by Kaan Turnali, Global Senior Director, Enterprise Analytics

Proof 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?

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.

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:

1. Insights: 13 deep dive articles on various aspects of business analytics like industry specific approaches, embedded analytics and many more
2. Strategy: 13 chapters talking analytics strategy related subjects and topics like defining your BI roadmap or the closed loop portfolio
3. 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
4. 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 Closed Loop portfolio in Analytics

Authored by Iver van de Zand, SAP

We talked about the overwhelming power of analytics in Retail and B2C market-segments earlier and one of the topics discussed there, was the integration of operational business activities with operational analytics. In the example we saw the stock manager using analytics to change his stock-buying-behavior. He adjusted his order system by choosing another vendor and placing the order. Immediately his analytics are updated and he now requires to adjust his rolling planning or run a predictive simulation how the price-adjustment of his new stock might affect buying behavior of his customers. He might even want to adjust the governance rules with his new supplier or run a risk-assessment.

Below pictures visualizes the continuous integration of core business activities with business analytics, indicating examples of core processes with their accompanying analytical perspectives. These are just examples and not exhaustive at all.

Performance Management closed loop

Basically what the stock manager in our example needs, is a full – real-time – integration of business analytics with his core business activities over all aspects of his performance management domain. A predictive simulation of changing buying behavior lead to new analytical insights on product mix which might influence the companies’ budget and causes a risk analysis for new vendors.

To do so, a closed loop is required of following core components driven by the continuous flow of Discover – Plan – Inform – Anticipate:

• online Analytics on big data with interactive user involvement
• ability to adjust and monitor a rolling Planning for budgets, forecasts. A planning that that allows for delegation and distribution from the corporate level into lower levels
• GRC software to perform risk analyses on for example vendors or suppliers
• online Predictive analyses components to apply predictive models like decision trees, forecasting models or other R algorithms. Predictive analyses allow to look for patterns in the data that “regular” analytics is not able to discover. The scope of predictive analytics is gigantic: think not only sentiment analyses for social media, but also basket analyses in retail markets, attrition rates in HR and many, many more.

This so-called closed loop of predictive analytics, planning and performance management, business analytics and GRC is NOT a sequential process at all. They interact randomly towards each other in real-time and at any moment needed. They are also dependent towards each other, since Digital Transformation requires us to be so agile, we have to constantly execute and collaborate on the interoperability of the components and monitor the outcome. Lastly, the closed loop platform interacts on core operational activities (real-time insights in operational data) and as such the analytics are defined as Operational Analytics.

Closed loop platforms more than anything else require business users to drive its content and purpose. They drive the agility to the platform that is so heavily needed in the Digital Transformation era. On the other hand, the technical-driven architects do make a difference too since closed loop platforms are very sensitive to respect governance principles. A special role is allocated to the CFO or Office of Finance here; they will drive the bigger part of the Planning and Budgeting cycle.

One can imagine the calculation processes behind the closed-loop platform are huge and therefore a business case for an in-memory system is a sine qua non.

Imagine the possibilities

Needless to say that the closed loop model applies to all industries and not only in the retail example that I used here. I can list plenty of examples here but just to name a few:

• HR: attrition rates of employees
• Banking & Insurance: customer segmentation, product basket analyses
• Telco & Communications: churn and market segmentation nut also network utilization
• Public Government: Fraud detection and Risk-Mitigation
• Hospital: personalized healthcare

Apart from imagining the possibilities per market segment, we can also change perspectives and look at the possibilities per role within companies applying the closed-loop platform. Below picture provides capabilities the closed loop components could offer to various user communities. The potential is huge and extremely powerful when used in an integrated platform. This is also the weaker point of the closed loop platform: the components must be integrated not to miss their leveraging effect on each other.

A solution is available today

With its Cloud for Analytics offering, SAP is today the only provider with an integrated offering for the closed-loop platform. Even more: SAP Cloud for Analytics is integrated in one tool offering analytics, planning, GRC and predictive capabilities. One tool?? …. Yes, one tool is completely Cloud-driven and utilizes the in-memory HANA Cloud Platform it is running on. One tool that seamlessly lets analytics and planning interact with each other. A tool where you can run your predictive models and analyses and visualize the outcome with the analytics section. A tool that allows access to both your on premise data, your Cloud data and/or Hadoop stored data. And lastly a tool with fully embedded collaboration techniques to share your insights with colleagues but also involve them with planning or others. Our dream becomes reality.