Month: March 2021
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Commentary: Engineers tend to think the best code will win. Unfortunately, that’s not true.
Image: iStock/SeventyFour
“I’m not that technical” is something I’ve said, by way of apology, many times over the course of my career. In an industry that celebrates engineering, it’s a way of pleading for a bit of mercy. After all, if you can’t write code, what good are you?
The problem with this engineering machismo is that it completely misunderstands how products–whether built with software or a comic book–get sold. I’ve written about the importance of other disciplines like marketing in tech before, but the topic is worth repeating now.
SEE: 10 ways to prevent developer burnout (free PDF) (TechRepublic)
If you build it…
“[T]he hardest thing for most engineers to grapple with is the idea that not only doesn’t the best tech win every time, it doesn’t win most of the time.” Those aren’t my words–they’re Steven O’Grady’s, and he should know: O’Grady is an analyst with RedMonk, which coined the tagline “Developers are the new kingmakers.” Of course, “best tech” is completely subjective. Which “Linux” is the “best?” Red Hatters might say RHEL, Ubuntu folks are going to pick Ubuntu, etc. “Best” depends on what you want–on your preferences.
And, in some instances, it also depends on what some “I’m not that technical” marketer has told you to try.
Oh, I know the counterargument: “Developers are impervious to marketing.” I’m sorry, but that is complete and utter garbage. Developers, like every other human being, respond to marketing. Maybe it’s different marketing (documentation, blogs, Reddit answers, a hackathon) than we normally associate with that term, but it’s marketing, all the same.
Thank goodness. Without marketing, the “best engineering” simply sits on the shelf and waits for buyers. Joyce Park, founder of Renkoo and 106 Miles, recently noted, “This is maybe THE hardest thing to get across to the entrepreneurial engineers at 106 Miles. They always think ‘If you build it they will come’.” They won’t. Not without marketing.
This isn’t to suggest that technology doesn’t matter. As O’Grady pointed out, “[T]he lesson isn’t that tech doesn’t matter at all, but rather that it doesn’t matter nearly as much as many engineers fervently believe it does.” If you’re an engineer and think that marketing or sales or some other non-engineering function is “useless,” well, Hilary Mason has some advice for you: “If you think an entire job function (marketing, HR, engineering managers, etc.) is useless, you’ve most likely never seen it done well.”
So what does good marketing look like?
…they will come
It looks like storytelling. Steve Jobs once said, “It’s in Apple’s DNA that technology alone is not enough—it’s technology married with liberal arts, married with the humanities, that yields us the result that makes our heart sing.” It’s why Jobs could get up on stage and talk about how special a new product was (which suspiciously looked like a handful of competing Android devices or PCs), and we believed him. And we bought. Again. And again.
This is why former Twitter and Google executive Santosh Jayaram once told The Wall Street Journal, “English majors are exactly the people I’m looking for,” precisely because they can create stories, or narratives, around otherwise uncommunicative ones and zeroes. Engineers can build the “what” of the product, but liberal arts folks, often in Marketing and Sales roles, help to sell the why.”
When I ran community at MongoDB, we focused on answering both of those questions. We gave would-be adopters of the database technical reasons to prefer a document database like MongoDB over their relational defaults (flexible schema, etc.). But we also told stories about freedom and scale. Both of these methods combined to make MongoDB one of the world’s most popular databases. It didn’t happen simply because Eliot and Dwight (the two cofounders) built it–they also marketed it.
For any successful product, you need both great engineering and great marketing. But you just might need the latter even more than you need the former. Yes, really.
Disclosure: I work for AWS, but the views expressed herein are mine.
Also see
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What happened
Shares of MongoDB Inc. (NASDAQ:MDB) were rising this afternoon even after an analyst lowered his price target for the tech stock today.
MongoDB’s shares were up 4% as of 3:16 p.m. EDT.
So what
Citi analyst Tyler Radke lowered his price target for MongoDB’s stock to $445 today, down from his previous price target of $500, but Radke maintained his buy rating for the company’s stock.
Image source: Getty Images.
Investors tend to react negatively when an analyst lowers their price target, but MongoDB’s share price was rising modestly today.
That may have more to do with the fact that some investors are beginning to come back to the tech sector after several months of shunning tech stocks. MongoDB’s stock is down about 25% year to date as investors have looked beyond the tech stock boom that occurred over the past 12 months.
Many investors focused their attention on the technology sector for growth during the pandemic, but as the economy is starting to open back up, some investors have sold off their tech positions.
Now what
Today’s share price jump is likely a small shift in investor sentiment back toward technology stocks, but MongoDB investors should probably prepare for some more volatility. Investors are still trying to figure out where to put their money after a strong year in tech stock gains, and that means that MongoDB’s share price may see some dips and pops in the short term.
This article represents the opinion of the writer, who may disagree with the “official” recommendation position of a Motley Fool premium advisory service. We’re motley! Questioning an investing thesis — even one of our own — helps us all think critically about investing and make decisions that help us become smarter, happier, and richer.
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Bad data is worse than no data at all.- What is “good” data and where do you find it?
- Best practices for data analysis.
There’s no such thing as perfect data, but there are several factors that qualify data as good [1]:
- It’s readable and well-documented,
- It’s readily available. For example, it’s accessible through a trusted digital repository.
- The data is tidy and re-usable by others with a focus on ease of (re-)executability and reliance on deterministically obtained results [2].
Following a few best practices will ensure that any data you collect and analyze will be as good as it gets.
1. Collect Data Carefully
Good data sets will come with flaws, and these flaws should be readily apparent. For example, an honest data set will have any errors or limitations clearly noted. However, it’s really up to you, the analyst, to make an informed decision about the quality of data once you have it in hand. Use the same due diligence you would take in making a major purchase: once you’ve found your “perfect” data set, perform more web-searches with the goal of uncovering any flaws.
Some key questions to consider [3] :
- Where did the numbers come from? What do they mean?
- How was the data collected?
- Is the data current?
- How accurate is the data?
Three great sources to collect data from
US Census Bureau
U.S. Census Bureau data is available to anyone for free. To download a CSV file:
- Go to data.census.gov[4]
- Search for the topic you’re interested in.
- Select the “Download” button.
The wide range of good data held by the Census Bureau is staggering. For example, I typed “Institutional” to bring up the population in institutional facilities by sex and age, while data scientist Emily Kubiceka used U.S. Census Bureau data to compare hearing and deaf Americans [5].
Data.gov
Data.gov [6] contains data from many different US government agencies including climate, food safety, and government budgets. There’s a staggering amount of information to be gleaned. As an example, I found 40,261 datasets for “covid-19” including:
- Louisville Metro Government estimated expenditures related to COVID-19.
- State of Connecticut statistics for Connecticut correctional facilities.
- Locations offering COVID-19 testing in Chicago.
Kaggle
Kaggle [7] is a huge repository for public and private data. It’s where you’ll find data from The University of California, Irvine’s Machine Learning Repository, data on the Zika virus outbreak, and even data on people attempting to buy firearms. Unlike the government websites listed above, you’ll need to check the license information for re-use of a particular dataset. Plus, not all data sets are wholly reliable: check your sources carefully before use.
2. Analyze with Care
So, you’ve found the ideal data set, and you’ve checked it to make sure it’s not riddled with flaws. Your analysis is going to be passed along to many people, most (or all) of whom aren’t mind readers. They may not know what steps you took in analyzing your data, so make sure your steps are clear with the following best practices [3]:
- Don’t use X, Y or Z for variable names or units. Do use descriptive names like “2020 prison population” or “Number of ice creams sold.”
- Don’t guess which models fit. Do perform exploratory data analysis, check residuals, and validate your results with out-of-sample testing when possible.
- Don’t create visual puzzles. Do create well-scaled and well-labeled graphs with appropriate titles and labels. Other tips [8]: Use readable fonts, small and neat legends and avoid overlapping text.
- Don’t assume that regression is a magic tool. Do test for linearity and normality, transforming variables if necessary.
- Don’t pass on a model unless you know exactly what it means. Do be prepared to explain the logic behind the model, including any assumptions made.
- Don’t leave out uncertainty. Do report your standard errors and confidence intervals.
- Don’t delete your modeling scratch paper. Do leave a paper trail, like annotated files, for others to follow. Your predecessor (when you’ve moved along to better pastures) will thank you.
3. Don’t be the weak link in the chain
Bad data doesn’t appear from nowhere. That data set you started with was created by someone, possibly several people, in several different stages. If they too have followed these best practices, then the result will be a helpful piece of data analysis. But if you introduce error, and fail to account for it, those errors are going to be compounded as the data gets passed along.
References
Data set image: Pro8055, CC BY-SA 4.0 via Wikimedia Commons
[3] How to avoid trouble: principles of good data analysis
[4] United States Census Bureau
[5] Better data lead to better forecasts
[6] Data.gov
[7] Kaggle
Global Graph Database Market Impressive Growth 2021 | ArangoDB, Kompass (UK) Ltd, Sparsity …
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Global Graph Database Market covering key business segments and wide scope geographies to get deep dive analysed market data. Global Graph Database Market Report gives the definite Study of the major Graph Database industry driving professionals alongside the organization profiles and systems embraced by them. An alternate segment with Graph Database industry enter makes is incorporated into the report, which gives shipment, value, income, net benefit, talk with the record, business circulation CAGR etc.. This empowers the purchaser of the answer to pick up an adaptive perspective of the aggressive scene and plan the methodologies in a required manner. Some are the key & emerging players that are part of coverage and have being profiled are Stardog Union, Ontotext, Bitnine Co, Ltd., Cambridge Semantics, ArangoDB, Kompass (UK) Ltd, Sparsity Technologies, Objectivity Inc., Teradata, MongoDB, Inc., among others.
Get Exclusive Sample of Report on Graph Database Market spread across 350 pages, profiling Top Market Players is available at https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-graph-database-market&DP
Global graph database market is expected register a 24.2% CAGR in the forecast period of 2019-2026. Growing adoption and need in identifying the complex patterns along with the rapid use of virtualization for Big Data analytics are expected grow global graph database market
Global Graph Database Market Dynamics:
Market Drivers:
- Growing real-time big data mining with effect of visualization is expected to drive the growth of the market
- Growing demand of system that has capability to process low-latency queries is expected to drive the growth of the market
- Increasing demand of AI-based graph database tools and services is also expected to boost the growth of the market
- Technological advancements in the graph databases software and prevailing demand from the healthcare industry for enhanced accuracy is another factor uplifting the market growth
Market Restraints:
- Lack of awareness among consumers is expected to restrict the growth of the market
- Dearth of standardization and programming ease is another factor which hamper the market growth
- Scarcity of technical experts along with high initial expenditure also acts as market restraint
Important Features of the Global Graph Database Market Report:
1) What all companies are currently profiled in the report?
List of players that are currently profiled in the report- Oracle, IBM, Microsoft, Amazon Web Services, Inc., Neo4j, Inc., TIBCO Software Inc., Franz Inc, OpenLink Software, TigerGraph, MarkLogic Corporation, Cray Inc, DataStax, Inc,
** List of companies mentioned may vary in the final report subject to Name Change / Merger etc.
2) What all regional segmentation covered? Can specific country of interest be added?
Currently, research report gives special attention and focus on following regions:
North America, Europe, Asia-Pacific etc.
** One country of specific interest can be included at no added cost. For inclusion of more regional segment quote may vary.
3) Can inclusion of additional Segmentation / Market breakdown is possible?
Yes, inclusion of additional segmentation / Market breakdown is possible subject to data availability and difficulty of survey. However a detailed requirement needs to be shared with our research before giving final confirmation to client.
** Depending upon the requirement the deliverable time and quote will vary.
Global Graph Database Market Segmentation:
By Type
- Resource Description Framework
- Property Graph
By Application
- Recommendation Engines
- Fraud Detection
- Customer Analytics
- Risk and Compliance Management
By Organization Size
- Small and Medium-Sized Enterprises
- Large Enterprises
By Industry Vertical
- Banking, Financial Services, and Insurance
- Retail and e-commerce
- Transportation and Logistics
- Government and Public Sector
- Manufacturing
- Telecom and IT
- Healthcare and Life Sciences
- Others
By Deployment Mode
- Cloud
- On-Premises
By Component
- Tools
- Services
By Geography
- North America
- U.S.
- Canada
- Mexico
- Europe
- Germany
- Italy
- U.K.
- France
- Spain
- Netherlands
- Belgium
- Switzerland
- Turkey
- Russia
- Rest of Europe
- Asia-Pacific
- Japan
- China
- India
- South Korea
- Australia
- Singapore
- Malaysia
- Thailand
- Indonesia
- Philippines
- Rest of Asia-Pacific
- South America
- Brazil
- Argentina
- Rest of South America
- Middle East and Africa
- Saudi Arabia
- UAE
- South Africa
- Egypt
- Israel
- Rest of Middle East and Africa
New Business Strategies, Challenges & Policies are mentioned in Table of Content, Request FREE TOC @ https://www.databridgemarketresearch.com/toc/?dbmr=global-graph-database-market&DP
Strategic Points Covered in Table of Content of Global Graph Database Market:
Chapter 1: Introduction, market driving force product Objective of Study and Research Scope Graph Database market
Chapter 2: Exclusive Summary – the basic information of Graph Database Market.
Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges of Float-Zone Silicon
Chapter 4: Presenting Graph Database Market Factor Analysis Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.
Chapter 5: Displaying the by Type, End User and Region 2013-2018
Chapter 6: Evaluating the leading manufacturers of Graph Database market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile
Chapter 7: To evaluate the market by segments, by countries and by manufacturers with revenue share and sales by key countries in these various regions.
Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source
Region wise analysis of the top producers and consumers, focus on product capacity, production, value, consumption, market share and growth opportunity in below mentioned key regions:
North America – U.S., Canada, Mexico
Europe : U.K, France, Italy, Germany, Russia, Spain, etc.
Asia-Pacific – China, Japan, India, Southeast Asia etc.
South America – Brazil, Argentina, etc.
Middle East & Africa – Saudi Arabia, African countries etc.
Strategic Key Insights Of The Graph Database Report:
Production Analysis – Production of the Patient Handling Equipment is analyzed with respect to different regions, types and applications. Here, price analysis of various Graph Database Market key players is also covered.
Sales and Revenue Analysis – Both, sales and revenue are studied for the different regions of the Graph Database Market. Another major aspect, price, which plays an important part in the revenue generation, is also assessed in this section for the various regions.
Supply and Consumption – In continuation of sales, this section studies supply and consumption for the Graph Database Market. This part also sheds light on the gap between supply and consumption. Import and export figures are also given in this part.
Competitors – In this section, various Graph Database industry leading players are studied with respect to their company profile, product portfolio, capacity, price, cost, and revenue.
Analytical Tools – The Graph Database Market report consists the precisely studied and evaluated information of the key players and their market scope using several analytical tools, including SWOT analysis, Porter’s five forces analysis, investment return analysis, and feasibility study. These tools have been used to efficiently study the growth of the major industry participants.
- The 360-degree Graph Database overview based on a global and regional level. Market share, value, volume, and production capacity is analyzed on global, regional and country level. And a complete and useful guide for new market aspirants
- Facilitates decision making in view of noteworthy and gauging information also the drivers and limitations available of the market.
Queries Related to the Graph Database Market:
- Which application segments will perform better and achieve success in worldwide through the forecast years?
- What are the key factors driving the market growth?
- Who are the key vendors in this Industry?
- Which are the impressive business sectors where best players want their own expansion in future?
- What are the market dynamics?
- What are the limits ruining the development rate?
- What is the focused circumstance to advance development?
- What are the opportunities and threats faced by the performers in the global market?
- What are the development rates for this Industry?
About Data Bridge Market Research:
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Data Bridge set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market.
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The food industry is among the largest industries in the world. Perhaps nothing serves as a better testament to its importance. The global food industry not only survived the pandemic even as pretty much every other sector suffered the wrath of shutdowns, but it thrived. The growth Zomato, Swiggy, UberEats and more managed to achieve in the past year is incredible. Now, it is clear to see that this sector has an abundance of potential to offer, but with great potential comes even greater competition. And it’s not only the humongous competition — but companies also have to contend with the natural challenges of operating in this industry. For all that and more, the sector has found great respite in various modern technologies.
However, in particular, one has evinced incredible interest from the food industry, on account of its exceptional potential, of course: Big Data. You see, this technology has increasingly proven its potential to transform the food and delivery business for the better completely. How? In countless ways, actually, for starters, it can help companies identify the most profitable and highest revenue-generating items on their menu. It can be beneficial in the context of the supply chain and allow companies to keep an eye on factors such as weather conditions for farms they work with, monitor traffic on delivery routes, and so much more. Allow us to walk you through some of the other benefits big data offers to this industry.
- Quicker deliveries: Ensuring timely food delivery is one of the fundamental factors for success in this industry. Unfortunately, given the myriad things that can affect deliveries, ensuring punctuality can be quite a challenge. Not with big data by your side, though, for it can be used to run analysis on traffic, weather, routes, etc. To determine the most efficient and quickest ways for delivery to ensure food reaches customers on time.
- Quality control: The quality of food is another linchpin of a company’s success in this sector. Once again, this can be slightly tricky to master, especially when dealing with temperature-sensitive food items or those with a short shelf life. Big data can be used in this context by employing data sourced from IoT sensors and other relevant sources. And to monitor the freshness and quality of products and ensure they are replaced, the need arises.
- Improved efficiency: A restaurant or any other type of food establishment typically generates an ocean-load of data, which is the perfect opportunity to put big data to work. Food businesses can develop a better understanding of their market and customers and their processes and identify any opportunities for improvement. It allows companies to streamline operations and processes, thus boosting efficiency.
To conclude, online food ordering and delivery software development can immensely benefit any food company when fortified with technologies such as big data. So, what are you waiting for? Go find a service provider and get started on integrating big data and other technologies into your food business right away!
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Data science platform market is estimated to rise with a CAGR of 31.1% by generating a revenue of $224.3 billion by 2026. Asia-Pacific holds the highest growth rate, expecting to reach $80.3 billion during the forecast period.
Data science is the preparation, extraction, visualization, and maintenance of information. Data science uses scientific methods and processes to draw the outcomes from the data. With the help of data science tools and practices one can recognize the data patterns. The person dealing with data science tools and practices uses meaningful insights from the data to assist the companies to take the necessary decision. Basically, data science helps the system to function smarter and can take autonomous decisions based on historical data.
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Many companies have a large set of data that are not being utilized. Data science is majorly used as a method to find specific information from a large set of unstructured and structured data. Concisely, data science is a vast and new field which helps to build, asses and control the data by the user. These analytical tools help in assessing business strategies and taking decisions. The rising use of data analytics tools in data science is considered to be major driving factor for the data science platform market.
Data science is mostly used to find hidden information from the data so that business decisions and strategies can be conceived. If the data prediction goes wrong, business has to face a lot of consequences. Therefore, professional expertise are required to handle the data carefully. But as the data science platform is new, the availability of the workforce with relevant experience is considered to be the biggest threat to the market.
Service type is predicted to have the maximum growth rate in the estimated period. Service segment is projected to grow at a CAGR of 32.0% by generating a revenue of $76.0 billion by 2026. Increasing difficulties in terms of operational work in many companies and rising use of Business Intelligence (BI) tools are predicted to be major drivers for the service type segment.
Manufacturing is predicted to have the highest growth rate in the forecast period. Data scientists have acquired a key position in the manufacturing industries. Data science is being broadly used for increasing production, reducing the cost of production and boosting profit in manufacturing area. Data science has also helped the companies to predict potential problems, monitor the work and analyze the flow of work in the manufacturing work area. Manufacturing segment is expected to grow at a CAGR of 31.9% and is predicted to generate a revenue of $43.28 billion by 2026.
North Americas has the largest market size in 2018. North America market is predicted to grow at a CAGR of 30.1% by generating a revenue of $80.3 billion by 2026. The presence of large number of multinational companies and rising use of data with the help of analytical tools in these companies gives a boost to the market in this region. Asia-Pacific region is predicted to grow at a CAGR of 31.9% by generating a revenue of $48.0 billion by 2026. Asia-Pacific is accounted to have the highest growth due to increasing investments by companies and the increased use of artificial intelligence, cloud, and machine learning.
The major key players in the market are Microsoft Corporation, Altair Engineering, Inc., IBM Corporation, Anaconda, Inc., Cloudera, Inc., Civis Analytics, Dataiku, Domino Data Lab, Inc., Alphabet Inc. (Google), and Databricks among others.
Global NoSQL Database Market SWOT Analysis, Key Indicators, Forecast 2027 : NoSQL database …
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The report by Zion Market Research titled “ NoSQL Database Market: Global Industry Analysis, Size, Share, Growth, Trends, And Forecast, 2020-2026 Market Research Report” presents a profound comprehension regarding the functioning and expansion of the NoSQL Database market on regional and global level. This analysis report is the collation of all the wide-ranginginformation relating to the market statistics during the recent years as well as forecasts for coming years. To begin with, the report comprises the major players actively participating and competing within the NoSQL Database market; it entails several companies, manufacturers, suppliers, organizations, and so on. Thus, the report will assist in understanding the initiatives and approaches implemented by these players to create and reinforce their market presence. The thoroughanalysis presents a wide-ranging comprehension of the global market in a knowledgeableway. The client can merely point out the steps of the firm by having details regarding their global revenue, market share, price, production & capacity, andrecent developments during the forecast period.
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Global NoSQL Database Market: Competitive Players
NoSQL database market are DynamoDB, ObjectLabs Corporation, Skyll, MarkLogic, InfiniteGraph, Oracle, MapR TechnologiesInc., The Apache Software Foundation, Basho Technologies, and Aerospike.
The research report includes the outline of the global NoSQL Database market such as definition, classifications, and applications. Apart from this, it entails the comprehensive assessment of a number of factors like constraints, opportunities, drivers, challenges,and risk. Further, it the global NoSQL Database market is bifurcated on the basis of diverse parameters into respective segments as well as sub-segments. The report alsoencompasses the existing, previous, and likely growth trends within the market for each segment and sub-segment. Additionally, the market is also segregated based on regions North America, Europe, Asia-Pacific and Latin America. along with detailed evaluation of their growth, key developments & strategies, opportunities, and the key patterns influencing the market expansion in those regions.The report will further also entail a particular part putting forth the changes and of the ongoing COVID-19pandemic. It comprisesin-depth market analysis rooted on the predictions of post-COVID-19 market circumstances together with data on the existing impacts on the NoSQL Database market of the pandemic.
Promising Regions & Countries Mentioned In The NoSQL Database Market Report:
- North America ( United States)
- Europe ( Germany, France, UK)
- Asia-Pacific ( China, Japan, India)
- Latin America ( Brazil)
- The Middle East & Africa
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The research report also highlights the wide array of tacticalsteps, such as latest business deals, joint ventures, partnerships, M&As, technological developments, and launch of new products taking placing in the market. In addition, it scrutinizesseveral patterns of the global NoSQL Database market, entailing the rules, criteria, and policy deviationsimplemented by the private companies and government on the market over the last few years. As a final point, the analysis includes forecasts and historic data making it a beneficial asset for experts, industry executives, presentation, sales & product managers, consultants, and every other person or organization looking for essential market data and statistics.
Key Details & USPs of the Existing Report Study:
- Worldwide-level market size of NoSQL Database Market in terms of Volume (K Units) and Value (USD Million) for historical period (2016 – 2019) and projected years (2020 – 2026)
- Region-level (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa) market size of NoSQL Database Market in terms of Volume (K Units) and Value (USD Million) for historical period (2016 – 2019) and projected years (2020 – 2026)
- Country-level (U.S., Canada, Germany, UK, France, Spain, Italy, China, Japan, India, South Korea, Southeast Asia, Brazil, Mexico, GCC, South Africa, RoW) market size of NoSQL Database Market in terms of Volume (K Units) and Value (USD Million) for historical period (2016 – 2019) and projected years (2020 – 2026)
- Type market size bifurcated into its individual Product Type (Concentration, Temperature, Combustion, Conductivity, and Others) in terms of Volume (K Units) and Value (USD Million) for historical period (2016 – 2019) and projected years (2020 – 2026)
- Demand Side and Supply Side Perspective and analysis
- Company/Players/Manufacturers/Vendors/Service Providers Market Share
- Competitive Landscape, Competition Matrix, and Player Positioning Analysis
- Market Dynamics, Trends, Factors affecting market growth during upcoming year
- Key Buyers and End-User Analysis
- Value Chain & Supply Chain Analysis including Distribution and Sales Channels as well as Forward and Backward Integration scenarios
- Manufacturing Cost Structure Analysis
- Key Raw Materials Analysis
- Key Pricing Strategies adopted in the market
- Key Marketing Strategies adopted in the market
- Porters Five Forces Analysis
- SWOT Analysis
- PESTLE Analysis
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What Reports Provides
- Full in-depth analysis of the parent market
- Important changes in market dynamics
- Segmentation details of the market
- Former, on-going, and projected market analysis in terms of volume and value
- Assessment of niche industry developments
- Market share analysis
- Key strategies of major players
- Emerging segments and regional markets
- Testimonials to companies in order to fortify their foothold in the market.
Also, Research Report Examines:
- Competitive companies and manufacturers in global market
- By Product Type, Applications & Growth Factors
- Industry Status and Outlook for Major Applications / End Users / Usage Area
Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe or Asia.
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Microsoft, Google, Igalia, and other industry partners are joining hands to improve browser compatibility. The cross-browser effort, named #Compat2021, will focus on the top five compatibility pain points, all CSS-related: CSS Flexbox, CSS Grid, CSS position: sticky, the CSS aspect-ratio property, and CSS transforms.
Google explained the rationale behind the project:
Compatibility on the web has always been a big challenge for developers. […] The goal in 2021 is to eliminate browser compatibility problems in five key focus areas so developers can confidently build on them as reliable foundations. This effort is called #Compat2021.
The joint working group identified the key compatibility pain points from feature usage data, bugs reported to each vendor’s tracking system, surveys, current compatibility level, and test results measured by the web-platform-tests auditing website. The five areas of focus have been selected to maximize the impact on developers and web users.
CSS Flexbox, one of the five identified pain points, is for instance used in 75% of all page views, and growing. The feature nonetheless only has 85% test coverage across browsers while being the top issue mentioned in the latest MDN Browser Compatibility Report. The Igalia team explained in detail on its blog the Flexbox issues that they found and fixed. One example of fixed behavior in Webkit, together with the CSS code, is as follows:
.flexbox {
display: flex;
flex-direction: column;
height: 500px;
justify-content: flex-start;
align-items: flex-start;
}
.flexbox > * {
flex: 1;
min-width: 0;
min-height: 0;
}
<div class="flexbox">
<img src="cat1.jpg>
</div>
Black and white cat by pixabay
The previous example shows that while in both cases the image’s height is correctly set (500px), in the erroneous case, the width of the image does not respect the image ratio.
Google described at length in a blog post the five browser compatibility pain points chosen (CSS Flexbox, CSS Grid, position: sticky, aspect-ratio, and CSS transforms) and the rationale behind the choice.
The necessary work will be divided among the working group. The Microsoft Edge team reports that it intends to get Chromium to pass 100% of CSS Grid tests this year. The broader web community may also contribute to the joint effort by filing bugs in the appropriate project (Chromium, Webkit, or Gecko) when they encounter compatibility issues. Developers may follow the progress for each focus area in the Compat 2021 Dashboard. The current snapshot of the dashboard taken at the time of publication is as follows:
Igalia also experimented last year with the crowd-funding of Web APIs that they termed as open prioritization. Igalia is an open-source consultancy that contributed a large part of the CSS Grid implementation in WebKit and Chromium.
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Data As A GalaxyOne of the more significant “quiet” trends that I’ve observed in the last few years has been the migration of data to the cloud and with it the rise of Data as a Service (DaaS). This trend has had an interesting impact, in that it has rendered moot the question of whether it is better to centralize or decentralize data. There have always been pros and cons on both sides of this debate, and they are generally legitimate concerns. Centralization usually means greater control by an authority, but it can also force a bottleneck as everyone attempts to use the same resources. Decentralization, on the other hand, puts the data at the edges where it is most useful, but at the cost of potential pollution of namespaces, duplication and contamination. Spinning up another MySQL instance might seem like a good idea at the time, but inevitably the moment that you bring a database into existence, it takes on a life of its own. What seems to be emerging in the last few years is the belief that an enterprise data architecture should consist of multiple, concentric tiers of content, from highly curated and highly indexed data that represents the objects that are most significant to the organization, then increasingly looser, less curated content that represents the operational lifeblood of an organization, and outward from there to data that is generally not controlled by the organization and exists primarily in a transient state. Efficient data management means recognizing that there is both a cost and a benefit to data authority. A manufacturer’s data about its products is unique to that company, and as such, it should be seen as being authoritative. This data and metadata about what it produces has significant value both to itself and to the users of those products, and this tier usually requires significant curational management but also represents the greatest value to that company’s customers. Customer databases, on the other hand, may seem like they should be essential to an organization, but in practice, they usually aren’t. This is because customers, while important to a company from a revenue standpoint, are also fickle, difficult to categorize, and frequently subject to change their minds based upon differing needs, market forces, and so forth beyond the control of any single company. This data is usually better suited for the mills of machine learning, where precision takes a back seat to gist. Finally, on the outer edges of this galactic data, you get into the manifestation of data as social media. There is no benefit to trying to consume all of Google or even Twitter without taking on all of the headaches of being Google or Twitter without any of the benefits. This is data that is sampled, like taking soundings or wind measurements in the middle of a boat race. The individual measurements are relatively unimportant, only the broader term implications. From an organizational standpoint, it is crucial to understand the fact that the value of data differs based upon its context, authority, and connectedness. Analytics, ultimately, exists to enrich the value of the authoritative content that an organization has while determining what information has only transient relevance. A data lake or operational warehouse that contains the tailings from social media is likely a waste of time and effort unless the purpose of that data lake is to hold that data in order to glean transient trends, something that machine learning is eminently well suited for. This is why we run Data Science Central, and why we are expanding its focus to consider the width and breadth of digital transformation in our society. Data Science Central is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to submit original articles and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think. In media res, DSC Featured Articles
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