Business outcomes and Agile approach to IoT, Bigdata, Analytics, MachineLearning, cybersecurity

It's great to start off trying out Digital with a Proof of Concept or Pilots in the beginning since there are a number of aspects a traditional business has to contend with such as existing systems of record and basis systems of engagement and legacy technologies. Beyond the Pilots, there has to be a purpose and vision of what the future state of the enterprise will be; with definitive business outcomes supported by a robust transformation strategy, Product road-map and agile methodology based execution and business benefits timelines

Fuelled by benefits from initial pilots and limited capabilities introduction, Automobile companies are now embarking on a journey to accelerate the size, breadth and capability of their IIoT, IoT, BigData and Analytics organization. The purpose of this article is to briefly envision the Analytics context, provide an “outside in” perspective of the range of Analytics possibilities including Big data and IoT and an evolutionary roadmap for such endeavours at Automobile companies. Towards that end, I believe it is mandatory to have a Business Outcomes based vision to execute an evolutionary approach that maximises Analytics benefits as technological capabilities evolve.

Gamut of Analytics possibilities at Automobile companies

Analytics Implementation will help understand what happened with descriptive analytics, ascertain patterns with diagnostic analytics, forecast possibilities with predictive analytics and take decisions with prescriptive analytics. Automobile companies, whilst manufacturing their products, used to engage their customers more through their network of Dealers and Authorised service providers. Customer solicitation & Feedback, if any, used to come through this route. Online and Social media has now given a more direct route including marketing. Manufacturers hardly had any direct interaction with their customers. However, with the advent of embedded technology and the evolved IoT and edgeanalytics, manufacturers now have more direct visibility of their end users experience. Manufacturers also have the capability, with investments into Digital, to have direct/indirect visibility into the Supply chain performance.

As depicted above, the gamut of and insights possibilities are vast. The Systems of Record, Systems of Engagement, Systems of Planning and IoT are all essential net feeders of data into the many BI and Big data stores that underpin analytics. IoT and IIoT are likely to cause massive data explosion in Automobile companies. When harnessed properly, this will yield massive benefits. From business outcomes perspective, the range of possibilities includes, but not limited to:

Increased sales: Listening into social with web crawlers & sentiment analysis, website visits, configurator usage, carwow or other online car review websites usage, campaign response, brand feedback, dealer insights, base model choices, add-on choices, company car leases analytics, Global/Regional/Country macro and micro economics factoring to come up with predictive models. Conversion ratio predictive analytics is useful to focus on the right prospects. Statistics and Statisticians could help come up with the formulae for predictive and prescriptive modelling. Data collection, hosting and analytics scripting are equally essential. As MachineLearning and insights improve, cognitive computing and integrated analytics will help improve prescriptive analytics for discounts, Order-to-cash and forecast-to-delivery.

Improved customer experience: Use of social mentions, configurator usage, and competitive products comparison to come up with appropriate predictive discounts. Many Automobile companies have been increasing the In Car Electronics & Digital and the IoT with sensors capabilities are only going to increase. A fine balance of In-car edge analytics vs. Back-end analytics must be implemented for Preventive maintenance, call in for Predictive maintenance based on events logged or forecast prescriptive analytics based on a combination of In-Car IOT Analytics as well as periodic services. For example, Automobile service providers send assessments of current car following a service however, refrain from providing any decision options and when a decision be made to replace/change parts, which befuddles the customer. Fuel economy prediction based on weather or surface dynamics, hybrid modelling prediction and “wear & tear” parts usage optimisation are all possibilities based on driving behaviour & car usage analytics. How about navigation maps update? How about the software updates to provide added functionality? These are but a few examples of planning the road ahead for Analytics implementation. Analytics implementation is also knowing the IoT, Big data, hybrid cloud – SaaS and IaaS and determining the crux which is the hybrid of technologies to be exploited for Edge analytics vs. central analytics. Needless to say, planning for Cyber security assurance is a must. You can’t have a run-away hijacked car leaving a stranded customer.

Enhanced productivity & profitability: Automobile companies use Telemetry/IoT to gain In-car as well as On-track feedback during testing. Similarly, Automobile companies also use Assembly line Telemetry, quality checks, etc. Quite soon, Automobile companies will further accelerate to IIoT (Industrial IoT) adapting to Manufacturing 4.0 principles. As sensor capabilities evolve, Automobile companies could use Predictive and Prescriptive Analytics for Plant maintenance reducing downtime, integrated workforce time tracking, and leaves to optimize production and finished products time-to-deliver with enhanced route optimization of delivery trucks. One easy quick-win for route optimisation is an avoidance of trucks stuck in motorways in peak hour traffic. Analytics could be used to achieve improved cash-flow by optimising the gap between Account payables and Account receivables. Analytics could help optimise the Supply chain; for example quality analytics per supplier could help supplier management and optimise deliverable cycles. Analytics can help Procurement optimisation by balancing off repetitive standardised procurement vs. bespoke procurement. Beyond statistical modelling, envisioning the evolution of IoT, Big data and Analytics with the right combination of technologies offering the best ROI/ROCE is crucial to the success of Analytics implementation in the long term.

Enlivening employee experience: Employees lose time at the factory gates or parking owing to congestion. Analytics could be used to determine entry, exit and parking time optimization to improve the experience as well as improve productivity. Analytics could be used to enable People manager training & coaching based on employee surveys thereby improving retention. User persona analytics is used for optimizing the number of digital devices with an employee, re-balance the work-from-home vs. work-from-office and determine the right work-life balance. Analytics could help plan for the workforce of the future, succession planning, recruitment management, talent planning and many more. Knowing the Systems of record, engagement and planning are essential to determining the right systems of insights and analytics.

Living up to the brand values with Corporate Social Responsibility (#CSR): From automobiles to grocery shopping, the influence of CSR emotions on consumers’ decisions can be noticed everywhere. Reports suggest that companies with a “fully engaged,” emotionally connected customers make more profit than others, and can better withstand the stress of economic downturns. Sustainability is a core emotion driving buying behaviours. My wife bought a hybrid car in November 2016 citing this as the single most decision criteria over riding any other brand value phenomenon. With this backdrop, for example, In-car IoT and Edge Analytics dashboard really helps to change driving patterns to improve fuel economy & reduced carbon emissions with prescriptive analytics and decision recommendations. BIM, in-factory and in-office sensors and edge analytics can help reduce carbon emissions with prescriptive analytics. Truck-delivery timing and route optimization could reduce carbon emissions.

The success of Analytics implementation in Automobile companies is very much dependent on knowing the business outcomes that is possible with secure IoT, Big data and Analytics.

Evolutionary Approach for Analytics Implementation

Data Warehouse and BI have been there all along. Most Automobile companies use Analytics for many purposes performing specific Analytics and gathering specific Insights.

The focus of this article therefore is to build on this foundation and articulate a roadmap on how to evolve in the future. In the main, the implementation must first focus on assessing current Analytics and Insights as well as networking with key stakeholders within the organization to determine what good looks like; of utmost importance is what would good look like for customers first before determining what is good for the organization. The picture below depicts an “Outside In” point of view of an evolutionary approach to Analytics adoption.

IoT, Machine learning, Big & Small data and Analytics are all still Work in Progress. This is so as IoT, Machine Learning and Data sources are all evolving. It is only apt an evolutionary approach to Analytics is undertaken as opposed to a One time Big Bang implementation. As part of way forward,

Strategic Design Thinking workshop with involvement of key stakeholders across the many business functions is a first step to (a) Agree on new outcomes, (b) evolutionary path to Analytics improvement, (c) underpinned by a micro-services architecture.

Crossfunctional leadership to be established including Business representatives, Product owners and agile implementation owners from IT, IT-CTO organization, IT Programme & Run organization to agree on Minimum Viable Products (Analytics) and evolutionary sprints.

Analytics Programme organization to be revisited, which aligns with updated vision and mission as established as part of the Strategic design thinking workshop and Cross functional leadership

Two Track Governance reporting to the Executive steering committee to be established, of which the Programme implementation Governance will focus on the Implementation & Deliverables as well as the Cybersecurity and Regulatory compliance. An Enterprise Big Data/IoT/Analytics Architecture must be established and must be periodically reviewed for currency as the Analytics capability evolves. The Ongoing management governance will focus on enhancements, benefits realisation, ethical and responsible automation, participate in forums, continue to engage with Big data & Analytics products and implementation providers to continually learn the innovation in the marketplace. As more Edge Analytics gets implemented, the focus on DataQuality and Datamanagement must be intensified to ensure that Analytics do not produce incorrect Predictive and Prescriptive analytics. To underpin Datagovernance, Cybersecurity must also focus on securing the IoT edge as much as protecting the in-house and cloud environs.

In Summary

The first generation of Analytics implementation in Automobile companies focused mostly on sales outcomes. This article only provides a few examples of business outcomes possibilities. Industry 4.0 is also dynamically reshaping organizational thinking. Statisticians and Statistic modelling are absolutely essential to framing the Analytics scripts that underpin the visualisation presented by Analytics tools. The best of Analytics is yet to come and for that, in the Automobile industry, knowledge of evolution of the Automobile industry, the business processes, the performance metrics, the data sources are all essential to gain maximum business benefits. IoT, IIoT, Analytics and Cybersecurity are all still evolving. A Big Bang approach may not be really practical; however, automobile companies can envision an end state and adopt an #Agile methodology & Minimum Viable Product Design thinking approach to dynamically adapt to the new capabilities.

About the Author

This article was first published by Jagadish Rao Raghavendra on September 1, 2017

Jagadish is a Strategic Board adviser, senior Executive leader with specialisation in reimagining business models; who has been successful at building and/or turnaround Practice advisory, Presales, Sales enablement, Solution consulting capabilities for:

- Applications & Enterprise applications

- Modernising and Transformation to a secure hybrid Cloud

- Service Integration and Management in a multi-cloud environment

- Digital workplace solutions

- Digital transformation using IoT, IIoT, Big Data, Analytics, Artificial Intelligence, Drones and Robots

- IT Infrastructure services

More recently, Jagadish has influenced re-imagining a digitally enabled business model for a leading enterprise

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