Tag Archives: Ashith Bolar

SAP Business Suite 4 SAP HANA – Let’s Start at the Beginning

by Ashith Bolar, Director AmBr Labs, Amick Brown

Every week at Amick Brown, we are questioned about HANA There is a lot of confusion in the market with the multiple options. As well, there are many questions about timing and business application. You have asked, so we will start a series of HANA articles to address your questions.

grow blue suit

SAP Business Suite 4 SAP HANA, shortened to SAP S/4HANA,  is a big strategic play from SAP. Here is why you need to take heed.

The new SAP S/4HANA is supposed to replace the SAP Business Suite (formerly R/3) over the next few years. This announcement and the launch of the software lay a roadmap for SAP in the coming years.

What led to this launch?

SAP is a leader in ERP worldwide. However, in the recent past, a new trend is taking over in the business world. Cloud-based software services also known as Software-as-a-Service. SAP has SaaS components to it, but its main business model has been selling software the old way: software installed at the customers’ premise.

Other cloud companies  have been slowly chipping away at SAP’s market share. And this is SAP’s answer.

Name

The R in R/3 stood for real-time. The S in the S/4 stands for Simple. This is the big idea. SAP is planning on simplifying the ERP system with this release.

Database

While SAP R/3 Business Suite ran on any database, S/4 runs exclusively on HANA. SAP has spent considerable financial resources and effort on building up the in-memory database over the past few years. SAP HANA has tremendous performance advantages compared to the older disk-based database solutions. This large-scale change has enabled SAP to dramatically simplify both the data-model as well as the user-experience.

One significant aspect of HANA is that it is an in-memory appliance. This means data-access times (disk read/writes) are not an issue anymore, allowing developers to focus more on business logic than performance. This lends itself to the other motivation for S/4 – simplicity.

The Cloud

SAP S/4HANA is mostly a movement of SAP’s premier software from customer premise to the cloud. However, on-premise solutions will still be available. SAP offers 3 options:

  1. Public Cloud – Completely managed by SAP. Multi-tenancy shared by all public cloud customers
  2. Private Cloud – Partially managed by SAP. Exclusive database per customer.
  3. On-Premise – Software installed on client’s hardware. Client pays for user-licenses.

Software

SAP S/4HANA will allow customization to S/4HANA on the HANA Cloud Platform (HCP). This means ABAP developers will get to continue to use their skills. If you don’t know OO, it is a good time to learn it.

UI

Let’s admit it, SAP R/3 has not been known for its stellar user-experience. UI on SAP R/3 has been clunky, rigid and unwelcoming. But the S/4HANA user-interface will be based on SAP’s Fiori UX platform. SAP Fiori, launched earlier in 2014, gives the software a new look-and-feel. The fact it does not have licensing cost should make it attractive to customers with an existing SAP installation.

Conclusion

Co-founder, Hasso Plattner said “If this doesn’t work, we’re dead. Flat-out dead.” This may be just Hasso Plattner being the passionate visionary that he is. But this indeed is a huge launch from SAP, one whose initial roll out is expected to be 3-5 years, and customer transitions lasting more 10 years.

The story on S/4 HANA continues here.  Watch this space and Follow Amick Brown on LinkedIn

The Human Aspect of Predictive Analytics

By Ashith Bolar , Director AmBr Labs, Amick Brown

The past decade and a half has seen a steady increase in Business Intelligence (BI). Every company boasts a solid portfolio of BI software and applications. The fundamental feature of BI is Data Analytics. Corporations that boasts large data do indeed derive a lot of value from their Data Analytics. A natural progression of Data Analytics is Predictive Analytics (PA).

Think of data analytics as a forensic exercise in measuring the past and the current state of the system. Predictive Analytics is the extension of this exercise: Instead of just analyzing the past and evaluating the current, predictive analytics applies that insight to determine, or rather shape the future.

Predictive Analytics is the natural progression of BI.

The current state of PA in the general business world is, for the most part, at its inception. Experts in the field talk about PA as the panacea – as the be-all and end-all solution to all business problems. This is very reminiscent of the early stages of BI towards the turn of the century. Hype as it may be, BI did end up taking the center stage over the ensuing years. BI was not the solution to the business problems anymore; it was indeed mandatory for the very survival of a company. Companies don’t implement BI to be on the leading-edge of the industry anymore. They implement BI just to keep up. Without BI, most companies would not be competitive enough to survive the market forces.

Very soon, PA will be in a similar state. PA will not be the leading-edge paradigm to get a headstart over other companies. Instead PA is what you do to just survive. All technologies go through this phase transition – from leading-edge to must-have-to-survive. And PA is no different.

predict the future

Having made this prediction, let’s take a look at where PA stands. Some industries (and some organizations) have been using predictive analytics for several decades. One such not-so-obvious example is the financial industry. The ubiquity of FICO scores in our daily lives does not make it obvious, but they are predictive analytics at work. Your fico score predicts, with a certain degree of accuracy, the likelihood of you defaulting on a loan. A simple number, that may or may not be accurate in individual cases, arguably has been the fuel to the behemoth economic machinery of this country, saving trillions of dollars for the banking industry as well as the common people such as you and me.

Another example would be that of the marketing departments of large retail stores. They have put formal PA to use for several years now, in a variety of applications such as product placement, etc. If it works for them, there is no reason it should not work for you.

This is easier said than done. Implementing Predictive Analytics is not a trivial task. It’s not like you buy a piece of software from the Internet, install it on a laptop, and boom – you’re predicting the future. Although I have to admit that that is a good starting point. Implementing the initial infrastructure for PA does require meticulous planning. It’s a time-consuming effort, but at this point in time, a worthy effort.

Let’s take a look at high-level task list for this project

  1. Build the PA infrastructure
  2. Choose/build predictive model(s)
  3. Provision Data
  4. Predict!
  5. Ensure there’s company-wide adoption of the new predictive model. Make PA a key part of the organization’s operational framework. Ensure that folks in the company trust the predictive model and not try to override it with their human intelligence.

Steps 1 thru 4 are the easy bits. It’s the 5th step that requires a significant effort.

Most of us have relied on our superior intellect when it comes to making serious decisions. And most of us believe that such decision-making process yields the best decisions. It is hard for us to imagine that a few numbers and a simple algorithm would yield better decisions than those from the depths of our intellect.

However, it is important to change your organization’s mindset about predictive analytics. If you are considering your business to be consistently run on mathematical predictive models, acceptance from the user community is crucial. Implementing PA is a substantial effort in Organizational Change Management.

Remember the financial services industry. They don’t let their loan officers make spot decisions on the loan-eligibility of their clients based on their appearance, style of speech or any such human sensory cues. Although, if you ask the loan officer, they might claim to be better judges of character – the financial industry does not rely on their superior human intellect to measure the risk of loan default. Generally, a single 3-digit number makes that decision for them.

The next time you go to the supermarket for a loaf of bread, and return with a shopping cart full of merchandise that you serendipitously found on the way back from the bread aisle including the merchandise along the cash-counter, you can thank (or curse) the predictive analytics employed by the store headquarters located probably a thousand miles away from you.

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Predictive Analytics 101 – The Real Business Intelligence, part 2

by Ashith Bolar,  Director AmBr Data Labs @ Amick Brown

In my first post, “The Real Business Intelligence” ,  I emphasized on the significance of Predictive Analytics in the Business Intelligence space.  Let us take a deeper look at Predictive Analytics by way of more concrete examples.

As a refresher, Predictive Analytics is a set of tools and techniques based on statistical and mathematical techniques to analyze historical data and subsequently predict the future. The basic premise is that by analyzing historical data, determining relationships, more specifically correlations between related (and sometimes seemingly unrelated) attributes and entities, one can derive significant insights into a system. These insights can be further used to make predictions.

Let’s take a look at this process step by step.

The fundamental component in Predictive Analytics is a Predictive Model, or just model. A Predictive Model is set of data points plus a series of algorithms working on that data. It attempts to capture the relationships between the data points by means of applying mathematical or statistical computations deployed as algorithms.

The output of a model is typically a single number — called a score. The score essentially is an quantitative value for a specific prediction by the model based on historical data. Higher the score, the more likely a certain behavior is predicted. Lower the score, the more likely the opposite behaviour is predicted.

Predictive models can be built for a wide variety of problems. But the most common predictive models, especially in the context of a business application, is one that predicts people’s behaviors. Predictive models are designed to predict how people behave under new circumstances, given what we know about how they behaved in the past with other known circumstances. For instance, Netflix’s movie recommendations — based on the movies that you have seen and rated highly (known circumstances) recommendations for new movies (unknown circumstances) are generated.

You will hear terms like “Machine Learning”, “Artificial Intelligence”, “Regression Analysis”, etc. While, each one is an independent area of mathematics and computing, for a Predictive Analytics suite, these are just different algorithms (computing models) that are employed in the process of predicting.

Let’s dig deeper into the concept of Scores. Let’s take two classical examples of scores generated on customers.

  1. Based on the movies you have seen and rated in the past, Netflix tries to determine if you will like a new movie or not. Say for instance, a scoring of 0-10: 10 being a prediction of you absolutely loving the new movie, and 0 being a prediction of you absolute not caring about it. This type of a score is called a Probability Score. In essence, the score tells you the probability of you liking a movie.
  2. Another type of score is called the Quantitative Score. Here the prediction is not the probability of whether you will like the movie or not, instead to quantitatively predict the amount of something. For instance, Life Insurance companies try to predict how long a certain customer will live based on the life choices and other circumstances of the customer.

In case of the Netflix model (Probability Score), if a customer gets a 8 (out of 10) for the likelihood of liking a particular movie, it can be rephrased as “There’s a 80% chance that the customer will like this movie, and a 20% chance that they will not like it”. Such a prediction is basing its prediction on the spread of probabilities (probability distribution). Another way of looking at this score of 8 (out of 10 is) “The customer might not absolutely love this movie (which would be a 10/10), but definitely not absolutely hate the movie (0/10). Instead the customer is more likely actually liking the movie to some extent (8/10), rather than completely disinterested (5/10).

In either case, a careful examination of this score tells us that all the system is doing is categorizing people’s behaviours into a set number of ranks. Therefore, predictive models which generate probability scores are usually called classification models. On the other hand, the quantitative scoring (predicted life expectancy of an insurance customer) is really a quantitative number. Another classic example is customer spend which is how much a customer is willing to pay for a new product or service. The actual value is reached at by means of various statistical and mathematical computations. These models are typically known as regression models.

A good predictive model might not be accurate in every single case, but given a large set of data (read target customers), the model regresses to the predicted mean of the behavior.

In the following posts, we will delve deeper into predictive algorithms, and try to gain a better understanding of how they work, and more importantly why they work, and why they are important in your corporate strategy.

 

The Real Business Intelligence

Ashith Bolar, Director of Research, Amick Brown

In the state of the art of computing, every company generates a large amount of data, and it goes without saying that every organization does some sort of data analysis on this data. Big and small companies invest in Business Intelligence in some shape or form. The ubiquity of big data infrastructures, such as those from SAP, as well as Hadoop and its various distributions also has enabled even smaller and medium sized businesses (SMB) to perform data analytics at scale.

glass wall meeting

Loosely speaking, Business Intelligence (BI) is a set of techniques and tools used to transform raw data into meaningful and actionable information. However, simply based on that definition, virtually every exercise in data analytics can be considered as Business Intelligence. The true value of a BI is only realized when the latest tools and technologies are applied in order to determine the historic, current and predictive views of the business.

One of the applications of BI is Predictive Analytics (PA). PA encompasses a large and fluid set of tools based on statistical and mathematical techniques to analyze historical data and subsequently build predictive models. The idea is that historical data contains patterns that, if recognized and correctly applied, can be used in predicting the future. This enables a company to predict and proactively respond to the future state of the business, say customer behavior, market conditions, etc.

growth graph

Take a look at your current BI system. Are you only analyzing  historical data, and at best enumerating the current state of your business? Or does your Business Intelligence platform help you model the future and enable you to predict the course of your business? Are you able to identify risks and opportunities well before they occur?

PA captures patterns and relationships among the various factors in your business, giving you a deeper perspective of risks or potentials associated with your current course of business. This enables you to make optimal changes to your business in order to make the most of the current and future market conditions.

Current technology offers very cost-effective means of Predictive Analytics that SMBs could implement with virtually no upfront cost.  SAP’s Predictive Analytics Software gives you a robust set of tools in this space. These tools run on your enterprise data and any supplementary data that you provide.

SAP’s Predictive Analytics Library (SAP-PAL) provides the following list of capabilities:

  • Predictive Modeling : automated set of tools to build predictive models
  • Predictive Scoring : identify and evaluate relevant variables in predicting
  • Predictive Model Management : enable end-users with limited knowledge of the science of predictive analytics to ask what-if questions
  • Predictive Network and Link Analysis : explore the links between your customers and network of strong social influencers with analytics
  • Predictive Data Management : automated data-set preparation for predictive modeling

Amick Brown can help you realize the predictive powers that you have with your SAP and BI platform provides you.