There has never been a time when more options were available to stand up a process-driven and/or platform-driven Data Architecture. In recent years, some companies find themselves with an embarrassment of riches, having every tool known to humankind. Using tools à la carte, with a different tool for each solution, works for some organizations. Other organizations have core applications in place; there are data flows, but they’re very operational in nature, and there’s nothing even relating to Data Architecture. Still others are trying to use one tool to accomplish everything, end to end.
One Size Does Not Fit All
“There’s no one right way,” said George Yuhasz, VP and Head of Enterprise Data at NewRez, because the demands and the value that Data Architecture practices bring to an organization are as varied as the number of firms trying to get value from data. Yuhasz was speaking at DATAVERSITY® Data Architecture Online Conference.
The very definition of Data Architecture varies as well, he says, so get clarity among stakeholders to understand the constraints and barriers in which Data Architecture needs to fit. Will the organization prioritize process alone? Or process, platforms, and infrastructure? Or will it be folded into a larger enterprise architecture? Without a clear definition, it’s impossible to determine key success criteria, or to know what success is, both in the short term and long term.
The definition should be simple enough to be understood by a diverse group of stakeholders, and elegant enough to handle sophistication and nuance. Without it, he said, the tendency will be to “drop everything that even relates to the term ‘data’ onto your plate.”
No matter where the organization sits on the spectrum of Data Literacy, performing an honest assessment of the current state of data literacy is necessary for success:
• Organizational Data Literacy: To what degree does the organization knowingly use data to both drive positive outcomes and to build a catalogue of benefits over time?
• Operational and Core Platform Footprint: What is the core platform footprint? Is it primarily one or two sets of platforms or many things on a core?
• Framework for Understanding Data as an Asset: What capacity does your organization have for understanding data as an asset? How much capacity for transition does your company have?
• Technology Roadmap: To what extent does the Data Architecture align with the activities of the technology organization?
The Need for a Tailored Approach
There are common approaches and frameworks to Data Architecture, but they must be aligned with the type of organization, the industry in which it operates, and its specific needs and goals. Healthcare, financial services, and manufacturing companies have differing needs, and a startup will have a different profile from a long-established company. Culture is also a significant consideration as organizations with a top-down management style will need a different strategy than one with a bottom-up management style.
Adapt to the Culture and Influence Stakeholders Over Time
Yuhasz shared practical steps for successful Data Architecture implementation. His first recommendation was to approach the process with a sense of humility. This idea is often interpreted as simply agreeing to everything, yet he stressed that it’s more about respecting others as professionals, giving them the benefit of the doubt and assuming that everyone wants to provide value to each other and to the firm. “Even if you don’t feel like you’re getting it back, sometimes you need to be the one that sets the example.”
Leading with empathy and a willingness to listen provides essential insights into the culture and an understanding of stakeholder perspective. Rather than being seen as an external party trying to be disruptive, humility also allows a manager to adapt to the culture and become “baked into the organization,” he said.
Raise Awareness and Education
The feeling of being at a conference can be so inspiring: “It’s a wonderful sense of feeling like we’re there amongst our people,” he said, but it can be a challenge to curb enthusiasm when it’s time to bring it back to constituents at home who haven’t shared the experience. It’s important to meet them where they are as well as working over time to raise their awareness and education. Focus on mutually important results and goals and work as partners to get there, using the new tools you’ve acquired, rather than just explaining what you’ve learned, he said. “They can see the value in that, and you’re going to win them over.”
Act as a Change Agent Appropriate to the Organization
The key to implementing successful change is to start small, be humble, and build inventory and capability from there. Talking about making “a couple tweaks”’ here and there will provide opportunities for quick, small successes that can build momentum quickly, especially if the process involves listening and shared problem-solving. At the same time, partner with those in your organization who seek to innovate, improve, and transform, and find out how are they getting people excited about opportunities.
Foster Innovation, Minimize Disruption
Successful transformation also requires an awareness of how much potential there is for disrupting people’s lives, Yuhasz said: “This is the empathy part.” Disruptions might be for all the right reasons, but understanding and acknowledging the impact will help engender an understanding of the benefits.
Evangelize the Power of Data to Drive Outcomes
Keep making connections between the outcomes achieved and the role of Data Architecture in deriving value from the activities that led to those outcomes. In a perfect world when the story of the project is shared, he said, others will voluntarily tell the story. “And you’re just agreeing and saying what a great job they did in helping to get the value.” Sharing the credit, sharing the wealth, and building others up pays back huge dividends.
Have alignment on the definition of what a good outcome should be, using the current roadmap. Find a shared understanding of the problem and ensure that everyone is working toward the same goal. Work toward a baseline shared perception of “good” with stakeholders and users, as well as best practices for getting there.
Know When “Best” is Necessary
The concept of “good” is in relationship with “best” and there’s a lot of room in between those two spaces to deliver a lot of value, he said. However, when it comes to areas that are critical to keeping the lights on, that’s where the highest standard is important.
Simplify the Big Picture
This starts with a problem statement, clarifying the value in doing X or Y, and outlining the construct by which they’ll be implemented. Be careful about the amount of detail, though, because “The more you throw on the page, the farther you get from ‘simple.’”
A simple overview should answer the questions: Why do I care? What am I doing? What should I expect to get out of it, “And then absolutely be able to drill down in detail from there.”
Champion Quick Wins
“The death of many a Data Architecture [or] strategic initiative was that they didn’t stack up enough quick wins up front.” Asking stakeholders to wait six months before starting to see any value is a mistake, Yuhasz said. Be able to articulate at which points they will start to see some real value.
Be Specific About Outcomes
Although it’s tempting to make big promises up front, providing quantifiable outcomes in relationship to the amount of disruption makes it easier to get through transitions with fewer people grumbling. Metrics that illustrate the value of a project engender a sense of cooperation and trust: “We’ll be slowing down by 25%, but the end result will make us 50% more trustworthy.”
Understand the User Community
Stewards, governors, quality admins, analysts, report authors, developers, data scientists — they each have a role in curation and governance. Some will want to focus on determining value to the business and others will be more focused on operations. Listen to learn how each of them prefers to work, recognize the constraints they are bound by, and then empower them, he said, but with guardrails. “You must figure out a way that lets your users do what they must do, while both protecting your firm and providing a promotion path for each individual’s insights and discoveries.”
Communication is Key
Clarity about Data Architecture in the ecosystem is paramount, he said, and how it informs your governance and analytical communities. Also important is clarity about where data will be stored, how to access it, and how the firm will get value from the data. Outline places where experimentation with data is allowed. Align platforms and technologies with the abilities of stewards and stakeholders and talk about how iterations will occur.
Let the Culture Be the Guide
“We all like to say it’s about people, process, and technology,” Yuhasz said, but ultimately, the culture decides how well the people, process, and technology will work together. “Cultural readiness, the skills, the platforms, and adoption: Those will yield your outcomes.” Even with a very basic skills platform and very narrow adoption, he said, good outcomes are possible.
Want to learn more about DATAVERSITY’s upcoming events? Check out our current lineup of online and face-to-face conferences here.
Here is the video of the Data Architecture Online presentation:
Exclusive-Toshiba’s No.2 shareholder calls for immediate resignation of board chair, 3 directors
By Makiko Yamazaki
TOKYO (Reuters) -Toshiba Corp’s second-biggest shareholder on Sunday demanded the board chairman and three other directors immediately resign after an investigation found the company had colluded with the Japanese government to pressure foreign investors.
The letter, seen by Reuters, is from 3D Investment Partners, which owns a 7.2% stake in Toshiba. It was sent to the four on Sunday, according to people with direct knowledge of the process.
It is likely to heighten scrutiny into governance at Toshiba, a renowned industrial conglomerate in crisis sparked by Thursday’s report. The shareholder-commissioned report marked an explosive turn in a long battle between the Japanese company’s management and foreign shareholders.
In addition to 3D, these shareholders include activist investors and Harvard University’s endowment fund.
The revelations in the report “are deeply troubling and represent one of the most prominent and shocking corporate governance failures among large public companies anywhere in the world in the last decade,” the 3D letter says.
The letter, addressed to board chair Osamu Nagayama and three current audit committee members, describes Nagayama as “ultimately responsible for Toshiba’s recent governance failures, including the flawed internal investigation and the board’s determination to oppose an outside, independent investigation.”
“It is also troubling that you have been silent about the investigative report and have failed to accept responsibility for the misconduct that occurred under your oversight as chair of the board,” the letter says.
Toshiba declined to comment on the letter, telling Reuters in a statement it was “carefully reviewing the content of the investigation report and plans to announce its comments towards this investigation result after the review.”
The company was holding an emergency meeting on Sunday to discuss reassigning the candidates for three key board committees ahead of a June 25 shareholder meeting. Major shareholder advisory firms recommended against some of the candidates, including the four addressed in the 3D letter.
Four independent directors, all non-Japanese, have said in a sign of revolt that they were no longer in support of the full slate of director candidates nominated by Toshiba.
(Reporting by Makiko Yamazaki; Editing by William Mallard)
Image Credit: Reuters
The rising importance of Fintech innovation in the new age
The rise of fintech has opened an array of opportunities for smart cities to develop and thrive. Its importance has actually increased in the age of the pandemic that calls for social distancing or contactless transactions.
The leading global payment solutions provider Visa recently indicated the increasing role of digital payments. Thanks to the expanding role of fintech, digital payments are expected to enter different smart city sectors.
Reportedly, fintech application is going to be instrumental in the transportation sector. It will come to people in different forms of contactless payments. It will also ease the process of paying for parking or hiring bikes and scooters.
More than that, whether it’s about loans, money transfer, investment, accounting and bookkeeping, airtime or fundraising. Smart cities and businesses are going to hugely rely on fintech in the coming future.
Going ahead, we are delving into understanding the fintech situation in three smart cities. All three are important fintech hubs that the entire world looks upon.
In the smart city culture, London has the reputation of being the ‘fintech capital’ of the world. The number of fintech giants in the city is valued at more than $1 billion.
However, the pandemic has caused a number of businesses to shut down. At the same time, it has also catalysed the shift to digital and contactless. Businesses are now adopting new ways to support their customers.
Even in this time of crisis, London is at the foremost position of producing the next generation of fintech leaders. This is as per the Ed Lane, VP of Sales for the EMEA region at nCino, a US-based cloud banking provider.
Remote work is becoming a necessity due to COVID-19. Hence, investments in different technologies and solutions in financial organisations and service providers are “more important than ever”. And so Lane claims that this has increased the adoption of cloud-based banking software developed by his firm.
The UK recently introduced the Bounce Back Loan Scheme and the Coronavirus Business Interruption Loan Scheme (CBILS). This is helping Lane’s company nCino and others. They are offering a Bank Operating System to aid SMEs with effective processing of loan applications.
Fintech companies are surviving and tapping into benefits in the COVID-19 age due to their disruptive mindset. The dot.com crash of 2001 and the financial crash of 2008 are drivers that lead them to become proactive.
Innovatively, fintech companies started offering mobile banking, online money management tools and other personalised solutions. Today, the same is enabling them to prevail during this pandemic. Besides all, partnerships have proven to be key strategies in achieving even the impossible, as experts say.
Singapore is showcasing a pioneering move in the fintech industry. Fintech is at the core of Singapore’s vision to become a ‘Smart Nation’ with a “Smart Financial Centre.”
To achieve the dream, the city-state has been showing constant efforts by using innovative technology. With this, it intends to pave the way for new opportunities, enhance efficiency and improve national management of financial risks.
Until 2019, Singapore was already home to over 600 fintech firms. These companies attracted more than half of the total funding for the same year. And amidst the COVID-19 pandemic, the Monetary Authority of Singapore (MAS) introduced two major support packages.
First on April 8, 2020, it announced a S$125 million COVID-19 care package for the financial and fintech sectors. This package aims at aiding the sectors in fighting the challenges from the COVID-19 health crisis. It will help in supporting workers, accelerate digitalisation, and improve operational readiness and resilience.
Second, on May 13, 2020, MAS, the Singapore Fintech Association (SFA) and AMTD Foundation launched the MAS-SFA-AMTD Fintech Solidarity Grant. The S$6 million grant proposes to support Singapore-based fintech firms.
A specific focus is on managing cash flow, producing new sales and seeking growth strategies. At the individual level, many industry participants have launched their own initiatives to support the sector.
HongKong’s fintech startup sector tells us a different story which involves the role of blockchain. Blockchain-based companies are dominating the city’s startup sector.
In 2019, enterprise DLT and crypto-assets exchanges earned rankings as the most popular sectors in Hong Kong’s fintech industry. The report comes from the Financial Services and Treasury Bureau. It confirms that blockchain startups make up 40% of the 57 Fintech firms established in the city in 2019.
As per reports, 45% of new companies are focused on developing applications for large businesses. This is the reason that enterprise blockchain firms were the most popular. Another 27% account for blockchain-related firms in Hong Kong involved in digital currency.
The increase in the number of blockchain-based fintech startups is due to the Special Administrative Region of the People’s Republic of China. The authority introduced new policies towards blockchain tech development – making it a priority.
Blockchain is thriving in Hong Kong due to a number of reasons. The city has laid down clear regulatory guidelines for blockchain-related businesses. Many have leveraged the benefits of the QMAS program. It enables applicants to settle down in the region before having to look for employment. This has immensely encouraged several blockchain specialists to move to Hong Kong.
The city government is also entering partnerships to expand its fintech footprint in the right direction. For example, in November 2019, the government collaborated with Thailand’s officials to explore the development of Central Bank Digital Currencies (CBDCs). Blockchain is a promising technology for the fintech industry. It supports quick, secure and cost-effective transaction-related services.
More importantly, it provides transparency that other traditional technologies were not capable of. Thanks to the use of encrypted distributed ledgers. These enable real-time verification of transactions without the need for mediators such as correspondent banks.
Why Is Fintech Innovation Important For The Development Of Smart Cities?
Advanced cities that are now smart cities have been using fintech for their development. With that, they are also leading the way for others to follow. Many experts confirm that innovation in fintech is a must for any city to become a ‘smart city.’
It enables easy national as well as international business. For the residents, it makes life more convenient by encouraging contactless, economical, sustainable and efficient payment-related operations.
One important aspect that smart city development and fintech innovation has in common is their determination to cut bureaucracy. A city that manages to enable speedy and inexpensive international transfers will also enable its citizens with greater access to the global market. This is as said by Hans W. Winterhoff from KPMG in one of his articles.
Furthermore, fintech innovations of the past have demonstrated their success. Some fintech applications have simplified procedures that became unnecessarily complex over time. Traditional banking services are one of the biggest examples.
The innovative fintech services opened doors for online shopping and easy international money transfers. Fintech is able to provide the same product or service to consumers. But that’s happening in less time, with fewer steps, and at more affordable rates.
Besides, transparency is another important factor that is allowing consumers to have faith in fintech services. With the current potential of fintech, we can now say that it is one of the essential pillars of successful smart city development. The results are already here in the age of this pandemic.
If you did not already know
In the last decade, a variety of topic models have been proposed for text engineering. However, except Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA), most of existing topic models are seldom applied or considered in industrial scenarios. This phenomenon is caused by the fact that there are very few convenient tools to support these topic models so far. Intimidated by the demanding expertise and labor of designing and implementing parameter inference algorithms, software engineers are prone to simply resort to PLSA/LDA, without considering whether it is proper for their problem at hand or not. In this paper, we propose a configurable topic modeling framework named Familia, in order to bridge the huge gap between academic research fruits and current industrial practice. Familia supports an important line of topic models that are widely applicable in text engineering scenarios. In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models. Simply through changing the data organization of Familia, software engineers are able to easily explore a broad spectrum of existing topic models or even design their own topic models, and find the one that best suits the problem at hand. With its superior extendability, Familia has a novel sampling mechanism that strikes balance between effectiveness and efficiency of parameter inference. Furthermore, Familia is essentially a big topic modeling framework that supports parallel parameter inference and distributed parameter storage. The utilities and necessity of Familia are demonstrated in real-life industrial applications. Familia would significantly enlarge software engineers’ arsenal of topic models and pave the way for utilizing highly customized topic models in real-life problems. …
In statistics, the median absolute deviation (MAD) is a robust measure of the variability of a univariate sample of quantitative data. It can also refer to the population parameter that is estimated by the MAD calculated from a sample. Consider the data (1, 1, 2, 2, 4, 6, 9). It has a median value of 2. The absolute deviations about 2 are (1, 1, 0, 0, 2, 4, 7) which in turn have a median value of 1 (because the sorted absolute deviations are (0, 0, 1, 1, 2, 4, 7)). So the median absolute deviation for this data is 1. …
Most work on temporal action detection is formulated in an offline manner, in which the start and end times of actions are determined after the entire video is fully observed. However, real-time applications including surveillance and driver assistance systems require identifying actions as soon as each video frame arrives, based only on current and historical observations. In this paper, we propose a novel framework, Temporal Recurrent Networks (TRNs), to model greater temporal context of a video frame by simultaneously performing online action detection and anticipation of the immediate future. At each moment in time, our approach makes use of both accumulated historical evidence and predicted future information to better recognize the action that is currently occurring, and integrates both of these into a unified end-to-end architecture. We evaluate our approach on two popular online action detection datasets, HDD and TVSeries, as well as another widely used dataset, THUMOS’14. The results show that TRN significantly outperforms the state-of-the-art. …
CDF2PDF is a method of PDF estimation by approximating CDF. The original idea of it was previously proposed in  called SIC. However, SIC requires additional hyper-parameter tunning, and no algorithms for computing higher order derivative from a trained NN are provided in . CDF2PDF improves SIC by avoiding the time-consuming hyper-parameter tuning part and enabling higher order derivative computation to be done in polynomial time. Experiments of this method for one-dimensional data shows promising results. …
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