Category: Data

Posts related to Data & Analytics

Coronavirus – If you don’t measure it, you can manage it

It’s been interesting over the past weeks and months to observe the different approaches taken by countries in response to the Coronavirus pandemic. Several countries such as the UK and US have been providing CoVID-19 data at a granular level, enabling their populations to stay much more accurately informed. For example, the UK health department has been very transparent with their data regarding where the coronavirus cases are located, by area/postcode. This allows for more effective management and planning.

Innovative workarounds have been developed in Australia, (such as SnewPit and AIBuild), but it’s a shame that in Australia we’ve had to resort to approaches such as reverse engineering geolocation data by scraping social media feeds, to be able to more accurately map information related to CoVID-19.

While the current situation raises some data governance/ethics questions, it’s interesting that European countries governed under legislation such as GDPR have been more open with their data than Australia.

As we say in business, “if you don’t measure it, you can’t manage it”. Better access to more timely, more accurate, information invariably leads to better decisions.

With better access to data similar to that provided by other countries, data experts can provide local communities with much better information, which could help reduce the sense of panic and frustration being caused by state and federal government’s sometimes confusing messaging and misinformation.

The data visualisations below were built using some datasets recently released by the NSW Government. While this data is by no means perfect (I uncovered numerous data quality errors while building these dashboards), it at least a start, allowing us to keep our local communities more accurately informed.

Note: the base COVID-19 case data was enriched by adding Australian Bureau of Statistics demographic data (Census 2016), including population, median age, median weekly income to help identify, for example, ‘at risk’ suburbs (suburbs with a high proportion of the population over 65)

Smart Cities: from Hype to Action

We’ll never live in the Smart Cities of tomorrow if we continue to rely on the technologies and infrastructure of yesterday.

Data has surpassed Oil as the world’s most valuable asset. 3 out of 4 jobs will soon require some level of technology skills – and children in disadvantaged areas without access to the latest technology will continue to fall further behind – the digital divide. This has major implications for Smart Cities initiatives.

At the recent Colorado Smart Cities Symposium, we learned of the growing social divide due to lack of broadband access, with only around 60% in the Denver area currently having access. City population growth only exacerbates the problem, with over 400,000 new residents arriving in the past 6 years.

Smart Cities for All (SmartCities4All.org)

According to James Thurston, Managing Director of SmartCities4All, while Smart Cities initiatives aim to provide social inclusion, most are actually increasing the digital divide, due to lack of focus around accessibility, particularly as it relates to accessibility for persons of disability and older persons.

In order to create smart cities and regions, CIOs need to leverage data and technology while also implementing programs that promote community engagement to help make citizens’ lives better and boost economic development. Many Smart Cities aim to leverage real time open data driven solutions and take advantage of disruptive new technology in transport, communications and energy efficiency, to grow the economy. However, according to Gartner, 30% of smart city initiatives will be discontinued due to lack of integrated services and data analysis, by 2023.

“Smart city platforms need to extend beyond just IoT platforms to deliver full solutions that encompass citizen engagement and experience, ecosystems, data analytics and AI.” (Source: Gartner Predicts 2019: Smart Cities)

While infrastructure such as fast, reliable and affordable broadband internet is key, it’s also essential to be able to make effective and efficient use of the many sources of data available to feed a Smart City project, and integrate these sources into smart, personalised digital experiences. For example, “Ten Gigabit Adelaide” has been rightfully seen as a game changer, helping make a somewhat isolated city, struggling to attract investment, tranform itself to be globally competitive. As Peter Auhl, the driving force behind the initiative, commented: “This world first infrastructure is showing the impact digital infrastructure can have on an economy and a city”.

Yet many Smart City CIOs are still struggling to pull together all the disparate sources of data (IoT, Open Government Data, internal data, 3rd party data e.g. Strava data, Weather data, sentiment data, survey data etc) and present it in a seamless digital experience for internal and external users. 

In an ideal world, integrated digital experiences – for citizens, agencies, employees would just work – securely, seamlessly, functionally, and with delight. But they don’t. Instead, most Smart Cities have a growing problem of too many systems and silos, redundant or overlapping tools and data, and compounding communication challenges because of the “systems of chaos”.

No Smart City is immune to this challenge – and all Smart City CIOs wish vendor to vendor systems worked better together.  

Aloha Cloud for Smarter Cities - distributed real-time data and collaboration digital experiences

This is where AppFusions can help. The AppFusions AlohaCloud platform addresses these issues by providing dynamic, real-time, collaborative digital experiences incorporating enterprise-grade blockchain (including seamless SSO/authentications where required).

The AlohaCloud platform is unique in that while addressing the Data side of the equation through deep integrations for realtime IoT data, Open Data etc, it also incorporates a full Digital Workplace (DWP) for Collaboration.

This provides data-driven, fact-based collaboration with context. Particularly in the current environment of “fake news” and alternative facts, it’s now more important than ever for Smart Cities initiatives to be accountable, measurable, auditable.

 

Smart Cities need to become ‘collaborative, innovative problem solvers’. As Professor Gary Hamel commented: “The problem with the future is that it is different. If you are unable to think differently, the future will always arrive as a surprise”.

A great example of “thinking differently” is the US Department of Energy in Idaho, which has a vision to provide clean, secure connected transportation, addressing critical zero-emission transportation system challenges (e.g. grid capabilities and charging strategies). The 10-20 year strategic objective is to demonstrate Autonomous, Connected, Electrified and Shared mobility transportation solutions. A short term goal is to provide a bus electrification management system digital experience, engaging everyone from passengers and drivers to fleet planners, operators and manufacturers. AlohaCloud is the digital experiences platform powering this visionary initiative.

We will never have sustainable, vibrant and future-proofed local economies unless we embrace digital transformation to equip our local workforces and communities with the skills they need to for the future. AlohaCloud enables this digital transformation, bridging the digital divide. Making Smart Cities smarter.

We’ll never live in the smart cities of tomorrow if we continue to rely on the technologies and infrastructure of yesterday

 

We’ll never live in the cities of tomorrow if we continue to rely on the technologies and infrastructure of yesterday.

 

 

Lessons on data analytics from a tween

I recently received a note from my 11 year old son’s maths tutor, which read:

“Jack will be covering Data, specifically: collecting categorical and numerical data thru observations and surveys, Constructing data displays including dot plots, column graphs and line graphs, naming and labelling vertical and horizontal axes, Using scale to determine placement of each point when drawing a line graph.”

“Aha! Welcome to the world of Business Intelligence!” I thought with a smile. Excitedly, I introduced Jack to my veritable library of books on the subject of charting and data visualization, including of course, all my favourites such as “Signal: Understanding What Matters in a World of Noise” by Stephen Few, “The Wall Street Guide to Information Graphics (the do’s and don’ts of presenting data, facts and figures)”, “Data Points: Visualization That Means Something” and many others. With no words and a tweenage look of disdain, he resumed his game of Roblox (Minecraft was no longer “cool” since it’s acquisition by Microsoft, I was off-handedly informed…)

Then, almost in passing, Jack mused: “I wonder how the number of Roblox users by year would look in a line chart…?”

Challenge accepted. A quick Google search, drop the data into a self-service data visualization tool, and we had our answer.

What was quite interesting to me was that he was thinking in terms of the data, wanting to answer a question empirically, and wanting to see the data visually to interpret the results. Now, as I listen to my son in the background, collaborating in real-time with one of his school friends in multi-player Roblox while they chat and interact simultaneously through Facetime, it’s occurs to me that future generations of ‘knowledge worker’ are likely to be much more collaborative and team-oriented, much more visual and data savvy, much less inclined to accept decision-making which cannot be supported by data. Which, of course, reminds me of a classic Dilbert moment:

Couple the inquisitive, knowledge-hungry mindset of our younger generations with the relentless pace of change and innovation, and we can anticipate that our future business users will not be tolerant of poor decision-making or slow time to action caused by the outdated insights provided by traditional analytics infrastructures. Today more than ever, analytics is a key to business success, but it needs simplicity, real-time speed and security: how fast can a user access and blend all the disparate data they need, analyze and share it, then take action, all in a secure and controlled environment? How can we provide solutions which enrich data with context, to build consensus? How can we empowering teams with the right data, providing machine learning-driven insights and personalization? Cognitive, collaborative analytics helps teams take action in real-time, to work on the right things at the right time. Our future decision-makers will expect nothing less.

Footnote – 10 of my favourite books on Data Visualization and Presentation:

Collaborative Mainframing

This week in San Jose, I attended my first SHARE conference, both as an attendee and as a speaker.

Having grown up in the world of business intelligence and analytics, it was interesting to get an insight into the world of the mainframe – and to meet many of the world’s leading experts in this timeless platform. During my session (“Information at the Speed of Thought — How Social Business Solutions Power Collaborative Analytics”) we had active discussions around a couple of very interesting topics:

  1. The need for curation and governance in support of self-service business decision-making;
  2. Balancing the “need for speed” in accessing and collaborating around business information with the need for security and controls around the data.

In both cases, I found it interesting to learn that the mainframe is the perfect power tool to facilitate these needs. By providing an easy mechanism to connect directly to the underlying mainframe data, visualize it, and then share it appropriately, users (including just about every bank in the world) avoid the need for a costly, time-consuming “ETL” approach with all the inherent limitations that approach introduces, including timeliness of data, effective governance and lineage of the data, difficulty keeping pace with business users’ evolving information needs, and the like.

One analogy, which seemed to resonate during my session, was the idea that finding a business insight and sharing it with colleagues should be as easy as “taking a picture and posting it on Facebook.” The increasing complexity of the world we all work in is making it imperative for us to work collaboratively to solve business problems. The old world of silos and “information hoarding” is not effective, and the information needed to support business decision-making needs to be quickly accessible, wherever it resides.

In this new world of #alternativefacts and #fakenews, it’s now more important than ever to be able to infuse business social networks with curated, governed data in support of informed, fact-based decision-making.