An Ex-Influencer’s take on influencing

I read this today morning and thought it was good advice . Influencers – they are an invaluable source of information to any vendor , and the good ones can help you do course corrections before you do something awful .

For a brief period , SAP considered me as an influencer . First as an SAP mentor and then also as a blogger . It also probably played some part in SAP hiring me . And now I deal with several influencers as an SAP employee, similar to what I did as an IBMer till last year.

I never quite figured out why there are multiple categories of influencers – analysts, bloggers, press , mentors et al. I am not a communications expert – so I trust there is some good reason that such distinctions exist . As someone who talks to most of the “50 shades of influencers” , I don’t personally see any difference in the quality of input I get . Maybe it is just organizational inertia to change an existing model .

In my opinion – choosing Influencers is exactly like choosing your mentors . It is never easy . It is a complex balancing act – you need to establish long term relationship with the best of them, but you also need to keep bringing in new ones to negate any bias . All influencers have some bias – which is why you should have more than one to begin with . However , if you stick to the same ones for an extended period – your chance of getting fresh new ideas will decrease quite a bit . And after some time passes, you would have influenced your influencers too much in reverse and will start painfully wondering why you seem to be stuck in echo chambers all the time .

In my opinion, it is probably safer for both sides to introduce a retirement scheme for all influencer programs . A change of scenery can do wonders for ones perspective . And if it is a planned activity, it will feel more like a nice vacation than a divorce . Sure you will lose a bit of continuity and comfort feel – but it is a small punishment compared to the doomed echo chamber !

Big Data Solutions – Do Questions Matter ?

I have Ray Wang to thank for this post. Off late, I have a serious problem of writers block. I just cant find a topic interesting enough to write about, and consequently have become a ratherirregular blogger – at least compared to last year. Any way – back to the topic of this post.

Ray tweeted this few minutes ago

A lot of BI blueprinting sessions from my consulting career flashed through my mind when I saw that. A key principle for a good BI system design is in finding out upfront most of the questions a user would ask the system, and then designing a solution around that. Unfortunately this is a blessing and a curse – while we can really optimize getting fast and accurate responses to predefined questions , this also curtails our ability to change our mind and ask different questions. More experienced BI experts will second guess other questions that users “may” ask and leave some room in design to cater for that, but it is clearly not a scalable way to do things.

Somehow, users were also trained along the way to agree to some lack of flexibility in BI systems. While the complaints never went away fully, most users think by now that it is normal for BI team to ask for some time to change the datamodels and create new reports and so on. It is a sort of “marriage of convenience” if you will – with tradeoffs understood by both sides.

So when we let go of “ordinary” data and embrace “big” data – what should change? I think we should use the big data momentum to make BI systems more intelligent than the rudimentary things it is capable of doing today. And this intelligence should be done with some business savvy. In other words both “B” and “I” of BI need some serious tweaking.

In my opinion, what should change right away is the expectation of business users needing to state most of their potential questions upfront at design time of the system . Or more clearly – the expectation should be significantly lowered, and business users should be allowed to ask more ad-hoc questions than they have done so far. Of course we can never guarantee full flexibility – so some subjectivity is necessary on where we draw the line. Just that the line should be drawn musch farther from where it is drawn today.

Accuracy of result for ad-hoc questions is not enough – the results should come back in a predictable and short time frame too. Ideally, all questions should come back with answers ( or a heads up to user that this is going to take longer ) within a predefined timeframe (say like 3 to 5 seconds or less).

Then there is the question of how the users ask these questions. SQL or NoSQL – querying languages do not provide democratic access to data. People should be allowed to ask questions in English ( or whatever language they use for business ). Some training might be needed for the system and for the users to understand the restrictions – but no user should be constrained with the need to know how things work behind the scenes. A minority of people should have the skills to educate the computer – the rest of us should not be burdened with that. Instead, the computers should be smart enough to tell them answers to what questions users ask.

There are very seldom exact answers to questions in business ( or life) – even apparently simple questions like “what is my margin in North America ? ” is ambiguous to answer. Most clients I have had have many different meanings to “margin” and “North America” and “My” within their organization. In real life, if these questions are asked of a human analyst, she will ask follow up questions to you to clarify and then provide an answer with necessary caveats. Why can’t systems do that? Wouldn’t life of users be vastly improved if systems answered problems like humans did, in a way humans understand? of course with more speed than humans 🙂

Big data or otherwise, there is always an issue of trust in the data from user’s perspective. Most analysts spend nearly as much time explaining how they arrived at their results, as they take for compiling and analyzing the data. The system goes through all the computation any way – even today in the non big data world. Why can’t our BI systems explain to the user how it arrived at the result all the way from source to target or backwards? Wouldn’t that increase productivity a lot?

When users ask questions – they usually will also combine it with external data (google, spreadsheets etc) before they take a final decision. Would it be possible for a BI system to present some useful contextual data to the questions from internet and intranet and allow the user to choose/combine what he needs?

And one last thing – if the system is intelligent enough to find answers, why can’t it have the smarts to also figure out the best possible presentation for the results? Today – we mostly have to predefine how output looks like visually. Why put that load on users? Can’t systems be smart enough to look at the question and the answers and figure out the best way to represent it to the user? This is not a “big data” problem – this should have been the case all along, but somehow never quite happened in a mainstream kind of way.

This is by no means an exhaustive list – I left out plenty of things like collaboration, predictive responses, closed loop BI and so on. I didn’t do so because they are unimportant, but only because of the boredom factor. These types of things are already happening to some extent, and hopefully will catch on more as time progresses.

So there you have it – its my birthday wishlist. And thanks again Ray for that much needed spark to blog again 🙂

Is “Out Of The Box” a Myth ?

I had a brief exchange of tweets with Ram Manohar Tiwari ( @rmtiwari on twitter ) recently on out of the box thinking , and since then a lot of thoughts have been brewing in my mind about this .

Everyone I know in corporate circles is a fan of “out of the box” . I don’t think a day has passed in my working life these past few years without someone mentioning it explicitly . It almost gives me an impression that there is this huge big box , full of regular joes like me – and a handful of smart people who stand outside and try their best to get us on their side .

My view on this matter is that there is no such thing as “out of the box”. People maybe able to get out of “A” box , but they will be in “some box” all the time . And because people are different from each other – most people should be able to tell others to get out of the box . All they can do is get out of their current box, get into a box with more space , and when that box fills up – then jump to yet another box .

Some times you might even have to visit the box you were in earlier – the one that you took pains to get out . If you need an example : think of people moving out of mainframes and now going to a similar model in cloud computing 🙂

I also doubt we are just in one box at a time – my feeling is that we are at the intersection of several ones at any point in time . This makes me claustrophobic just thinking about it . This is also the reason getting out if a box is hard – it is like a relational DB. All the dependencies need to be taken care off before you whack a table 🙂

All things considered , I now think teams will have more success by making use of diversity in its members than making a homogeneous team try hard to get out of the boxes they are in .

I think I have sufficiently bored you by now . If you need to be “un-bored”, try some out of the box thinking ….NOT