Pragmatism: A guide to unlocking data’s strategic value

Abstract

Based on my recent Data Analytics & AI conversations with multiple large enterprise customers, irrespective of their stage in BI, Analytics & AI Maturity Journey. There seems to be similar key themes posing challenge in unlocking the strategic value of data for consistent business growth. There are inherent complexities and ground realities in building a data-driven culture to drive business forward in a consistent, forward thinking, agile and informed way. Hence, there is clear emphasis by majority of the Enterprise Customers to focus efforts towards formulating a coherent Data Strategy to achieve desired business outcomes.

Key Themes

If we carefully analyse the above key themes, they do relate to important facets of a coherent Data Strategy and one of the definitions which explains it nicely:

“A data strategy is the foundation to leveraging data as an asset and driving business forward. It’s not a patch job for data problems. It’s a long-term, guiding plan that defines the people, processes, and technology to put in place to solve data challenges.”

However, understandably putting a Data Strategy is an exercise which could be daunting and long enduring process, there are plenty of great blog posts / whitepapers already available in terms of how to build a Data Strategy and once such example is 3 part blog series on how to build & execute Data Strategy (This can be used as a Reference point for acquiring further details on individual topic areas). Creating a strategy is one thing but to execute one on enterprise scale poses a great challenge to organisation’s existing culture, people, processes, and technology choices. It requires a great deal of commitment and clearer ownership at all levels of an organisation. This is reflective in quite a few examples that even having commitment from the top-level leadership, still the process of demonstrating value isn’t as agile as it should be.

Let’s take a quick look at the strategic principles and why they are so important in the current context of things:

Increasing Efficiencies

Formulation of Data Strategy could take longer to execute and with COVID-19 the business challenges have increased tremendously, more than ever before it has imposed the need for organisations to adapt quickly and bringing efficiencies in all areas of business. According to the report by Gartner on emerging risks, while organizations continue to focus on digital initiatives and invest in them, two-thirds not only fail to deliver upon expectations, but also demonstrate enterprise weaknesses.

Data Democratisation

Central IT has slowly been shifting to shadow IT becoming more de-centralised ( to know more there is a detailed blog about Centralised vs De-Centralised Data Architecture approach) and that’s what has led to the resurgence of Data Mesh concept as well. They want to innovate quickly by having access to enterprise-wide unified quality data in self-serve fashion, in order to meet challenging & timely business requirements. There are many reasons why businesses fail to tap into the full potential of their data – business functions working in separate silos, each team using different tools & standards for data analysis, or a failure to link KPIs to overall business goals.

Data Democratisation is the key to delivering that value back to business and achieving the challenging business growth targets.

Governance

In relation to Data Governance there must be a right balance in the decentralised world of data democratisation approach. If governance is enforced too hard that could possibly stifle innovation but at the same not having some core principles & processes in place could also endanger creating “Data Wild West”. Resulting in damaging organisations reputation and potential revenues. So, a holistic Data Governance approach is fundamental to unlocking strategic value of data in a consistent manner.

Here is a nice blog to understand as how to build an effective Modern Data Governance Strategy

In the absence of a well thought-out Data Strategy for organisations (not applicable in all cases) and mounting pressures of delivering key insights in timely manner, there is a clear need to just “get going” and quickly start demonstrating value back to business. In this pursuit, acting on the above Key themes or using them as strategic principles wrapped in a framework could not only lead to addressing current business problems but even assist in the formation of future holistic data strategy in an iterative way with validation (not tactical or throw away exercise), yet yielding quick timely results. This seems to be the need of the hour and it’s reflective of the current rapidly changing business environment.

Solution

This write-up is aimed to provide a clear and pragmatic guide for business & technology leaders to help develop the mindset and strategies required to generate value from data and scale quickly in a simplified structured manner.

Iterative Data Strategy Validation Framework

1- Business Aligned Objectives

Identifying key priorities in line with business vision and keeping “Think big, start small, and act fast” ideology is the fundamental key for success. Picking up the right use case(s) doesn’t always need to be long haul difficult vetting process, it could be an on-going problem in any business unit where there is enough data to validate its ROI, more appetite and easy buy-in. So, there is no analysis paralysis enigma and things can move quickly, as that’s where most of the organisation could be struggling to get started.

Otherwise, there are other approaches to validate your key business priorities against technical capabilities holistically also e.g.

Aligning strategic business priorities with technical complexities

For further Details: how-to-build-and-deliver-an-effective-data-strategy-part-1

2 – Commitment

There has to be clear understanding around the adopted approach and desired common business objectives among all the key roles involved in making this a success i.e. Leadership Team (C – Level), Business Unit, IT, Ops etc.

3 – Capability & Skills

Clearly mapping out the Product, Services or Tools capabilities Maturity Assessment along with People skills, who will help deliver on the achieving the overall objectives.

The above is not an exhaustive list by any means and could vary depending on the organisation type/structure.

4 – Assess Capability Maturity

There must be an exercise in assessing the Data Analytics & AI capabilities required to deliver on a specific use case, holistically or at an organisational level. However, there has to be some guiding principles/process to go about the assessment:

• Define current capabilities and ambition
• Identify risks and blockers to progress
• Clearly state benefits and key stakeholders
• Link benefits to stated business objectives
• Identify key dependencies

As a next step, look at Azure native services, and start mapping what you need to deliver success.

Along with capability Maturity Assessment, Culture is also another important aspect which is the key focus of this framework to make it successful.

The details are covered here: how-to-build-and-deliver-an-effective-data-strategy-part-2

5 – Communication

Communication being one of the most important elements of this framework and requires communication process to be devised between Leadership, Business, IT & Delivery Teams.

As this would not only assist in delivering effectively in the context of current project but establishing a forum to stay in line, up-to date and focused on the overall objective of formulating a Holistic Data Strategy for future.

There are couple of key strategic processes which would help in delivering successfully in the context of this framework:

1. Formation of CoE
2. Adopting Agile Delivery Method

 Formation of CoE

Centre of Excellence would assist in:

• A Centre of Excellence helps drive adoption, Standards, best practices and innovation
• Funded team for full delivery and specialist skills provision
• Deep technical skills in key technologies
• Active participation and evangelising in Communities of Practice

Adopting Agile Delivery Method

Agile is the ability to create and respond to change. It is a way of dealing with, and ultimately succeeding in, an uncertain and turbulent environment.

It’s about thinking through how you can understand what’s going on in the environment that you’re in today, identify what uncertainty you’re facing, and figure out how you can adapt to that as you go along.

For further details on benefits, manifesto and why adopt please see: agilealliance.org

Some of the agile processes for BI, Analytics & AI are referenced below:

AgileBI

Team Data Science Process

6 – Reference Architecture OR Technology Solution

This is another area where customers seek clarity or prescriptive guidance before starting to carve out the technical details for a specific use case, project or an End-to-End Data Analytics & AI platform. This can be a stumbling block as how to ensure that all the strategic principles not only in the scope of current use case but also required for the formulation of the overall Data Strategy are considered.

So, keeping this challenge in view and speed up the delivery of end-to-end insights journey, Microsoft has developed a prescriptive offering in the context of an Enterprise Scale Analytics which marries up perfectly to the key themes discussed above.

“Caution: It’s going through initial feedback and prescriptive guidance is in Private Preview – Subject to change”

Having said that, not necessarily this is the only architectural approach to fully benefit using the above framework, in fact it’s totally down to each organisation’s preferred Cloud provider, Skills & Technological choices.

Enterprise Scale Analytics & AI

Enterprise scale Analytics & AI is part of Microsoft’s Cloud Adoption Framework and designed to enable customers building enterprise data platform which provides prescriptive guidance, coupled with best practices, on cloud operating model, reference architecture and platform templates based on real-world learnings from some of our most challenging, sophisticated, and complex environments.

Additionally,

• Supports multiple topologies ranging across Data Centric, Lakehouse, Data Fabric and Data Mesh.
• Based on inputs from a diverse international group of specialists working with a range of customers.
• Separate guidance tailored to Small-Medium and Large enterprises.
• ~80% prescribed viewpoint with 20% client customisation

For further details: Enterprise-Scale-Analytics Overview

7 – Data Product Delivery Ops Model

To deliver effectively the Enterprise Scale Analytics and AI moves teams from working horizontally across each tier such as Ingest, Processing, Analysis, Consumption and Visualization to vertical Data Landing Zone Groups coexisting together in an agile team.

8 – Deliver Value

Finally, Data product delivery against the set-out success criteria specified in above “Business Aligned Objectives” section is going to validate the above iterative framework. Additionally, taking the learning and continuously innovating will help in building business confidence and widening out of the Data Strategy strategic goals, for clearer and quicker adoption across business.

Summary

Hope the above Iterative Data Strategy Framework provided some insights or pointers in the case of organisations either starting to build a new Data Strategy or looking to refresh an existing one, in the light of changing business environment / objectives. The above approach is built around unlocking Data’s strategic value in an agile iterative way, contributing towards building a data driven culture.

How to get going with Modern Analytics Sandpit Environment using Azure in no time…(Part 2)

The previous post in this series of 3 was regarding the background / purpose of “how to get started with Modern Analytics” and in this post we will try to cover the actual approach adopted and why?

As the name suggests “Modern Analytics” the approach has to be unconventional regarding Information Management to support the idea of bringing all the different types of analytics together (Descriptive, Diagnostic, Predictive & Prescriptive). Especially, the shift that has drastically challenged traditional analytics in the form of “Big Data“, and for that we have to draw some parallels between the old and new worlds (ETL vs ELT) as following:

Information Management Approach

There are two approaches to doing information management for analytics and deriving actionable Insights:

1. Top-down (deductive approach)

This is where analytics is done, starting with a clear understanding of corporate strategy where theories and hypothesis are made up front. The right data model is then designed and implemented prior to any data collection. Oftentimes, the top-down approach is good for descriptive and diagnostic analytics. What happened in the past and why did it happen?

2. Bottom-up (inductive approach)

This is the approach where data is collected up front before any theories and hypothesis are made. All data is kept so that patterns and conclusions can be derived from the data itself. This type of analysis allows for more advanced analytics such as doing predictive or prescriptive analytics: what will happen and/or how can we make it happen?

The following image very nicely  visualises the differences between the two and the types of analytics they cover.

In Gartner’s 2013 study, “Big Data Business Benefits Are Hampered by ‘Culture Clash,’” they make the argument that both approaches are needed for innovation to be successful. Oftentimes what happens in the bottom-up approach becomes part of the top-down approach and exactly this forms the basis of my approach towards accumulating / simplifying different types of Analytics within “Modern Analytics”.

This leads onto a basic idea of how to start with bottom-up approach as we are already familiar with Top-down in form of traditional BI / Relational paradigm? Yes, Using Data Lake, as it helps to land all the different data sources in one single place to derive actionable insights, irrespective of data volume, velocity, variety or Veracity challenges.

Data Lake Framework

However, before jumping straight into Data Acquisition and Ingestion steps there has to be some careful thinking to avoid Data Lakes from turning into Data Swamps, and especially thinking around “What about Data Management structure considering data democratisation within an organisation?”, “How to apply Data Governance?”, “How to cater for Data Quality”, “How to apply Data Security based on certain Roles within organisation”, “How to store data for Real-time and batch Processing?” without making this too onerous having some basic framework around these fundamental questions does assist in delivering profound, relevant and quality analytics even in the Sandpit environment, above all, also helps in formulating certain aspects of Enterprise Data Strategy.

Additionally, consideration on these basic questions would form the solid basis of Best Practices and Patterns for delivering on Data Science & Advance Analytics projects in an effective manner.

We can go into a lot of details but already there has been some quality blogs written by the likes of my colleague Tony Smith and especially Adatis – which is referenced below and the one used as an adopted approach for answering the questions regarding Data Governance & Data Management mentioned above.

Though, just to highlight one of the core areas which is quite significant and fundamental to all the above questions, and that’s regarding the Big Data Architecture applicable for managing the Real-Time and Batch Processing within Data Lake.

Lambda Architecture

Briefly, this architecture approach divides data processing into  “speed” (near real time) and “batch” (Raw, Base, Curated) layers.  This design is well established and is a relatively common implementation pattern on Azure.

Framework

The rest of the details for Data Lake Framework are articulated very nicely in the following blogs especially around the carving of Data Lake for better Data Governance and Management based on the above Lambda Architecture.

Here is a screenshot of the carved Azure Data Lake discussed in detail within the following blogs:

Must reads…

Shaping The Lake: Data Lake Framework

Azure Data Lake Store–Storage and Best Practices

Architectural Components or Azure Building blocks for the Environment

The main architectural components or building blocks referenced for this environment are as following:

1. Azure Data Lake Store
2. Data Science Virtual Machines
3. App Service
4. Event Hubs
5. Stream Analytics
6. Azure Blob Storage
7. Power BI

Modern Analytics Project Delivery Process (mainly Data Science and Advance Analytics) & Key Personas

The Project delivery process has been narrowed down specifically for Data Science and Advance Analytics  in the form of TDSP (Team Data Science Process).

The Team Data Science Process (TDSP) provides a recommended lifecycle that you can use to structure your data-science projects. The lifecycle outlines the steps, from start to finish, that projects usually follow when they are executed.

The TDSP lifecycle is composed of five major stages that are executed iteratively. These stages include:

1. Business understanding
2. Data acquisition and understanding
3. Modeling
4. Deployment
5. Customer acceptance

Here is a visual representation of the TDSP lifecycle:

The key personas involved in the delivery of a Data Science & Advance Analytics project could slightly differ depending on the availability / organisational structure based on size/maturity but in majority of the cases the team structure would look like:

1. Solution Architect
2. Data Scientist
3. Data Engineer
4. Project Lead / Manager
5. Developer (Integrating the Insights into downstream Apps)

For further details click here regarding standardised project structure, Infrastructure Resources, Tools & Utilities for delivering Data Science / Adavcne Analytics projects in an effective manner.

This is by no means the definitive / exhaustive details for DS & AA project delivery process as already mentioned could vary / tailored depending on numerous factors but as the topic for blog series suggests to “Get started” will surely be quite helpful.

The final post in this series of 3 will demonstrate the entire above approach in form of a practical Example Project (Cisco Meraki)

 

Some relevant blogs

Making Sense of the Swamp – Azure Data Catalog for your Data Lake

Granting Permissions In Azure Data Lake

Assigning Resource Management Permissions For Azure Data Lake Store (Part 2)

Assigning Data Permissions For Azure Data Lake Store (Part 3)

Buck Woody’s DevOps for Data Science Series

How to get going with Modern Analytics Sandpit Environment using Azure in no time…

Purpose

The objective behind referring to this environment as Modern Analytics Environment is due to the fact that it covers all types of Analytical projects whether Big Data, Modern BI/DW, Data Science and Advance Analytics.

It will be 3 blog post series, starting with the purpose then details of the approach and lastly a working example / Solution (using ARM) covering all the aspects discussed in this blog post series to get going on the Modern Analytics journey.

By no means this approach is recommended for production environments as it’s merely to get organisations started with a sandpit environment for doing mainly experimentation around Data Science and Advance Analytics related use cases.

I have been thinking of writing this blog for a while reason being in all my recent customer interactions irrespective of organisation size or sector there has been a single biggest challenge stopping them to get going mainly on their Data Science and Advance Analytical journey is “How do we get started”?

“How do we get started” question can very quickly unfold into an extensive debate seeking all eventualities, hence, in a lot of cases leading to inconclusive outcomes. Just to give a flavour of those, discussion could be in the form of queries like “How do we do different analytical projects within same environment?”, “How do we ingest data?”,”How do we store data keeping our organisation and source of data in mind?”, “How to catalog data?”, “How could Data Scientist access the relevant data and do exploratory analysis with ease to find key insights?” “How to ensure secure access to the data and manage azure data services spend?”, “How could the environment follow DevOps process?” and the list could go on and precisely for this very reason, to just “get going”, I have adopted a rather simplistic still solid enough approach in building an effective Modern Analytics sandpit environment which allows organisations to start transforming raw data into intelligent action and reinvent their business processes in a very quick efficient manner. And, once the organisation maturity level starts improving the environment and processes can be transformed for much coherent Modern Analytics projects delivery / management practices because the foundations of the environment will still still be intact.

The key and sole purpose of this blog is to provide a Modern Analytics sandpit environment boilerplate keeping industry best practices and basic enterprise readiness requirements in perceptive i.e. Data Governance, Security, Scalability, High Availability, Monitoring, Lower TCO and most importantly Agility.

Approach

Here is the background to rather simplistic still solid approach for the proposed Modern Analytics Sandpit environment:

1. Information Management Approach
2. Data Lake Framework
3. Architectural Components or Azure Building blocks for the Environment
4. Example Project (Cisco Meraki)
5. Key Personas (mainly Data Science and Advance Analytics)
6. Modern Analytics Project Delivery Process (mainly Data Science and Advance Analytics)

The environment code will bet available via a Github repository later so watch this space.

Solution

Now, before we go into details of each one of the core areas of the above mentioned approach, here is the high level view of the proposed Sandpit environment using Azure Data & Analytics offering to keep things in perspective for next blog post:

Also, just to highlight here that the above high level architecture diagram represents the logical grouping of resources using Azure Resource Groups as an example implementation, the above implementations can be different depending on Organisation/Teams structure, Security/compliance etc.

Azure Resource groups are logical containers that allow to group individual resources such as virtual machines, storage accounts, websites and databases so they can be managed together.

The key benefits of the Azure Resource Groups are in the form of Cost Management, Security, Agility, Repeatability etc.

For complete benefit details please click on the following link:

https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#the-benefits-of-using-resource-manager

Majority of the Services used in the proposed high level design are either PaaS or Managed Services to provide lower TCO and greater agility in deriving key actionable insights.

All PaaS services include cloud features such as scalability, high-availability, multi-tenant capability and Resiliency

Resource Group	Details	Technologies to be used
1 – rgCommonDev	Shared resources to be used by all types of projects mainly for Information Management perspective and most important as well	Azure Data Lake Store
2 – rgAnalyticsTeamDev	Development resources required for the Big Data, Data Science and Analytics Projects	Data Science Virtual Machines times no. of Data Scientists/Engineers Azure Blob Storage
3 – rgMerakiDev	Resources required for delivering the Meraki project which is to demonstrate a type of Advance Analytics example project	Web App Event Hub Stream Analytics Azure SQL Power BI

1. rgCommonDev

In this resource group, the only resource will be Azure Data lake Store as that’s the most important and critical part of the implementation. Any data ingested from multiple data sources will be stored in here in an organised manner based on best practices / patterns.

More details will be added in the next series of this blog post Data Lake Framework process regarding the structure of the Data Lake.

2. rgAnalyticsTeamDev

rgAnalyticsTeamDev (Resource Group) contains 1 Data Science Virtual Machine but this is bare minimum setup and could be altered based on number of Data Scientists and Data Engineers usage. Regarding, Blob storage it will be used for storing ad-hoc data files, artefacts related to development environment.

Mainly, DSVMs will be used by the Data Scientists to perform exploratory data analysis by accessing data stored within Azure Data Lake Store, finding patterns and creating predictive models.

Talking explicitly around Azure Machine Learning & AI Platform portfolio the following image shows landscape and can be employed depending on organisation needs but to keep things simple have gone with DSVMs (details in next blogpost).

For further options regarding machine learning offerings see here…

3. rgMerakiDev

 The sample project to be deployed in this environment is Meraki. I have blogged earlier regarding this project here. This project helps demonstrating:

a) How different analytical projects can be deployed in Modern Analytics Environment side by side with completely separate governance framework

b) It’s also got the Lambda Architecture implementation which helps in showcasing the underlying Data architecture and framework whilst using Azure Data Lake store.

The key components of the project are: Web App, Event Hub, Stream Analytics, Azure SQL DB and Power BI. All these resources are already wrapped in ARM template within a Visual Studio Solution to automate the deployment process and ease of repeatability reasons which can be accessed separately by going to Github repository.

This project will also help in developing better understanding regarding the environment and processes involved in managing it going forward.

Details of the Modern Analytics approach mentioned above to follow in next blog post…

Just to give a quick glance of the Modern Analytics Pipeline in Azure with multiple offerings in each stage.

Some relevant blogposts:

Buck Woody’s DevOps for Data Science series

Making sense of the swamp

 

 

 

IoT in Action – In Store Location Analytics on Microsoft Cloud

In store Internet of Things (IoT) analytics is a key area for retail organisations.  The nature of retail stores and the importance of maximising investment of staff and retail space makes this one of the leading areas in the IOT space.  However, getting started can seem daunting for many IT teams:  “What equipment do we need?” , “How do we collate the IoT data streams?”, “What should we measure?”.  This post introduces a Microsoft Cloud (Azure) based GitHub IoT project which acts as an end to end example of an IoT in store analytics implementation as well as demonstrating how Azure PaaS (Platform as a Service) Services can be used to quickly implement Enterprise class IoT solutions.

GitHub project

If you want to get straight into the project it’s available on GitHub as “Azure PaaS Implementation using Lambda Architecture of Cisco Meraki In-Store Location Analytics“.  The project is fully documented and self contained.

Overview of Solution

Almost any public place can become a “smart building“, retail stores, universities, hospitals can all benefit from implementing IoT devices & sensors.  This particular solution solves a common retail IoT problem which can be expressed as:

“Provide a chart of customer footfall in real time across selected areas in store” 

However, although that specific issue is addressed this solution more broadly demonstrates the art of the possible in IoT Analytics on Azure.  The implementation can be extended to apply to any building equipped with WAPs (Wireless Access Points) and a number of analytics objectives including:

• Staff Optimisation
• Store Layout Optimisation
• Product Recommendation

As well as many other advanced use cases. The project delivers an end to end Azure based solution for IoT analytics from initial capture of events via the in store WAPs, real time analysis of the event stream, archiving of events onto a persistent storage layer and finally visualization of the real time & historical combined results.  Throughout, Azure PaaS services (in conjunction with Cisco Meraki Cloud) are used to provide a scalable, robust, extensible IoT analytics platform.The outcome of the solution is the result shown below, a foot fall chart operating in real time:

Note: Azure PaaS & Cisco Meraki Cloud – Azure PaaS and Cisco Meraki cloud are the 2 main technology stacks used in this solution.  Cisco Meraki Location Analytics displays real-time location statistics to improve customer engagement and loyalty across sites, and is built in to Cisco Meraki Access Points with no additional cost or complexity.

Cisco Meraki does offer some insights out of the Box, however, this approach extends more in-depth analysis by having the flexibility to correlate other data sources on top of events data for richer / deeper actionable insights. Also, this solution can be extended to in-corporate other Vendors similar to Cisco Meraki in similar fashion.

Azure PaaS Services are part of the Microsoft Azure cloud platform.  They offer robust and extensible capabilities which are cloud first in design, meaning they are scalable, serverless (no patching or maintenance of VMs is required) and typically operate on a “pay as you go” model.

Architecture

The diagram below gives the high level architecture for the approach, which follows the established Lambda Architecture.  Briefly, this architecture approach divides data processing into  “speed” (near real time) and “batch” (historical, cleansed, aggregated) layers.  This design is well established and is a relatively common implementation pattern on Azure.The general workflow is as follows:

Note: This  solution comes prepackaged with sample Cisco Meraki event data which is sufficient for end to end testing & evaluation.  However the solution can easily be integrated into a working Meraki installation.

Cisco Meraki tracks events as customers/visitors move around the building – each customer will lose one Wireless Access Point & acquire another and this loss/acquisition translates into movement around the building.  These events are passed in real time (1) to Azure and collated using an Azure Event Hub instance (2).  In turn, the event hub forwards the events to Azure Stream Analytics, the azure real time event processing engine which can operate on individual events or aggregated events over a rolling time window. Stream Analytics workflow

Following the Lambda architecture the Stream Analytics engine now routes its output to the “speed” (5) & “batch”  (4) processing streams.  In the “batch” stream events are captured for historical analysis on both Blob storage and an Azure SQL Database instance – from here, events can be cleansed, merged with other data or aggregated for further analysis.Meanwhile the output of the real time analytic processes is also forwarded directly to a Power BI Dashboard.  Here it be displayed as a real time data stream however it can also be combined with data from the historical store (6).As mentioned above, the outcome is a real time chart showing footfall for the selected area in the building.

Deployment & Outcome

As mentioned the GitHub solution is fully self contained and documented.  It includes all necessary code, sample “real time” Meraki Cloud data and provides step by step deployment guidance.

Azure PaaS Implementation using Lambda Architecture of Cisco Meraki In-Store Location Analytics

Example guidance

The upfront requirements (Azure subscription, Power BI etc) are listed in the documentation.  At the end of the deployment (which should only take a few hours) you’ll have a full end to end Lambda compliant Azure Cloud based IoT solution.  You’ll also have worked with some of the key event & IoT processing engines within Azure as well as the Power BI visualization tool.  Together this should provide a solid foundation to build richer and more complex IoT analytics as outlined in the introduction.