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What you should consider when embarking on an Advanced Analytics journey?

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By Paul Pallath, PHD,  Chief Data Scientist & Director Advanced Analytics, SAP

In my previous Predictive blog, I introduced four main considerations that organizations need to keep in mind when they’re beginning that journey. Today, I’ll cover them in more detail.

1. How Do We Measure Business Value and Return on Investment?

An advanced analytics solution must make a measureable impact. If not, the solution doesn’t get noticed, never mind appreciated. This holds even more true, if the return on investment (ROI) can’t be realized as a significant opportunity to drive business growth or new market opportunities.

Take the example of a marketing campaign. The ROI is in having the intelligence to target the customers who are likely (if persuaded) to buy your product rather than finding customers who would have bought the product without any marketing required.

An advanced analytics solution will be short-lived if it creates a “wow” effect, but nothing else.  The solution must generate recurrent value, revenue, and business opportunities.

2. How Do We Use Advanced Analytics Effectively?

For your business, good questions to ask at the start of the journey are:

  • Is the enterprise truly digital?
  • Is there a single source of truth of all the data that is generated/captured by various functions of the enterprise?

These questions are important considerations. Why? Because businesses often approach advanced analytics in an ineffective manner.

Remember, advanced analytics drive value to every business function, be it marketing, finance, human resources, and so on. However, enterprise functions want often to embed advanced analytics into their business workflow and embark on advanced analytics initiatives in silos. Though there is value in doing so, the results can be underwhelming.

This is because they’re using adoptions of various technologies, methodologies, practices to address the use cases that might exists— but without an enterprise-wide vision for advanced analytics. Therefore, walls rather than bridges are built between the various functions.

The problem becomes self-perpetuating. With increasing adoption of advanced analytics solutions in various business units, the business as a whole finds it difficult to consolidate all the activities into a central initiative and have proper discipline and governance.

The solution is to create the vision and execute it across all functions— even if the pilot starts from one or two activities. The functions must agree that advanced analytics is an enterprise-wide mission. Leadership must demonstrate belief in an analytics-driven business that it is going to provide competitive advantage. In this way, advanced analytics becomes a true company asset.

3. Is Advanced Analytics Just Another Technology Project?

Advanced Analytics is not just another technology project. If considered to be a technology project, the business understands only the technical feasibility and not its business impact.

As mentioned, an advanced analytics initiative is the means by which a business gains a competitive advantage. It follows that outcomes provide the data to help make well-informed decisions.

A lesser or confined approach is a step in the wrong direction. There is no ROI associated with technology-only thinking, because no tangible results are expected as an outcome. An initiative to embrace advanced analytics must be inseparable from your business strategy.

4. Is Big Data Equal to High Quality Insight?

Big Data is not equal to high quality insight.  A traditional business approach is to think, “We’ve captured huge amounts of data, but how do we  make sense of it?” This is a wrong start.

The right approach is to start with a business question in mind. That way, you can ask if the data that you have is sufficient enough to provide the answer.

These are several pieces of the puzzle that need to be put together for one to find meaningful, actionable insights from the data. This is, after all,  the quest that we embarked on.

As we now know, advanced analytics is about business change, insight and value.

“The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data”-Sunset salvo. The American Statistician 40 (1).

Follow Amick Brown on LinkedIn for the best SAP Analytics and Reporting topics.

Analytics, SAP

Are you Planning to Embark on an Advanced Analytics Journey?

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by Paul Pallath, PhD, Chief Data Scientist & Director, Advanced Analytics, SAP

Businessman Analyzing Graph

Welcome to the new world!  The manner in which data is generated and captured today has come of age. Traditionally the way of generating data for the most part from B2C/B2B2C business processes, was by having interactions captured as part of transactional systems in a highly structured format. But with changing technological landscape, much has changed in how data is generated and captured.

What data supports this view?

According to the ESG Digital Archive Market Forecast, the growth in data volumes that is driven by unstructured data amounts to more than 88% as compared to structured data. What’s more, Computer World states that unstructured information may account for more than 70% to 80% of all data in organizations.

The change has come because we in the 21st Century have redefined the way business is conducted. Significant advancement in internet technology has forced the need for digital online presence for most businesses to stay relevant. Likewise, every interaction that a customer has in the digital online ecosystem leaves behind a digital foot print containing huge amount of information.

Social media presences, for individuals and businesses, have increased the speed at which information travels. This has made it possible to share opinions as blogs or multimedia content. The result is the constant generation of large amounts of unstructured data.

All that Unstructured Data Is Good News for Data Scientists

However, this is good news for data scientists.

The previous figures imply that we have now Yottabytes [1024] of data at our disposal for deriving business value—and that amount of data is about to increase.

The Internet of Things with its emphasis on completely connected systems has resulted in the availability of high speed streaming data. This makes it possible for new innovations that use data to build technologies to enable machines talk to one another (and perhaps eventually become intelligent enough to remove humans from the loop)! Taking the trend into consideration, Brontobytes [1027] of data to work with will soon be a reality for data scientists.

So, what is the best way for a business to capture and benefit from this information? Of course, capturing the massive swathes of data available is an important part of the Big Data story. But it’s not the most important part.

The most vital activity is to generate insights that add value to your business. This takes vision, it takes change, it takes…advanced analytics.

For organizations embarking on a journey into advanced analytics, it’s vital to keep in mind these important considerations:

  • How Do We Measure Business Value and Return on Investment?
  • How Do We Use Advanced Analytics Effectively?
  • Is Advanced Analytics Just Another Technology Project?
  • Is Big Data Equal to High Quality Insight?

In future blogs, I’ll discuss each one of these considerations in more detail.

Let me know what you think. Did I leave one off the list?

Amick Brown would sincerely like to Thank Dr, Pallath for his contribution today .

Analytics, Big data, Business Intelligence, SAP

“Passionate on Analytics” , new book available

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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 from the Apple iBooks Store via this link.

Since I am evangelizing on interactive analytics every single day, I decided to create aninteractive 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

 

SAP

What is Design Thinking?

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Contributed by Kaan Turnali, SAP
“Customer-centric design is about looking out from the inside—rather than outside in”

Today’s organizations face multifaceted problems that are part of increasingly complex business models. Continued expansion of global transactions, supported by partnerships that can span large ecosystems, create unique opportunities and unique challenges for businesses.

These challenges demand multidimensional solutions and require more than just basic applications of current products and services. This is where design thinking comes into play. By applying this framework, organizations can not only address everyday business problems and challenges but also gain a competitive edge.

To stay relevant, companies must innovate without disruption to drive growth and profitability. As Tim Brown, CEO of the design and innovation firm IDEO, puts it in a Forbes.com interview, “Design thinking is all about upgrading within constraints.”

What-Is-Design-Thinking

What Is Design Thinking
In its simplest form, design thinking is a process—applicable to all walks of life—of creating new and innovative ideas and solving problems. It is not limited to a specific industry or area of expertise.

It can be as effective in technology or education as it may be in services or manufacturing. It could result in new products and services for customers or improved processes and productivity gains for internal operations. If applied with equal fervor, it can even transform HR, finance, marketing, or operations teams—turning them into lean and agile profit centers.

The award-winning documentary Design & Thinking explores this idea and provides perspectives by well-known subject-matter experts in this space. The Hasso-Plattner-Institute (HPI) at the University of Potsdam School of Design Thinking, in Germany and the Hasso-Plattner-Institute of Design at Stanford University in California are the two leading educational institutions in this field.

Developed by IDEO founder David Kelley, design thinking is defined as “a human-centered approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.” Thus, the method focuses on three main elements of a product or solution: people, technology, and business. All of these aspects evolve around the customer.

The customer drives the current and future state of any business. Products and services, whether they are delivered to internal or external customers, must create intrinsic value and address specific business needs. This cannot be done unless the customer is an integral part of the entire product lifecycle—not an afterthought.

It is accepted that businesses, projects, or teams that lack customer focus are bound to fail. Design thinking makes the customer the main focal point of design for any solution. Plus, it consistently applies the values embraced by this approach, such as empathy, diversity, and ambiguity, as well as recognizing the importance of multidisciplinary teams. Many of these principles reflect ideas that stem from well-known principles and/or best practices. However, design thinking, in essence, incorporates them into a coherent and repeatable process.

Here are my interpretations of some of these principles. You can find others listed here.

  • Success comes from designing integrated solutions in which each part completes the system in whole—not designing fragmented pieces that make up a stack.
  • By getting closer to current or potential users and going beyond distant observation, we have a chance to design by looking out from the inside—rather than outside in.
  • Empathy opens up nerve endings so we can feel what it is like to be in another’s shoes—a prerequisite for customer-centric design. We need to get as frustrated as the users/customers so we can better understand the pain points.
  • By bringing multidisciplinary teams together at the table, we leverage the power of collective expertise.
  • Embracing ambiguity opens the door for human ingenuity—allowing us to chase opportunities for new ideas we would otherwise miss.
  • Promoting the philosophy of “fail early and often” is the key to harnessing the power of rapid prototypes and delivering proof of concepts that resonate and encourage feedback from actual users and customers.

This last one is the closest to my heart because it often reminds me of two famous quotes (available in several variations on the Web). First, there is Edison’s quote about failure and inventing the light bulb: “I have not failed, not once. I’ve discovered ten thousand ways that don’t work.” Then there is Frank Lloyd Wright’s insight “You can use an eraser on the drafting table or a sledgehammer on the construction site.”

To me, design thinking is a framework for ingenuity. It can generate excitement for new ideas, leading to solutions that address unmet needs. Just as business intelligence can be the enabler for faster, better-informed decisions, design thinking can be the driver for better-designed solutions for products and services.

I always argue that passion is the fire that ignites engagement by inspiring what is possible. In the context of business and technology, ideas create the demand for technology that can translate into solutions that drive growth and profitability.

Connect with me on Twitter (@KaanTurnali), LinkedIn and turnali.com

SAP

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

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Women With Shopping Bags --- Image by © Tim Pannell/Corbis
Women With Shopping Bags — Image by © Tim Pannell/Corbis

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 OffersMan holding gift bags --- Image by © Ocean/Corbis

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

– See more at: http://blogs.sap.com/analytics/2015/12/09/the-overwhelming-power-of-analytics-in-retailing-and-b2c-part-one/#sthash.ozusXrxq.dpuf

Business Intelligence, SAP

The Closed Loop portfolio in Analytics

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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 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 therefor 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 completely Cloud driven and utilizing 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.