A good Data Governance Program, Sandwell said, creates trust in the data, so that end-users see it as a valuable, accessible resource for decision making. The goal is to provide a program that is consistent, high quality, and understandable, making it easy for end users to derive value from the data. This, in turn, fosters transparency and accountability for data assets and their management, which is essential for “creating trust in your enterprise data,” he said. ... Data Modeling allows an organization to work out a plan before offering it up to users. It’s accepted that the right way to design relational databases is to take time for modeling, do the analysis, understand the challenges and risks, and work out the “what-if’s” before ever showing that database or offering it up for use.
AI and cognitive computing are managed in the same way as many other information and technology governance programs. They require executive sponsorship, charters, roles and responsibilities, decisionmaking protocols, escalation processes, defined agendas, and linkage to specific business objectives and processes. These initiatives are a subset of digital transformation and are linked to customer life cycles and internal value chains. Because the objective is always to affect a process outcome, all AI and cognitive computing programs are closely aligned with ongoing metrics at multiple levels of detail-from content and data quality to process effectiveness and satisfaction of business imperatives-and ultimately are linked to the organizational competitive and market strategy.
In order to prevent things from running out of control, the tech industry has a responsibility to help the society to adapt to the major shift that is overcoming the socio-economic landscape and smoothly transition toward a future where robots will be occupying more and more jobs. Teaching new tech skills to people who are losing or might lose their jobs to AI in the future can complement the efforts. In tandem, tech companies can employ rising trends such as cognitive computing and natural language generation and processing to help break down the complexity of tasks and lower the bar for entry into tech jobs, making them available to more people. In the long run governments and corporations must consider initiatives such as Universal Basic Income (UBI), unconditional monthly or yearly payments to all citizens, as we slowly inch toward the day where all work will be carried out by robots.
ASIC said it expects the application of DLT to grow exponentially over time, but noted its existing regulatory framework is able to accommodate the DLT use cases it has come across to date. However, as DLT matures, the government body anticipates that additional regulatory considerations will arise. "Our approach to developments in the fintech sector is to work to harness opportunities and economic benefits, not stand in the way of innovation and development," ASIC's guidelines state. "At the same time, we need to mitigate any potential risks of new business models through the use of new technologies." In reinforcing its regulatory remit, ASIC has established an Innovation Hub to help fintech startups developing "innovative" financial products or services to navigate its regulatory system.
Founded as a non-profit organization and based in Toronto, Canada, the group of founding members include Accenture, IBM, SAP, Digital Asset, NASDAQ, Pepsico, the Province of Ontario and Nuco Inc. The group is expected to spend its first year studying the impact of blockchain on eight industries, or "vertical opportunities", including energy, media, technology, healthcare and government. Associate members, including Hyperledger, Enterprise Ethereum Alliance and the Chamber of Digital Commerce, are then aiming to help minimize the amount of redundant work. "We don’t want to duplicate anything that’s already being done," Tapscott said. Including access to monthly webinars and a private website to view published material, the membership – which Tapscott expects will reach 30 by the time the program launches next month – will receive a custom executive report based on the institute's findings and aimed specifically at the members' demands.
As the saying goes, everything is bigger in Texas, and for data governance, it was the massive landscape of different industries and geographies coming together to create something significant and sustainable for effectively managing data. For many, the focus was to prepare challenges around data and analytics, including: Establishing effective information governance for better quality, privacy, and security; Maximizing the impact of business intelligence and MDM programs; Preparing for trends such as AI, Hadoop, Internet of Things (IoT) and blockchain; and Building and executing an effective, holistic data and analytics strategy. Organizations of all sizes and types were present at the conference to learn and shared about their data governance programs. It’s amazing to see how these organizations have transformed the process of enterprise data governance.
Microsoft’s view is that the mindset of governance around data needs to change from being that of “data management” to “data as a strategic advantage”. Once the organization understands that the use of data can strategically change the way it does business, the requirement of the governing body (typically the board of directors) to become involved is obvious. After all, it is the board that is responsible for the overall strategy of the organization. And if the organization is to transform itself to become more of a “data business” then it is the board that is accountable for the success of that transformation. The digital transformation journey for Ryman Healthcare, a leading retirement village operator in New Zealand, started two years ago precisely that way. The management team initially set out to mitigate risks of documentation errors as they felt that it was risky to depend on manual and paper-based documentation, especially when it comes to patient care.
Some organizations have mastered data governance, but they are in the minority. As data volumes continue to grow, most businesses are finding it hard to keep up. "You're going to do this one way or another," said Shannon Fuller, director of data governance at Carolinas Healthcare System. "You can do it in a controlled, methodical manner or you can do it when your hair's on fire." Poor data governance can result in lawsuits, regulatory fines, security breaches and other data-related risks that can be expensive and damaging to a company's reputation. "We don't have regulation about data lineage and reporting and all that, but it's going to come," said Fuller. "Do you want to prepare for that now or do you want to be like Bank of America and spend billions of dollars complying with the law? Most healthcare organizations don't have that kind of cash lying around."
The research of 1,500 IT decision makers from multiple vertical industries across the US, Europe, Asia-Pacific and South Africa, reveals that hybrid IT is becoming a standard enterprise model, but there’s no single playbook to get there. Looking at the top motivators to move to hybrid IT by country, Hong Kong, UK and US companies highlighted end-user demand most often, while respondents in France, Singapore and South Africa most often noted cost. Malaysian firms listed hiring challenges, and German firms mentioned limited data centre capacity as the most common motivating factors. The Success Factors for Managing Hybrid IT report points to the fact that management of the hybrid IT environment (41% of respondents) is one of the top three challenges in deployment.”
The relationship between lean and agile is complex. Some agilists do not even see a direct relationship. Even those who recognize that agile mindset is based on lean principles of value delivery, reduction of waste, and system thinking, frequently have a perception that while lean is a manufacturing approach that focuses on minimizing costs by eliminating waste and improving process efficiencies, agile is just the application of lean mindset to software delivery with a set of processes around it. This is only partially accurate because agile, and specifically Scrum, bring two important concepts into lean: incremental delivery and cross-functional team-based execution. Our Lean Pilot framework implements lean six sigma DMAIC cycle at cadence using Scrum framework. DMAIC is a data-driven quality strategy used to improve processes. It is an integral part of a Six Sigma initiative, but in general can be implemented as a standalone quality improvement procedure.
Quote for the day:
"New capabilities emerge just by virtue of having smart people with access to state-of-the-art technology." -- Robert E. Kahn