Crazy One!

Red tapism, corporate innovation and growth

Innovate or die – haven’t we all heard this? Add red tapism and innovation in the same sentence, sounds conflicting, right?

Organisations try to live up to “Innovate or die” by trying to become Agile or migrating to cloud services or adopting OKRs, basically moving in the right direction to be able to deliver better and faster. As technology advances exponentially, customers become more demaning, organisations must now be able to quickly respond to market demands in order to compete, which requires agility.

However, trying to be agile in an established, organization with legacy systems and non-agile methods is a big challenge. Untangling teams and systems that have always worked in silos is a big hurdle and the hardest part of any organisational transformation, not to forget the red tape created by outdated processes. Earlier, organisations created processes and procedures to ensure predictable outcomes, to mitigate risks. The processes designed did not have much room for experimentation or agility. But in the current digital landscape, this type of bureaucracy is simply too time consuming and not at all cost-effective.

  • Most enterprises have standardised tedious approval processes where some of the people approving do not even possess the technical know how to judge or review the matter in question. This ends up in unending rounds of justifying the simplest decision.
  • Procurement teams also add to the red tapism making it difficult for teams to acquire services and products that can speed up their development. We have all been through this – waiting months to get Slack or JIRA approved. And IT Security will not allow Trello or Google Drive, so go figure!
  • And then there is the fear of cloud solutions. There is no denying privacy is a major concern when it comes to data and customers want to ensure that the services and products they use, handle their data well. But a cloud solution provider is more likely to have robust, well-configured firewalls and data security practices than an average enterprise, as it is the focus of their business. Keeping in mind that the cost of regulatory compliance will be substantial, but the cost of non-compliance will be higher, is important while choosing cloud service vendors.
  • To top it all there is the fear of unknown, which is a huge blocker for innovation, it is therefore important to educate and get a buy-in from everyone involved on a transformation journey.

To be able to innovate, enterprises need to deliver end-to-end business value in increments, test and validate results before starting full scale development. Creating a culture of testing and experimentation demands processes and methodologies that support faster delivery of customer-centric value, with constant room for improvement.

Starting off testing a few assumptions that could lead to a potential minimum viable product should not require written approval from legal, compliance, finance, risk management, procurement, etc. Experimenation could be conducted with data masking and that should not entail long winded paperwork such as a detailed risk analysis and architectural artefacts.
Red tapism is a sure shot way to kill creativity which in turn ensures no innovation or improvement.

If organisations really want faster qualitative deliveries, freethinking leaders should not be afraid to rock the corporate boat and cut some slack in terms of obsolete processes and procedures.

Building blocks for digital enterprises

Every organisation now aims to be digital, innovative, competitive, agile and fast growing apart from being profit-making at minimal cost. Any sane organisation would set it’s sights on the aforementioned targets but the path to achieving the same is tricky. The problem, sometimes, is lack of alignment between the business goals and implementation of it and sometimes (often), businesses are so shrouded with buzzwords that they prioritize being associated with buzzwords rather than building capabilities to achieve business opportunities or solve business problems.

Abuzz with buzzwords

The path to agile digitalization and innovation is carved by identifying use cases based on business goals and thereafter defining the business as well as technical enablers required to accomplish those use cases. One or more capabilities can facilitate one or more use cases, but the mapping exercise needs to be done prior to implementation, bridging the gap between strategic business objectives and capabilities. The capabilities required by a business (the what) tend to remain comparatively constant while, the implementation process (the how) is likely to change frequently. I have come across multiple instances where organizations want to implement AI or chatbots and often without a clear goal or vision in mind, resulting in shadow IT, additional costs and often failed attempts to productionalize the efforts.

Many organisations lack governance, making every new requirement a burdensome task, taking several years to deliver even minute features. I have also come across organisations that have some form of governance, group of enterprise architects, working on process mapping, information modelling etc. but there is a huge gap between the enterprise architecture and the actual IT development. A lack of visibility of the path between purpose and implementation creates silos, making it tedious to deliver enablers for digitalization.

Approach for implementation

Business capability is about identifying what factors facilitate accomplishment of business goals. Be it customer experience, pricing strategy, promotion and distribution of ideas, products and services, the idea is to satisfy the strategic objectives of an enterprise.

Mapping business capabilities to strategic goals makes the business strategy tangible and more visible to the entire enterprise. This leads to a more effective way of using technology to achieve business goals, eliminating enterprise-wide redundancies. A capability-centric organization also helps overcome the common problem of organizational silos.  Visualizing use case -> business capability-> technological capability leverages transparency, integrates, constructs, and reconfigures resources and competences to achieve high performance. The resulting organization is a more agile and adaptable one, leading to faster time-to-market.

Architecting Modern Data Platforms

As organisations struggle to capture and leverage multitudes of data, there is a surge of technological options to choose from. Well designed data platforms facilitate experimentation, have shorter time to markets, have faster adaptation to latest advancements in data technologies, promote self-service thereby accelerating data adoption.  Data being the key enabler for business transformations, it is vital to build platforms that accelerate validation of use cases and can handle scaling of use cases and users. Designing a platform which is elastic enough to embody all the above can be quite a daunting task.

MDA

The primary points to consider when architecting modern data platforms:

  • Customer centric

Organisations battle immensely with legacy data technologies to deliver personalization, and customer experience, despite there being so much emphasis on hyper personalization. Thinking on the lines of creating 360 ° customer view helps align technological choices after business pain points.

  • Cloud Native

Cloud solutions support elastic scaling, high availability  and secure fully managed services with integration to a range of enterprise security systems including LDAP, Active Directory, Kerberos and SAML. Cloud  solutions allow pluggable architecture – replacing components if better options are available with minimum reconstructing. Cloud platforms eliminate the time-consuming work of provisioning resources and infrastructure, thereby reducing time to market.

  • Multi-platform architectures

Be it multi-cloud or multiple data storage patters, it should be the use cases that dictate the architectural patterns and not vice versa. Datawarehouses, datalakes and NoSQL databases can all co-exist on multi-cloud platforms if the use cases demand so. Organisations should avoid platform/vendor lock-ins, because then businesses are forced to make technology choices that are not in the best interests of the company.

  • Microservice-enabled

It is critical to  envision data as not just a means for visualization like a diagnostic tool, data is critical to help organizations adapt to change, in evolving business environments and to innovate and every company wants to expedite the process to be the first ones to come up with innovative products and services. Data plays a key role in this aspect. Monolithic applications are a major bottleneck in this case. In microservices based design small decoupled services are developed completely independent of each other  to achieve business requirements, faster, generally through REST APIs or event streams.

  • Flexible

Modern data platforms should be flexible enough to accomodate rapidly evolving business requirements. Be it integrating new data sources or feeding data into futurist data products. Modern data platforms should simplify testing new ideas on a small scale prior to making heavy investments in infrastructure.

Modernization continues to be a strong trend in data platforms, whether on Hadoop or RDBMS or multi-tenant solutions. It is the ease of integrating new data sources, TCO, prototyping functionalities, security and scaling that matter most in modern platform architectures.

 

Three reasons why Big Data projects fail

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I have not been regular with my personal blog because I have been blogging elsewhere.

Here are the links to my latest blog posts about why Big Data projects fail and how to attract more women into tech.

Having worked extensively in the Big data & IoT space I have closely observed failures over and over again and the reasons for failure being repetitive :

  • Wrong use cases
  • Wrongly staffed projects
  • Obsolete technology

Read the blog post for more details:

Three reasons why Big Data projects so often fail

Being a woman in tech or woman in data I am often the only woman in meetings, trainings and discussions which feels weird. With not many women in tech it gets easier to discriminate the few that do exist. Incidents of mansplaining, gaslighting are rampant and it’s the victim that gets labelled as drama queen while the abusers fo scot free. Organisations that are serious about increasing the number of women in tech need to address glass ceiling, gender wage gaps & bro-culture and cultivate an inclusive work atmosphere. Read my post on how to get more women into tech.

How to Get More Women in Tech

How to become big data – data analyst

Anyone who works in the tech industry is aware of the rising demand of Analytics/ Machine learning professionals. More and more organisations have been jumping on to the data driven decision making bandwagon, thereby accumulating loads of data pertaining to their business. In order to make sense of all the data gathered, organisations will require Big Data Analysts to decipher the data.

  Data Analysts have traditionally worked with pre formatted data, that was served by the IT departments, to perform analysis. But with the need for real time or near-real time Analytics to serve end customers better and faster, analysis needs to be performed faster, thereby making the dependency on IT departments a bottleneck. Analysts are required to understand data streams that ingest millions of records into databases or file systems, Lambda architecture and batch processing of data to understand the influx of data.

Also analysing larger amounts of data requires skills that range from understanding the business complexities, the market and the competitors to a wide range of technical skills in data extraction, data cleaning and transformation, data modelling and statistical methods.

Analytics being a relatively new field, is struggling to resource the market demands with highly skilled Big Data Analysts. Being a Big Data Analyst requires a thorough understanding of data architecture and the data flow from source systems into the big data platform. One can always stick to a specific industry domain and specialize within that, for example Healthcare Analytics, Marketing Analytics, Financial Analytics, Operations Analytics, People Analytics, Gaming Analytics etc. But mastering the end-to-end data chain management can lead to plenty of opportunities, irrespective of industry domain.

The entire Data and Analytics suite includes the following gamut of stages:

  • Data integrations – connecting disparate data sources
  • Data security and governance – ensuring data integrity and access rights
  • Master data management – ensuring consistency and uniformity of data
  • Data Extraction, Transformation and Loading – making raw data business user friendly
  • Hadoop and HDFS – big data storage mechanisms
  • SQL/ Hive / Pig – data query languages
  • R/ Python –  for data analysis and mining programming languages
  • Data science algorithms like Naive Bayes, K-means, AdaBoost etc. – Machine learning algorithms for clustering, classification
  • Data Architecture – solutionizing all the above in an optimized way to deliver business insights

The new age data analysts or a versatile Big Data Analyst is one who understands the complexity of data integrations using APIs or connectors or ETL (Extraction, Transformation and Loading), designs data flow from disparate systems keeping in mind data security and quality issues, can code in SQL or Hive and R or Python and is well acquainted with the machine learning algorithms and has a knack at understanding business complexities.

Since Big Data and Analytics is constantly evolving, it is imperative for anyone aiming at a career within the same, to be well versed with the latest tech stack and architectural breakthroughs. Some ways of doing so:

  • Following knowledgeable industry leaders or big data thought leaders on Twitter
  • Joining Big Data related groups on LinkedIn
  • Following Big Data influencers on LinkedIn
  • Attending events, conferences and seminars on Big Data
  • Connecting with peers within the Big Data industry
  • Last but not the least (probably the most important) enrolling in MOOC (Massive Open Online Course) and/ or Big Data books

Since Analytics is a vast field, encompassing several operations, one could choose to specialise in parts of the Analytics chain like data engineers – specializing in highly scalable data management systems or data scientists specializing in machine learning algorithms or data architects – specializing in the overall data integrations, data flow and storage mechanisms. But in order to excel and future proof a career in the world of Big Data, one needs to master more than one area. A data analyst who is acquainted with all the steps involved in data analysis from data extraction to insights is an asset to any organization and will be much sought after!

Managing corporate innovation

The time is ripe for corporates to embark on a journey of innovation. Having said that it is a rocky road for enterprises that have been in existence for half a century or more, having made hay when the sun shined and thus gathering a good amount of legacy systems and processes on the way. Some organisations that have have chosen to invest in innovation prefer to run the innovation labs separately, far from the core business, the reason being that innovation should not get bogged down by corporate beauracracy. A big part of innovation is experimentation — experimentation with thoughts and ideas and prototyping. Business leaders have a herculean task of ensuring innovation for the businesses of the future while continuously improving the incumbent corporate machinery that generates the revenue necessary for future investments.

Innovation is about inventing new products or services that solve customer needs and can be monetised. Innovation involves trial and error and learning from the same, a structured method of experimentation leads to better tracking of ROI. The pentathlon framework of innovation articulates the methodology from ideation to market launch. The influx of ideas, which form the innovation backlog, can be either disruptive ideas or new ways of resolving existing business challenges.

Each part of the innovation funnel has to imbibe a fail fastand and an iterative approach for further ideation with feedback loops. Every new invention undergoes inception, improvements and adoption followed by stability and subsequent depreciation. If there are many trains of thought in the innovation projects pipeline, there has to be an order in the way of prioritizing the projects. Prioritization of innovation projects should depend on

  • Marketability — is there a potential market for the product/service?
  • Feasibility — is it possible to deliver the project with the resources the company can afford?
  • ROI — How soon is the breakeven point?
  • Time to market — how soon can the MVP be launched?

The implementation of innovation projects are not very different from other corporate projects, involving a portfolio selection based on the prioritization and business urgency. The next stage involves scoping and development, preceeded by prototyping before scaling. Post validation and launch, the impact has to be measured and analyzed to understand the product adoption and customer experience. The insights from measuring innovation efforts lead to newer ideas or incremental improvements to exisiting business processes and/or the innovative product under consideration. There has to be a relentless flow of insights into the innovation funnel to finetune the ideas being considered for prioritization, implementation and launch.

The very existence of an innovation foundry within a coporate house has to be justified to the investors and shareholders. As a means to create a process of accountability there should be KPIs defined, some examples being:

  • Number of ideas considered for prioritization
  • Number of ideas that were productionalized
  • Number ideas that have lead to business process improvement
  • Value added (Value = Accrued benefits — costs)
  • Time to breakeven

The major factors that influence an innovative culture at a corporate level depend on the company culture – the ability to thrive during change and adaptability to new market conditions. It is of utmost importance to recognise and reward the people who contribute to innovation and showcase and communicate the succesful results. Innovation labs within corporates cannot be run separately forever, the outputs from the innovation exercises need to flow back into the day to day business and the challenges from the core business need to be worked upon in the innovation labs.

With innovation being a crucial area of focus for most organisations, there are a lot of ideas floating regarding innovation management. It can be rather chaotic with buzzwords like Big data, AI, Robotics, Augmented Reality etc. being thrown around callously. Technology is a great enabler for business strategy, but great ideas arise from understanding customer requirements and the lack of products or services that fulfill the same.The most significant point of focus in any corporate innovation should be customer centricity and business gain.

Continuous customer experience — service designing

Clayton M. Christensen, one of the world’s top experts on innovation and growth, in his book – Competing Against Luck elaborates on why some companies excel at satisfying customer needs while others fail. He calls it the “Jobs to be done theory” which essentially is a means to identify customer needs and build products and service around those needs, instead of pushing a product and expecting the customer to fall for the bait. Customers want a certain product or service to solve a void in their lives and if the product/service not only fills that gap but also resolves multiple other needs and adds value, then even better.

Enhancing the customer experience requires organisations to understand different service interactions that customers experience and the potential for value addition within the same. The product/service provider should not confine their design thinking to their immediate customer but also envision the needs of the extended customer, the consumer of the customer (B2B2C). A seamless customer experience entails identifying the gaps in the customer needs across the touch-points and connecting the dots. Most of the times customers perceive the gaps in the services when they are handed from one division to another, within an organisation, making the behind the scene silos obvious.

Customer journey landscape

Creating continuous or seamless customer experiences should begin with journey maps centered around the journey the customers indulge in while navigating through interactions and touch-points, across multiple devices. A customer journey is never a linear path as the way customers engage with a product or service can be manifold. Charting the fragments of the journey on a single map, makes it easier to design the entire landscape including the front and backstage bits of the customer journey. A well researched concept plotted as journey map leads to ideation which can be refined in iterations culminating in service blueprints. Every part of the journey map jointly contributes to the entire customer experience. Tech enabled business innovation can enhance each customer interaction to yield a holistic contented experience.

Tech enabled touch points — The use of blockchain in supply-chain not only prevents fraud but could aid the customer experience in terms of story telling or driving sustainability where customers can trace the fair trade ecological sourcing of products. Smart labels and smart tags on wine bottlesand clothes aid the B2B customers with supply-chain and logistics Analytics while making the end user’s experience smarter with IoT solutions. The consumer shopping for a piece of clothing can continue to remain engaged with the service provider by engaging with the smart wardrobe apps that allow refurbishing or recycling the garments, suggest new trends based on the consumer’s preferences, help in maintaining an inventory of the wardrobe. The challenge, however, for the service provider is to be able to gather product/service usage data and provide AI driven services within the realms of data privacy and compliance. The understanding of patterns or deviations in the patterns of the product usage leads to innovating new products or services or business models like a contractual business model or partnerships with other service providers, which jointly make a service appealing, cementing the discontinuities. The tech know-how enables the design execution but the prelude is visualizing stories as part of the customer journey map.

New business models that cater to both B2B and B2C customers using data driven approaches to enhance customer experience

It is important to consider loss aversion and recyclability while designing products/services. As more and more people embrace minimalism as a way of life, sales figures dwindle. Refurbishing, recycling or donating to charity, as mentioned in the circular economy should also be considered as part of the product/service design as the number of environment conscious consumers rises.

To serve the customers a non-fragmented customer experience fueled by design thinking at every interaction demands a robust ecosystem of business acumen, big data solutions, IoT, augmented reality and blockchain implementations, actionable insights and KPIs, abolishing organisational silos and an ambidextrous leadership. Designing the front and backstage touch points that support the journey by orchestrating connections across the different interaction points with a service design mindset delights the customer by hitting the spot!

Chasing dopamine — The Neo-generalist

Dopamine a chemical released by nerve cells, which plays a major role in reward-motivated behavior. For some of us the adrenaline rush comes seeking and mastering new challenges and then moving onto the next.

Chasing dopamine

This habit of seeking new problems to solve goes beyond job titles, roles and responsibilities, educational background and age. These polymaths, knowledge seekers or autodidacts bloom where planted. They thrive when things need to be fixed, they use the knowledge gained from one industry on others, experience with one method leading to another. Monotonous tasks seem arduous.

“It still holds true that man is most uniquely human when he turns obstacles into opportunities.”–Eric Hoffer

I find it hard to grasp that organisations hire specialists to break down silos to facilitate continuous flow of information between different business units but individuals on the other hand are encouraged to be specialists within a certain discipline. Unless there are people who can handle multidisciplinary roles transcending departmental borders, I do not see a solution to organisational silos. The divide between tech and business is one such area of concern. People with tech skills are assumed to have little grasp of business acumen while people with strong business understanding are assumed to have limited IT proficiency and people with both business and tech expertise are perceived as average in both. That’s due to our obsession with specialism. Generalists are looked down upon, likened to jack of several trades and master of none. On the contrary, the monkey minds are an asset, being able to connect several dots and improvise solutions based on their creative thinking, envisioning paths beyond their job titles.

My constant dilemma

Hailing from India, Jugaad was part of the everyday vocabulary. Jugaad in Hindi means makeshift solutions which requires resourcefulness. Resourcefulness is not part of any syllabus, it comes naturally when there’s a scarcity of means. Being able to do more with less. Doing more has to do with understanding several disciplines to put together a solution beyond frameworks and recognized theories.

I came across a book The Neo-Generalist by Kenneth Mikkelsen and Richard Martin which was sort of narrating my mental state — the nomad state, in search of the next problem irrespective of domain, technological challenges or borders. We, the neo-generalists are happy as long as our brains are being harnessed and we are involved in something meaningful. If you identify yourself as neo-generalist then it is a must read. You’re not alone, there’s a whole tribe of us, restless souls, trying to juggle several disciplines at the same time and loving every moment of it!

Settle down — this word does not appeal to neo-generalists. Constant learning and treading on paths not previously traveled are our only focus.

“Listen baby, ain’t no mountain high,
Ain’t no valley low, ain’t no river wide enough”

Analytics – Implications on Digitization

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Digital is all about data, contrary to the prevalent method of creating Analytics as a silo all by itself. Analytics should be seen as one of the fundamental underlying processes that support the core business processes like product development, marketing, sales, customer relationship, finance and innovation. Data and Analytics provide value to core processes, for continuous improvement.

Most organisations are keen on innovation. Innovation could entail new market opportunities and could be an entirely new value proposition, discovered on a strategy canvas. But innovation could also be a by-product of a business process improvement. Such opportunities can only arise when business processes are tracked, measured and analyzed. Organisations that indulge in hypothesis driven product development or mass marketing could benefit by introducing  a data driven approach to the above processes, thereby uncovering the customer needs and product usage. Businesses may launch products with a certain outcome in mind, but sales, social media feedback and web analytics data may have another story to tell. It is in this story, that new opportunities can be unearthed. Understanding customer behavior is a way of discovering new marketing and/or product/service development opportunities.

Many organisations investing heavily in digitization, charting customer journeys, aimed at improving customer experience across all touch points, seemingly forget to make Analytics an integral part of this process.  The key to understanding  major business drivers like customer retention, ROMI, growth, customer engagement, monetization, finding new customer segments depend on deciphering the business data generated.

Analytics, therefore should be embedded in all business processes to capture the way the end customers perceive products or services or marketing and branding efforts made by any organisation. Analyzing the business data from existing processes could possibly give rise to future business prospects. To tread on a path of continuous improvement and innovation, companies will have to make Analytics a fundamental part of every business strategy.

Future proof your digital career

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If we consider Santa’s career – it has been traditional conveyor belt industry model of mass production. The distribution model is nowhere close to hi-tech, sleighs with reindeers!

Fortunately or unfortunately, Santa will have to embrace the new era of digitalisation to remain in business. Or consumers could turn to AI driven Santas. There is no more a linear career progression path, anymore. Business transformation is heavily data-driven, now, and is constantly evolving, it is of utmost importance for anyone aiming or sustaining at a career within the same, to be well versed with the latest industry trends and technological breakthroughs that will help monetisation of a business idea. Some ways of doing so:

  • Following knowledgeable industry leaders or thought leaders on Twitter/ LinkedIn
  • Joining future trend and related groups on LinkedIn
  • Following influencers on LinkedIn
  • Attending Meetups, events, conferences and seminars
  • Connecting with peers within the industry
  • Last but not the least (probably the most important) enrolling in MOOC (Massive Open Online Course)
  • Updating the reading list and reading the books too 😉

As they say Life begins at the end of your comfort zone.