Data in Product Management & Agility

Transformations under the looking glass ...

As COVID causes continual catastrophic change to our day to day needs at a personal level the economic and business effects are still being digested. For small business the outcomes have been almost immediate and devastating. For Enterprise business, beyond the economic downturns the secondary affects of increase pressure to perform as more Digital organisations and less as a traditional transaction focused business is pinching at some raw nerves. The underperforming basics of Product Management control effective delivery of competitive differentiators, and of course these in time become obvious in poor innovations or lagging product performance.The PM basics causing waves are ...

• the ability to continually improve multiple dependent systems in an organisation simultaneously or in complimentary 'value chains',
• to ability comfortably measure and respond to customer value/satisfaction beyond transactions/NPS/CSAT while embracing better automation and AIML activations,
• the ability to activate around the said learnings and scale up and down effective value delivery to improve return on investments while fine tuning BAU and ensuring innovation (reputation and integration) is real!
• Note : Data Product Management lifecycle is not a comprehensive Agile performance analytics strategy or Ways of working charter although these are certainly complimentary.
• Keep your eyes on the prize, it is Organisational Agility and how it relates to Product Management lifecycles

How do companies lose track of PM capabilities?

These key abilities are all directly out of any Digital Product Management playbook, whichever one you lean towards. Often the trend I see is tier 1 companies becoming complacent with their responsiveness and with diversified internal ownership landscapes, deepening compliance and regulatory needs and struggles of legacy maintenance. No surprises there, but as I mentioned in my last article there seems to be a crisis of 'digital identity and adoption' for enterprises that is often around response to change demand. Being partially agile is not suiting tier 1 organisations and the trajectory there is eating at their brand, margins and capabilities. The overarching trend for younger audience segments from 'gen Y' onwards is that moving away from various 'bricks and mortar' brands and towards market disrupters is fine.

Such as in the example of Big Banks screaming at the possibility of Facebook, Apple and Google (who own much of their relevant data) becoming firstly payment platforms and then neo banks themselves. Financial Services groups are often beholden to the needs of shareholders and continue performance also demonstrate the behaviour of continual Procurement downwards cycles. The ever growing dividends demands pushing internal savings needs which clearly demonstrate a low appetite towards agile engineering acceleration practices and as a result it is only when a crisis point is reached that true platform change occurs.

So what is the case for Product management strategy continual improvement?

Simple in the mind of Digital natives, it is the cost of entry in the market right? Well, mature businesses are on mass finally considering the serious gaps in portfolio and product management agility, in the cadence of analyis and in their Data and Analytics capabilities? Often it is in the access to the critical insights and practices of basic product understanding that have become clouded. If traditional Product managers that have not made the jump to understanding the lean and digital ways of enabling insights their teams too will be lacking skills and falling behind in the effective use of critical data functions.

To be clear, by this I am not discussing the AIML and Information and Data Management capabilities but more core Data Product Management routines as a sub domain. For example in Digital and Marketing Transformations implementing automated activations. This is a serious weak spot for most organisations.

How is the organisation measuring more ambiguous atypical customer journeys across the product landscape where automations play a role driven by Events driven triggers?

The gap I am referring to is far more the critical and key to the cyclical change in going beyond the "marketing lifecycles" and "transactions performance" analytics of previous. In summary I am suggesting to look more critically at what core practices Digital enterprise can lose through the responsibilities of size and scale.

Minimal Digital Product Data Management should include ...

• Yes Product Transaction vs Campaign vs Funnels are a critical focus regardless of the maturity but what key indicators are there that bubble up into this are important.
• Being deeply affective in organic SEO for baselined product/services and content performance and transformations requires funnel based assessments for ...

'acquisition > on-boarding > engagement > transactions > and extended value to cross sell... through this funnel your community or customer value and wellbeing.'

• Continual Product team design capability uplift - namely continual Data Analysis skills uplift for understanding automated and ML driven capabilities,
• Product not procurement input for Delivery and performance effectiveness of Modular reuse of dynamic Digital Assets, curation, automations, DevOps automation and testing accelerations,
• Moving your Product practice mindset to focus on the new key (more events driven) Product funnels, metrics and analytic cadences that your Operating systems enable,
• Developing Cross Channel product strategy, design, delivery and measurement required in Omni-channel approaches,
• Improving Marketing automation and Digital asset integration for events driven architecture,
• Strategically understanding emerging thresholds and their key data drivers for Attrition, On-boarding, Funnel success and fails, automating responses to threshold activations, cross sell, channel propensity,
• Tactical linked to Strategic works : Ensuring that the legacy modernisation is assisting in the development of these more accessible systems (so contributing to the Architecture outcomes).

And again I will underline here, all of this before the Machine learning and Artificial Intelligence universe becomes a part of the Product team makeup.

In summary, Senior or lead Product Mangers must better enable their key competencies first before looking at pulling those product levers. This mean guiding the evolution of the Technology transformations for business.

Symptoms here include answering some key questions around the business enablement from the technology owners ...

1. How is technology really enabling Product Assess and Pivot capabilities?,
2. DATA analysis for algorithm/automated performance process?,
3. Identifying the nuances of dynamic profiling and dynamic ontology ownership against product engagement and customer value activations,
4. and ownership cadence for Domain focused Information and Data management capabilities.

These are the new agendas in Digital Product Management 3.0 and are going to be the cost of entry for competitive Digital Enterprise future success. The enterprise that can compete with disruption, that can response to the needs of accelerating market demand. Again I will underline the Data focus here at every stage, ones performance as a Product Manager will only be as valuable as your Product Data Managers capabilities in analysis systems, key data extractions, data audit/quality assurance and delivery of insights in the product and services lifecycle.

The Product Management lifecycle must run from Portfolio strategy to Product to Apps Development and Managed Services feature enhancements ...

Of course this question is one that Transformation Leaders need to answer every day. For the majority of clients I see and collaborate with, the practices I manage tend to separate our focus areas into distinct quadrants of activity for lifecycles of Digital Business Operations, Products and Services. This is based around effective Innovation and Product Maturity lifecycles. (*Please see previous articles on the these Innovation Maturity models)

This approach is somewhat aligned to Portfolio Investment strategy and the agile business understanding of,

1. Differentiation and Disrupting Innovations, (Transparency and 'Problems on the table')
2. Incubation of accepted Innovations and BAU readiness
3. Business As Usual Continual Improvement, enhancements & pivots alike
4. Audit and retirement of capability for Product, Tech, Ops and Capability regardless of divisional ownership.

• In summary, together these cycles or phases of Product management activity are the key macros phases for organisations to understand, in some ways they are "the bible of Digital survival". More often than not, external consultancies see glaring holes in the adaptations of these key analysis cycles despite many companies have a 'maturation lifecycles approach' in place, it is often not an effective or strong guardrail for Digital Business Practices or for the Data focused Product Management activities that will help organisations grow the lean intuitive and accessible Data driven value chains and capabilities for a successful future state. Lastly to repeat an earlier point...

Keep your eyes on the prize, it is Organisational Agility and how it relates to Product Management lifecycles

In the HCL Digital Consulting practice we are inspired by helping our clients in these areas where we often see struggles to keep pace with the constant change and growing needs. Assisting organisations of all types to build momentum and continual improvement approaches to this is an essential part of how we drive value and create inspiration and excitement about Digital Product Management now and in the future.

Next article ... the concept of Citizen Developers, have we been down this road before?

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