The new application that detects a cheating HANA partner

 

As someone once said 'Cheating is a choice, not a mistake'

AUTHOR'S NOTE: What follows represents an actionable roadmap of possible paths in our global HANA decisions. If you consider this story farfetched then do some independent Gartner and Google research and come to your own conclusions. You will hear a lot of key finding repeated in my papers, and the reason for that is though I have been publishing and stating them for over 3 years I find no one listening. Two reasons for that either it challenges everything they, the big x influencers, have believed in for the last decade and built their processes and practice on it, or they are better off not resonating with the Gartner and other statements as that will mean a change of their methodology into something they are not familiar with.

***

I work as a HANA solution architect for one of the big implementing partners and have lived a perfect life in selling in the SAP BI space for the last 14 years. I have been working with this fortune 100 corporation and have been their preferred advisor for over ten years. They trust me and my advice and I am living close to a perfect life, but then this new HANA app ruined everything.

My name is John Dimming, and for almost the last two decades I have happily lived two lives. Most days I am the perfect advisor, a near perfect BI solution architect, SAP BI leader that is dependent, reliable and highly capable. When my customers need any advice I am there for them. 

No one except my select customers knew me and that for me represented my perfect life. I lived in the shadows of perfection and my advice has been taken without much challenge.

I come from an engineering background and got into BI and BI sales as I passed out from the university. My education, my training and my mentors taught me one thing – that BI technology has reached the pinnacle of capabilities and can today, this was in 2002, answer any question that a business user needs. In fact I firmly believed that if a business group needed 60 reports to meet their requirements my business content could give them 300 and far exceed their expectations. I followed the path that over 90% of BI solution architects have followed for the last decade or so.  However, in the back of my mind there was a gnawing feeling that I was not doing a perfect business solution. What we provided were perfect technical solutions.

Then I read the global Gartner report of 2003 that rocked my mind and split my mind into two distinct paradigms. The report, though simple, had a profound impact on the future of BI. It clearly stated that according to the feedback of over 1500 CIO’s “Fewer than 50% of BI reports would meet business expectations”.

This report took almost 2 years to sink-in when I happened to read it again because one of my CFO’s asked me how that related to them. It was followed by another report that stated that ‘98% of BI projects are declared successful in week one of Go Live, yet less than 50% of them remain successful by week 10”

 Looking back I realized though we did 2 to 4 multi-million BI projects a year yet very few of them were truly reference able. In each of them we had popped champagne bottles after go live and never looked back as we celebrated in Mexico or Las Vegas for another project well done. We never looked back – and that is something I started doing around 2010.

I started to see a common pattern of ‘The BI project was an IT success, but a BI failure’ where the client management and key stakeholders could rarely go back to their stockholders and state that their multi-million BI project was a failure. This pattern was consistent whether the project was Oracle, Teradata or any other BI application.

By 2011 I realized that I was part of a big deception of selling very high quantity of reports and custom objects, something that cost clients large budgets, but more often than not very little information. It was common to deliver 360 FI reports but rarely did we measure what business users could actually use.

Business users, who were they, in all our projects we actually forbade them to come into the project war-room. If one of them inadvertently entered the room I personally raised the red flag to the CEO or CFO recommending they keep business users out of the project room as they were an unnecessary distraction. Our statement was - our company was a global consulting leader and we had deployed hundreds of BI projects and we knew more about what the company needed than most business users. We never lost.

Very soon I realized that though I was talented my talent was limited to deception. Having worked with CxO level stakeholders for so long I am trained and can construct perfectly logical success stories and convince my customers. But now a gnawing sense of insecurity crept in as I realized I was continuing to sell application support, architecture, modeling with a solid track record of over 70% failure. But there was nothing else I knew so I continued with my deception and my bonuses.

For all these years I had a free pass that got me a lot of satisfaction that required me to completely cut loose from my forebodings and concerns and after each meeting and sale I would come back a renewed man more convinced that maybe Gartner was all wrong about this.

Then in 2011 an 8 million dollar BI project that I was involved in failed most user tests eight weeks before go live. The BI lead, having read the 2003 Gartner report, asked if they could meet at least 40% user satisfaction they would consider the project a success. I requested my company to assign me to another project which they immediately complied. The project in question was postponed by six months but that is now someone elses concern.

In 2012 I was part of the same BI project where my counterpart had convinced them that their failure was not because of bad design, which I knew to be not true at all, but the platform. We recommended moving to HANA and once again I put my professional garb of deception and convinced my customer that because we had been providing them application and business support. When one of the business stakeholders asked why they should spend on HANA if their current BI could not meet over 80% of business reporting expectations, we shut the person down with protocol efficiency.

Then the VP of sales opened the iPhone application ‘BInformed’ and even till today it kills me that I had never heard of this scientific application designed to expose erroneous decisions on big-data decisions and trap cheating, or inexperienced, advisors, lying SI’s and what BI Valuenomics terms as technical criminals in a global heist. At that meeting I thought it was a humorous game and nothing more.  

Here is how it went down as the game was played by the very VP of sales I had buffed using my CIO protocol card. The game started with the detection of advice along random questions that it dared my team to beat. Being the lead honcho, and the BI Solution advisor, I took it upon myself to represent my company with a firm belief to prove to Mr. VP that he was a fool trying to play a business game with an IT demagogue.

Here’s how it went down.

The game started by asking if we used the standard ‘Z’ and ‘Y’ for custom BW developments and I proudly answered Yes.

It then asked what was the recommended BI architecture Data marts, EDW or FEDW- I knew there was nothing called FEDW and selected EDW.

The next question asked if we recommended taking all of the BW as is to BW on HANA and As I had crossed this bridge just a few weeks ago I again answered a solid and loud ‘Yes’.

This continued and in my and each time it was like hitting a jackpot for every question being asked was something I had been familiar with for over a decade.

By Question 9 or 10 I was feeling on top of the world. The question asked whether we could reduce the price of HANA SW and/or HW and I knew the answer both from SAP and HW partners and stated a clear ‘NO’ and asked for the next question.

This pesky game was trying to read my mind on questions that I had answers to. If I had made up the questions myself I could not have done a better job… I waited to hit the next question out of the park…

Then surprisingly, Mr. VP then stated that though there were 22 more question there was no need waste any more time as the result of these answers would provide an indicative success factor.

I smiled waiting for the result as did the foolish business VP who I would crush for wasting the valuable time of over 27 key stakeholders with this stupid game.

The game pulled out the score and visually and with data stated, in front of my 27 followers and key stakeholders that if the program followed the current path the probability of strategic success was less than 18% and that of tactical success under 24%

I could hear the air punched out of my Client managers guts. I could not believe it.

The CEO asked my manager to explain why the result of our questions was so alarming and whether we should continue with the remaining  questions. To which she unfortunately agreed and asked me to continue answering.
The end result was worse than before. We then challenged the game and were proven wrong once again.
You can guess what happened next. Our companies went for a simple divorce and so did the next three as they were measured on the same silly game. We hear that one company scored over 80% and have been trying to get their answers but each time we reach out to our old loyalists they shrink away in fear.

We not only divorced from that company but the application may result in divorcing me from my company. I have been challenging this vehemently and blogging about this useless game over my social network only to cause more and more people to use this business focused evil game. Who cares about business they are all fools and I will get my Technocrats back on the path that I have been pimping for the last decade. .

Authors note:

1.      This is a fictional story, but is has a string of truth in it.

2.      It is a reminder that if you are a cheater and your BI projects are a string of non-reference able failures maybe you need to retool your solutions, and recommendations, before you make the next HANA pitch

      3.      HANA is certainly not a technical implementation, but a business solution.

4.      My goal here is to place the question in the head of every big x consulting company: What will you’re your life be like when the truth finally becomes inescapable?

5.      And, for business stakeholders: it’s time you took ownership and accountability in every stage and every deliverable from your BI investments, all it takes is two hour of your time.

Gartner’s check:

1.      There is a very high probability that over 50% of reports in your production environment do not meet, or will never meet, business expectations
2.      In 2012 Gartner ran the same report and the result of over 2,400 CIO’s indicated that ‘Less than 30% of BI projects will meet business expectations in 2012-14” (author can send the report deconstructed for free- send email)

What’s in your pocket..

7 emerging trends in Big Data – SAP BW-on-HANA focused

Today it does not matter where on the planet one lives if we’re involved with SAP then HANA is a very bright light that is no longer on the horizon but getting closer every day. Many large customer are now inside this light and are reaping the benefits of moving to HANA, some are struggling to get to terms. Here are the 7 trends that are ensuring that HANA initiatives are not only successful but also the ROI is substantially lower. Today no matter where you live on the globe you are living in your own silicon valley

1. HANA is not a technology installation but a business solution

Customers and service providers: Despite repeating this singularity since 2010 we still see companies taking to HANA as just a technology installation. A technology installation is defined as lets take our current BW as is and migrate to HANA.  This is a very engineering and technocratic view of deploying HANA and is delivering results but lacking in maximizing its true potential. To these customers we ask a few fundamental questions.

Question 1: What is the business-benefit of accelerating your 700 second query to 1 second if businesses will never use the query?

Question 2: What is your interpretation when Gartner in 2003 stated that ‘fewer than 50% of BI projects will meet business expectations’ or when in 2012 they revalidated that ‘less than 30% of BI project will meet business expectations? Our interpretation is that there are over 50%, or 70% in 2012, of queries in your production BW that business does not need or is not using. Why take all these to HANA

HANA is very sexy but there are bonus points for all concerned to ensure that we clean up the landscape and environment before we migrate to HANA. We have witnessed anywhere from 20% to 60% reduction in database size by clean-up efforts. This translates to 20 to 60% reduction in initial costs and annual SLA’s that can be millions of dollars in each bucket.

2. Apps, Apps and more Apps

Service Providers: The shortest way to HANA success is in building applications for HANA. SAP has already committed a $1 billion fund for HANA start-ups, and this is the actionable roadmap for thinkers and doers to launch their billion dollar company and idea. Without a doubt, this solution cannot be a technocratic solution but need to be a business solution that impacts the business bottom line. The higher the demonstrable impact the higher the probability of success.

3. Big data is now a way of business-success-life

As we dive deeper into traditional RDBMS databases meeting our commitment to provide a query response in a 10 second timeframe is becoming more and more difficult. One simple solution is to decrease the time frame. So when we started BW we saw a year worth of data, then we shrank this to six months, then a quarter, then a month. With each spurt of growth our decision vision is becoming smaller. In driving we call this a tunnel-vision and in driving this can cause accidents. Another method of meeting the 10 second query response is caching, pre-summarizing, and other ‘tunnel-vision’ solutions.

Now suddenly, as big data is becoming more and more a reality from things like social network analysis, customer emotion analysis, running global scenarios, looking at five year trends and other such analytics as a minimum requirement. In addition customer are no longer willing to wait for once a month cost and profitability analysis because it takes 24 hours to run, and this includes global MRP runs, financial closings detailed P&L reports by plant and sales office, etc. All these business expectations and having to deal with very large and very complex runs are converging tin what is becoming best business practice.

So big data is here to stay and grow, business expectations are not going to go backwards and we all need to create dynamic and real-time business solutions to meet, and often, exceed business expectations

4. The Socially connected economy

Days when customers waited for days or weeks before responding to their stock allocations are now gone – people trade in real time and business needs to react similarly. Days when customer lived outside the firewall and marketing firms provided customer sentiments once a year are now gone- now companies measure customer emotions in real-time as they release new products or trade promotions. Days when customers talked with one or two individuals with little global impact are gone- now customer constantly communicate over social networks and can literally make of break a product launch in a matter of hours- companies now track thee customer in real-time and we are now living in a PR golden years as we can see emotions in real time. Days when service was only dispatched when a machine broke-down are now gone- now machine parts talk to other machine parts and call for service parts before they breakdown- companies are now working with machine talk in order to save billions of dollars in machine downtime and service outages. Our cars routinely tell us it is time for servicing or something is not optimal, like the tire pressure is low.  All of this is just the beginning of the socially connected economy where not only human but all kinds of devices talk to each other and automated consolidators for global optimization.

5. The globally linked economy

More and more people are now citizens of social groups some larger that many nations. These groups talk about families in Facebook or focused discussions on HANA like in ‘In-Memory SAP HANA’ group and learn and share their experiences with other members in the socially connected groups. Each of these members is influencing others in small and great ways and these links are becoming the source of global sharing of ideas and concerns.

6. SAP HANA is truly not as costly as you perceive it to be

All companies that have taken the route to ‘technocratic-HANA-Installation’ have mostly overpaid for their assets, and often compromised their ROI to a very large degree. Proactive companies are coming out with scientific principles on how to increase quality, lower costs and decrease time for deployment. Today there are few hurdles, as many customers have built all the proof points and what customers get today is extremely efficient ways to decrease their HANA costs, accelerators to rethink their global HANA methodology, many ways to reduce their HANA costs and more ways to get higher and higher user satisfaction. Very few of these are based on simple HANA installations. What one gets reminded day after day on the road is the 2010 Gartner statement ‘Without business in business intelligence, BI is dead’. So business stakeholders roll-up your sleeves and become part of the HANA deployment, architecture, modeling and deliverables decision flow.

 7. What got you here will not take you there

If we believe Gartner’s statements in -section 1, question 2- are true then our current architecture, standards and processes are currently pretty flawed. In fact they represent a 50% to 70% flaw. Proactive companies are spending a little time planning and laying a strong foundation of ‘Global HANA methodology’ and working with new HANA based paradigms, architecture, modelling, standards and processes and realizing user success scores over and above 85%- after 20 weeks of go-live. I state this based on another research that came back with ‘98% of BI projects are declared successful in week 1 after go live, yet only 50% remain successful by week 10’.
So your thought of the day is to measure the success of your BI project only 15 weeks after go-live, i.e. when business is actually using and familiar with that they have received.

IS SAP HANA simply a technical install or a business solution

Is HANA a technical installation or a business solution.

BW-on-HANA Example

Technocratic Installation roadmap
1. Buy HANA
2. Ask your existing partner what to do with it
3. Use the same old 'Z' and 'Y' naming conventions
4. Use the same ole EDW architecture
5. Deliver the same old efficiencies
6. Use the same ole application guidelines and legacy resources, who rapidly revert to their ole habits
7. Take your BW 'As-Is' and migrate as you feel that is your lowest cost option
In other words use your same ole partner and their same ole resources
8. Migrate to HANA
What you get:

• You accelerate a 800 second query to 1 second, that business never used and will never use.
• You implement the same architecture, models and standards that currently deliver over 70% of queries that your users don't need.
• So what you are doing is simply accelerating your inefficiencies.
• What you will find out is that for every dollar you saved from planning you will now need to spend $4 to $40 to try and fix it after go live.

Check:
If most of the queries in your production system are queries that do not meet 80% of your business needs - then you are on this roadmap

Business Solution
Tell me the advantage of accelerating a query from 700 seconds to 1 second if business never use it - and you are now comming face-to-face the tip of the iceberg.
1. Understand the capabilities and limitations of HANA
2. Train your executives on 'Do's and Don'ts' of HANA
3. Remodel on the new FEDW (Gartner) Architecture
4. Delete and replace all your legacy ineffeciencies
5. Get an audit of your BW landscape and systems
6. Increase quality assist with decreasing costs
7. Review all alternatives
8. Drop TCO by 50-60%
What you get:

• Your quality of information becomes 20-30% better
• Your initial cost for HANA migration drops by 40-50%
• Your initial cost of HW drops by 40-50%
• Your annual support cost, for HW and SW, drops proportionally
• Your business users understand exactly what they are getting and what not
• You end up eliminating inefficiencies and accelerating decisions

Check:
If your business users are happy on week 1 - its no guarantee of a good BI project. If they are happy after 20 weeks that is something to be very proud of
Retinink architecture, modeling, standards and lower costs by atleast 50%

The Magnificent 7 BI Quotes- Famous quotes to make every HANA projects a success

Often when one reads a great quote it has the power of firstly making you pause in sheer delight. Then if it good it makes you ponder and relate it to one’s life experience. If it is really astounding it has the power to alter the way we look at life and in this case alter the way we move ahead with how we look at the delivery and design of information management initiatives. This includes BI or deployment of BI projects. Throughout my life I have been blessed with words that have influenced me and some that have cohesively change the way I look at my past behavior, with an impact of changing how I would do things henceforth. Just like data is the center-of-the-universe in a data warehouse, words are the winds that guide our ship of enlightenment and imagination.

My favorite is

1.      “Without business in business intelligence, BI is dead” Gartner

o   Translation: The more you involve business in every BI decision the more successful your BI project will be. In every BI project where I have involved business we have beaten the Gartner nightmare (see 4 & 5). If your SI asks that business users be kept outside your BI project doors – you are highly recommended to quickly show them yours.

After that

2.      “98% of BI projects are declared successful on week 1, yet only 50% of them remain successful by week 10” 2010 BI Valuenomics

o   Translation: “The success of a BI project can only be measured by how satisfied your business users are 15-20 weeks after go live.

3.      “Show me a person who has never made a mistake, and I’ll show you a fool” Bernard Shaw

o   Translation: Mistakes are the foundation of evolution. But, only if we can learn from them. If someone is arrogant enough to state they have always been right either they have no creative ideas, or they are liars.

4.      “Fewer than 50% of BI projects will meet business expectations” Gartner 2003

o   Translation: There is a high probability that if your BI project went live between 2003 and 2010 that over 50% of the reports in your production environment are not being used by your business users”

5.      “Fewer than 30% of BI projects will meet business expectations from 2012 to 2014” Gartner 2014

o   Translation: As we continue to follow the same old rules on to new technologies all we are doing is accelerating our acceleration towards defects and failure.

6.      “Insanity is doing the same thing again and again, and expecting different results” Albert Einstein

o   Translation: Tangential thinking (Edward de Bono)  logic stats that we all feel most comfortable doing things that we have done before. This means that if we introduce a developer with legacy standards to new HANA standards and processes they will most likely revert to their old methods unless we they are audited and regulated frequently – until the new order becomes they way of their life.

7.       On big data and new technologies “This is a time of accelerating change, where your current IT architecture will be rendered obsolete,” Mr. Sondergaard, Gartner Analyst, said. “You must lead through this change, selectively destroy low impact systems, and aggressively change your IT cost structure. This is the New World of the Nexus, the next age of computing.”

o   Translation: With HANA as the new SAP platform you will need to change your architecture to global FEDW (Gartner 2013) designs, work with new HANA standards and processes and take modeling to focus on new challenges. The only way forward is scientific change that selectively destroys old inefficiencies and replaces them with modern methodologies that increase the overall data quality, decreases the overall cost and time in one single step.

To me, the first key takeaway is to push yourself through fear and uncertainty and place your life not on current Technocratic authoritarian assumptions that technology alone can answer all questions. The second is to realize that HANA is not just another technical installation but a business solutions. The third is that companies that follow the old technocratic methodologies will either go the way of Gartner's predictions for 2012 (see #5) or land into the insanity bracket of Albert Einstein (see 6). The choice is clear and now the writing's on the wall

Gartner's 2013 CLOUD Recommendations & your enterprise

The new Cloud Services Broker (CSB) is a service that more and more companies are adopting to to facilitate cloud adoption as IT organization in small, medium and large organizations adopt to internal and external CSB service offerings.

Some of the key reasons companies are adopting these trends are

1. As the pace of technology adoption accelerates internal IT departments are finding it challenging to accommodate all the demands of their business users and information consumers. As the delta between business expectations and deliverables increases more and more companies, from mid-sized to large companies, are adopting to private and secure CBS service providers.

2. Management, upgrades, modernization, integration and consolidations are becoming more and more important an dproactive companies are opting for deploying cloud strategies especially in areas where the competitive advantages of technology and speed are becoming the key competitive differentiators.

3. My input) Cloud service enable customer to remain on the current technology via a subscription service that is both predictable and strategically aligned to a principle of ‘each partner does what they are best at’ methodology. With IT costs becoming unpredictable cloud offerings are the option most companies are choosing to enhance their competitive advantage in a ever flattening world.

4. Note: When thinking Cloud for the enterprise think Security. Clouds are great but they are open to all so think private vs. public, think Fort Knox Security vs. loosing your crown jewels to an apology - like loosing all your employee social security details into a cloud offering.. For SAP customers think Afaria and partner with providers that know and understand Afaria.



For more reading Source: http://www.gartner.com/technology/cio-priorities/

Data Scientist- Critical Attributes for BI - 1 (ver Jan 2013)

I have received many email on this topic and met some who claimed to be data scientists.

Here is an executive brief of what is takes to make one in the SAP BI environment.


Just like IT, BI and HANA - Data scientist is not a technical only skill. Data scientist keeps 'Meet Business Expectations as the center of this universe'
Read the independent statements in level 0.4 and get aligned in your thinking. This singularity could save your company millions of dollars.

Here is a brief of what all you need to know / understand to become one, or claim to be one..

LEVEL 0

1. Understanding that Business Intelligence has been and is about two factors. The first being business and the second intelligence.

2. Resonating with Gartners 2010 comment ' without business in business intelligence, BI is dead'

3. Working on a 'Business First and Business Last' focus in all things business

4. Knowing that
   4.1 'Less that 50% of BI projects will meet business expectations' Gartner in 2003
   4.2 'Less that 30% of BI projects will meet business expectations by 2014' Gartner 2012
   4.3 '98% of BI projects declare success in week 1 of their go-live, yet less than 50% remain
         so by week 10' BI Valuenomics 2010

LEVEL A
1. Conceptual: As a data science it is critical that your conceptual understanding of Business Intelligence is solid not only from a technology standpoint but also from a business side of BI
2. Technical: It is critical to understand the technology that one is dealing with along with its capabilities and limitations. It is also critical to understand alternatives for the business need
3. Judgmental: Understand Data Architecture, TDQM, and the data impact from metadata to maser data. Understand the various statistical methodologies and the ability to build algorithms.

LEVEL B

SAP BI: Understand the fundamental of SAP BI. This includes deep understanding of SAP BW, BW Accelerator, BusinessObjects, BO Explorer and the recent HANA. It also includes understanding how each of these applications manages data along with strengths and weaknesses of each application area.
Global BI Architecture: Understanding Data Marts, Enterprise Data Warehouses with a singular purpose of building global FEDW environments (Federated Enterprise DW's) that allow global standards and 100% local independence.
Automated Modeling optimization: Just like most of us cannot put together a Rubik’s Cube in any optimized time we need to accept that we certainly cannot model a cube with 10 dimensions and 60 characters under any circumstance. Use automated modeling tools
Data Flow optimization: A clear understanding in the impact of differential data flows and the maintenance of Data Quality within each data element and the impact it has on the entire data warehouse.

LEVEL C

Basic Understanding:
0. Matching Algorithms: This won the 2012 Nobel Prize. Stable matching, Optimal pairing, Incentive compatibility, one and two sided matching, medical markets, experimental evidence, market designs..
1. Mathematical basics like an understanding of exponentials, logs, distribution types, continuous and random variations of data sources and elements
2. Econometrics and Modeling: The economics of language based system commands, descriptive statistics, Brownian movements in data, ARCH/GARC modeling, Monte Carlo Simulations, Auto regressive modeling, etc
3. Mean variance optimization: Quadratic optimization, Tracing out efficient frontiers, Covariance or combinations of portfolios, and other portfolio analytics
4. Textual data management: Extracting information from news and blogs; framework of textual data management, word count classifiers, vector distance classifiers, confusion matrix, accuracy, etc..
5. Bayesian modeling: joint probability administration, correlated default applications, Bayes net, Accounting fraud, etc.
6. Predictive modeling: Predicting growth in markets, product and services, Bass modeling, Peak growth calibration, artificial intelligence algorithms, organic growth modeling, etc.
7. Large data extractions: Discriminative analysis, Eigen systems, Factor analysis
8. Auctions financial models: Auctions methodology, Theory of auctions, Auction and bidder types, Optimization of bids, Discriminating pricing, Collusions in auctions, Advertising by auctions, Next price auctions, etc.
9. Network financial modeling: Graph theory, Strongly connected components, Shortest path algorithms, VC Web, Centrality, etc..
10. Financial Neural networks: Non linear regression, Perceptions, Squashing functions, Feedback/backward propagations, neural nets, etc
11. Mathematical Speculation: Gambling, Odds, Edge, Book makers, Kelly criterion, Entropy analysis, Casino games, day trading
12. Cluster analysis and Prediction trees: K-mean clustering, Hierarchical clustering, Prediction classification and regression trees, etc
13. Storage and speed in big data: Distributed computing from Hadoop, Map-reduce concepts, Parallel processing engines, Prototyping, advanced language usage,
14. Misc: Dynamic programming, Fourier analysis, artificial intelligence clustering, stable matching, optimal pairing, Incentive compatibility,

So welcome to the world of the data scientist in the new world of too much data and very little information.

From Big Baloney to Big Opportunity in Big Data

In early 2012 Big-Data was a most hyped concept in the tech industry with a lot of technocratic pundits wondering what the need for any big data was in the first place. Now it is becoming more than evident that big0-data is but starting.

The amount of data we create as a planet is growing exponentially. Data is bubbling forth from many sources including cameras, sensors, signals, blogs, social media, tweets, browsing trails we leave behind, and google searches we conduct. Include into these mix e-commerce transactions, product complaints via emails and texts, medical records, DNA details, drug contradictions, GPS locations of various smart devices and vehicles and we seem to be just scratching the surface.

The world has yet seen nothing of what big data informatics can actually yield. Proactive companies are already starting to invest into the new 360 degree informatics - analytics that mash-up external and enterprise data in an efficient manner.
Predictive analytics allows you to know the weather before you start your day, or before you head for the hills for a skiing holiday. Our GPS traffic tells us the traffic 10 to 50 miles ahead and provides actionable predictive solutions by providing alternative routes when traffic is very heavy, it also provides alternative routes when the information consumer takes a wrong turn for example. It is now routine to know what time the next train will arrive that we don’t event think of it as predictive analytics.

Take it one step further and we can today predict the next hotspot for a battle in Afghanistan simply by analyzing social, text and behavioral patterns to an accuracy where the nascent algorithms could predict altercations up to 24 hours ahead of it happening in 46% of cases.
Google Now, shows us the first glimpe on the predictive power of NOW.

Two things to remember

First start seeing patterns - as we move forward and merge human genome data, with weather, health, chemical contaminations, where each person lives we will certainly be able to predict the impact of genome, lifestyle, disease and life expectancy.

Second big data is the biggest data sets you require to meet your analytics reach. For an enterprise it could be 20 to a 100 terabytes. For Google it could be 20 to 100 Exabyte’s and for the NSA it could be a few Yotabytes
All individuals need not start thinking Yotabytes of data management right now. Focus on your needs only.
Within a few years, expect to be able to do Google-like searches to learn what diseases those with similar genetics have had and what medications worked for them. Eventually, when researchers combine the medical records of a hundred million people with their genome data, work habits, and other information, such as weather and pollution data based on where they live, they will all but certainly be able to determine the correlation between genome, disease and lifestyle.

HP's SAP HANA 2013 Vendor selection Benchmark

Welcome to the world of 'The Scientific Principles of Decision Enhancement'

Here is the HANA update from an HP perspective. Use this as your benchmark for selecting your 2013 HANA Partner.

 HW ATTRIBUTE                                                     Jan 2013 Facts 

01. SAP Certified Global HANA Partner                      Yes

02. No of HANA Installation to Date                           Over 280 installation (Nov 2012)

03. What does this represent                                   47% of global HANA Installations

04. SAP Certified in 'Disaster Recovery'                  Yes

05. SAP Certified in 'Scale-Out' HW                         Yes

06.Current HANA Scale Out Capabilities                    42 TB (first record)

07. SAP Certified for 'High Availability'                   Yes

08. SAP Certified HANA HW Partner                         Yes

09. SAP Certified HANA RDS Partner                         Yes

10. Has their own HANA RDS Solutions                      Yes (Fast Track)

11. SAP BI and HANA Hosting Partner                        Yes

12. SAP Cloud Solution Partner                                Yes

13. # of HANA Certified Consultants                         Over 80 (Dec 2012)

14. # of HANA certified consultants (Dec 2013)         Plan for 500

15. Certified for SAP BW Upgrade for4 HANA          Yes

CRITICAL NOTE: This is just 1 of 5 checklists, Hardware Parrner,  that each executive stakeholder needs to run in order to ensure they take strategically professional decisions

Others

2. BVA  ATTRIBUTE                       Jan 2013 Facts

3. Methodology ATTRIBUTE           Jan 2013 Facts

4. HANA SI Attributes                    Jan 2013 Facts

5. Current SAP BI State Check       Jan 2013 Fats

How 2 - ensure success in SAP HANA

By Jan 2013 as an executive you already have way too much on your plate. AS a Business Intelligence Leader, CIO and business owner you have a lot more on your plate. Staying focused on the technology alternatives can be a tough ask- with Gartner reporting that “..less than 30% of BI projects will meet business expectations..” in th 2011-14 period. . Staying focused can be tough when your business users are clamoring that they do not have access to their decision based analytics. Staying focused can be difficult if you just finished a $3, or 5, million BI project and your business users can hardly use the delivered reports, i.e. user satisfaction is low.


Is it the same for HANA projects?

The simple answer for January 2013 is that the same rules that have applied for your traditional BI deployments apply to a very large degree to your HANA BI Appliances.

Now as we enter the new world of HANA we need to minimize defects and thereby increase the probability of success. Proactive executives need to ascertain that we do not fall down the Albert Einstein chute of ‘Doing the same things and this time expecting different results” Which simply translates that if you continue to implement your HANA BI initiative as a technology only solution then your HANA results could be quite similar to your BI results of the past. As one CIO quoted “Our BI project was a technical success, but a total business failure”

So the advice for 2013, BI Implementations and HANA initiatives is 3 critical facts

1. Brutally Honest Partners and advisors

2. Without business in business intelligence BI is dead (Gartner 2010)

3. Take the 2x2 hour training for business stakeholders prior to moving ahead with your HANA initiatives. It is full of checklists, totally vendor agnostic and absolutely scientific in design.

Our brains are finely attuned to distraction, and in today's digital environment makes it especially hard to focus.

Uno: Brutally Honest HANA Advisors

1.1 Undertake a Brutally honest ‘Current BI State Strategic Workshop. Find out whare you currently are.

1.2 Understand where your business needs to be. Document their expectations and then design to meet their expectations

1.3 Get brutally honest findings with minimal interpretations, for unless we accept our current state of reality we may simply step into another fog.

Dos: ‘Without business in business intelligence, BI is dead” Gartner 2010

This statement was made by Gartner in 2010 and stands true from 1004 till date. Decisions enhancement and operational performance measures are not technical solutions but business ones.

The critical difference is between ‘Value’ and ‘BVA’, or Business Value Attainment.

The former is an external definition of value and what your business needs. It normally consists of generic measures that at best will make all companies working with the exact same method of measuring their business and its performance. No company will have any unique competitive differentiators.

BVA on the other hand is an internal and professional method of understanding your business expectations and your unique competitive advantages

Tres: Take HP’s 2x2 hour Executive Stakeholder training for HANA

Understand your weaknesses: We are used to working on our strengths. However, in BI it is critical to understand that the CIO, CFO, VP Sales, VP Procurement, i.e. your Key Stakeholders know little or nothing of SAP HANA or how to undertake a professional selection. Use the 2 x 2 hour HANA executive workshop

Strengthen your weaknesses: The workshop is basically a 2 hour session that comes with checklists and documents that can be used by non-IT stakeholders to verify that the selection process. The second 2 hour is to actually assist the team fill the checklists in a professional manner. It empowers stakeholders to undertake strategically professionally decisions.

Seven Critical tips to ‘Meet Business Expectations in BI’

Just as LinkedIn, Google and Facebook are businesses started with a vision, so do Global BI Projects. The background of this paper is  Gartner's 2012 BI Report that empirically states --> less than 30% of BI projects will meet business expectations in 2012-14 period. Please send an email for your free copy of the ‘Gartner 2012 BI report Deconstructed’

Here are seven tips about how to simply start and deploy a successful Business Intelligence Program/Project. Here are the top seven tips I’ve picked up over the years.

1. Listen More to Business and Align Deliverables Accordingly

It has been proven beyond reasonable doubt that involving actual business owners and stakeholders in BI projects is the only way to assured BI success. IT is critical but so is business. To be a good BI leader one has to listen to business and then use the IT alternatives to meet, and sometimes exceed business expectations. First and foremost an IT leader has to be a good listener and less of a talker of technocratic solutions. The collective knowledge of the Deployment team must be leveraged as great ideas can spring from the most unlikely sources. Keep your ears open to even the shrewdest advice. Get into the midst of business users, identify leaders and analytics owners, listen to them, draw business into the BI development process and cooperate to build the ultimate BI solution as a team.

As Gartner stated in 2010 ‘Without business in business intelligence, BI is dead’

2. Eliminate Surprises

Define your BI success criterias very early and very clearly, then track and report it on a weekly basis. I call it BVA or Business Value Attainment.

Most BI Projects do not track ‘Meet Business Expectations’ scores through the lifecycle of the project, yet are surprised when it does not meet their requirements towards the end of the project. The key to a successful BI project is to eliminate any scope of surprise after ‘Go-Live’
BI Valuenomics reported in 2010: ‘98% of BI Projects are declared successful in week 1 after go-live, yet less than 50%* remain successful by week 10’. So planning must ensure that the BI project success does not fall off the cliff as it nears or passes the ‘Go-Live’ date.

*Note: this was published in 2010 when, according to Gartner, failure rate of BI projects was reported at over 50%. In 2012 the failure rate is now estimated at over 70%. So today you must read this as less than 30%..

3. Think BVA**

The scientific principles of BI mandate that BI projects plan for ‘**Business Value Attainment’, versus simply deploying a BI technology. The decision is [1] Meet Business Expectations in BI; or [2] Deploy a technocratic BI application. Some key considerations

• Your charter must conform to BVA definitions

• At each stage key stakeholders must review a BVA Checklist and prioritize what needs to be accomplished when
• Your PM should conform to BVA project reporting components

• Your project must be mentored by a ‘BI Business Value Architect’ whose focus is aligning all technology decisions with a ‘Business First’ process of filtration

• Your project must include a ‘BI Business value Owner’ that has to be an internal resource

• Your Project RACI must be clearly communicated prior to commencement of the project with clear business ownership and accountabilities

Current projects are run on a technocratic assumption that 'technology alone can provide all the answers' to business needs. However, the past decade and a half has clearly proven this assumption to be erroneous.

4. Based on Proven Scientific Principles

Large and small customers, alike, need to analyze their business with competitive positioning as a fundamental background. What makes your business unique, and successful, is the way you do business and your business stakeholders.

In BI your analytical skills have to be radically different from your competition in order for your business to stand out in the competitive landscape. But this does not have to be technocratically complex. Each business stakeholder and each enterprise has critical business analysis needs just waiting to be solved in a more efficient manner. For example running a national CO-PA report once a month in a 20 hour run vs. having it run in real-time in under a few minutes. Maintain a focus on ‘True Business Needs’ and innovation. Don’t try to reinvent the wheel and never believe that quantity of reports is better than quality of business focused analytics. Remember that a simple change for the better is far more effective than five complicated changes for the worse.


5. Take pride in your work

Last two years I enjoyed my favorites of all BI projects. In one we scored 96% business satisfaction, based on user feedback, in week 1, 10 and 20 and did not have any emergency transport, i.e. defect mitigation, in the first two weeks. In the other we scored 102% project success, based on user feedback on, week 1, 22 and 30. In both projects we deployed the BVA process and it was a team effort of each individual going an extra mile to make the project a success. In a Gartner reported background of fewer than 30% success this is exemplarily solid outliers from the average. With so many different business units, business needs, divisions, nationalities under one project it was interesting that the only bonding link to all developments was our scientifically documented ‘Global Enterprise BI Cookbook’. Remember that your business users are your final judge, jury and executioners and your biggest advocates. Focusing on them, helping them take ownership with pride will always shine through how they treat and use your delivered BI content.


6. Keep it Simple & Have Fun
Dont get dazzeled by flashing lights and technocratic promizes. focus first on business needs, develop a business vision then proceed with an alternatives analysis. After that document. By documenting clear directives for standards, processes, Architecture, automated modeling, etc guidelines in your ‘Global Enterprise BI Cookbook’ we eliminate the element of subjective assumptions and replace them with a solid global methodology. This simplifies not only the deployment but also the job of auditors and stewards. By keeping it simple team members tend to have fun.

If your team is not having fun, then we are all doing it wrong. In such cases it is critical to stop, pause and review. If your resources get up in the morning thinking their work is a chore, then the methodology is wrong, and they should be trying something else. When employees are having fun and there is a work-life balance in the project then the project is indeed on the right path.

Great project are nourishment, give a chance to be positive and are good for all parties and individuals concerned.

7. When you fail – Sit Back and Analyze Next Steps

This one is far, far easier said than done. When BI projects fail it seems far easier to immediately start unplanned projects to meet business expectations. This is not only costly but also counterproductive.

When, and if, your BI Projects is going, or has already gone, south:

• Welcome to the club: more than 70% of BI projects actually do not meet business expectations. You are certainly not alone. Every BI PM and resource has experienced some flavor of this, some will accept it others not. The ones that accept actually learn, the ones that done continue on their same technocratic path to the next failure. Don’t panic, don’t get disheartened, instead dust off the errors and get yourself a trusted ‘BI Business value Architect’. Someone with a solid business background and an equally solid technology understanding. Find out what and why things went wrong. This can be accomplished in a matter of weeks. Identify the negatives, or defects, and avoid them. Identify the positives and work on them. This is then your starting point.

• Sometimes it is shorter to ‘Rip it Up and Start Again: Once again a difficult decision if the company has spent a few million dollars and the end result is dissatisfaction with business users. But it is important to remember the Einstein quote

• Albert Einstein stated that insanity is ‘.. doing the same thing over and over again and expecting different results’: All too often failed BI projects are the result of failed business participation, processes and methodologies. When BI projects fail it is not uncommon to find companies have one or more of the following patterns:-
   o Doing the same thing that got you in trouble at the start. Low/ No business ownership
      and accountability. Lack of Methodology, standards or processes.
   o Terminate the BI Team, i.e. replace the BI SI with a new one
   o Offshore the BI development

   o Hire lower cost contractors and make them work from 8am till 11pm every night and
      through weekends
   o Loose all the good resources in the process

   o See an escalation in cost, business dissatisfaction and budgets

   o See a decrease in business sponsors, true solutions and strategic BI developments

Pause, rewind and plan before you take the next leap of faith into another round of lunacy. Conduct a short ‘Strategic BI Health-check’ and then prioritize the next steps in a professional and planned manner.