Big Data on the other side of the Trough of Disillusionment

5. Don’t implement a technology infrastructure but the end-to-end pipeline a.k.a. Bytes To Business

SImple Reason : Business doesn’t care about a shiny infrastructure, but about capabilities they can take to market …


4. Think Business Relevance and agility from multiple points of view

Aggregate Even Bigger Datasets, Scenarios and Use Cases

  • Be flexible, tell your stories, leveraging smart data, based on ever changing crisp business use cases & requirements

3. Big Data cuts across enterprise silos – facilitate organization change and adoption

  • Data always has been siloed, with each function having it’s own datasets – transactional as well as data marts
  • Big Data, by definition is heterogeneous & muti-schema
  • Data refresh, source of truth, organizational politics and even fear comes in the picture. Deal with them in a positive way

2. Build Data Products

1. tbd

  • One more for the road …

Jeff Dean : Lessons Learned While Building Infrastructure Software at Google

Last week I attended the XLDB Conference and the invited Workshop at Stanford. I am planning on a series of blogs highlighting the talks. Of course, you should read thru all the XLDB 2013 presentation slides.

Google’s Jeff Dean had an interesting presentation about his experience building GFS, MapReduce, BigTable & Spanner. For those interested in these papers, I have organized them – A Path through NOSQL Reading 

Highlights in pictures (Full slides at XLDB 2013 site):



The Big Data Convergence

As we scan the concepts, technologies, products and the practices in the big data space, lot of things get muddier.

Neither the progression nor the boundaries are clear. We are still in the descriptive stage in terms of the application of the analytics technologies.

I had a good conversation with Bob Friday yesterday – his question was “What prevents us from answering 80% of the questions via automatic inferences ?” And that is the “Adaptive” stage we need to be …

I think a diagram is much better than me writing 100,000 words. So here it is :


In many ways, a lot of the underlying technologies are converging.

For example, A(rtificial) I(ntelligence) = NLP + N(atural) L(anguage) U(nderstanding) + ML + K(nowledge) R(epresentation) + Reasoning
Are Amazing Intelligent Machines in the works ?

Big Data State Of The Union

An informative study by TCS on the current state of Big Data “The Emerging Big Returns on Big Data”



Of course, you should download and read the whole report. Some interesting highlights:

  • There’s a polarity in spending on Big Data, with a minority of companies
    spending massive amounts and a larger number spending very little
  • The business functions expecting the greatest ROI on Big Data are not the ones
    you may think – while Sales & Marketing have initiatives, finance & logistics are betting on big data for efficiences & insights
  • The biggest challenges to getting business value from Big Data are as much
    cultural as they are technological
  • Nearly half the data (49%) is unstructured or semi-structured, while 51% is
    structured. The heavy use of unstructured data is remarkable given that
    just a few years ago it was nearly zero in most companies – Enterprises have gone multi-structured !
  • Monitoring how customers use their products to detect product and design
    flaws is seen as a critical application for Big Data

Cheers & Happy Reading …

5 Steps to Pragmatic Data …er… Big Data

It is 2013 & Big Data is big news … Time to revisit my older (Nov’11) blog “Top 10 Steps to A Pragmatic Big Data Pipeline” … Some things have changed but many have remained the same …

5.  Chuck the hype, embrace the concept …

This seems to the first obvious step for organizations. From Ed Dumbill (“Big data” is an imprecise term...) to TechCrunch (“Perhaps it’s about the actual functionality of apps vs. the data“) agree with the concept, but the terms and marketing hypes have hit the proverbial roof. The point is, there are many ponies this pile & there is tremendous business value (so long as one is willing to discount the hype and think Big Data = All Data) …

I really like Mike Gualtieri’s very insightful definition of Big Data as

… the frontier of a firm’s ability to store, process, and access (SPA) all the data it needs to operate effectively, make decisions, reduce risks, and serve customers

Big Data 01

4. Don’t implement a Technology, implement THE Big Data pipeline

Think of Big Data in multiple dimensions than a point technology & evolve the pipeline focussing on all the aspects of the stages

Data Science 02

The technologies, the skill sets and the tools are evolving, so are the business requirements.

Chris Taylor addresses this very clearly (“Big Data must not be an elephant riding a bicycle“) – viz. One has to address the entire spectrum to get value …

Simply applying distributed storage and processing (like Hadoop) to extremely large data sets is like putting an elephant on a bicycle .. it just doesn’t make business sense — Chris Taylor

3. Think Hybrid – Big Data Apps, Appliances & Infrastructure

I had addressed this one in my earlier blog(“Big Data Borgs, Rise of the Big Data Machines & Revenge of the Fallen Algorithms“)

The morale of the story : Think out-of-the box & inside-the-box.

Match the impedence of the use cases with appropriate technologies

2. Tell your stories, leveraging smart data, based on crisp business use cases & requirements

Evolve the systems incrementally focussing on the business values that determine the stories to tell, the inferences to derive, the feature sets to influence & the recommendations to make

Augment, not replace the current BI systems

Notice the comma (I am NOT saying “Augment not, Replace”!)

“Replace Teradata with Hadoop” is not a valid use case, given the current state of the technologies. In fact, integration with BI is an interesting challenge for Big Data …

No doubt Hadoop & NOSQL can add a lot of value, but make the case for co-existence leveraging currently installed technologies & skill set. Products like Hive also minimizes barrier to entry for folks who are familiar with SQL

From a business perspective Patrick Keddy of Iron Mountain has a few excellent suggestions on managing Big Data: 

Big data informs and enhances judgement and intuition, it should not replace them

Opt for progress over perfection

View the data in context

1. Apply the art of Data Science & Smart Data, paying attention to touch points

This still remains my #1. Data Science is the key differentiator resulting in new insights, new products, order of magnitude performance, new customer base et al – “a cohesive narrative from the numbers & statistics”

Data science is about trying to create a process that allows you to create new ways of thinking about problems that are novel, or you are trying to use data to create or make something.” says D.J.Patil

Smart Data = Big Data + context + inference + declaratively interactive visualization


  • Smart Data is (inference) model driven & declaratively interactive
  • For example,
    • The information like Wikipedia is big data; the in-memory representation Watson referred to is smart data
    • Device logs from 1000 good mobile handsets and 1000 not-so-good phones is big data;  a gam or glm over the log data after running through several stages of MapReduce is smart data, because it could give you an insight as to what factors or combination of factors make a good phone a bad phone

Focus not only on the Vs (ie Volume,Velocity,Variability & variety) but also on the Cs (ie. Connectedness & Context)

The two main Big Data challenges in 2013 would be:

1st : Data integration across silos to get the comprehensive view &

2nd : Matching the real-time velocity of business viz. CEP, sense & respond et al.

 For example, I have already seen folking looking outside Hadoop for CEP and near-realtime response

“.. 85% of respondents say the issue is not about the volume of data but the ability to analyze and act on data in real timesays Ryan Hollenbeck quoting a 2012 Cap Gemini study (Italics mine)

Big Data Borgs, Rise of the Big Data Machines & Revenge of the Fallen Algorithms

I have been following the 2013 predictions for Big Data. Naturally lots of interesting predictions. Here are a few that I understand and (sort of) agree :

What or Who is a Data Scientist ?

DataScientist = Part Hacker + Part Technologist + Part Detective + Part Scientist + Part Business Analyst + Part  Visual Artist

Is Hadoop the new stored procedure ?

Two things happened today for me to ask this question and am not offering any serious answer, yet !

  • First I had some quick chat with folks at MongoDB and as a result got me thinking about the MapReduce in Mongo and where could it go. MongoDB also has the new declarative aggregation framework.
  • My thesis is that, while now the MongoDB aggregation framework is JSON semantics+$ keywords, it could look a lot like a functional programming language – with high-order declarative functions like map/reduce, discriminated unions (like F#) and currying.
  • And later in the day I read Edd’s blog “5 Big Data Predictions”, also in Forbes. (While both are the same blog, there might ne interesting comments in each)
  • Lots of interesting observations from Edd. He is predicting better programming language support, but may be we are looking at it the wrong way – what we need is a better stored procedure support in the data layer. It also could the next point Edd was talking about-Streaming data processing ! Where best could we have that feature than at the data layer ?
  • Would we be able to write a social science data platform using the MongoDB aggregators ? Would MongoDB mapReduce fit the bill now ? If not, what would it take to make it so ?
  • There are two obvious paths – connector to an application artifact for example Hadoop connector or embed the map/reduce in the data layer. Both have their advantages and disadvantages. With the connector the mapReduce can scale orthogonally, but with the embedded feature, one can achieve real-time processing (within limits). May be this is the time for an application data store !
  • Would the datastores like MongoDB gain features like the Twitter Storm, Real-Time map reduce, hierarchical iterative functional aggregators  and so forth ?
  • GreenPlum’s Chorus is interesting – Can NOSQL datastores gain some of the relevant capabilities that Chorus has?

Finally, the beginning as the end,

  1. Is hadoop the new stored procedure or would the new stored procedures look like Hadoop ?
  2. Is Data and Application becoming inseparable at scale ?
  3. What says thee?

Top 10 Steps to a Pragmatic Big Data Pipeline

As you know Big Data is capturing lots of press time. Which is good, but what does it mean to the person in the trenches ? Some thoughts … as a Top 10 List :

[update 11/25/11 : Copy of my guest lecture for Ph.D students at the Naval Post Graduate School The Art Of Big Data is at Slideshare]

10. Think of the data pipeline in multiple dimensions than a point technology & Evolve the pipeline with focus on all the aspects of the stages

  • While technologies are interesting, they do not work in insolation and neither should you think that way
  • Dimension 1 : Big Data (I had touched upon this in my earlier blog “What is Big Data anyway“) One should not only look at the Volume-Velocity-Variety-Variability but also at the Connectedness – Context dimensions.
  • Dimension 2 : Stages – The degrees of separation as in collect, store, transform, model/reason & infer stages
  • Dimension 3 : Technology – This is the discussion SQL vs. NOSQL, mapreduce vs Dryad, BI vs other forms et al
  • I have captured the dimensions in the picture. Did I succeed ? Let me know

9. Evolve incrementally focussing on the business values – stories to tell, inferences to derive, feature sets to influence & recommendations to make

Don’t get into the technologies & pipeline until there are valid business cases. The use cases are not hard to find, but they won’t come if you are caught up in the hype and forgrt to do the homework and due diligence …

8. Augment, not replace the current BI systems

Notice the comma (I am NOT saying “Augment not, Replace”!)

“Replace Teradata with Hadoop” is not a valid use case, given the current state of the technologies. No doubt Hadoop & NOSQL can add a lot of value, but make the case for co-existence leveraging currently installed technologies & skill set. Products like Hive also minimizes barrier to entry for folks who are familiar with SQL

7. Match the impedance of the use case with the technologies

The stack in my diagram earlier is not required for all cases:

  • for example if you want to leverage big data for a Product Metrics from logs in Splunk, you might only need a modest hadoop infrastructure plus an interface to existing dashboard plus Hive for analysts who want to perform analytics
  • But if you want Behavioral Analytics with A/B testing with a 10min latency, a full fledged Big Data infrastructure with say hadoop, HDFS, HBase plus some modeling interfaces, would be appropriate
  • I had written an earlier blog about the Hadoop infrastructure as a function of the degrees of separation from the analytics end point

6. Don’t be afraid to jump the chasm when the time is appropriate

Big Data systems have a critical mass at each stage – that means lots of storage or may be a few fast machines for analytics, depending on the proposed project. If you have done your homework from a business and technology perspective, and have proven your chops with effective projects on a modest budget, this would be a good time to make your move for a higher budget. And when the time is right, be ready to get the support for a dramatic increase & make the move …

5. Trust But Verify

True for working with Teenagers, arms treaty between superpowers, a card game, and more closer to our discussion, Big Data Analytics. In fact, one of the core competency of a Data Scientist is a healthy dose of skepticism – said John Rauser [here & here] . I would add that as you rely more and more inferences to a big data infrastructure across the stages, make sure there are checks and balances, independent verification of some of the stuff the big data is telling you.

Another side note in the same line is the oscillation – as the feedback rate, volume and velocity increases there is also a tendency to overreact. Don’t equate the feedback velocity to the response velocity – for example don’t change your product feature set based on high velocity big data based product metrics, at a faster rate than the users can consume. Have a healthy respect for the cycles involved. For example I came across an article that talks about fast & slow big data – interesting. OTOH, be ready to make dramatic changes when you get faster feedbacks that indicate things are not working well, for whatever reason.

4.   Morph from Reactive to Predictive & Adaptive Analytics, thus simplifying and leveraging the power of Big Data

As I was writing this blog, came across Vinod Khosla’s speech at Nasscom meeting. A must read – here & here. His #1 and #2 in ‘cool dozen’ were about Big Data! The ability to infer the essentials from an onslaught of data is in fact the core of a big data infrastructure. Always make sure you can make a few fundamental succinct inferences that matter, out of your infrastructure. In short deliver “actionable” …

3. Pay attention to How & the Who

Edd wrote about this in Google+. Traditional IT builds the infrastructure for Collect and Store stages in a Big Data Pipeline. It also builds and maintains infrastructure for analytics processing, like Hadoop and visualization layer like Tableau. But the realm of Analyze,Model, Reason and the rest, requires a business view, which a Data Analyst or a Data Scientist would provide. Pontifying further, it makes sense for IT to move in this direction by providing a ‘landing zone’ for the business savvy Data Scientists & Analysts and thus lead the new way of thinking about computing, computing resources and talents …

2. Create a Big Data Strategy considering the agility, latencies & the transitory nature of the underlying world

[1/28/12] Two interesting articles – one “7 steps for business success with big data” in GigaOm and another “Why Big Data Won’t Make You Smart, Rich, Or Pretty” in Fast Company. Putting aside the well published big data projects, a normal big data project in an organization has it’s own pitfalls and opportunities for success.  Prototyping is essential, modelling and verifying the models is essential and above all have a fluid strategy that can leverage this domain …

<This is WIP. Am collecting the thoughts and working thru the list – delibeately keeping two slots (and may be one more to make a baker’s dozen!, pl bear with me … But I know how it ends ;o)>

1. And, develop the Art of Data Science

As Ed points out in Google+, big data is also about exploration and the art of Data Science is an essential element. IMHO this involves more work in the contextual, modeling and inference space, with R and so forth – resulting in new insights, new products, order of magnitude performance, new customer base et al.  While this stage is effortless and obvious in some domains, it is not that easy in others …