Tag Archives: In-Memory Computing

2016 and Business Analytics: Be Prepared for a Smashing Year: Part One

by Iver van de Zand, SAP

The end of the year is always a time to reflect, but also a time to look ahead and think about what might be different or innovating next year.three-spheres

Reflecting on 2015, three things immediately come to mind:

  1. Interactive self-service business intelligence (BI) has definitely landed and earned its permanent place. Every top-100 customer I talked to has self-service business intelligence in its BI strategy plans.
  2. “Traditional” business analytics (as in managed reporting and dash boarding) is not sufficient anymore for full performance management. A closed loop portfolio of analytical, predictive, planning, and GRC information is becoming a necessity in today’s management of processes and business flows.
  3. The value of in-memory platforms is now being recognized by leading companies. They massively adopt in-memory platforms to not only run their core applications, but also to integrate business data and facilitate analytics.

Looking forward, I’m sure you’ll agree with me that analytics is heavily influenced by the readiness of organizations to adapt to change resulting from the Digital Transformation. Connected economies and networks, data that’s available at any moment at any level, and sensor techniques allowing for new business models—they all heavily influence our needs for insights. As such, they heavily influence the 2016 trends for business analytics.

Did Tableau Lose Its Head?

Recently, I did the Google search exercise for “BI Trends 2016″and was both shocked and amazed. Our friends from Tableau’s marketing department have succeeded in monopolizing  80% of the first 20 hits! However, if you read closely, you’ll notice they are all referring to the exact same article. (Though they all seem to be different articles, they all cover identical things.)

I was further shocked  by the lack of insights these identical articles cover.  My feeling is that the articles point out  BI trends for 2014 (or earlier). “Governance and self-service become best friends,” it says. Dear people from Tableau, self-service BI can only exist by the sake of data governance. If self-service BI is not governed properly, there is no sense for it. And the trend mentioned as “Data Integration gets Exciting”? This was something everybody focused upon in 2012.

Analytical Projections for 2016

So what can we expect for 2016? Personally, I can only reflect on what I see and hear when talking analytics with key customers every single day. For me, these discussions have provided food for thought. Listening to the plans that my customers have, I can extract five key trends for business analytics in 2016:

  1. Self-service BI will become a commodity
  2. Business will embrace the portfolio loop
  3. Companies will really analyze Big Data
  4. Cloud BI adoption will accelerate
  5. Operational BI footprint will grow

Let’s take a closer look at the first two trends in today’s blog.

  1. Self-Service BI Becomes Commodity

Governed self-service BI will further find its way to all echelons of organizations. And the reason is simple— business users finally have the opportunity to drive analytics in their organizations. While 2015 was the year of adopting self-service BI, 2016 will be the year of the massive roll-out. Self-service BI is becoming a commodity in 2016 with the number of business users growing rapidly. From a functional perspective, the success of self-service BI is greatly determined by its ability to:

●  Interact with the user. Self-service BI can be adopted quickly because end users are able to interact with massive amounts of structured and unstructured sources of information.

●  Make data and insights easily visible. Business users really recognize the value of making insights visible. The simple but clever idea of using visualizations and analyses to create your own stories (storytelling and infographics) is very successful. Nice examples are GEO-driven stories, dashboards , and D3 open- source visualizations. These, combined  with interactivity, make self-service BI a stunning combo. As I’ve mentioned before,  “our meetings will never be the same.” We can now use interactive, visualized insights to discuss and monitor the heartbeat of our company in real time!

●  Be agile with new and ever-changing data. A third success factor (what’s in a name J) to self-service BI is its agility. This agility is a huge value-add because it allows business users to really simply acquire and enrich new data and use it for analyses. Bear in mind, this also applies to Big Data using in-memory computing.

  1. Business Embraces the Portfolio LoopReal-time business wheel

I’ve made my point on the importance of the closed loop portfolio in earlier blogs. Every key customer I met last year who’s willing to embrace Digital Transformation is seeking an integrated and governed platform to analyze, plan, predict, and assess risks in a constant and permanent loop.

I use the word ‘integrated’ on purpose here, since here is where the difference is made—customers seek to have real-time integration between their business analytics, their detailed planning, and the predictive models that affect, for example, product mix or pricing strategy. The integration also needs to be on operational financials and towards risks and compliancy cases when needed.

Many of my customers have accomplished this on a near-integrated level that isn’t real time by using individual components that access each other’s data. Products like SAP Cloud for Analytics are revolutionary here since they provide the closed loop portfolio covering real-time, interactive integration on all mentioned areas. Markets have been waiting for this for quite some time and are eager to adopt. It allows them to interact with market fluctuations that speed up due to the Digital Transformation. You can look at the examples I described in a previous blog for the retailing sector to understand the scope of the closed loop portfolio.

Stay tuned for my next blog. I’ll discuss the other three trends I see for business analytics in 2016: analyzing Big Data, the acceleration of cloud BI, and  the growth of operational BI.

Follow me on Twitter @IverVandeZand.

Amick Brown is here for you.

 

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

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