The cost of precision in BI

What percentage of decisions need precise data right upfront ? My guess is less than 10% or even lower .

A big decision for an average person is purchasing a home . Having gone through that exercise in two countries – and knowing many people who have done this before and after me , I am convinced that the data needed to make that decision did not need much precision . Based on your financial position – you could judge affordability within a plus or minus range of some amount . Another factor was school district rating – how many of us will care if the score was “9 out of 10” vs “10 out of 10” ?

Decision making is progressive – you find a “cluster” based on some characteristics like location , price etc . As you narrow down – you cluster again – flooring , yard size etc comes into play ( but again you don’t need super precision – if I am looking for a 2500 sqft house , I won’t overlook a 2400 sqft house because it didn’t precisely meet my criteria ) . And then comes the ultimate short list that needs some precision , and a final decision that needs excellent precision since you need to pay it to the seller .

Some version of this process is followed in all decision making , including sales , marketing, purchasing etc . Apart from legal and financial – i think almost no business function needs the type of upfront precision that it has chased since the beginning of time .

Look at an average BI project in enterprise world – 90% of time is spent on plumbing data – designing schemas , defining exception workflows , writing transformations and so on . Remaining 10% is used to make reports useful to its audience . This is unavoidable because BI is very static in nature – even what is called as-hoc analysis is limited by schemas in back end . In short – even the best BI solutions cannot mimic how human beings make decisions .

The quest for extreme upfront precision is what works against BI being useful – ironic as it might sound . And BI has no chance of being seriously disrupted till it stops expecting tightly defined schemas on back end , and high precision right upfront in all cases .

Context is way more valuable than precision . That is how we make decisions eventually in real life . And context changes with time – which means BI has no chance to keep up given its hard dependency on static things . BI world needs to think in terms of real world entities – not in some arbitrarily defined data models .

Good news is that technology and data science have progressed enough to do that in (more or less) repeatable and cost effective ways . Bad news is that the world of BI won’t go to the promised land without blood curdling shrieks , kicking and screaming .

Keep calm – our world of BI is changing , hopefully for the better .

Talent cannot be managed

Most people can and should be managed in an organization – but not talent . And talent is not a word that should be used in a light hearted and generalist way . That is how it loses relevance .

Interestingly, I did not learn this originally from corporate world or business school – I learned this from training and working with dogs . My intention is not to compare dogs to humans – just that I learned something from my hobby that empirically seem to also apply in my real career .

Most novice trainers go around looking for smart and intelligent dogs . What they don’t realize is that you can’t train smart dogs with novice training skills . It is one of those things that people won’t learn without making their own mistakes . Been there done that and learned , I think I learned at least . The dog for novice trainers is the one that is highly motivated by food , and an over eagerness to please the owner . That is – a pretty dumb dog , the opposite of an intelligent dog . Once they are trained – the dumb dogs will perform spectacularly to please their handler – but they usually can’t think for themselves to save their lives .

That is pretty much the case with management too . If you really want to work with smart people – you cannot manage them . They don’t thrive under management . They need collaboration and leadership . Set them in a direction , and get out of their way . Check in periodically and let them know you can be approached for help whenever they need . And then don’t let them down when they come to you .

“Talent” is not scalable – there is no such thing as a talented team of 100 people . I wish there was but I have never seen or heard of it . So be prepared to run smaller teams if you want to work with real talent .

“Talent” doesn’t mix with non talent – the moment they mix with people less smart than them , they lose interest . That is when turf wars and politics and all start – and they will out wit everyone else , even if the result is that the team will not deliver on goals . So if you decide that talent is what you are after – you need to be super careful as a leader to not lower your hiring standards .

“Talent” is fiercely loyal to their own ideas – and this is why they cannot be managed . It is a test of character for the leader to see if you can gain their agreement on a team goal . Or at least get them to disagree , but commit . And you will be the biggest idiot if you don’t consider their ideas carefully – because “I said so” is not what they consider as rational criteria . If you dismiss their idea – you need to beat them to the punch at an intellectual level they are at . Very very hard to do .

“Talent thrives on loyalty” – they value integrity in their leaders . If you fight for them when they needed , they will usually lay down on the tracks for you. Conversely – screw them over and they will screw you over harder than your worst nightmare . Don’t make promises you can’t keep and don’t give them standard company lines as excuses . They know exceptions can be made almost in every case . If they suspect you are not doing everything for them – that loyalty goes away in a hurry .

“Talent” will challenge you every step of the way. Don’t hire them if you can’t deal with it constantly . They will make you think when you would rather sleep or have a beer . It is not easy – you are either in the game with them , or you are in the cheap stands . No bench in this game. Always on !

“Talent” doesn’t tolerate breach of trust – they know that they won’t always get managers who are as smart as them . But as long as they trust the leader – this is not an issue . The issue is that they won’t usually give you a second chance if you break their trust . Be open and fair as a leader . And be consistent in being open and fair.

“Talent” needs direction – especially because they are quite capable of thinking about many ways to do things . And they will get bored if there is not enough challenge in their jobs . They will also be pissed off if you set unrealistic goals . It is a fine balance to strike .

“Talent” needs money , but money won’t keep them there for long – these folks don’t come cheap and most of them know their value quite well . But that is the easier part . The harder part is retaining them and keeping them positive without turning them into bitter employees .

“Talent” will walk away – but they probably will give you some time to get your act together . They might even politely remind you that they are losing patience . But they won’t sit around for ever to bitch and moan . They know that they are in demand irrespective of the economy or general market conditions . So they will walk away . And they won’t usually come back if you do counter offers – because they would have computed that in their decision making process before they chose to walk . So if they are leaving , allow them to leave on pleasant terms . You never know when you need each other . It is a small world .

Nothing but problems in general – so why bother with talent at all then ? Because these are the few people who will take the big swings and hit home runs . And that is what separates you at the end from competitors . If you are happy with status quo – don’t worry about talent

But here is the thing – you need the rest of the organization to work at peak efficiency so that the “talent” can be given the freedom to make big swings . If the rest of the organization is not disciplined – it is reckless to just depend on big swings to change your fortunes . That is hope – hope is not a strategy . There is a management concept called “policy by lapse” – that is not an admirable strategy , to say the least .

This is where rubber meets the road for companies with big innovation agendas . They tend to over do it by trying to light as many fires as they can – hoping that something will catch on . In this process – they forget that “talent” can easily deal with it , but others probably cannot do it to the same degree . And when “talent” sees the lack of differentiation in what got assigned to them – their passion will evaporate. And pure recklessness results – with no goals being met . The smarter companies know what part of the team needs to be industrialized and what is the portion of the team that can be allowed to make those big swings .

And in the process of (mis)managing “talent” – the real hard job is to take adequate care of everyone else . Today’s bills are paid by everyone else . “Talent” can only pay tomorrows bills . And every leader needs to keep that in mind .

Now , which part of the team would you like to lead ? All “talent” , All “everyone else”, or a mix ?

Big data – You can start small, but start today !

I am back at Houston Airport after an excellent customer event where I did a key note on big data . One of the side conversations that happened today was on where to start big data programs . Jon Reed had nudged me couple of days ago on the same topic . So here are a few things that come to mind .

I am a big fan of starting small but starting soon . Technology lets you scale as you go . So don’t wait for the most amazing earth shattering use case . You will need some time to get a first hand feel of how things work in big data land .

There are many ways to skin the animal that big data is – but if you want the easiest way to do it , my suggestion is to start with corporate data and work your way into bringing real world entities into the mix to see how they work together usefully .

My favorite way of finding a big data problem – which allegedly is influenced by an interest in corporate finance in b school back in the day – is to start with your financial data reported to the street . This is a summary representation of what is happening in your company and if you compare it across periods – you can spot what needs most attention .

Lets say Accounts Receivable is the one you spotted , and of course verified with other sources of data to make sure it is worth exploring . Now – what makes AR ? It is all those customers who bought your wares who haven’t paid you yet . What do you know about those customers ? Have they paid you in time before ? How is their credit rating ? Did collections department make a hundred calls before they paid last invoice ? This needs more data – granular data that you probably need to load from somewhere else .

What is happening with those customers in social media ? Not just twitter and FB – what do financial analysts think of them ?

Can you combine the AR and collections info with social media input to assess the chance of you getting paid ? How about seeing if the customer is holding payments because of poor service ? Have you tried to analyze the trouble tickets and service documents and see any trends ?

You could keep narrowing down till you find something you didn’t know before . Once you figured out what influences other data have on the AR info you start with – you can map it to the transaction processing . Should you discount more ? Should you put a billing block on the customer ? Can you have an API that can make there decisions while a transaction is in process ?

That is a full closed loop – moving from a small set of data to a bigger set data , only to find what tiny insights you can find in the context of a given transaction . And once you have solved that – cast the net wider , and keep going after all the opportunities you can find .

The trick here is to not waste time chasing every fork in the road . You will get a lot mod false signals that you need to smartly move past . Fail fast and move on . And if everything looks ok on AR , try another part of your financial statement trend that stand out.

Another way I attempt this is to talk to the person in charge and the persons who are at the farthest end of the business process . Ignore the layers in the middle . Like the VP of Sales and the shipping clerk / Store manager / sales person etc . More often than not – their perspectives will be different . Start from summary data and see of you can relate the source data from each of the others to see impact . As you add more and more sources – you probably will run into interesting trends , either with the impact on summary or between sources themselves . Stop and analyze and ask the business users on possible explanations . If you smell a rat , don’t assume something is wrong . But if the actual business users smell a rat – you have enough validation usually to dig deeper.

There are many caveats here . Organizational and Political motivations can easily derail you if you are dealing with data from multiple sources . Constantly verify data with multiple sources – systems and people . It is as much an art as it is a science .

Also keep in mind that data can be interpreted in many ways to suit someone’s needs . And just because you see correlation doesn’t mean you can assume something is good or bad . This is why I repeat myself all the time that data interpretation needs to be verified by people in the business who understand the context .

And always remember to close the loop – end of the day , data is good only if you can act on it . So make sure that what the data tells you is factored into your business process – maybe you need to enhance a transaction , maybe you need to empower a call
Center agent way more than today , may be you need less approvals in your work flow . You haven’t done justice till the loop is closed . Even then – you will need to keep tweaking it as the world around us changes faster than we can keep up .

As technology improves and more machine learning and AI gets mainstream , the effort to do all of these will come down for sure . But then, the complexity of problems we will attempt to solve in mainstream business will also be orders of magnitude bigger . So – start small , but start today .

Got to run – my flight is boarding 🙂

Holding a tiger’s tail

Where I grew up , our elders used to advise us that we should never hold a tiger’s tail . The story is that if you do catch hold of a tiger’s tail – you will have to round and round with the tiger pretty much for ever . People will cheer for you as long as you don’t let go – the moment you let go , people will cheer even more because they get to watch the tiger eat you .

Whenever a big company CEO steps down, and I start reading the commentary in press and social media – I fondly remember the proverbial tiger and its tail .

Look at Steve Ballmer . He grew the company’s revenue and profit , yet the market cap nose dived and share price stopped swinging up . And now, the world wants Bill Gates to come back and do his magic . No one particularly worries about trivial thoughts like “if Gates indeed had a bright idea, he could have implemented it at MS any time Ballmer has been CEO”. Personally , I think the world needs Gates to continue his humanitarian pursuits way more than running MS . But I digress, the point is – Ballmer held the tiger’s tail and no glory came with it .

Look at IBM – Sam Palmisano figured out that revenue didn’t matter all that much , investors just wanted steady growth in EPS . He boldly announced a plan for $20 EPS by 2015 . And he didn’t miss a beat . Then he retired, Rommety took over and continued down the path at a pace Palmisano set . And she did miss a couple of beats and voila – there she is holding the tiger’s tail . I hope she finds a few good ways to get the great company to grow again .

Then there is Apple . Steve Jobs took it to almost stratosphere and made it extremely highly valued . Three back to back successes with iPod , iPhone and iPad . And profit margins out of the world . Top line and bottom line in excellent shape . And then he passed the baton to Tim Cook . Cook didn’t miss a beat on operations – and company still has awesome financials . Yet, since the next iThingy hasn’t come yet – there he is holding the tiger’s tail too.

Pretty much the same story at HP, Intel and pretty much everywhere else . Michael Dell tried to avoid the tiger by going private – but lets see how that plays out .

It doesn’t matter whether the company is in good financial shape or bad . Everything that had an upward trend at any point has to keep going up – for ever . Not one thing , not some things , not majority of things – everything . Otherwise CEOs have to tell upfront what exactly will go up – and then they usually get a pass for everything else . But then if they slip on the one thing they promised will go up – tiger eats them immediately . That is the deal .

The tiger will maul and eat them in public – not in some closed room . And the public that watches them will be liberal with unsolicited advice throughout the process – for ever . No breaks – no time outs .

With this well known background , I am amazed how many people still raise their hands to be counted when a CEO search is going on . More power to them !

Big Data Deployment – Planning is everything

When data becomes big – and it gives pretty cool insights of high value , the big hurdle facing customers will be their own deployment challenges. Where should this magic solution live ? And how exactly will we find out ?

Several factors play into this – and I am just mentioning a few that came to mind first.

1. Hardware is cheap-ish

Cheap is relative . When you have a million dollars to spend in a hard economy , would you buy hardware or will you do something else like hire more sales people , spend more on marketing etc ?

Would you buy or would you rent ? Or will you start small by renting and then buy when you need a scale that makes renting uneconomical ?

If you buy , are you going to buy cheap servers and live with extra redundancy? Or would you rather invest in fewer industrial strength servers with great HA and DR ?

2. Skills , or lack there of

Even if you have cheap hardware lying around , do you have skills and manpower to install and patch on all the machines ? Is it cheaper to hire/train internally or should you hire a consulting company to do your big data technical work ?

What about business users ? If big data tells them something new – are they empowered to act on it ? Or will a real time insight need a batch mode committee of people to act on ?

Does the business user have enough training to understand the context of what big data solution tells them ?

What is the minimum usability requirement ? (Not everyone is a data scientist – and majority of use cases will need stupid simple usability , ideally with little to no training )

3. Ever improving technology

Big data technology is benefitting from rapid innovation from open source world and commercial vendors . How much appetite do you have for keeping up with fast evolution of technology ?

Tactically , when will you replicate and when will you federate ?

4. Quantifying the value

Investments are worth only when value is greater than cost over a reasonable period of time . Cost is a straight forward calculation and so is OPEX vs CAPEX . But do you have the ability to quantify value and benchmark against the best in the industry ?

How does this play with existing strategies on BYOD , security and everything else that you have a strategy for ? Can they all work together ?

5. Platform and Applications

What will you buy and what will you build ? Do you have guidelines on deciding what factors will make you ask a vendor to create an app for you (and others) as opposed to building it yourself ? Do you have criteria for evaluating all the platform options ? Do you expect ERP like security for big data or will you relax it ?

6. Legal , ethical and privacy stuff

Are you aware of what the government thinks of your data ? Do you have ideas on how best to keep your big data solution legal and ethical ? Have you considered opt-in and opt-out scenarios for users ?

In short – there are a large number of deployment considerations for big data . The options available are increasing and improving almost every day . So definitely a good first step is to spend some time deciding on your big data strategy – while remaining pragmatic that your strategy will evolve over time , and probably at a rate faster than BI strategies etc of past .

In my opinion , accelerated value from big data is possible only if all or part of the solution is cloud based . A customer should not have to worry about the deep mechanics of big data – they should be focused on the quality of insights . The mechanics of this should be offloaded to a vendor you TRUST to partner with . Big data comes with big responsibilities – so choose the partner wisely and for long term .

Such a vendor should be able to shield customers from a lot of the flux – and at a cost that is cheaper than if you tried to do it yourself . Of course 100% cloud like deployment is not practical for many reasons as made obvious in the discussion above – but vast majority of big data landscapes will need to be cloud based if value realization had to happen at a big scale . So like it or not – plenty of hybrid solutions will crop up to support big data .

So what is the end game ? Wish I knew – but I do have a dream . A network of big data is my vision of an end game . A network where data is shared across a huge ecosystem where people collaborate securely on data without everyone having to keep a redundant copy and build custom solutions on top . Of course not all data can be shared – but in almost all industries I am familiar with , not even 5% of data of common interest is shared freely . Lets see how long it takes before such a network will show up in our lives – or maybe it never will , and I will have to find a new dream 🙂

Defining Big Data, by listening to customers

Ever since I started working on SAP’s big data initiative , I have been having a hard time defining what exactly is big data . So, my gang and I had many a discussion and figured out that the best way to figure this out is to talk to customers . I called this a “listening tour ” – visiting customers to not sell anything or to solve any problem, but just to listen to them on what they think of big data .

One CIO put it explicitly on my face – the 3V model doesn’t work for him at all . In his eyes – this is just a vendor view of the world . Volume , variety , velocity , etc are all characteristics of data and doesn’t tell him what is the BIG deal . I mentioned this to Doug Laney on twitter and he confirmed that of course it is a data management theme that he meant when he/Gartner came up with it in 2001 .

Other customers also gave me versions of this view – they all are curious about big data , but they can’t see what the big deal is . Doing the same analysis that they do today with just a lot more data didn’t sound like anything worthwhile .

“Real time” and “Right time” access to data resonates with most of them – and they see value in that . This is of course a good degree different from just a few years ago .

As I looked across all the responses – one thing stood out . There is only one big V that matters to customers – BIG VALUE.

Two somewhat related definitions came out in how customers seem to think about the concept of BIG VALUE .

1. insights that cannot be figured out by current options (in techniques, storage , usability etc ) that can some how be made available by BIG DATA

2. “power of one” – if insight can be narrowed down to one customer , one user , one product etc – in the context of a useful transaction , then BIG VALUE is delivered . This is also the aspect where real time and right time comes into play meaningfully for customers .

3. Big value is equally obtained by figuring out what “mistakes” happened it past that no one found so far , and by forward looking predictive and prescriptive insights

So how do customers expect this BIG VALUE to be delivered to them ?

1. Via a Platform – that lets them store and compute without worrying about an upper limit , at an affordable price point . Security and other “enterprisey” things are a given – big data doesn’t get a big pass .

2. Via special skills – as in data science skills .

3. Via applications – apps that give users easy access to insights in an interactive and visual manner , without having to know how data is organized behind the scenes , or learning special technical skills to use the app .

Listening tour will continue , and if there is anything interesting I can share , I will post it here

Helping Employees Avoid Mid-Career Crisis

First things first – individuals own their career , not companies or bosses or coaches or anyone else . Others can help , but only you can execute . I had this conversation about mid career crisis situations quite a few times recently in some variant with my mentors , and with people that I mentor . I thought I will jot down a few points that came up – mostly for me to come back to for a refresher from time to time . Feel free to add , challenge etc as needed via comments .

1. What got you here won’t get you there

Nothing changed my career for the better than this one lesson that I learned from Bill Smilie , at an executive training program few years ago . The essence of the idea is that you need to constantly evolve your thinking and approach to get to the next level . If you are an ace sales person , just doing what you do best year after year will maximize your commission – but won’t get you to be a sales leader who manages many sales people .

2. Hard/Smart work is required , but nearly not enough if you don’t know where you are headed

You should know where you want to go. Ideally with a plan B and C . If you can’t clearly define what is it that you want – certainly don’t expect your boss to do the thinking for you . Run ideas by everyone you trust – and do it periodically . But decide for yourself .

And for those of us who help others – we might not have the ability to solve all problems that are brought to us . So be prepared to coach your mentees to the level you can and hand them off to another mentor who can take them to the next level . This happens in sports coaching all the time , and is easy to do in career situations too .

3. Don’t wait indefinitely for things to change for better

Loyalty is a great trait – but don’t let that be a one way street all the time . Give time for decisions to be made and processes to finish . If it doesn’t happen at first shot, try few more times if there is some thing to be added to the approach . If all reasonable approaches seem to not work – stop wasting your time and go to plan B . Just don’t get yourself tightly wound up by repeatedly doing the same thing over and over expecting a different result .

4. If things don’t work and you have to move on , ALWAYS leave on pleasant terms

Be it a customer who never gives you business , a boss who never gives a raise or a vendor who is habitually late on delivery – if you are getting out of a business relationship , do so without a big fuss . I can say from first hand experience – time and distance heals most hurt and disappointments in business . Learn what you can from the relationship and move on – save yourself an ulcer in the process 🙂

5. Plant those saplings every chance you get and pay it forward

We all need help from time to time . But you will need to establish the support system before crisis hits you . This is another reason why leaving on pleasant terms is so important . Also – never hesitate to pay it forward . It is not always possible to reciprocate help – for example , your CEO might help you with making an important customer introduction. But your odds of doing that for him is low . But if you nurture that relationship and make an intro to someone else coming after you – it will be very useful . Don’t try to keep a count – make time to help .

I try my best to give time for anyone who asks – and as long as they are genuine , I will give them time again and again . It didn’t come to me naturally – I learned it from my mentors . And they taught me to not waste their time .

6. When things are going well for you , learn to present better and negotiate better

I very rarely meet people with poor ideas , or who don’t deserve what they ask for . However – in many cases , they can’t articulate their ideas and they don’t have good negotiation skills . The time to learn is when you are on a roll – but if you didn’t do it then , do it now . Take classes , practice , meditate , watch it on you tube – whatever works for you . But do it .

The whole idea of effective presentations and negotiations is simple – make it personal to who you are dealing with , and give them options to choose from wherever possible . Yes and No are not the only options I am talking about here 🙂

What would convince a CFO won’t convince the AP clerk . And listen – usually they will give you enough clues on what will work with them . My way of making a presentation or negotiation simple is to avoid slides and use a piece of paper or a white board . Try a few different ways and see what works for you and polish it .

7. Align your goals to a higher purpose that others can relate to

No , not world peace or anything of that proportion . I just meant it in a more tactical way .

In corporate world , I am surprised how many employees don’t think outside their narrow responsibilities . I had a difficult sales situation once with my management not willing to accept my idea on how to close the deal . And the customer chose to stand their ground as well . I could not close the deal that quarter as I had committed , and had to take a week off to keep my sanity . That was the first time ever that I failed in such a magnitude and sort of in such a public way .

In that time , I caught up on reading a lot of business magazines and so on and quickly it dawned on me that if I recraft my proposal to help in an area that was a strategic concern to the company – I might get it approved . So I went back to my bosses with a new plan – and put it in context of the strategy of the company . I got approval in 5 minutes – and they even offered additional concessions I didn’t even ask for . And the very next day – I closed the deal with customer . You bet it changed my whole approach to dealing with my superiors .

This is one point I need reminders on – I knew how to do this with customers for a long time . It just didn’t occur to me till this incident that I should have done it with my own employer too . You live and learn , eh ? Icing on the cake was a few weeks ago , one of my mentors called me to thank for this approach which he learned from me and used successfully in negotiating something big.

That’s it for now – my dogs are pawing me to go play with them . Off I go