Category Archives: Tech Strategy

Posts in various domains of technology strategy and how things can be shaped in various technology domains

F.A.S.T Approach To Product Strategy

A few weeks back I wrote a blog post on how you can bring technology and growth more close to realization by adopting 5S Tech Strategy The blog post talked about taking a 5S approach towards product development that could help create a more robust & user centric product. I am extending my thinking now at strategic level to make an attempt at addressing the problem we face today :- How to counter the rapid change happening around us.

In so many years , the only thing I would ever speculate would be about an outcome of a cricket match or weather in cities I would have visited , but that still came with certain level of confidence; Today policies , businesses & impact on people has gotten reduced to a level of speculation never seen before! Feedback loop that drives future has shortened and is demanding faster reaction time.

In this blog , I will talk about the F(lexible) A(daptable) S(wift) T(ransformational) approach to Product Strategy. Each of the four pillars have been described by taking examples that are recent and defining in how they are impacting lives & society.

Flexible

Pandemic has shown us that flexibility is a key attribute that any design will have to cater for future. If we do not carry that trait our ability to respond will be constricted.

Health & fitness as a product is currently driving the Indian urban market. People now are willing to spend a good amount of their disposable income towards gyms, health devices like Fitbit and many more. The running events or rather the Marathon events are slowly gaining popularity in India, especially in Metros. Number of professional runners in India who train themselves through out the year to participate in Marathons is increasing by triple digit figures year on year.

But due to pandemic this activity has been drastically , where the whole joy of running in groups , meeting up and combining social activity with physical sport has gone for a toss. So should they stop organizing events ?

The obvious answer is NO , most of these event management firms have shifted to a virtual format where-in they are still able to charge 50% of registration fee than a normal event and provided for a simple mechanism to upload results post your run. They are trying to be flexible about where you run , when you run and how you measure performance.

Key Take Away : The product should be Flexible enough to continue to cater to its addressable market and foster engagement with its user base

Adaptable

Breweries and distilleries will make the switch from booze to hand gel as the government relaxed manufacturing rules in a bid to make up for shortages of alcohol-based disinfectants. Credit: Stock image/Pixabay

In late March , Belgium gave the go-ahead to breweries, distilleries and other recognized alcohol-makers to exceptionally produce disinfecting hand gel as demand surges due to the coronavirus (Covid-19) pandemic.

The Rubbens Distillery was one of the first businesses to cater to a new pandemic-related niche — making hand sanitizer in addition to gin. The pandemic has forced many European businesses to improvise. This Belgian gin distillery now has a popular line in hand sanitizer!

More examples across Europe reflect upon how the crisis forced thousands of companies to adapt — either to stay afloat, or to cater to a new pandemic-related niche. In France, perfumeries made disinfectant. In Denmark, a gourmet restaurant now sells only burgers. Elsewhere in this Belgian town, a lingerie manufacturer pivoted from corsets to COVID, and now produces face masks.

Key Take Away : The product / solution should be Adaptable to serve unrelated needs emanating from its customer base.

Swift

Swiftness to respond , is a great attribute to have. We often see inflexibility that comes when using different technology stacks and it becomes difficult for engineering teams to respond swiftly to changing market dynamics and retain market share. If you look at the new wave of platform hypothesis you would come across the following terms more often than ever :

Microservices based, API-first, Cloud-native SaaS and Headless.

It is agile and nimble, always up to date approach that can help provide swiftness to your solution if the underlying platform is able to support the above tenets.

Whilst above is an example from how digital needs to transform and respond to changing business strategy. The following example is from manufacturing where 3D-printing platform have countered supply chain disruptions enabling on demand solutions for needs ranging from personal protection equipment to medical devices and isolation wards. the digital versatility and quick prototyping of 3D-printing has enabled the rapid mobilization of the technology and a swift response to emergencies in a closed loop economy.

An Italian engineering company, Isinnova, came up with a 3D printable respirator parts for free to help keep coronavirus patients alive , called a venturi valve, it connects to a patient’s face mask to deliver oxygen at a fixed concentration. The valves need to be replaced for each patient. The biggest supply crunch is with ventilators, but respirator parts like the ones in Italy and even simple nasopharyngeal swabs for testing are all in short supply. Meanwhile, the technology of 3D printing, which allows digital design of parts and the “printing” of them off a machine that creates them layer by layer, is ideally suited to emergency manufacturing because it is fast, cheap and can be done without a big factory

COURTESY OF ISINNOVA

Key Take Away : The platform / solution should be enabled to provide Swift response to the challenges that are thrown by macro factors governing the market.

Transformational

With above tenets in design , the product offering needs to be truly transformational to disrupt the market. May 26th was historic day in spaceflight era. It was a start of new journey. For the first time ever on this day , SpaceX launched a crewed mission to the International Space Station . NASA astronauts Bob Behnken and Douglas Hurley took this journey on Crew Dragon spacecraft and shepherded it into a new era of space exploration.

At $1.7 billion dollars, SpaceX’s Crew Dragon vehicle is the least expensive spacecraft developed since the Mercury Program, which, adjusted for inflation, cost the agency $2.7 billion!

The SpaceX Crew Dragon spacecraft for Demo-2 inside the company’s hangar at NASA’s Kennedy Space Center.

Another fun fact is that the astronauts Bob Behnken and Doug Hurley drove to the launchpad in an electric car manufactured by Tesla, another of Musk’s pioneering companies, foregoing the “tin-can” Astrovan that has been the traditional crew transport since the US began sending humans into space in 1961 🙂 .

Self driving cars and reusable space rockets , we would have never imagined to move out from prototypes to production systems , but that is a reality now and maturing everyday!

Key Take Away : For a product to truly disrupt the market it has to be Transformational unless that happens competition will catch on and take over.

5S Tech Strategy

Giving some break to my Tech Hacks writing and getting back to strategy again ( it is about switching gears all the time !) .

I have been working through the years on many initiatives and they have varied in scale as well complexity but there are common pieces to the over-all strategy that are very critical for making Technology work towards delivering Growth.

I have put the tenants through a visual that will help connect the dots along side definitions which will help build the context. Let us go over the definitions one by one:

Technology

In today’s world this is bedrock for any industry and/or strategy. Be it pure play tech idea or tech enabled , the word tech seems to glue itself with firm roots into the foundation of any business model

Potential

Potential needs to exhibited by using technology has grounding principle. Unless we architect and design products , services and/or solutions in a manner that they can demonstrate potential value , the Technology lever does not effectively come into play.

Engagement

Producing endlessly products , services and/or solutions is not a great idea and hence engagement with potential users is of great value. The earlier they get integrated into feedback cycle the faster you are able to generate feedback to either go ahead or trash the concept. Sometimes , the potential users may get confused with the value proposition but showing persistence in providing answers to their relevant problems via engagement helps to solidify over a period of time the connect with the potential.

Growth

Without Growth there is nothing that can be achieved. The subtle difference between output and outcome lies in how well are you doing on your growth hypothesis. Engagement provides for evaluation of potential , Growth makes it real!

Scale

For growth to operate at stable levels and maintain a steady ship , it requires Scale which has to be provided by Technology. The Scale is driven by

  • Infrastructure
  • Inversion of Control(IOC)
  • Separation of Concerns(SOC)

Leave enough on table to help drive growth to next level without creating bottleneck.

With above explanations lets look at the 5S on how it connects the dots between these elements and create a feedback loop that can help drive right objectives for the Technology Strategy.

  • Technology needs to Show Potential
  • Potential needs to Steer Engagement
  • Engagement Shapes Growth
  • Technology Supports Scale
  • Scale Sustains Growth

Obvious Benefits For Teams

  • The connected 5S strategy becomes very self-explanatory in defining how we have to rally our teams to focus on creating value from the technology
  • The teams can be given clear goals that are aimed at working towards a more coherent strategy
  • Create iterable development model that can operate with constant feedback loops
  • From business or customer standpoint , the value drivers are clear on how potential can be monetized

I hope these insights are useful for the next big what you might be planning and as always for any feedback , questions or comments please leave it on the post!

Photo by David Travis on Unsplash

Habit Forming Platforms Part I

In my previous blog I had talked about Technology Evolution & touched upon how we have seen waves come in and go. I am converting that into a series of posts. First of the many posts related to Habit forming products & platforms. It captures my reflections around customer engagement mostly inspired from my readings of the book by Nir Eyal : HOOKED : How To Build Habit-Forming Products 

Nir Eyal is an Israeli-born American author, lecturer and investor known for his bestselling book, Hooked: How to Build Habit-Forming Products.  He teaches and has expertise in areas of psychology, technology & business.

Everybody you meet , there is always a common thread on talks of how to improve customer engagement. I also do realize that we are trying to make sincere efforts to improve it all the time but still keep failing at it ! It is important to retrospect why this is the case and why do we keep losing engagement from our customers , not making the value proposition compelling enough to keep their attention live and fresh !

When I started reading the book , it became very clear to me how forming habits is imperative for the survival of many products. The current pandemic is a living example where the consumption patterns and habits are rapidly shaping to create survivability  , continuity , & pivoting away from the pandemic.

Back in 2001 , when I joined the industry internet was coming out of womb and world was still about rich desktop applications. Some of us would remember Power Builder front-ends on Windows ! People at that time would expect the technology on web to be just like that , comparison point of totally then divergent tool sets ! there was expectation that web should replicate every aspect of experience there by underscoring the other tranformational impact of internet.  It was a struggle on how to manage this transition with scores of teams involved trying to get this right .  the books offers a set of learning on how such situations should be addressed from a platforms stand-point.

  1. Companies need to change behavior by presenting users with an implicit choice between old and new.
  2. Platform services should be enjoyable for the sake of its customers.
  3. Building Platforms that are marginally better than others will never shake the old habits of customers , with broad adoption base.

A classic paper by John Gourville , a professor of marketing at Harvard Business School stipulates that

Many innovations fail because consumers irrationally overvalue old while companies irrationally overvalue the new.

As we build platforms

  • We need to be better by  miles to even stand a chance for customers to get hooked to us.
  • If the platform and products require high degree of behavior change , then they are doomed to fail even if the benefits of using the new product are clear and substantial !
  • We need dramatic improvement to our software design or restatement of problem to break the users out of their old routines. 

Quoting another example from the book is that of the QWERTY keyboard , which was developed in 1870s ! Simply putting this layout prevented users from jamming metal type bars of early machines. Many people have tried to since then reinvent keyboards and relate it to better ergonomics BUT QWERTY still remains a standard. How does it survive ?

For a simple reason that there is very high costs attached to changing the user behavior and challenge the stored value for it within its customers. The whole process of relearning and adopting stands little or very less chance of success!

Business heads , platform architects , designers & developers need to:

  • Engage
  • Gauge
  • Modify

to make important decisions regarding how platform should be developed to trigger engagement for customers to get hooked to it.

We will talk in upcoming blog posts more around how to challenge and change the stored value in customer’s mind in order to increase likelihood of adoption. In the mean time , if you have any feedback or comments , please do share !

 

Technology Stack Evolution

Technology paradigms have been making shift through decades. Trends are moving fast in terms of offering agile & pivoting solutions to problems at hand.

From an engineering stand point I have seen following evolutionary trend as waves:

IT Application Engineering → Product Engineering → Platform Engineering

With every wave our approach on how we conceptualize a solution from self-build to best-of-breed has gone through ideological change:

IT Application Engineering

  • Point-to-Point
  • Narrow audience. Build for one works for one

Product Engineering

  • Vertical in terms of features
  • Broader Audience . Build for features serves many
  • Customization may give birth to unmanageable monolith

Platform Engineering

  • Horizontal in terms of features
  • Wider Audience through knob controls on infrastructure seeding
  • Extensible through API design
  • Build on top of it and not within
  • Scales

With the above evolution & need for diversity in addressing different problem statements , one needs to keep following points in mind :

  1. Cookie-cutter approach does not work for diverse business models
  2. “Thought partners” are required to co-develop solutions , listen and adopt design inputs instead of simply being vendors
  3. Need to address data infrastructure, visualization and distributed microservices
  4. Concepts around minimum viable product help understand customers’ journey at a high level and evaluate the technology needs

At such a massive scale, it is always beneficial to develop a set of design principles that can guide your decision-making.

  1. Choose tech solutions made by challengers and visionaries with an extensible, API-first mindset
  2. Avoid legacy companies that might be lagging behind as they try to evolve their monolithic platforms.
  3. Do lot of proof-of-concepts that build hands-on understanding

Platform approach comes with certain amount of decentralization embedded in it for use and extension thus allowing elasticity in solution to serve diverse needs :

decentralization

When we start thinking on above lines it helps us to become creative across two major parameters:

  1. Expanding or Testing into a new market
  2. Expanding or Testing a new product line

Technology should enable this entrepreneurial spirit from start-up to scale across various sub-streams.

I hope this short read provides condensed view of how to evolve your technology stack.

Additional Reference

You can find more details and insights in my catch-up with  Segment.com , that got published on their blog last month on How Our Stack Evolved At Cimpress   . Thanks to Geoffrey Keating from Segment for taking the interview. 

 

"Cranes by A-Runway of Haneda Airport" by ykanazawa1999 is licensed under CC BY-NC-SA 2.0

Re-platforming – Lows & Highs

Evolution is all about looking forward – Gerard Pique

"Cranes by A-Runway of Haneda Airport" by ykanazawa1999 is licensed under CC BY-NC-SA 2.0

“Cranes by A-Runway of Haneda Airport” by ykanazawa1999 is licensed under CC BY-NC-SA 2.0

When you talk about re-platforming to an online business , it starts to give butterflies to everybody around you! The feeling is an obvious one given that it is not going to be straight forward and will touch everybody in the organization. A re-platforming phase will cause strain on internal resources and the impact to business is also not ruled out, as one starts to touch the very foundation which it uses to conduct it’s business operations.

First reflections always would be to label re-platform as a bad idea but in reality it is a necessity not only to keep the stack fresh and adopt the next wave of tech but also show doors to huge opportunities that might be in front of us.

While many would view global expansion or launching to new marketplaces as an exciting business evolution, re-platforming also needs to be treated as a first class citizen for its successful outcome. Re-platforming is not migration or a parity generator for past but a re-look into where the market is, where the business fits in the current (and future) landscape and reviewing everything from customer experience to technology. For example being on mobile was considered competitive advantage but now it is part of standard behavior. As new age start-ups come into vogue and adopt newest stacks , things that were considered high barrier to tech adoption have become default offering. Continue reading

Platforms & More…

It has been time since I shared any thoughts. Have been irregular for sames reasons always .. caught in web of work and finding time for everything ! I tend to forget always that “yes I can do anything but I cannot do everything !” . Enough of self-infliction , will try to get back into groove and be honest to myself to do things which I like doing !

I am an avid read of Founding Fuel(who have the vision of creating  playbook of entrepreneurship through multi-channel knowledge sharing using their esteemed network of thought leaders) . While reading one of their articles from last week , I found it interesting enough for sharing it as blog post with some personal retrospection on it.

Read On…

Roughly last week on Founding Fuel an article was published about how much control is too much control for a platform. This article talks about some interesting history in early 1930s of how one the Bell Lab engineers Clarence Hickman developed a working model of answering machine but the underlying technology around magnetic storage and the research around it was shutdown by Bell Labs due to reasons related to people’s perception in relation to its usage and ramifications thereof on Bell Labs future ( Remember that Bell Labs remains most prolific entity in world that would have produced most Nobel laureates outside university / academia infrastructure as private company !) ( refer Tim Wu On the Master Switch )

Another powerful example is related to guiding principles of TCP/IP by Vint Cerf and Robert Kahn aimed at interconnecting computers. It had no central control and does not offer any specific optimization for an application. an extreme end of platform design

Above and many other examples mentioned in the article relate to following facts:

  • Businesses heavily control platform strategy
  • Controls are often misguided with too much or / less making a platform subservient to stakeholders or / negative usage
  • Enterprises struggle with definition of platform success
  • Enterprises struggle to see if the platform is balancing act for every stakeholder
  • & Platform itself does not know when it has failed on above parameters!

Now-a-days technology has made it possible to create a platform vision as a foundation to any business idea , but its economic model definition remains conflicting and suited to business itself on how it sees or visualizes the same but it is important to keep referring to core definitions of a platform business.

A platform is a business model that

  • Creates value by facilitating exchanges between interdependent groups. To make these exchanges happen, platforms harness and create scalable networks or users and resources.
  • Platform business needs to facilitate transactions and boost efficiency from the perspective of supply, demand or supply-demand relation.
  • A platform business, should ensure that transactions are carried out efficiently and profitably.

For a platform business, the essence is to ensure that transactions are carried out efficiently and profitably.  The key, undoubtedly, lies in whether a platform can deliver increased transaction efficiency from the perspective of supply, demand or supply-demand relation?

If a business can manage that, it will be able to rise above the fierce competition , ensure effective guardrails with inversion of control , and eventually grow bigger. When it grows bigger in terms of economic value than the creators itself , it starts to become a platform .

Scaling Analytics With Agility Part II

This is last in the two part series where I have have tried to explain approaches to achieving agility with data. If you have not already gone through part I , then follow this link.

Reminder of what we are trying to achieve by adopting any one or hybrid approach is as follows: 

  • Optimize Query performance 
  • Common Query Language 
  • Central data model for business analysts
  • Fast access to data insights 

The part – I of this series helped us understand the single Physical Data Store approach and now we are going to talk about Logical Data Store Approach

Logical Data Store Approach

In this approach we do not execute a Load of data to single store but tend to hand off more directly to data analysts ability to construct logical view or data models across various data sources without the need of lifting and shifting the data. There is a need to construct logical data models and to a large extent removes the need of developers to get involved straight up in any process.

capture

The above landscape tells us that Single Data Store architecture does provide some inhibitions to agility at the end of the day and this is something which logical data ware house architecture is looking to address.

Typical Architecture

The main theme here is that we are centralizing the data models as opposed to the data itself.

Let us now summarize the approaches across both major themes to achieve agility:

Considerations to Single Physical Data Store Approach

Pros

  • Brings data to one place and then use the store to do transformations

  • Takes an approach where the lake contains all relevant information in raw state post ingestion on continuous basis to cater to multiple personas

  • If used in conjunction to ELT architecture, it provides for a fine balance between developer and analyst community. The schematization of raw data is helpful and allows analysts to create logical data models post transformation within the store

  • Extent of development required depends on choice of ELT infrastructure adopted

  • It is not a hard choice or decision of CTO’s organization and in essence with less engineering resources you may still achieve quite a lot

Cons

  • It is dependent on the architecture that the teams would have followed in bringing data to a single store, implying that if customer connector architecture or ETL approach has been adopted with wrong choices then, the friction to get data in the store will remain very high
  • Storage of data and connecting to DWH will determine pricing of bring it all together along with other investments to standardize the ingestion pipeline architecture

Considerations to Logical Data Store Approach

Pros

  • It centralizes the data modelling and not the actual raw data store
  • It centralizes the modeled data for BI exposure
  • It provides for more self-service BI architecture

Cons

  • Maturity of organization and type of skill set to operate this kind of infrastructure
  • At what size should this be recommended?
  • How much help would be required for multiple businesses become self-serve on this model?
  • The CTO organization can make a choice for this but would need Data Ops to work alongside BI for creating & enabling data models that allow you to operate and leverage the power or else this can get reduced to being just another ELT infra that may not justify its deployment

Summary

Through this mini-series , one would get general idea of various methods by which agility can be achieved to unlock the golden joins ( as I call it ) that drives maximum value for the organization and provides data when it is needed most. 

According to me in order to make a choice , try to introspect and define the maturity index of following three parameters

  • Analyst Org
  • Engineering Org
  • Current DWH infrastructure
  • Budget 
  • Data set sizes 

In addition to this also be reminded that hybrid approach will always bean option if the organization is quite large and centralization in general to drive all the personas might not fit through one or the other working model.

 

Scaling Analytics With Agility Part I

Design principles and guiding forces for achieving unified analytics in the world of distributed data sources can vary. I thought it might be a good idea to just digitize some of my thoughts & where does it make sense to bring them all together and what are the trade-offs in doing so. In a multi-part series we will explore some approaches and then analyse what parameters are necessary to measure in order to pick an approach.

The common goal which we are always trying to search is geared towards any one of all of the following in combination:

  • Optimize Query performance 
  • Common Query Language 
  • Central data model for business analysts
  • Fast access to data insights 

Analyst Use Cases

While you can have many ways of looking at analytics , I generally tend classify things in two buckets to keep it simple. 

Report or Dashboard View

Capture

Non-Dashboard view

  • Combine data to generate insights, or to do data scientific activities which drive marketing behavior
  • Process for pulling data together in the lake to analyze and create marketing pipelines.
  • Online / offline share, share on complaints and sample orders
  • Combining touchpoints => is customer searching on site, then contacting customer care and placing an order via e-mail => what kind of product?

Single Physical Data Store Approach

This approach requires you house all your data in place and follows the paradigm of building analytics on top of single warehouse technology

Data Ingestion Approach

Data ingestion approach is driven by adopting custom connectors or ELT architecture that allows you to get the data to your central data store

Custom Connectors

This is a very traditional approach where developers internally work using any programming language to run batch mode extractors and bring a highly developer centric approach to developing / deploying extractors. It lacks standards of extraction architecture and does not follow a templated based connector architecture paradigm. This approach comes with least flexibility and agility into ingesting data with agility into your store. The custom connectors basically serve as ELT pipelines and are prone to continuous upkeep.

Standard ELT Pipelines

Industry offers many standard ELT pipelines and these platforms are standardized as architecture approach to provide for wide variety of connectors. Two most popular ELT architecture platforms are Stitch and FiveTran

There would be more and other ways , I am not contesting that but trying to just convey the pipeline flow and certain things can be achieved.

Stitch

It has been around for quite some time in market and now has been acquired by Talend , it is a blend of following traits

  • Provides for standard connectors certified by Stitch, these are around 90+
  • Provides for standard Tap-Target architecture which is open source. Read more about it at singer.io
  • Offers Schematization in standard as well open architecture development
  • Has limited exposure of Google related connectors and meta information
  • You can control historical import of information
  • Fosters open source development
  • Great community support
  • Has got a good User Experience
  • It is now backed by a world leader in data pipeline architecture
Skillset requirements

As a Data Analyst you can deal with this Stitch easily, while if you do not have a connector then you can develop one using Pyhton skillset using a standards-based approach as offered by Singer and get certified by Stitch. Using Stitch Import API in conjunction to Lambda functions also allows you to send data to Stitch.

Stitch Approaches Summarized
Using Stitch’s Standard Connectors

Stitch supports more than 90 integrations

Using Stitch’s Import API

If building and maintaining a script to retrieve and push data from Google Search Console is feasible for you or your team, Stitch’s Import API integration can be used as a receiving point for JSON or TRANSIT posts that would then be loaded to the destination warehouse connected to your Stitch account.

Singer Development

Stitch also works with integrations built as part of our open source project Singer. Singer includes a specification that allows integrations to be built by anyone, and our team provides support to members of the community who want to contribute code to the project via the Singer Slack group.

Any community built integrations can also be submitted by their creator to our team for review and potential inclusion in Stitch as a Community Integration using this form. Otherwise, integrations can be run within your infrastructure with the Stitch target and directed toward an Import API integration within your account.

If you or a member of your team is interested in building a Singer integration, for Google Search Console or otherwise, I would recommend checking out our getting started guide, and bringing any development-focused questions to the Singer Slack group.

Sponsored Development

If this is especially time-sensitive or building an in-house solution isn’t feasible for your team, Stitch’s Enterprise plan can include arrangements for the development of custom integrations that are built to ensure your requirements are met.

Typical Architecture

Capture

FiveTran

This is a new age ELT pipeline platform that focused on bring rich schematization & large connector architecture base to its users. It is blend of following traits

  • Provides very large connector base that covers almost all tools available
  • Is continuously evolving
  • Offers rich Schematization
  • Boasts of handling very large dataset with optimality
  • Is highly optimized to Snowflake
  • Comes with multiple destinations architecture
  • Provides for event stream ingestion architecture
  • API driven economy is available but evolving
  • Has in-depth exposure of Google related data stores
Skillset requirements

As a Data Analyst you can deal Fivetran easily. It is touted more as a data analyst friendly platform and while developers can get involved using cloud functions architecture, this is not something that is considered as an open source standard, you need to define and architect it as per the needs of the FiveTran platform.

Using FiveTran’s Standard Connectors

Leverage 100+ connectors from FiveTran

Using FiveTran’s Cloud Function Architecture

This is achieved using cloud function architecture where you need to deploy cloud functions on their platform and make that connector available for consumption

Sponsored Development

This is possible using enterprise contract

Typical Architecture

capture-2.png

Summary

I explained data centralization approaches using above facts and in next part I will continue to talk about virtual datawarehouse architecture and what kind of benefits it might entail.

Customer Data Platforms

One the key things which is going through an evolutionary cycle is the whole idea of getting to know our customer better through multiple touch points. Companies which have traditionally working in space of Campaign automation through multiple interfaces are making aggressive foray into this area by coming with cloud driven integrations to construct a 360 degree profile of the customers.

Customer data platforms (CDP) are gaining momentum faster than any other marketing technology, even though many marketers are not yet familiar with the technology, it promises to provide the key to comprehensive data-driven marketing, a very attractive marketing concept where all your customer data is combined for marketing (and other) uses.

This blog is intended to give you a brief overview of Customer Data Platforms(CDP) . The target audience are novice like me , who have not heard about this and are making effort to collect some facts to improve general knowledge around trends in area of marketing.

Without the data and the management of the data, the ‘marketing brain’ that allows for smarter campaigns simply can’t function. Bringing together the data is very important and here CDP comes into picture. The aim of the CDP is to bring together all customer data and stitch the data together into unified customer profiles, so a marketer can easily work with it.

From a business perspective the ownership of all customer touch points becomes increasingly important: Facebook & Co. often know more about a customer then the company how owns the data. To turn it around: The only chance to better personalize/customize the customer experience is the ownership of proprietary data and the ability to execute towards your business goals accordingly.

It is regular to hear that marketers have tools which can function as a hub or central place for customer data & yes there has certainly never been a shortage of technologies that claim to provide a ‘single customer view’or a ‘360 degree customer profile’ but in reality there hasn’t been one platform type out there that has the potential to bring together all the data and at the same time properly make it useful for the marketer.

Quote from David Raab, a respected martech analyst, was the first to define the CDP category in 2013, and the definition reads as follows:

“A customer data platform is a marketer-managed system that creates a persistent, unified customer database that is accessible to other systems.”

So, what is the difference with other systems?

The difference between a Customer Data Platform (CDP) and a Marketing Cloud (also called “Multi-Channel Campaign Management” by market research institute Gartner, or MCCM) is not always obvious to many marketers at first glance.

This is mainly because there are no fixed definitions for these terms and therefore the market has many providers scrambling for position, who vary from each other in terms of their focus. What follows is therefore a general definition of the two categories, to the extent that is possible.

A pictorial sum-up of what we have talked until is given below.

cdp

I will continue to collect and share more information on this topic , as I find or read more !

Vertical Decomposition Of Software Systems

This is a very light post on vertical decomposition and by no means is most comprehensive read on this topic! It is meant for engineers and product managers that are looking after or working in such initiative to resurrect their current approach or pick something new from the examples.

Vertical decomposition powered by micro-services is a handy approach while trying to break down applications which appear to be black-boxed and contain objects with too many responsibilities. This complex overlap causes unwanted extension in data structures and creates convoluted schema that albeit powers the application but fails to provide right level of elasticity for absorbing ever changing needs of business.

Vertical decomposition can help you break down the application that encapsulates single business domain such as order , search , customer , product etc. In turn these single business domains should have concrete resource definitions with right level of granularity powered by micro-services to expose their

  • Functions
  • Configuration
  • Events
  • Discoverbility

While looking at vertical decomposition , one needs to spend time and decide which pieces need to be decomposed and how an incremental migration of same would take place.

Some great examples where you will find a particular resource having multiple responsibilities is as follows [ not comprehensive but enough to drive home the point ] :

  • Cross Domain
    • Customer carries definition on discounting that is linked to products
    • Customer specific products are linked to the product model
  • Complex Structure
    • Customer carries definition of tiers with logic and rules attached to it
    • Ordering pattern combines buying and promotions together

Such scenarios clearly help you identify radius of operation and allow you to start breaking down aspects of complexities with primarily two things as drivers:

  • Break things which are simple in nature and help you lay foundation
    • Example : [ assigning loyalty tiers to customers ]
    • Break Loyalty program out from the customer
  • Identify dependencies that reduce reliance on your current monolith [ follow anti-corruption design in case dependency exists ]
    • Example : [ login service ]
  • Carve out independent services that can expose required functionality without going back to monolith
    • Example : [ Separate discount from customer as product discount or separate promotions from buying operations ]

If you are some where above and started to experiment then for sure your delivery teams are comfortable with building micro-services and ready to go. However you may hit roadblock as the deconstruction process is not an easy one. Domains are over-lapped and need to break down that one resource which does it all. One of the key things to achieve that is start imagining distributed systems and finding a “best-of”breed” approach to separate by keeping persons in mind mapped to domains and then to best of breed architecture. This way you would be able to spread the system as  a platform and created separation of concerns which can then start to shape up the action map for the persona and domain.

Achieving this is not a one day’s job and requires us to ramp up on many fronts as an organization. The safety net in doing so should be always kept in mind so that you can ensure to isolate or promote basis the phase outcomes before we proceed. The whole exercise will start with broad funnel approach and then start to narrow down eventually leading to spurt of services that control separate domains and talk to each other in a contractual approach.