Archive for category Business Related

Beware of the inspirational manager/supervisor/leader without substance

Many of us have @ one time or the other, come across inspirational leaders, in the form of a supervisor, team lead, or self-appointed leader. They motivate you to follow certain paths or go do things that seem counter-intuitive; their approach goes against everything you believe in or know is right; and yet, you follow their directive (or direction, depending on whether it is a supervisor or not). It is easy to come under their spell- they are strong, have conviction, and seem to have it all figured out. Beware….. and wake up and smell the coffee.

Validate their directive against actual facts and data, especially if the individual has little to no experience in the space. Lack of experience or knowledge does not stop these ‘leaders’ from trying to force their convictions on others- because, by their very nature, they are forceful and are often, sorry to say, so full of themselves, not in touch with reality. For them, their ‘leadership’ quality justifies everything.

Don’t get me wrong – there are examples of great motivational leaders- like Gandhi, MLK, JFK, Steve Jobs; these leaders had substance and in most cases based their convictions on certain facts and data. Steve Jobs did not try and give guidance on how the economy should be run; he focused in his area of expertise. He pushed his people to their max and helped them achieve their potential (though his means may be questioned by many!).

Inspirational leaders without substance and the right background can be downright dangerous for those they are leading and for the cause itself; they take diametrically opposite positions often just to prove their leadership – the only purpose for these positions is to show their leadership and provide them an opportunity to shine- it is all about them.

Such leaders are however fickle; when things start going wrong, they will be first ones to bail on you, leaving you standing lone with ‘your cause’ and ‘your position’. They have already found something else that looks more attractive and will give them a chance to get on the pedestal.

A good leader is not about himself or herself; they are focused on the success of the cause and do not want any attention to themselves. They base their guidance on recommendations on facts and good data; they ensure the team and the cause comes first and when things start going wrong, are the first to step up and take blame. They are people oriented and focus on the betterment of their team.

What has your experience been?


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Data Analytics Adoption – Its the people, stupid…..

Everyone and their brother is involved in data analysis, data science, KPIs. Big Data; it’s data, data, data, everywhere. This includes SW vendors, consulting firms, and data specialists, pushing their solutions, and strategies on data analytics. While there is a lot of value in data analysis, a fundamental requirement to make data analysis successful in an organization is the voice from the top and a buy-in from the executives on the strategy, and goals of the CEO. I am narrating (blogging) a life experience to substantiate my theory.

Several years ago, I was tasked with developing and deploying enterprise-level KPIs and metrics to enable our company to achieve certain organizational and operational efficiencies. The initiative originated from the CEO and the company had engaged a top-tier management consulting firm to help identify the right KPIs and metrics for this exercise.

My team worked with the management consulting firm to define, and refine the metrics and try and get consensus from all the EVPs; I vividly remember a big debate about how to calculate and report Headcount – to get to their target, some of the EVPs were arguing strongly for including, temps, open requisitions, and part-time employees. Next thing you know, there was a big discussion about who would be considered a ‘temp’, versus ‘full time’ – it was all in the definition.

After much debate and discussion, the consulting company finally got the agreement on the definitions of the KPIs and we rolled out the metrics company wide- and right away, the s….. hit the fan. VPs and EVPs whose metrics were not within the target range started attacking the data itself – offense is the best form of defense. Fortunately, we had good data governance and cadence and were able to substantiate the veracity of our metrics with the underlying data for the most part. But it was not without its challenges….

One incident stands out in my mind – I got called by the GM of a large division to his office; in front of him were 3 or 4 excel reports which he was manually cross-checking against the details behind our system generated KPI for spans and layers. He showed me how we had missed 10 employees and his list had 10 more employees than our KPI had captured. I was caught a bit off guards because I did not expect a GM to be sitting down and manually validating data. Upon recovering from the shock, I reasoned with the GM that it was only 10 counts, and not a big deal, given the data volume size. And here was his response – and this is the punch-line to this whole blog, and provides perspective to the blog title. He said:

“Yes, it may only be 10, but with the 10 included, my KPI goes up from 3.75 to 3.80 – 3.80 is closer to the 4.0 target, looks better, and easier to explain than 3.75”.

The GM, like many of his peers, had not bought into the whole exercise and its underlying cause, and without the right tone from the top, and not being aligned with the CEO on the corporate objectives, was looking @ this as something he had to get a ‘pass’ on. This is the challenge with rolling out enterprise-wide analytics; until and unless there is buy-in about the intent of the data, there will be resistance to adoption and acceptance. Else, such exercises and their outcome will be viewed as a stick instead of a tool for improvement.

I have had more success in deploying analytics by providing senior execs and GMs data and analytics that they can use internally (within their department) to manage their business better – for example, spend analytics, revenue and margin trends, etc. These internal metrics/KPIs are not viewed as a threat because these are not corporate mandated, not used to compare the different BUs, and do not have to be presented to the CEO during quarterly review in front of a bunch of people; instead these are analytics and metrics used to fine-tune the operations of the specific division and driven/mandated by the GM/EVP themselves. If these metrics/analytics help the individual(s) achieve their goals, they will help promote the program to deploy corporate metrics (since they now view these as enablers instead of a threat).

Hence the title of the blog……

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Down Memory Lane….. #ROLM Corporation

We recently closed down and moved out of our office on Old Ironsides Drive in Santa Clara; the building was going to be torn down and a new high-rise glass and chrome building built in its place. Ironically, I started my career @ a company right across the street from our office- ROLM Corporation,  which too is now in the process of being razed to the ground to make way for a high-rise building for a nearby company looking to expand its campus- signs of changing tastes and times.

For those not familiar with ROLM, it was started by 4 graduates from #Rice University in the late 60s, early 70s, led by a personable, charismatic, and visionary leader Ken #Oshman. Their #PBX phones and switches were state-of-the-art and their products world-class. Most of the big companies, hotels, universities, and other establishments worldwide used ROLM phones and switches (you will still find ROLM switches and sometimes even ROLM phones in use today). Their technology was pioneering and they were able to take on the giants like #AT&T, and Nortel. ROLM was one of the first companies to come out with the technology of activating the phone when it was removed from the cradle, via a magnet that in-turn activated the PBX switch- groundbreaking technology in the 80s.

ROLMs success can be attributed to its people, culture and Oshman’s leadership. It had a culture of openness, employee well-being and corporate responsibility. Way before the term ‘great place to work’ became a cliché, over-used by companies to try and promote themselves, Rolm was known as a great place to work and was nicknamed “G.P.W”, by its employees – true validation. Ken Oshman was a valley icon because of his vision, and foresight; he was an inspirational leader and the employees had great loyalty to Oshman and ROLM.

Rolm was very different from other companies in other ways as well, starting with the campus – it had been built to give it a college campus look and feel, with single story low slung buildings, plenty of open space, meandering streams, and calming fountains – a Zen like atmosphere.They had a state of the art gymnasium, with an Olympic size swimming pool, Jacuzzis, tennis courts, a full-size hardwood basketball court, racket-ball courts, and a workout room. The cafeteria had been designed with multiple levels, an outdoor eating area, and high quality food – all of which the likes of #Google, and #Facebook boast of, some 25 years later . Even before the words like flex-work, and work-life balance became buzz words, Rolm implemented these practices with minimal fanfare. Employees got 3 months paid sabbatical for every 7 years of full-time employment, had profit sharing, and other benefits – all this before stock options, and such benefits became common in the valley.

By the time I joined Rolm in the east coast, it has been sold to IBM and was known as #IBM/Rolm. Most of the middle and upper management were from the parent IBM company and the culture was classic IBM – dark suits, conservative ties, and heavy middle management.

IBM started changing the direction and strategy of ROLM and its products. They attempted to upgrade the ROLM products to make them more compatible with IBMs hardware as part of their strategy to integrate voice and data. This strategy did not really work and started creating all sorts of issues with the product.

More important, the two were very different cultures. One was a California based company, with an open culture and flexible approach whereas the other was an east coast based company, big, bureaucratic, inflexible, and set in its ways . ROLMans were passionate about the company and its products – there is an active online ROLM community where former employees stay connected and plan reunions, some 15-20+ years after they left the company – it was a family and former ROLMans try to maintain their camaraderie even today, with their reunions and local gatherings, across the US. IBM on the other hand was a bureaucratic and hierarchical organization, run like a large organization and very rigid in its decision making.

Ironically, my IBM colleagues back in the east coast did not think much of ROLM and its entrepreneurial ways; many of them had either been loaned from the parent IBM to ROLM or had moved from IBM to ROLM – and they were steeped in the IBM ways; they had little regard for the bohemian west coast. On the other hand, once I moved to the bay area, the original ROLMans would tell me how difficult it was to get IBMized- the sales guys would tell me stories of how during sales calls, 2 sales guys from ROLM and 20 guys from IBM, from various departments, would show up, completely overwhelming the client and the presentation. I had just finished reading the story of how #Xerox was not able to appreciate the value of #PARC – it was as if I was living the very cultural conflict I had read about.

A combination of the mismatched culture and fierce competition from #Nortel (another company that went by the wayside) caused ROLM to lose market share and bleed red heavily. IBM tried to salvage the situation by partnering with Siemens Telecomm., but after a couple of years, decided to get out of the PBX business and sold the entire company to #Siemens.

Siemens decided to close down the corporate office in the east coast and consolidate it with the corporate office in Santa Clara, renaming it Siemens-Rolm. I moved as part of this consolidation and the move from the glass-and-chrome high-rise buildings of the east coast to the college-like Rolm campus was an incredible experience. While I did not get to enjoy the culture of the original ROLM, I got to enjoy the state-of-the-art gymnasium, the high-quality cafeteria, and most important, work with some of the original ROLMans. Almost 10 years after Oshman left ROLM, the original employees still talked fondly about him and his leadership qualities.

As more and more of the original #ROLMans either quit or retired (some of them had made a lot of money during the hey days of ROLM and again when ROLM was sold to IBM for $1.5B in the 80s), ROLM started getting ‘Siemenized’, with folks from Siemens taking over the management and leadership. The only part of the original ROLM that remained was the campus – the spirit and culture of ROLM had been slowly eroding starting the late 80s.

I left ROLM in the mid-90s and did not pay much attention to it;  it had been renamed Siemens Telecommunications and had lost its original identity- but I fondly remember my first job, the ROLM campus, and the people after all these years. It was therefore with great sadness and nostalgia that I saw the last of the ROLM buildings being torn down. As they say, the only thing permanent is change. ROLM is one more example of a great company that lost its way due to a combination of the loss of its charismatic leader, and a cultural mismatch with its acquiring partner(s). The tear-down of the ROLM building and campus also shows that the valley has been changing in the past 20 years. Loyalty and camaraderie are not cherished values anymore- we are all too busy being busy.

For those interested in learning more about ROLM, I have attached a link to a YouTube video of an interview of the 4 founders of ROLM in 2004 at the Computer History Museum titled ‘Competing with Giants’:

Someone named Linda Boutin had posted online pictures of the ROLM Santa Clara campus on 4900 Old Ironsides Drive, from a couple of years back. I have posted a couple of these pictures – the first pix is of Bldg 4, the building I worked in; the other two are gym basketball court and the swimming pool in the rec center. The campus has been in a state of disrepair, vandalized, and left to the elements; they started tearing down the buildings about a year back and last I saw, there was only one of the former buildings still standing..

ROLM Bldg 4

Rolm Bldg 4

ROLM Basketball Court

ROLM Basketball Court

ROLM Swimming Pool

ROLM Swimming Pool

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Death (Dirth) of the Data Scientist with improved Predictive Analytics Software?

Starting a couple years back, there has been a lot of hype about data scientists and how this was going to be the hottest job in the coming years; analysts were predicting that the need for analyzing big data and finding patterns and trends would require thousands of data scientists who can build statistical and predictive analysis tools.

Terms like R and Python suddenly became sexy and all the statistical nerds starting coming out of the woodworks. Caching, sharding, clustering, classification, overfitting, underfitting, and scalability became terms to be thrown out @ random in social events and parties. And to make sure resume scanning programs found the buzz words, Hadoop, MapReduce, Hive, and HBase became must have words on resumes.

The dirth of qualified data scientists was a hot topic of discussion and following the projected supply demand laws of economics, companies were scrambling to hire the few data scientists available and paying them highest dollars (or pounds, or Euro, or bitcoins…..). Of course, not to miss out on the hype and opportunity, academia jumped on the bandwagon and started hyping up their data science programs or offerings. Boring and unpopular statistics and econometrics classes suddenly got rebranded as ‘data science’ courses – marketing @ its best.

A 2011 McKinsey report estimates there will be 140,000 to 190,000 unfilled positions of U.S. data analytics experts by 2018. In response, universities are scrambling to improve their existing degree programs and create entirely new offerings.

Note: Ironically, while the companies like Facebook, and Twitter that generate much of the data are in California, there are only a handful of universities in California offering a dedicated data science program.

I have not tracked if companies like jumped into the fray as well promoting the socially challenged candidates trying to find a match, but would not be surprised if they did (“John does not talk much, has no interests or activities, but is great @ projecting the outcome of a coin toss…….”).

Recently though, the hype seems to have gone down.

One of the possible reasons could be the improvement in new predictive analysis tools like #SAS, #SPSS, #KXEN (now called #InfiniteInsight after being acquired by SAP). I recently attended a 2-day workshop focused on SAP Insider Insight (formerly #KXEN) and I came away impressed. The tool allows you to build models using various statistical methods and predictive algorithms that can be used to analyze structured and unstructured data. With SAP HANA, one can process large volumes of data streams from various data sources (including Hadoop, O Data, etc.) and use SAP Infinite Insight to process this data and publish trends and co-relations. I was able to easily build various models with minimal training using the easy to use UI.

Unlike a data scientist, who has to constantly tweak and update his/her model manually with changing business conditions and data feed types, predictive analysis software tools can update models easily and be used to analyze data for predictions.

Infinite Insight incorporates R and other statistical capabilities and can be used independently or on HANA. Infinite Insight is going to be embedded into SAP #Hybris, according to the SAP roadmap; this will allow companies to use predictive analysis on their e-commerce sites.

Now back to the original topic – where does this leave the data scientist? My guess is while the data scientist may be hired to help build models in tools like Infinite Insight, it will not necessarily be a long-term employment. With more machine learning being developed almost on a daily basis, predictive analytics software will only get better @ modeling and predictions.

Now if only if there was a software to predict the future of the data scientist.

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BATNA – What you do when your boss puts you in a no-win situation

I ran into an old friend/acquaintance who used to be the head of IT of this company; he had quit his job and was looking for his next gig – and so we got talking. Here was his story…..

The auditors had identified a potential gap in the company’s  IT security and had flagged it as a SOX deficiency in their statement. Of course, they also wanted to  to increase their audit scope (and fees!). To fix this issue my friend went to get approval for a project to tighten up IT security by automating certain features and processes. His supervisor however had no interest in approving the funding for this project; instead the supervisor, who had minimal IT experience, lectured him on how to run his IT organization. My friend, realized that he was in a no-win situation; so he followed the BATNA approach.

For those who do not know what BATNA is, my definition of ‘BATNA’ is, ‘Best Alternative to No Alternative’; in this case, the best alternative he had was to quit because he had no other alternative- he was in a lose-lose situation.

Note: A good friend (must be a good friend because she actually takes time to read my ramblings, aka blogs) pointed out that the wiki definition for BATNA is ‘best alternative to negotiated alternative’. I find ‘best alternative to no alternative’ to be a better fit for my use because that that is what we usually face – there is no alternative. So maybe I should go create a BATNA 2.0 definition……

We have had situations in our career at one point or the other when our supervisor puts us in a no-win situation. Your boss asks you to follow a certain strategy or take a certain approach that he or she believes in; you know that the strategy and/or approach is either not the right one, or a recipe for failure.  And yet, you have to follow your supervisor’s recommendation. You may try and persuade your supervisor against the approach, but unless you and your supervisor share a very strong and open relationship (and bond) with frequent information and idea sharing, chances are, you will have to tow the line and do as told.

So what do you do in such a situation. You look @ your BATNA, and if you do not have the option to change departments and supervisors, your only alternative is to quit. Unless your supervisor is one of those rare individuals who has your back and will stand up and take the blame if the strategy or approach fails, chances are high that you will be the fall guy anyway. And when the failure happens, you cannot use the excuse pthat you followed the guidance given to you – it seldom works. So, the earlier you recognize the situation, the sooner you can start working on on your exit strategy, which might be your only BATNA.

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Why SAP HANA has not had more success than it deserves (no, I do not work for SAP)

I have been working with HANA and evangelizing about its use case and value with customers and prospects and yet I found it an uphill task to get engagement from the business (and in extreme cases,  even IT); this despite the fact that SAP HANA is a great product and has lots of uses. So I started researching the issue and came up with some hypothesis, based on conversations and observations.

I theorize that IT departments went ahead and acquired SAP HANA for one or more of the following reasons:

1. HANA is the newest shiny toy

2. The IT department had left over budget and had to go buy something

3. The SAP AE was a very good sales person and gave the customer a huge discount

4. The consulting partner convinced them SAP HANA would help solve all their problems

5. The IT department had to find something new and sexy to work on so the CIO and his/her staff could show they were working on the latest technology, and could put SAP HANA (aka ‘Big Data’) on their resume (how can you work in IT and not have ‘Big Data’ on your resume……).

Now that IT had acquired the new shiny toy, they had to go find something to go fix- create a solution and then go find a problem – we have seen this before, many times.

The challenge was two fold: (1) IT could not really articulate how SAP HANA would create value for the business, and (2) because of the lack of understanding of real business issues, IT picked the wrong use case or a very weak use case.

Let us now turn to the product itself; SAP HANA is great, and I believe there is huge value to be derived from this product, if rightly deployed. So why has HANA not been adopted more readily. Let us look @ some of the potential reasons:

1. HANA is an SAP product and everyone associates HANA with the core SAP ERP and CRM; so the target customers have been mostly current SAP clients.

2. Many people believe that you have to be an SAP shop to leverage HANA; I was talking to a friend who is trying to build a custom healthcare app; I told him he should try and leverage HANA to build his app, and his first response was that his product has nothing to do with SAP.

2A. Because of the belief that you have to be an SAP shop with significant SAP installations and SAP data volume to be able to use SAP HANA, many customers believe that they do not need HANA because their SAP data footprint is so small. The argument I often hear is, “We have less than 80 GB of total SAP data; why would we need HANA?”.

3. SAP HANA has been sold as an application, whereas it is more of a fast database and a development kit. When you buy an application, you expect to install it and start getting value out of it – like SAP ERP, Workday, Ariba, etc. HANA is not an application – it is a super-fast database AND an ADK – and similar to the Apple iOS, you can use this platform to build applications which can then be used to solve business problems- so SAP HANA is an enabler and not the end app itself.

4. Many people do not remember that SAP acquired Sybase a few years back and a lot of the Sybase IP is being integrated into SAP HANA; old timers like me would remember Sybase as a very robust and stable database with a lot of capabilities (Sybase IQ came with columnar data store capability). SAP  did not throw away all these features and functionality- instead these are being made part of SAP HANA.

A combination of these factors has hindered the adoption of SAP HANA both among SAP shops and non SAP shops.

For SAP shops, SAP HANA should be a no-brainer, just for using HANA Live; the pre-delivered analytics and calculated views alone can get someone trying to build a data analytics strategy a head start with SAP data. Here, of course the value of SAP HANA is dependent on the source data being SAP (see details about HANA Live in my earlier blog)

But there is a much bigger value for SAP HANA for non-SAP data; you can stage large data sets from the likes of Hadoop, Teradata, and other non-SAP sources into SAP HANA, using the likes of SAP SLT* or SAP Dataservices,  and use HANA to combine data from multiple sources almost real-time, and render the data using either SAP analytics tools like SAP Business Objects, or one of the many other analytics tools available in the market. (* SAP SLT does not work for all applications).

SAP is pushing down more and more development capability into SAP HANA and this will allow more application developers to build widgets on HANA and leverage the power and capabilities of HANA.

It is incumbent on SAP, the SAP AEs, and the consulting firms to educate customers on the real value of SAP HANA and more important, the fact that this is not a simple plug-and-play application- this needs planning, identifying appropriate use cases, and proving the true value of SAP HANA.

IT departments should find a good use case, build the application powered by HANA and show the application and its robust capabilities to the business- HANA should almost be not revealed or discussed with the business (how many times do you go and discuss what database you are using with your business users?). After the use case has been proved, the capability behind it, in this case SAP HANA should be revealed- lead the discussion with the business value and then talk about the technology enabling it.

Until such time, SAP HANA will remain largely a toy for IT departments and CIOs trying to show they are using cutting edge technology, with minimal penetration or adoption in the business.

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