By coding on a daily basis, you will gain the ability to integrate Data Science or other elementary concepts into your programming of developmental projects. The best part about practicing to code each day is it allows you, the coders, to subconsciously develop an innovative skill where you can solve computational tasks in the form of code. By gaining more information and learning more details, you can use code blocks more effectively and efficiently to solve complex programming architectures. You can even find solutions to Data Science tasks that you might have otherwise struggled with because you are in touch with the subject. And it also helps you gather discrete and more innovative ideas for implementation faster. This point is quite self-explanatory. Coding daily will sharpen your deduction abilities and tune you into a better programmer. By following the accurate principles of coding, you can improve your skills to the next level and develop yourself into an ultimate genius of programming. For example, let us take into consideration the Python programming language, which is most commonly used for Data Science. By developing your coding skills in Python, you are also advancing your skill level of Data Science to code complex Data Science projects.
Historically, relationships can be challenging cross-divisionally between InfoSec, IT, Legal, and business or product teams in the sense that the internal conversations and ongoing education required for these teams to sync on protocols and security measures can be extensive. Moreover, policy enforcement between these teams without proper technical safeguards can result in the accidental ー or in rare instances, malicious ー leakage of data that would put an individual or business’ reputation at risk. By taking a more contextual, technically enforced approach to data governance, the all-too-common risks associated with human-led processes can be eliminated and in turn, improve collaboration and enforcement between these internal teams. When the right technical controls are in place based on the data context, using privacy-enhancing techniques or in partnership with a reliable technology partner, enterprises no longer have to rely on the manual human processes that may lead to sensitive data leakage or re-identification risk. Only this alignment between internal teams will allow an enterprise’s overall data strategy to flourish. Many of the world’s most successful companies, such as Amazon or Unilever, have gained sizable market share through data collaboration, starting within their own enterprise.
The concept of value needs to be mastered and used by enterprise architects in a customer-driven enterprise, as shown in Figure 1 above. An organization usually provides several value propositions to its different customer segments (or persona) and partners that are delivered by value streams made of several value stages. Value stages have internal stakeholders, external stakeholders, and often the customer as participants. Value stages enable customer journey steps, are enabled by capabilities and operationalized by processes (level 2 or 3 usually). The TOGAF® Business Architecture: Value Stream Guide video provides a very clear and simple explanation, should you want to know more. Customer journeys are not strictly speaking part of business architecture, but still, be very useful to interface with business stakeholders. These value streams/stages cannot be realized out of thin air. An organization must have the ability to achieve a specific purpose, which is to provide value to the triggering stakeholder, in occurrence the customers. This ability is an enabling business capability. Without this capability, the organization cannot provide value to triggering stakeholders (customers). a capability enables a value stage and is operationalized by a business process.
A primary goal of metadata is to assist researchers in finding relevant information and discovering resources. Keywords used in the descriptions are called “meta tags.” Metadata is also used in organizing electronic resources, providing digital identification, and supporting the preservation and archiving of data. Metadata assists researchers in discovering resources by locating relevant criteria and providing location information. In terms of digital marketing, metadata can be used to organize and display content, maximizing marketing efforts. Metadata increases brand visibility and improves “findability.” Different metadata standards are used for different disciplines (such as digital audio files, websites, or museum collections). A web page, for example, a may contain metadata describing the software language, the tools used to create it, and the location of more information on the subject. ... Metadata found online and in digital marketing is a crucial tool for modern marketing. Metadata can help people find a website. It makes web content more searchable, and when used efficiently, metadata can increase the number of visits.
People privileged enough to be considered the default by data scientists and who are not directly impacted by algorithmic bias and other harms may see the underrepresentation of race or gender as inconsequential. Data Feminism authors Catherine D’Ignazio and Lauren Klein describe this as “privilege hazard.” As Alkhatib put it, “other people have to recognize that race, gender, their experience of disability, or other dimensions of their lives inextricably affect how they experience the world.” He also cautions against uncritically accepting AI’s promise of a better world. “AIs cause so much harm because they exhort us to live in their utopia,” the paper reads. “Framing AI as creating and imposing its own utopia against which people are judged is deliberately suggestive. The intention is to square us as designers and participants in systems against the reality that the world that computer scientists have captured in data is one that surveils, scrutinizes, and excludes the very groups that it most badly misreads. It squares us against the fact that the people we subject these systems to repeatedly endure abuse, harassment, and real violence precisely because they fall outside the paradigmatic model that the state — and now the algorithm — has constructed to describe the world.”
Thanks to recent developments, both the Internet of Things (IoT) and the Industrial Internet of Things (IIoT) are making Internet-enabled devices an increasingly common feature in business. This trend was already on the upswing prior to the global lockdown, with 46% of IT, telecommunications, and business managers saying their organizations had already invested in IoT applications and/or services, while another 30% were readying to invest in the next 12-24 months. But with millions of workers now working and communicating virtually from remote locations, the role of IoT in helping to realize the vision of the smart enterprise has drawn even greater interest. For one thing, IoT has gained traction as an enabler of productive remote work and unhindered team collaboration across industries, said Igor Efremov the head of HR at Itransition, a Denver, US-based software development company. Sectors that formerly could not continuously function without human labor such as manufacturing, can now rely on IIoT infrastructure including sensors, cameras, and endpoints to allow technicians to monitor and maintain asset performance in real time, without actually being physically present.
There is no such thing as a perfect work/life balance, of course. And while some employers understand the stress faced by their workers, not all do: Switched-on employers who care about their staff provide employee choice schemes, target-related bonuses, personal support, subsidized Wi-Fi, and a judgement-free attitude toward sick days. Those who don’t, insist workers stay on camera all day and refuse to accept excuses for absence on the basis of child care or any other need, while indulging in fire and re-hire policies. To a great extent, corporate responsibility around employee care in this environment has effectively been outsourced to employees themselves, even as productivity (and working hours) increase. And while Apple’s devices and third-party apps can help remote workers manage time more effectively, the need for all parties to develop new ways of working that don’t impact personal space remains challenging. This isn’t a platform-specific matter, of course: Windows or Mac, Android or iPhone, iPad or some other tablet, enterprise workers of every stripe face complex challenges as they juggle work and personal responsibilities.
As a Data Scientist, you will have to perform feature engineering, where you will isolate key features that contribute to the prediction of your model. In school or wherever you learned Data Science, you may have a perfect dataset that is already made for you, but in the real world, you will have to use SQL to query your database to start finding the necessary data. In addition to the columns that you already have in your tables, you will have to make new ones — usually, these are new features that can be aggregated metrics like clicks per user, for example. As a Data Analyst, you will practice SQL the most, and as a Data Scientist, it can be frustrating if all you know is Python or R — and you can not rely on Pandas all the time, and as a result, you cannot even start the model building process without knowing how to efficiently query your database. Similarly, the focus on analytics can allow you to practice creating subqueries and metrics like the one stated above so that you can add a few to at least, say 100, new features that are completely created from you that could be more important than the base data that you have now. ... A Data Analyst usually will master visualizations because they have to present findings in a way that is easily digestible for others in the company.
Companies today are quite good at collecting data - but still very poor at organizing and learning from it. Setting up a proper Data Governance organization, workflow, tools, and an effective data stack are essential tasks if a business wants to gain from it’s information. This book is for organizations of all sizes that want to build the right data stack for them - one that is both practical and enables them to be as informed as possible. It is a continually improving community driven book teaching modern data governance techniques for companies at different levels of data sophistication. In it we will progresses from the starting setup of a new startup to a mature data driven enterprise covering architectures, tools, team organizations, common pitfalls and best practices as data needs expand. The structure and original chapters of this book were written by the leadership and Data Advisor teams at Chartio, sharing our experiences working with hundreds of companies over the past decade. Here we’ve compiled our learnings and open sourced them in a free, open book.
Rather than providing a centralized solution for everything, a distributed cloud can meet each specific customer and country requirement. It also gives enterprises the ability to properly use their original investments in existing central clouds while executing a unified cloud strategy for location-based data needs. This is especially important when customers are looking to utilize SaaS solutions that depend on central clouds since they often can’t easily localize data independently. Hybrid clouds were originally intended to enable a unified strategy. Yet, enterprises continue to struggle to get the level of value they initially expected out of their private cloud deployments, especially in compliance-centric use cases where ongoing research and expertise is needed. However, enterprises can now consider a distributed cloud-based offering such as ‘Data Residency-as-a-Service’ to meet global compliance standards. As businesses look to expand into new countries that require data sets to be localized in different regions, it’s essential to stay ahead of these challenges or they risk losing business in the region altogether.
Quote for the day:
"Brilliant strategy is the best route to desirable ends with available means." -- Max McKeown