Enterprise big data and analytics cuts through the hype to make sense of data collection, storage, management, dissemination and discovery technologies, all employed collectively as a means of realizing corporate efficiencies and uncovering business opportunities.
The world will find its way through the COVID-19 pandemic with data.
Data analysts will be among the ordeal’s heroes, and organizations with strong data literacy throughout will recover the fastest. Invest now in data literacy.
Doctors, nurses, and hospitals are the frontline heroes for the acute victims of the COVID-19 virus. But data is everyone’s compass — today as ‘the curve’ signals danger and eventually as the curve signals hope.
It’s a good time to understand data. Many organizations in the western hemisphere now face their greatest peril of our lifetimes. Those that survive will eventually face another daunting task: rebuilding in a new, different economy. Understanding data – knowing how to read it, organize it, prepare it, analyze it, and explain it – will be crucial. Continue reading “COVID-19: Don’t Miss This Moment – Invest Now in Data Literacy”→
• Mobile operators have been offering aggregated location data for several years, with limited uptake from sectors like tourism, retail, and transportation.
• Those big data analytics solutions could be very useful to authorities and essential services in fighting COVID-19.
Telecom providers are finding their networks used in different ways since the start of measures being taken to limit the spread of COVID-19. For mobile operators, that now includes the use of user location data analytics to help governments and other entities to understand—and fight—the spread of the virus.
In the last three or four years, mobile network operators have been investing in big data analytics technologies in order to leverage the potential value of the vast amount of network and user data they collect. Accessing the technology was the easy part: the availability of open source tools, hyperscale cloud platforms, and investments already being made in the telcos’ own digital transformations led to innovative solutions launching as early as 2014. Some players in Europe went further, acquiring analytics start-ups and digital consulting firms, helping them to offer both standardized solutions providing insights on visitors, events, and journeys, as well as customized projects. Continue reading “COVID-19: Using Mobile User Location Data to Understand and Mitigate the Pandemic”→
• VirusBlockchain deployed this week to identify and monitor COVID-19 free zones
• The blockchain monitoring system is backed by technology provider Qlikchain
This week the tech industry partnered with a public health consortium to launch a blockchain-enabled monitoring system aimed at keeping communities at bay from the COVID-19 pandemic.
The Public Health Blockchain Consortium (PHBC) announced the new system which monitors healthy, uninfected individuals as they move between locations in order to automatically identify zones that are safe or unsafe. The system is built on a blockchain solution which combines AI, geographical information systems (GIS), and real-time information systems provided by virus surveillance providers.
Companies that have yet to jump on the remote working bandwagon may have their hand forced due to the self-isolation and social separation measures put in place by their respective national governments.
We will undoubtedly see an uptick in the adoption of telehealth technologies, including remote monitoring.
On the 11th March 2020, the World Health Organization (WHO) declared COVID-19 (Coronavirus) a pandemic. As of writing, there have been over 130,000 cases reported across 123 countries, areas or territories and almost 5,000 deaths from the virus, which emanated from Wuhan in China. We have witnessed a wide variety of responses to the threat including mass self-isolation in Italy, travel bans, fiscal stimulus packages, health insurance policy allowances, business and school closures, and the cancellation of large events such as Mobile World Congress in Barcelona and HIMSS20 in Orlando, at which U.S. President Trump was scheduled to address the situation. Continue reading “COVID 19: Keep Calm and Corona On – A Global Perspective”→
• China has become a test bed for the potential to harness IT to manage and mitigate the effects of major health crises like the coronavirus.
• The widespread use of technologies like AI, big data, robotics, and blockchain raises questions about wider applicability, and concerns about longer-term governance.
Recent weeks have seen China emerge as a test bed for the potential to harness IT to manage and mitigate the effects of major health crises like the coronavirus. As of 11th March, China had almost 80,800 confirmed cases of the virus, also known as COVID-19, which had killed over 3,000 people. The economic impact of the virus has also been widely documented and includes disruptions to supply chains and lost business for countless shops, bars, and restaurants.
But China has also seen several applications of IT to help combat and manage impact of the virus. Examples include the use of workplace collaboration tools such as Alibaba’s DingTalk, Tencent’s WeChat, and ByteDance’s Feishu by businesses, hospitals, schools, and universities. These platforms enable various remote working arrangements, as well as the use of online classrooms, which support distance learning. Others include the provision of digital mapping tools. Baidu has created an epidemic map feature on the Baidu Map App that offers real-time location information about confirmed and suspected cases of the virus, as well as travel disruptions caused by enforced quarantines. Meanwhile, Tencent provides a self-examination tool on its WeChat platform to help users experiencing symptoms such as a fever or cough self-evaluate their condition and make any necessary arrangements. Tencent also maintains a map depicting clinics and hospitals that treat coronavirus patients. Continue reading “China’s Use of IT to Fight the Coronavirus Prompts Wider Applicability and Governance Questions”→
• Looking to build good artificiaI intelligence (AI)? Don’t let the speed and availability of open source frameworks, modules, libraries, and languages lull you into a false sense of confidence.
• Good AI needs to start with good data and good data needs to be ingested, registered, described, validated, and processed well before it reaches the ready hands of AI practitioners.
These are heady times. Enterprises have at their disposal both the raw materials and the necessary tools to achieve great things with AI, be that something grandiose as self-driving cars or unassuming as a fraud detection algorithm. The trouble with an abundance of materials (e.g., data) and tools (e.g., open source machine learning models), however, is speed. Speed kills, as they say.
For AI practitioners, this means learning to run before learning to walk by hastily automating decisions via AI models that are built on unsound data. With a few simple open source frameworks, modules, libraries, and languages, seemingly useful but ultimately erroneous predictions and conclusions can be readily drawn from any old data set in very short order. What’s the answer? More or better tools? No. As with most human problems, good old human knowhow and understanding are necessary. And that begins with data.
The ‘democratization of analytics,’ essentially getting analytics tools and insights into the hands of the masses, is the next step forward in a world eager to leverage greater business intelligence.
Tableau is taking on the challenge by providing tools such as Explain Data and Ask Data, which are designed to make it easier for line-of-business users to extract insights from their data visualizations.
There is no doubt that the vast amounts of data being generated today contain a wealth of valuable information. But, unlocking the strategic insights contained within this treasure trove of material remains elusive to many. Sure, data scientists and data programmers have the tools to perform the analysis at their fingertips, but their techniques remain out of reach to many line-of-business users. Extracting insights from data and getting it into the hands of those outside of the IT department is a challenge. The ‘democratization of analytics,’ essentially getting analytics tools and insights into the hands of the masses, is the next step forward in a world eager to leverage greater business intelligence. Continue reading “Tableau Tackles the Challenge of ‘Democratizing Analytics’ by Offering New Tools”→
At its annual user conference, Tableau rolled out several data prep and management capabilities, highlighted by the ability for Tableau Prep Builder users to write out to third-party data stores, such as the popular Snowflake solution.
This, along with several ongoing cloud and database initiatives, marks a significant philosophical shift for the vendor away from pure analytics and toward a more complete solution to help buyers establish a company-wide data culture.
For a company powered by analytics, Tableau put very few on display during its annual user conference in Las Vegas last week. Certainly, there were numerous stats to be found, particularly relating to the adoption of Tableau Online, where there are now 15,000+ active customer accounts. What’s more, Tableau Online is maintaining 100% YoY growth, as reported by CEO Adam Selipsky – a fitting fact given that Tableau and its new, cloud-first parent company, Salesforce, are now free to talk integration and rationalization. Continue reading “Tableau Tackles Analytics at Scale, Not Through Tech Alone but with a ‘Culture of Data’”→
• Competition in the AI chipset space is heating up; new players are looking to join the fray and they are raising impressive amounts of capital.
• New vendors face stiff competition from tech heavyweights such as Nvidia, hyper-scale cloud providers such as Google and Amazon, and well-funded Chinese organizations.
Just as the market for AI platforms is heating up, so is competition in the AI chipset space. And it isn’t only the large well-established competitors such as Nvidia, Google, and Huawei vying for market share. New players are looking to join the fray as well, and they are raising impressive amounts of capital. Untether, a Toronto-based chip manufacturing start-up, announced in early November that it had raised $20 million in series A funding. The one year old company plans to release a chip designed for AI inference using near-memory design, reducing the distance data must travel, thereby moving data to processors at 2.5 petabits per second, which improves overall processing efficiency. Across the pond, Graphcore, a UK-based organization, has raised a substantial $200 million to develop its Intelligence Processing Units (IPUs), parallel processors designed for machine learning.