to handle massive data

Closed-loop capabilities of combining monitoring, planning, and predicting metrics on the fly while analyzing

Simulation functionality

SAP Digital Boardroom Insights

I used the SAP Digital Boardroom on top of in-memory platforms to analyze over 30 million records of public transport delays, incidents, and maintenance data. The SAP Digital Boardroom uses three touch screens to provide insights in real time on the transactional level. What impresses me most is its ability to combine versions of actual, planned, forecasted, and predicted data; in other words, I can analyze, adjust, predict, simulate, and re-analyze the end result. Now we’re talking!

It all starts with creating an agenda that lists the various insights required. The agenda items refer to models and stories with real-time insights. You can skip between agenda items with a single click, and every single insight can be further explored with new attributes, filters, or simulations.

Watch the video below for a full overview of using the SAP Digital Boardroom to address the challenges I’ve described. Please be aware that I anonymized the data though the algorithms, structures, and “cadence” of the data from a real-world example.

The post SAP Digital Boardroom Improves Public Transport Insights On Repairs, Delays, and More appeared first on Amick Brown.

BI Software Vendors Choose Their Strategies: On Premises, Cloud, or a Hybrid Approach?

Sheri Tate — Fri, 27 Jan 2017 19:42:08 +0000

by Iver van de Zand, Business Analytics Lead BeLux SAP

Where should we deploy our analytics and business intelligence (BI)? In the cloud or on premises? It’s a question that is thrown at me frequently. Most software vendors expect only 20% of the enterprises to run full cloud analytics in 2020. What about the rest? And what do you decide today? Is there an easy answer? It might seem not …..but a hybrid BI approach is!

The Evolution of Cloud Analytics

Before understanding why there is a discussion for cloud analytics and/or on-premises analytics anyway, we need to recap on the market situation a bit. When it comes to analytics, our world has changed dramatically and is still in this changing process. It is all part of digital transformation—where we initially shifted into renewed business networks with things like video collaboration, mobile devices, connected things and what have you, we’re now moving into insights-driven experiences using structured and unstructured data in real time and online. This change is massive.

Today we connect everything—devices, people, data, and processes. Enterprises, of course, undergo similar experiences; it changes the way they are competing. Data is their key asset in this competition and as such enterprises make data their strategic differentiator. Data becomes the new gold.

In the same time technology also emerged. Besides in-memory computing, we also got stable cloud platforms that leverage in-memory computing and boost performance tremendously. These platforms were welcomed a lot in today’s massive data generation. The cloud platforms are hugely attractive, given that maintenance and support efforts are limited down significantly, and in addition the platforms deliver the scalability that enterprises need today.

Analytics vendors got on top of this and provided cloud analytics tooling. SAP BusinessObjects Cloud is one of the best examples that even went a step further in answering the needs of enterprises by providing both monitoring, planning, and predictive capabilities all in ONE tool— the so-called closed loop portfolio.

The Perpetuation of On-Premises Analytics

Yet ….. it is expected that in 2020 only 20% of the enterprises will run full cloud analytics. Why only 20%? That is either because not everybody applies to today’s use cases to run analytics and business intelligence in the cloud, or because cultural differences mean that certain areas don’t feel ready to shift to cloud analytics.

So what about all the others? They still run on-premises analytics highly successfully. Software vendors keep on innovating the on-premises software, allowing it to handle the latest needs for agility, data volume and complexity, and real-time capabilities. And they succeeded in it. In the long term, we’re pretty sure all enterprises will shift to cloud analytics, but that will take another 7-10 years. In the meantime, analytics techniques need to further evolve to answer to the need of insights-driven experience and data-driven strategies. Now the question is, what should software vendors do? Should all the focus be on cloud analytics or should they continue to innovate the on-premises solutions?

The Shift from Descriptive Analytics to Predictive Analytics

To answer our question of whether software vendors should focus their developments on cloud analytics or on premises, some other elements are important too. These are the intentions of enterprises and their strategy on data insights. With enterprises using data as their core assets to be competitive, we see them shifting from the more traditional descriptive analytics (the typical monitoring) into predictive analytics. Simply put, where descriptive analytics answers “what happened” and “how did it happen,” predictive analytics tell us “what could happen.”

In the long term, this shift will further evolve towards pre-emptive analytics, quantum analytics, or even adaptive analytics:

Pre-emptive analytics: analytics focused on anticipating a certain outcome
Quantum analytics: machine intelligence and mathematics are used to “ask” the data questions (both automated and most relevant) for insights
Adaptive analytics: the ultimate goal towards fully adaptive enterprises where enterprises can predict which of the available analytical insights are really valuable

This shift to predictive analytics says a whole lot which we will discuss in other articles, but one thing is sure—it will definitely mean that we all need to bring analytics to where the data is created. That’s the place where we can create the data intelligence algorithms, so that the enterprise can predict what it needs to ask in terms of analytics questions, making them adaptive enterprises. Everything must be analytics-enabled.

We must have integration for predictive models and machine learning for a closed loop system. If we can all accomplish that, the world is ready for the opportunity of monetization and designing business models for subscribing to the outcomes of data.

Bringing the analytics to the data, means enabling almost any application with analytics. Given that the future of applications is in the cloud anyway (companies will also start using applications to their needs – a typical cloud model), we can also conclude that the future of analytics is in the cloud.

The Hybrid Approach Strategy

Enterprises, however, drive the speed and adoption towards adaptive organizations. They might shift iterative or in chunks. As such, they also drive the bigger part of the decision by software vendors to either focus on cloud or on premises. Therefore, the immediate answer is the hybrid analytics focus (it must be!).

This means that enterprises must innovate the on-premises software tooling in parallel with evolving the cloud analytics offerings. It also means that enterprises should whenever possible align them and make them interoperable. Cloud analytics must be able to interconnect with the vendor’s on-premises analytics and the other way around.

The long-term strategy of analytics vendors should be on cloud analytics. However, the adoption of cloud analytics is at a speed that in 2020 we can expect only 20% of all enterprises to be running full cloud analytics and the vast majority will still—partially—be running on premises.

Digital Transformation, however, keeps on going and enterprises need to go forward using their data insights as a competitive advantage. Short and midterm strategy for software vendors can thus only be hybrid.

See More on Business Intelligence Solutions

This post originally appeared on Iver van de Zand’s blog and has been republished with permission.

The post BI Software Vendors Choose Their Strategies: On Premises, Cloud, or a Hybrid Approach? appeared first on Amick Brown.

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

amick.brown — Fri, 28 Oct 2016 11:07:21 +0000

by Iver van de Zand, Business Analytics Lead BeLux

In 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.

The 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.

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.

AmickBrown.com

The post The Self-Service BI Application Dinner: Restaurant Guests and Home Cooks appeared first on Amick Brown.

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

amick.brown — Fri, 19 Aug 2016 11:49:34 +0000

by Iver van de Zand, Guest Blogger

Being 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 that 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 accessibility on any device, and 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
Online 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 application (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 log on 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 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?

So, 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.

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 dashboarding, 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

The post Part 1: Winning your End Users – SAP BusinessObjects Design Studio or SAP BusinessObjects Lumira or … appeared first on Amick Brown.

4 Best Practices to make your Storyboards more Dynamic and Appealing

amick.brown — Wed, 11 May 2016 11:02:14 +0000

By Iver van de Zand – Business Intelligence & Analytics – SAP – Visualization – DataViz – Evangelist – Author of “Passionate On Analytics”

Your end users will love it when you’d deliver your story- and dashboards in a more appealing and dynamic way. In these Let Me Guide series I discuss 4 easy to use best practices that will help you doing so:

Using backgrounds
Using Navigation
dynamic Vector Diagram pictures: SVG
Dynamic Text

Using Background

Backgrounds can better the looks and experience of story- and dashboards. Use the opacity to ensure the attention is not too much distracted from the actuals graphs and charts. I tends to create my backgrounds myself using PowerPoint: create a slide with a layout you like allocating space for KPI metrics and visualizations. Save the slide as JPG which you can import as background into SAP Lumira.

Using Navigation

If you have story- or dashboards with multiple pages, my experience is that custom navigation buttons help you users finding what they should read. I use custom navigation all the time on my storyboard’s landing pages for example. Here is how you do it:

Find a shape or picture that you want to use as clickable button and save it as xx.jpg
Import xx.jpg as picture in Lumira and drop it on your storyboard where you want it
Drag and drop a rectangle shape exactly over you newly created button and set its lines and fill-color both to “none”
Click you “invisible” shape and add the URL or page number to it
Save and preview

Example landing page A

Example of a core layout of a landing page for your storyboard. The color-coded tiles can be used as navigation buttons. The generic tiles act to show key metrics info. Save the core lay-out as JPG and use this JPG as core background in your storyboard. Now add an object over the color coded sections, make it invisible and add a page-link to the appropriate page in your story.

SVG files

Especially infographics gain on weight and meaningfulness if you use dynamic pictures as part of your charts and graphs. Bar- and line charts in SAP Lumira have the possibility to change its regular column and markers into a dynamic pictogram. You can use the embedded pictograms but also add your own. The pictograms need to be in the SVG dynamic vector format. Search for pictures on Google with the “ filetype:SVG” string to find SVG’s. Save and import them to Lumira and change the graphs properties. The results are impressive. It is easy to create your own SVG files: I use PowerPoint to create my own pictures and save them to JPG. Using conversion tools easily creates an SVG that you can use as dynamic chart/graph picture in your storyboards.

Dynamic Text

Dynamic Text is a powerful way to improve context sensitive messaging in your story- and dashboards. The dynamic text is based on a dataset attributes and thus changes when data is refreshed are filtered. Since SAP Lumira handles the dynamic text as any other attribute, you can also apply formulas against the text.

www.AmickBrown.com

The post 4 Best Practices to make your Storyboards more Dynamic and Appealing appeared first on Amick Brown.

Brainteaser: Storyboard or Dashboard…Self-Service or Managed…you choose

amick.brown — Fri, 08 Apr 2016 13:09:42 +0000

By Iver Van de Zand, SAP

If there is one term that always is food for discussion when I talk to customers, it is definitely “dashboard”. What exactly is a dashboard, how close is it to a storyboard, are dashboard only on summarized data and when to use a dashboard versus a storyboard. Tons of questions that already start in a bad shape because people have other perceptions of what a dashboard really is. And let’s be honest; take a canvas, put a few pies on it and a bar-chart, and people will already mention it as a dashboard. Let’s see whether we can fine-tune this discussion a bit.

A Dashboard

A business intelligence dashboard is a data visualization technique that displays the current status and/or historical trends of metrics and key performance indicators (KPIs) for an enterprise. Dashboards consolidate and arrange numbers, metrics and sometimes performance scorecards on a single screen. They may be tailored for a specific role and display metrics targeted for a single point of view or department. The essential features of a BI dashboard product include a customizable interface and the ability to pull real-time data from multiple sources. The latter is important since lots of people think dashboards are only on summarized data which is absolutely not the case; dashboards consolidate data that may be of the lowest grain available! Key properties of a dashboard are:

Simple and communicates easily and straight
Minimum distractions, since these could cause confusion
Supports organized business with meaning, insights, and useful data or information
Applies human visual perception to visual presentation of information: colors play a significant role here
Limited interactivity: filtering, sorting, what-if scenarios, drill down capabilities and sometimes some self-service features
They are often “managed” in a sense that the dashboards are centrally developed by ICT, key users or a competence center, and they are consumed by the end-users
Offer connectivity capabilities to other BI components for providing more detail. Often these are reports with are connected via query-parsing to the dashboards

A Storyboard

Is there a big difference between a storyboard and a dashboard? Mwah, not too much: they both focus on communicating key – consolidated – information in a highly visualized and way which ultimately leaves little room for misinterpretation. For both the same key words apply: simple, visual, minimum distraction.

The main difference between a dashboard and a storyboard is that the latter is fully interactive for the end user. The interactivity of the storyboard is reflected through capabilities for the end user to:

Sort
Filter data: include and exclude data
Change chart or graph types on the fly
Add new visualizations on the fly; store and share them
Drill down
Add or adjust calculated measures and dimensions
Add new data via wrangling, blending or joining
Adjust the full layout of the board
Create custom hierarchies or custom groupings
Allow for basic data quality improvements (rename, concatenate, upper and lower case etc)

Another big difference between dashboards and storyboards is that storyboards are self-service enabled boards meaning the end user creates them him/herself. Opposite to dashboards that are typically “managed” and as such are created centrally by ICT, key users or a BICC, and are consumed by the end user.

A Dashboard versus a Storyboard

So your question, dear reader, is “what is the day-to-day difference and what to you use when”? Well the answer is in the naming of both boards:

The purpose of a storyboard is to TELL A STORY: the user selects a certain scope of data (which might be blended upon various sources) and builds up a story around that data that provides insights in it from various perspectives. All in a governed way of course. The story is built upon various visualizations that are grouped together on the canvas of the storyboard. These visualizations can be interdependent – filtering on one affects the others – or not. The canvas is further enriched with comments, text, links or dynamic pictures … all with the purpose to complete the story.

Storyboarding has dramatically changed day-to-day business: the statement “your meeting will never be the same” applies definitely. Your meetings are now being prepared by creating a storyboard; meetings are held using storyboards to discuss on topics and make funded decisions, simulations on alternative decisions are done during the meetings using the storyboards and final conclusions can be shared via the storyboards. Governed, funded, based on real-insights!

A dashboard has a pattern of analyzing that is defined upfront. It is about KPI’s or trends of a certain domain, and you as a user consume that information. You can filter, sort or even drill down in the data, but you cannot change the core topic of data. If the KPI’s are on purchasing information, it is on purchasing information and stays like it. You neither can add data to compare it.

In a number of situations one does not want the end user to “interact” with the information since it is corporate fixed data that is shared on a frequent and consistent time. Enterprises want that information to be shared for insights in a consistent, regular and recognizable way. Users will recognize the dashboard, consume the information and – hopefully – act upon it. Think for example about weekly or monthly performance dashboards, or HR dashboards that provide insights in attrition on recurring moments in time.

Dashboards and Storyboards: the “SAP way”

The nuances made above on dashboards and storyboards are being reflected in SAP’s Business Intelligence Suite. Its component Design Studio is a definite managed dashboarding tool. Extremely capable of visualizing insights in a simple and highly attractive way while in the meantime able to have online connections to in-memory data sources, SAP BW or semantic layers. Storyboarding is offered via the on-premise SAP Lumira or via Cloud through the Cloud for Analyticscomponent.

If you have difficulties deciding what to offer to your end users, the BI Componentselection tool I made easily helps you understanding whether your users require dashboards or/and storyboards. You might want to try it.

Financial storyboard

Self-service storyboard created in around 45 minutes using SAP Lumira. On this page the heat-map section that allows for white spot analyses. Data can be exported at any time. User has numerous capabilities to add data, visualizations and additional pages

Financial storyboard

Self-service storyboard created in around 45 minutes using SAP Lumira. User has numerous capabilities to add data, visualizations and additional pages

The post Brainteaser: Storyboard or Dashboard…Self-Service or Managed…you choose appeared first on Amick Brown.

Context Awareness: Online or Real Time in Digital Transformation?

amick.brown — Tue, 08 Mar 2016 11:58:48 +0000

By Iver van de Zand , Business Analytics Leader, SAP

Excuse me? Online instead of real-time; isn’t that the same? Well, have I got news for you: It is not.

Driven by enterprise needs and technological capabilities, enterprises are massively going online. Why do they and how accurate is online? Why do I read about going online in the news sites all the time? Let me explain wearing my analytics glasses.

Definitions

First, let’s get some things straight: explaining the difference between real-time and online starts with a discussion on latency. Latency is a time interval between the stimulation and response, or, from a more general point of view, as a time delay between the cause and the effect of some physical change in the system being observed. Online means that there is some kind of interactivity involved, but doesn’t enforce limits in latency. Real time means that there are limits on latency. Pfff, need to re-read that a few times before it starts clearing for me.

Let me give an example: If you move your computer’s mouse, you expect the pointer to react immediately and precisely follow your actions. That’s real time. Another example is playing on a music keyboard controller and have some synthesizer program generating the sounds. Online, however, is when your actions show some response in some timely manner, but there’s no timely relationship enforced to it. For example, starting a video stream from a (remote controllable) webcam may show you the pictures with less than one-second latency, or even up to several minutes, yet be online.

My phrasing of “difference” should thus be adjusted—real-time and online don’t differ, they relate to each other.

The Market Out There: Context Awareness and New Business Models

According to Gartner’s recent Big Data Trends for Business Intelligence, by 2017 more than 30% of enterprise access to broadly based Big Data will be via intermediary data broker services, serving context to business decisions. These are massive amounts and it proves that digital businesses require real-time situation awareness. That covers full insight into the things going on inside and outside the organization. Retailers, for example, need to know in real time how weather patterns impact the buying behavior of their customers. The inventory manager requires real-time information when his supplier is in trouble delivering his goods, so he can immediately adjust and use analytics to find alternatives, re-plan and re-adjust for example his forecast.

The issue that occurs is that more and more the enterprises corporate data is insufficient to get the necessary context awareness required to adequately respond to digital business. Think of it; to compete in digital business, a combination of non-corporate data coming from outside the organization is required all the time. This—often unstructured—data could be about social behavior, environmental influences, and government or market trends, to name a few. Some of it is even from premium data brokers preparing data from specific industries or use cases.

We could say that the ability of enterprises to adopt digital transformation and digital business for a big part is influenced by their capabilities of curating, accessing, and interpreting their data to obtain context awareness.

Context awareness is crucial for any enterprise that wants to compete in digital business. Real-time availability of insights is the logical requirement to do so. We already recognized the need for real-time insights to corporate data, however, we also now recognize the need for real-time insights in contextual information:

Curating Insights

Digital business is about the agility to respond to market, customer and environmental influences and actors immediately when required. Digital business requires enterprises to act and respond almost real time to activities not registered in their corporate data.

Accessing data and insights

Digital business is about the agility to recognize and access information outside the enterprise that is necessary for curating insights immediately when it occurs.

Interpreting and act upon insights

Digital business is about the ability to interpret insights and act upon them instantly. This is not only about interacting with the insights, but especially about applying the closed loop portfolio of analytics: insights often generate follow-up actions that affect business planning, finance budget allocation, or require new predictive models to argument on influencing variables of the contextual information.

An interesting side effect of contextual awareness is the new business models that come with it. A new category of business-centric cloud services enters the market space that delivers data to be used as context in business decisions, whether human or automated. These information services (or data/decision brokers) will become an essential part of intelligent business operations and smart business decisions.

The Case for Online Analytics

Online analytics is primarily about cloud-based analytics. If we narrow down to business intelligence (BI), the cloud BI market will be worth $4 billion by 2017 whereas the current full BI market (software and services) is estimated at $86 billion.

But how important is the aspect of “being online” for context awareness? Well, it’s quite important:

Contextual information is very often residing on websites. Your company’s biggest database isn’t your transaction, CRM, ERP or other internal database. Rather it’s the Web itself and the world of exogenous data now available from syndicated and open data sources.
Products like SAP Cloud for Analytics connect to various cloud-based solutions like SAP S/4HANA, and others. It is obvious that online analytical tools integrate more easily with other cloud-based applications.
Reduced or eliminated capital costs. Because BI systems are managed on the cloud service provider’s hosted architecture, a user company has no up-front capital investments or multi-year equipment leases with depreciating value. It also stands to benefit from improved cash flow. The subscription fees charged by cloud vendors are treated as operational expenses and don’t incur additional interest charges, which can lead to better cash management and debt avoidance.
The simplicity of the online, cloud-based analytical applications is key when it comes to user adoption. If we realize that the people creating insights on contextual awareness are business users, you’d agree with me that simplicity makes the difference when it comes to adopting the applications and leverage their power.
Streamlined system design and increased elasticity. In the cloud, companies can rely on a provider to architect the BI environment, select the technologies that will power it, assemble systems and manage the hardware and software stacks. That frees them to focus their attention on running BI and analytics applications and interpreting the results.
Fully-integrated business analytics components into the so-called closed-loop portfolio. Analytical environments hosted in the cloud comprise a complete end-to-end architecture. SAP Cloud for Analytics for example, spans the ETL, data management, analytics, planning, predictive and risk spectrums, simplifying and speeding the deployment process for users. Cloud BI systems should be ready to use out of the box, so to speak, and the standard setups can quickly be augmented with templates that vendors have developed over the course of different customer engagements.

The Case for Real-Time and Online Analytics: Context Awareness

Man, does Digital Transformation bring us interesting times! There are so many aspects of it, with contextual awareness being just one of them. For me, it’s crystal clear that real-time contextual awareness is key to any enterprise that wants to be competitive in digital business. Given the flavor and behavior of the contextual information, online analytical applications can make a significant difference.

Follow me on Twitter @ivervandezand.

The post Context Awareness: Online or Real Time in Digital Transformation? appeared first on Amick Brown.

“Passionate on Analytics” , new book available

amick.brown — Fri, 05 Feb 2016 13:43:33 +0000

from Iver van de Zand

My book “Passionate On Analytics” is available now

Driven by a deep believe of the value of business analytics and business intelligence in the era of Digital Transformation, the book explains and comments with insights, best practices and strategic advices on how to apply analytics in the best possible way. 25 Years of analytics hands-on experience come together in one format that allows any analytics userHow proud can one be?

My first book titled “Passionate on Analytics” is now available at Amazon.

Since I am evangelizing on interactive analytics every single day, I decided to create an interactive ePub book. It contains over 60 best practice and tutorial videos, tons of valuable links and galleries and 33 extended articles providing insights on various analytics related topics.

Passionate on Analytics (206p) has 4 sections:

Insights: 13 deep dive articles on various aspects of business analytics like industry specific approaches, embedded analytics and many more
Strategy: 13 chapters talking analytics strategy related subjects and topics like defining your BI roadmap or the closed loop portfolio
Best Practices: 10 expert sessions showing and demonstrating best practices in business analytics like using Hitherto charts, how to make a Pareto or visualization techniques
Resources: a wealth (!) of resources on analytics

Please find below some screenshots.

I am very happy with the book with has brought up the best in me. Everything I learned, experienced or discussed during my 25 years tenure in business analytics, is expressed in this book. The book is fully interactive meaning you can tap pictures for background, swipe through galleries or start an tutorial video.

Special thanks goto Ty Miller, Timo Elliott, Patrick Vandeven and Waldemar Adams who I all admire a lot.

Iver

The post “Passionate on Analytics” , new book available appeared first on Amick Brown.

The Overwhelming Power of Analytics in Retailing and B2C: Part 2

amick.brown — Wed, 06 Jan 2016 16:13:12 +0000

Contributed by Iver van de Zand, SAP

Retailing and business-to-consumer (B2C) market requirements for online insights are relying heavily on the closed-loop portfolio. The permanent and online interaction of analytics towards rolling planning or predictive models applies all the time. As a follow-up to Part One, today I’ll discuss the various ways analytics is applied in retailing and B2C. The situations below are far from exhaustive, but at least they provide insights into what I’ve experienced in various engagements with the retailing sector.

Retailing and B2C segments rely on real-time availability of data insights. Customer behavior, societal influences, the distribution column—they all fluctuate drastically and affect commercial behavior so intensely, that only real-time insights empower the retailer to monitor and adjust the closed-loop portfolio. Needless to say, retailing and B2C require in-memory platforms, which provide the calculation and data handling power, plus the scalability, that are needed so badly.

At the base of getting insights are in-memory systems that track every single transaction done in the shops or online. An often-seen solution for this is called SAP Customer Activity Repository (CAR). SAP Customer Activity Repository is a foundation that collects transactional data that was previously spread over multiple independent applications in diverse formats.

Watch the SAP Customer Activity repository video HERE

The repository provides a common foundation and a harmonized, multichannel-transaction data model for all-consuming applications. Retailers can use SAP Customer Activity Repository to gradually transform their system landscapes from traditional database technology to revolutionary, in-memory database technology.

Assuming the real-time platform and SAP Customer Activity Repository are available, what is the typical scope for this market segment’s insights using analytical components from the closed loop? Let’s have a look.

Basket Analyses

Basket analyses are the core insights that provide information about what people buy at what moment and at what location. We get insights into what is in their “basket.” The mix of products consumers buy is especially interesting. For example, a retailer can use real-time predictive models to predict whether a young female teenager buying red trousers might also be interested in purchasing accompanying earrings.

Sensor techniques can really help the shop employees to focus their advice specifically to the customer’s needs. Consider this shopping scenario:

Sensors inform an employee that a customer is picking a blue shirt, sized XXL from the rack.
Online analytics and predictive models immediately tell the employee on a mobile device that the customer took the wrong size (based on his buying history) from the rack.
The buying history then indicates that the customer typically buys three pieces at once.
The employee is also informed that the customer’s profile indicates he might be interested in buying jeans to go with the shirt he’s looking at (based on predictive models).
Finally, loyalty card information indicates that if the customer today buys four pieces, an extra bonus will be provided to his savings card.

All this information helps the employee interact with and sell to the customer.

Shop Performance

With shop performance, I mean the ability to use real-time, closed-loop analytics on an
overseeing shop level. Sentiment analysis based on, for example, an impacting television show last evening where a popular boy’s band showed their new, hip-colored sneakers, might trigger the retailing group to discount a second article when customers buy similar sneakers. The agility here is crucial—sentiments are notified from social media analyses and action needs to be taken immediately.

Local influences could mean specific sizes of a product are sold very well in one place, but less well in other places. This might trigger shop management to re-allocate stock to other shops. The same thing applies to ranking capabilities—permanently monitoring top-bottom rankings per article, color-item or size is valuable, since the slightest social change (a big event in one specific city) might cause immediate changes in buying behavior locally.

Customer Loyalty

Customer loyalty cards provide the retailer with a wealth of information if used well. The loyalty cards “identify” the person buying. We can see the consumers’ buying behavior, what they buy, and when. Tracking techniques (picking up mobile devices’ signals when customers enter the store) show us in real-time exactly where each customer spends their time in our shop, what is the route that customer typically follows, and what is their average visiting time.

Retailers can go a step further by combining loyalty card information with the customer’s buying history and social media information. This further completes one profile, allowing the retailer to tailor make marketing initiatives on an individual level.

For example, I—as customer X—might receive a special offer for a new external hard drive from a retailer, since combining data shows that I like audiophile equipment, buy music magazines (basket analyses) and spend quite some time at the electronics department when visiting that shop. The data insight is that I might need (or want) a storage device to store my music.

Customer loyalty cards might also bring great value to the customer retention program. Customers nowadays really quickly change their providers of goods because they are enormously well informed.

Weather Influences

Weather conditions greatly impact the buying behavior of customers. In general, windy weather has proven to have a highly negative impact on retail sales revenue. Making other general statements is difficult since a specific weather condition can have a positive effect on one type of retail, and a negative on another. Think about how cold weather might improve sales of books but negatively affect sales of handbags, for example. (A nice article focusing on the impact of temperature on shopping can be found at the Summit Blog.)

Likewise, a retail shop’s location—inbound or outbound—and its availability of underground parking are very important in rainy conditions. For retailers, it’s important to realize that weather conditions have so much impact that they can’t be excluded from operational insights on shop performance. Thus, they must make them part of the closed loop portfolio.

Alternative Business Models

Retail and B2C markets are probably one of the most highly interesting market segments to follow. Why? Well, they’ll be under great change. The “Digital Transformation” age and the availability of information to both the retailer and the consumer are changing everything.

Consumers are not only wanting to know “everything” about their product, they are also shifting to buying (or should I say “renting”) a product experience rather than the product itself. For the latter, think about the accompanying services to a product that the retailer might want to offer. Have a look at this article from Forbes, which describes three trends for retail in the future—instant gratification, borrowing, and customization.

For me, there is enough food for thought to write a Part Three of the series on the overwhelming power of analytics within retailing and B2C. Stay tuned.

Follow me on Twitter – @IvervandeZand.

– See more at: http://blogs.sap.com/analytics/2016/01/06/the-overwhelming-power-of-analytics-in-retailing-and-b2c-part-two/#sthash.YIdYhjL9.dpuf

The post The Overwhelming Power of Analytics in Retailing and B2C: Part 2 appeared first on Amick Brown.

The Overwhelming Power of Analytics in Retailing and B2C: Part One

amick.brown — Mon, 28 Dec 2015 14:47:24 +0000

Thank you to Iver van de Zand, SAP

Online grocery shopping and personalized bonus cards – we all face these incentives every day. Each is strongly driven by the overwhelming power of the analytics that are behind them. This article will share my experiences on these topics providing examples of retailing and B2C customer journeys that I have been a part of. The below examples are not at all exhaustive; they are also not about the future but are what happens, and are in production, today!

One thing that makes the retail market segment so interesting is the extreme sensitivity to community influences. A small thing might happen in society that can immediately affect buying behavior: today people are connected everywhere and at any moment. A simple anecdote on social media is shared so quickly that it can influence consumer choices instantly. One simple bad review about, for example, a yogurt brand, can raise or lower the selling of this product the next day. If the retailer wants to act upon these influences, he needs state of art Insights and online operational analytics.

Retailers are Analyzing You

Your bonus card, combined with your social media credentials, tell the retailer a whole lot more about you than you might realize. Analytics, clustering, and predictive modeling inform the retailer about your family composition, your eating and clothing preferences, how many children and pets you probably have and even what kind of holidays you like. By smartly combining your information with reference groups, the amount of trustworthy information a retailer can predict is huge.

Now imagine that the retailer recognizes you based on your cellphone signal when you enter the store. This information is linked online to your bonus card and social media credentials: “the retailer knows exactly who is in the store”. Then based on the same cellphone signal, the retailer can follow (!) you through the store using GEO coordinates. It means the retailer knows you are in front of the vegetable section, and also knows – based on the bonus card info – that you like carrots a lot. The electronic banner automatically flips and messages about a special offer on “carrots that taste very good with a new white wine that you might want to try”. A message targeted at you.

Imagine?? Well, forget about “imagine” – this is done today and you are part of it.

Supply Chain Challenges

Imagine this scenario. The latest game controllers are very popular, so our retailer decides to order additional stock from one of his vendors. Using buying behavior and predictive algorithms, the retailer knows he will sell the controllers. Early in the morning the stock manager receives a message that the vendor’s truck driver is stuck at the border and will be very late. Order intake quickly searches for alternative vendors and places an online order. That order will influence consumer prices and using business analytics the retailer can immediately predict the effect this price change will have on today’s revenue. It also automatically adjusts the retailer’s forecast and rolling plan, even from its subsidiaries if they exist. Using basket analyses, the new type of game controller might be influential to the selling of USB cables too so the retailer decides to order additional USB sticks and the system automatically adjusts distributed forecasts and rolling planning. Imagine? Not at all!

Product Offers

Apart from understanding the buying behavior of a customer (using bonus cards and others), retailers spend a huge amount of effort in understanding where the demand will be. Trend forecast algorithms combine social media posts, web browsing behavior, and ad-buying data to predict what will cause a trend or buzz. Social media discussions on the clothing habits of a popular band might cause specific trousers to become popular. These sentiment analyses get even more complex if you realize that there is a heavy demographic component embedded together with economic indicators. Offerings on detective books will increase significantly if two things occur – the weather gets colder and at the same time a significant crime is discussed on social media.

In-Memory Computing and Interactive Insights Make the Difference

Retailers and B2Cs in today’s market dynamically follow and influence customer buying behavior. They have to because the consumer is so well informed and has so many alternatives for buying. Retailers have to act instantly on changing behavior. To do so the amount and complexity of information that needs to be analyzed is so big, only in-memory computing can handle it. Bear in mind that an individual retailer is never on its own but part of a brand, meaning individual shop performance is rolled-up to the corporate level. This corporate level manages online shop performance indicators, compares the various stores, and delegates rolling budgets down to the shops on a daily basis. These budgets vary daily given the changing demand analyses we talked about above.

These dynamics also require online interactive analytical capabilities. Information on buying and demand behavior varies daily and is analyzed permanently. Ever changing sources, unknown structures of new information, or simulation models require the analyst to interact with the data all the time.

In a future article, we will deep dive into some of the other use cases for business analytics in Retailing and B2C market spaces. One of them is basket analysis. Using predictive modeling combined with business analytics, it’s possible online to utilize the buying behavior of the consumer. These are techniques that are used today! Looking forward to share with you.

The post The Overwhelming Power of Analytics in Retailing and B2C: Part One appeared first on Amick Brown.