AI is a game-changing technology that has drastically changed how companies do business. Recent advances have enabled companies to use AI in ways they never have before. It’s not just about improving existing products; it’s also about discovering new possibilities they didn’t know existed.
AI helps companies find ways to improve their product development processes. AI can predict future trends, identify customer needs, and determine which products will be most profitable for your company.
This article explores the possibilities and limits of AI in research and development.
Use of AI in Research
Research and development (R&D) is a critical component for any business, especially in today’s data-dependent competitive world. Companies get valuable insights from research on improving their products and processes to meet customers’ needs and remain competitive. But then, there is a vast amount of information available that researchers need to analyze and synthesize when creating a new product. As such, companies must resort to efficient and fast product development technologies to conduct research and respond to the changing dynamics of the marketplace. And that’s where AI comes in handy.
Companies are using AI technologies to automatically analyze large amounts of data and identify patterns that would not be obvious to a human analyst. These patterns could then be used as the basis for additional experimentation by scientists or engineers. Product development Seattle companies can find solutions that humans may not have considered because they are too complex or abstract.
Generative Design
Generative design is a new approach to product development that uses artificial intelligence to generate and test many possible designs. These designs are analyzed to select the most promising ones. The technique is helping product design firm Seattle reduce costs and improve the quality of its products. It’s applicable in software design, architecture, and medicine, among other industries.
Assembly Line Optimization
Assembly line optimization is a process that allows companies to identify and optimize their production processes, from the design phase to the assembly line. Product development San Francisco firms are using artificial intelligence (AI) to predict how well a product will perform as it moves through different production phases.
In addition to helping companies identify problems with their products before they occur, AI can also help them determine how long it will take for each part to reach completion once it has entered production. This can be useful when deciding whether enough resources are available at one facility or another.
Automated Testing of Features
When creating a product or service, an organization may need to test its features. The company can use AI to automate this process and find out whether these features are working as intended. The goal is to verify that the features work as they were intended and to ensure that they do not cause problems with other parts of the product. AI can help the company save time, money, and effort when testing products and services.
Quality Assurance
Quality assurance (QA) is an integral part of the life cycle management of products and services. It involves tasks such as inspection, testing, and evaluation. QA teams are now using AI to help them with everything from testing to customer service. AI algorithms can check and validate if a product meets QA in real-time, significantly easing the process.
The Limitations Of AI
Though AI has many benefits in product R&D, it has some limitations in application. Below are some of them:
Massive Data Labeling and Training Data Sets
AI requires massive amounts of data labeling and training data sets to learn what is normal versus abnormal. Data labeling takes a lot of time and personnel, which can be costly. Also, obtaining large amounts of data sufficient to train an AI model can be challenging.
Bias in Data and Algorithms
If the data and algorithms companies use to train AI are inherently biased, that can lead to some big problems. One typical example of bias in data is the issue of racial profiling. If you’re training an AI program to recognize certain things (like faces), then it’s going to learn what humans have told it about those faces. And if people have been tagging those faces as “criminal,” then the AI will think that people who look like that are criminals. In the end, AI can cause a business more harm than benefits it wants to achieve.
The Explainability Problem
The Explainability Problem is the inability of machine learning systems to explain their decision-making processes. This is a serious issue, making it impossible for humans to understand how an AI system reaches its conclusions. Also, it’s difficult to determine whether an algorithm has been trained on biased data or if it uses outdated or inappropriate data sources.
Cost
Another limitation of AI in research and development is cost. The technology is expensive, and the time it takes to train an AI system can be prohibitively long. In addition, many companies don’t have the resources to train and maintain AI software.
Final Thoughts
AI is here to stay, and its future is bright. It is revolutionizing how companies approach research and product development. From data processing to feature testing and QA, AI can help companies create better products. However, companies should continually look for ways to address AI limitations.
Data analytics technology has proven to be very useful for the real estate sector. A report by McKinsey shows that a growing number of real estate professionals are leveraging big data to improve sales and customer satisfaction.
In order to make the most of data analytics in the real estate sector, it is important to be familiar with the different tools and types of data that can be used. Many real estate agents focus only on tapping traditional data. However, McKinsey author Gabriel Morgan Asaftei and his colleagues argue that 60% of the value comes from nontraditional variables. Therefore, real estate professionals shouldn’t be too discerning with the data that they take advantage of.
Data Analytics is Transforming the Real Estate Sector
Real estate marketing is a constantly changing, often volatile and unpredictable landscape. Property costs can vary wildly, influenced by a number of factors as well as the larger economic health of the nation.
Despite this, property remains an attractive and popular investment option for many people. It is a versatile and varied market, representing a viable investment alternative to more traditional routes like stocks, shares, or bonds. For an investor to be successful in any market, be it the stock market or real estate, effective and accurate market analysis is absolutely crucial. It can give you a deeper understanding of the processes involved and the systems in place and can give you an advantage over your competitors.
The good news is that big data can be incredibly useful for real estate investors and buyers. There are a number of tools available that help investors analyze the property market. Let’s take a look at some.
Why is Using Big Data Tools to Conduct Property Market Analyses Important?
Before we get into the tools that are available for property market analysis, let’s establish why it is so important in the first place. Investing in property is different from other investment methods such as shares or stocks. Shares can be bought, sold, and traded quickly, in reaction to or anticipation of broader market trends and movements. Investing in property, on the other hand, moves at a much slower pace. You can’t drop assets quickly and pick up more at speed. This is why proper analysis of market data is so important, it can work to confirm that you are making the right moves and making educated decisions.
What kind of property you buy, where it is, and what surrounds it can all have a significant impact on the current and future value of that property. To ensure you are making the right choice when looking to invest in property, let’s look at some of the analysis tools available.
Fortunately, a number of data analytics tools can be very helpful here. You will want to make the most of big data technology if you want to make sensible investment decisions.
Statistical Atlas
The Statistical Atlas is an invaluable data analytics tool that can give you detailed insights on a wide variety of national markets and areas. You can view the US by state, city, township, county, even all the way down to elementary or high school districts. Once you have selected your desired area, you can access a whole range of information and metrics, including data on age, sex, and ethnicity demographics, as well as information on the area’s household types, employment rates, and average income.
All these factors can have a significant impact on real estate values, and so this data is incredibly helpful for property investors. It can give you an idea of the potential costs and resale value a property in any area might have, whether you’re looking for a small purchase to get yourself on the ladder or a substantial multi-family home investment. One major bonus of the Statistical Atlas is that it’s absolutely free to use.
Google Street View
This is another free big data tool that can be surprisingly helpful when looking to analyze the surrounding area of a property you’re looking to invest in. First launched in 2007, Google Street View offers users a first-person perspective of nearly every street in the US. Use Google Street View to assess a property’s proximity to local amenities, attractions, road and highway access, and the overall feel and look of the neighborhood.
Dealcheck
Dealcheck is another great data analytics tool that is worth trying out. Let’s say you’ve found a property you like the look of. You’ve checked the Statistical Atlas and Google Street View and it looks promising; on the surface this would appear to be a perfect investment opportunity. But, how can you be sure? Software solutions like Dealcheck allow you to calculate property metrics and draw up detailed insights into a property’s worth and potential resale value by looking at various types of data, with options to generate long-term cash-flow and profit projections.
What’s more, Dealcheck can also be used to search for viable properties for future investment. It has one of the best data mining tools in the industry. By inputting a set of customizable parameters and criteria, you can screen properties within set areas to find investments that meet your requirements.
Zilculator
For investors looking to take their market analysis approach to the next level, Zilculator can be used to provide even more detailed insights and metrics for investors, real estate agents, and property developers. It offers a deal structuring service to assist buyers, while its comparison feature can allow you to assess the value of a potential deal. Zilculator compiles reports on properties with easily accessible data points that can be shared with other buyers and investors to streamline and speed up the sales process.
Data Analytics Tools Are Invaluable for Real Estate Investors and Buyers
More real estate investors and professionals are taking advantage of big data tools. The property market is proving to be a more and more attractive prospect for would be investors. With a highly variable and ever-changing landscape, many investors find real estate to be a more exciting, rewarding, and ultimately lucrative pursuit than they would find in traditional investment opportunities. Despite this, the property market can be extremely unpredictable, to ensure your investments and finances are secure you should strive to conduct effective market analysis using the range of tools available to do so. The right data analytics tools can be very valuable.
When it comes to the adoption of artificial intelligence (AI), we have reached a tipping point. Technologies that were once accessible to only a few are now broadly available. This has led to an explosion in AI investment. However, according to research firm McKinsey, for AI to make a sizable contribution to a company’s bottom line, they “must scale the technology across the organization, infusing it in core business processes” — and based on conversations with our customers, we couldn’t agree more.
While investments in pure data science continue to be essential for many, widespread adoption of AI increasingly involves a category of applications and services that we call AI Agents. These are technologies that let customers apply the best of AI to common business challenges, with limited technical expertise required by employees, and include Google Cloud products like Document AI and Contact Center AI. Today, at Google Cloud Next ‘22, we’re announcing new features to our existing AI Agents and a brand new one with Translation Hub.
“AI is becoming a key investment for many companies’ long term success. However, most companies are still in the experimental phases with AI and haven’t fully put the technology into production because of long deployment timelines, IT staffing needs, and more,” said Ritu Jyoti, group vice president, worldwide AI and automation research practice global AI research lead, at IDC. “Organizations need AI products that can be immediately applied to automate processes and solve business problems. Google Cloud is answering this problem by providing fully managed, scalable AI Agents that can be deployed fast and deliver immediate results.”
Translation Hub: An enterprise-scale translation AI Agent
At I/O this year, we announced the addition of 24 new languages to Google Translate to allow consumers in more locations, especially those whose languages aren’t represented in most technology, to help reduce communication barriers through the power of translation. Businesses strive for the same goals, but unfortunately it is often out of reach due to the high costs that come with scaling translation.
That’s why today, we are announcing Translation Hub, our AI Agent that provides customers with self-service document translation. With 135 languages, Translation Hub can create impactful, inclusive, and cost-effective global communications in a few clicks.
With Translation Hub, now researchers are able to share their findings instantly across the world, goods and services providers can reach underserved markets, and public sector administrators can reach more members of their communities in a language they understand — all of which ultimately help make for a more connected, inclusive world.
Translation Hub brings together Google Cloud AI technology, like Neural Machine Translation and AutoML, to help make it easy to ingest and translate content from the most common enterprise document types, including Google Docs and Slides, PDFs, and Microsoft Word. It not only preserves layouts and formatting, but also provides granular management controls such as support for post-editing human-in-the-loop feedback and document review.
“In just three months of using Translation Hub and AutoML translation models, we saw our translated page count go up by 700% and translation cost reduced by 90%,” said Murali Nathan, digital innovation and employee experience lead, at materials science company Avery Dennison. “Beyond numbers, Google’s enterprise translation technology is driving a feeling of inclusion among our employees. Every Avery Dennison employee has access to on-demand, general purpose, and company-specific translations. English language fluency is no longer a barrier, and our employees are beginning to broadly express themselves right in their native language.”
Document AI: A document processing AI Agent to automate workflows
Every organization needs to process documents, understand their content, and make them available to the appropriate people. Whether it’s during procurement cycles involving invoices and receipts, contract processes to close deals, or for general increases in efficiency, Document AI simplifies and automates various document processing. With two new features launching today, Document AI can allow employees to focus on higher impact tasks and better serve their own customers.
For example, payments provider Libeo used Document AI to uptrain an invoice parser with 1,600 documents and increase its testing accuracy from 75.6% to 83.9%. “Thanks to uptraining, the Document AI results now beat the results of a competitor and will help Libeo save ~20% on the overall cost for model training over the long run,” said Libeo chief technology officer, Pierre-Antoine Glandier.
Today, we’re announcing these new features to our existing Document AI Agent:
Document AI Workbench can remove the barriers around building custom document parsers, helping organizations extract fields of interest that are specific to their business needs. Relative to more traditional development approaches, it requires less training data and offers a simple interface for both labeling data and one-click model training.
Document AI Warehouse can eliminate the challenges that many enterprises face when tagging and extracting data in documents by bringing Google’s Search technologies to Document AI. This feature can make it simpler and easier to search for and manage documents like workflow controls to accommodate invoice processing, contracts, approvals, and custom workflows.
Contact Center AI: A contact center AI Agent to improve customer experiences
Scaling call center support can be expensive and difficult, especially when implementing AI technologies to support representatives. Contact Center AI is an AI Agent for virtually all contact center needs, from intelligently routing customers, to facilitating handoffs between virtual and human customer support representatives, to analyzing call center transcripts for trends and much more.
Just days ago, we announced that Contact Center AI Platform is now generally available to provide additional deployment choice and flexibility. With this addition to Contact Center AI, we are furthering our commitment to providing an AI Agent that can assist organizations to quickly scale their contact centers to improve customer experiences and create value via data-driven decisions.
Dean Kontul, division chief information officer at KeyBank, had this to say about powering their contact center with Contact Center AI from Google Cloud: “With Google Cloud and Contact Center AI, we will quickly move our contact center to the Cloud, supporting both our customers and agents with industry-leading customer experience innovations, all while streamlining operations through more efficient customer care operations.”
Start delivering business results with AI Agents, today!
If you’re ready to get started with Translation Hub, this Next ‘22 session has the details, including a deeper dive into Avery Dennison’s use of the AI Agent.
Data is the most valuable asset in any digital transformation. Yet limits on data are still too common, and prevent organizations from taking important steps forward — like launching a new digital business, understanding changes in consumer behavior, or even utilizing data to combat public health crises. Data complexity is at an all time high and as data volumes grow, data is becoming distributed across clouds, used in more workloads and accessed by more people than ever before. Only an open data cloud ecosystem can unlock the full potential of data and remove the barriers to digital transformation.
Already, more than 800 software companies are building their products using Google’s Data Cloud, and more than 40 data platform partners offer validated integrations through our Google Cloud Ready – BigQuery initiative. Earlier this year we launched the Data Cloud Alliance, now supported by 17 leaders in data working together to promote open standards and interoperability between popular data applications.
This week at Next, we’re announcing significant steps to our mission to provide the most open and extensible Data Cloud — that helps ensure customers can utilize all their data, from all sources, in all storage formats and styles of analysis, across all cloud providers and platforms of their choice. These include:
Launching a new capability to analyze unstructured and streaming data in BigQuery.
Adding support for major data formats in the industry, including Apache Iceberg, and the upcoming support for Linux Foundation Delta Lake, Apache Hudi.
A new integrated experience in BigQuery for Apache Spark.
Expanding the capabilities of Dataplex for automated data quality and data lineage to help ensure customers have greater confidence in their data.
Unifying our business intelligence portfolio under the Looker umbrella to begin creating a deep integration of Looker, Data Studio, and core Google technologies like AI and machine learning (ML).
Launching Vertex AI Vision, a new service that can make powerful computer vision and image recognition AI more accessible to data practitioners.
Expanding our integrations with many of the most popular enterprise data platforms, including Collibra, Elastic, MongoDB, Palantir Foundry, and ServiceNow to help remove barriers between data and give customers more choice and prevent data lock-in.
You can read more about each of these exciting updates below.
Unifying data, across source systems, with major formats
We believe that a data cloud should allow people to work with all kinds of data, no matter its storage format or location. To do this, we’re adding several exciting new capabilities to Google’s Data Cloud.
First, we’re adding support for unstructured data in BigQuery to help significantly expand the ability for people to work with all types of data. Most commonly, data teams have worked with structured data, using BigQuery to analyze data from operational databases and SaaS applications like Adobe, SAP, ServiceNow, and Workday as well as semi-structured data such as JSON log files.
But this represents a small portion of an organization’s information. Unstructured data may account for up to 90 percent of all data today, like video from television archives, audio from call centers and radio, and documents of many formats. Beginning now, data teams can manage, secure, and analyze structured and unstructured data in BigQuery, with easy access to many of Google Cloud’s capabilities in ML, speech recognition, computer vision, translation, and text processing, using BigQuery’s familiar SQL interface.
Second, we’re adding support for major data formats in use today.Our storage engine, BigLake, adds support for Apache Iceberg and support for Linux Foundation Delta Lake, and Apache Hudi will be added soon. By supporting these widely adopted data formats, we can help organizations gain the full value from their data faster.
“Google Cloud’s support for Delta is a testament to the demand for an open, multicloud lakehouse that gives customers the flexibility to leverage all of their data, regardless of where it resides,” said David Meyer, senior vice president of products at Databricks. “This partnership further exemplifies our joint commitment to open data sharing and the advancement of open standards like Delta Lake that make data more accessible, portable, and collaborative across teams and organizations.”
Third, we’re announcing a new integrated experience in BigQuery for Apache Spark, a leading open-source analytics engine for large-scale data processing. This new Spark integration, launching in preview today, allows data practitioners to create procedures in BigQuery, using Apache Spark, that integrate with their SQL pipelines. Organizations like Walmart use Google Cloud to improve Spark processing times by 23% and have reduced time to close financial books from five days to three.
In addition, we’ve launched Datastream for BigQuery which will help organizations more effectively replicate data in real-time, from sources including AlloyDB, PostgreSQL, MySQL and third-party databases like Oracle — directly into BigQuery. By helping to accelerate the ability to bring data from an array of sources into BigQuery, we enable you to get more insights from your data in real time. To learn more about these announcements read our dedicated post about key innovations with Google Databases.
Finally, a data cloud should enable organizations to manage, secure, and observe their data, which helps ensure their data is high quality and enable strong, flexible data management, and governance capabilities. To address data management, we’re announcing updates to Dataplex that will automate common processes associated with data quality. For instance, users will now be able to easily understand data lineage — where data originates and how it has transformed and moved over time — which can reduce the need for manual, time consuming processes.
The ability to let our customers work with all kinds of data, in the formats they choose, is the hallmark of an open data cloud. We’re committed to delivering the support and integrations that customers need to remove limits from their data and avoid data lock-in across clouds.
Supporting all styles of analysis and empowering analysts with AI
More than 10 million users access Google Cloud’s business intelligence solutions each month, including Looker and Google Data Studio. Now, we’re unifying these two popular tools under the Looker umbrella to start creating a deep integration of Looker, Data Studio, and core Google technologies like AI and ML. As part of that unification, Data Studio is now Looker Studio. This solution will help you go beyond dashboards and infuse your workflows and applications with the intelligence needed to help make data-driven decisions. To learn more about the next evolution of Looker and business intelligence, please read our dedicated post on Looker’s future.
We’re committed to enabling our customers to work with the business intelligence tools of their choice. We’ve already announced integrations between Looker and Tableau, and today we’re announcing enhancements for Looker and BigQuery with Microsoft Power BI — another significant step forward in providing customers with the most open data cloud. This means that Tableau and Microsoft customers can easily analyze trusted data from Looker and simply connect with BigQuery.
Increasingly, AI and ML are becoming important tools for modeling and managing data – particularly as organizations find ways to put these capabilities in the hands of users. Already, Vertex AI helps you get value from data more quickly by simplifying data access and ingestion, enabling model orchestration, and deploying ML models into production.
We are now releasing Vertex AI Vision to extend the capabilities of Vertex AI to be more accessible to data practitioners and developers. This new end-to-end application development environment helps you ingest, analyze, and store visual data: streaming video in manufacturing plants, for example, to help ensure safety, or streams from store shelves to improve inventory analysis, or following traffic lights for management of busy intersections. Vertex AI Vision allows you to easily build and deploy computer vision applications to understand and utilize this data.
Vertex AI Vision can reduce the time to create computer vision applications from weeks to hours at one-tenth the cost of current offerings. To help you achieve these efficiencies, Vertex AI Vision provides an easy-to-use, drag-and-drop interface and a library of pre-trained ML models for common tasks such as occupancy counting, product recognition, and object detection. It also provides the option to import your existing AutoML or custom ML models, from Vertex AI, into your Vertex AI Vision applications. As always, our new AI products also adhere to our AI Principles.
Plainsight, a leading provider of computer vision solutions, is using Google Cloud to increase speed and cost efficiency. “Vertex AI Vision is changing the game for use cases that for us were previously not viable at scale,” Elizabeth Spears, co-founder and chief product officer at Plainsight, said. “The ability to run computer vision models on streaming video with up to a 100-times cost reduction for Plainsight is creating entirely new business opportunities for our customers.”
Supporting an open data ecosystem
Giving customers flexibility to work across the data platforms of their choice is critical to prevent data lock-in. To keep Google’s data cloud open, we’re committed to partnering with major open data platforms, including companies like Collibra, Databricks, Elastic, Fivetran, MongoDB, Sisu Data, Reltio, Striim, and many others to ensure that help our joint customers can use these products with Google’s data cloud. We’re also working with the 17 members of the Data Cloud Alliance to promote open standards and interoperability in the data industry, and continuing our support for open-source database engines like MongoDB, MySQL, PostgreSQL, and Redis, in addition to Google Cloud databases like AlloyDB for PostgreSQL, Cloud Bigtable, Firestore, and Cloud Spanner.
At Next, we’re announcing important new updates and integrations with several of these partners, to help you more easily move data between the platforms of your choice and bring more of Google’s data cloud capabilities to partner platforms.
Collibra will integrate with Dataplex to help customers more easily discover data in business context, understand data lineage, and apply consistent controls on data stored across major clouds and on-premises environments.
Elasticis bringing its Elasticsearch capabilities to Google’s data cloud, giving customers the ability to federate their search queries to their data lakes on Google Cloud. This expands upon the existing integration already available to directly ingest data from BigQuery into Elastic for search use cases. We’re also extending Looker support to the Elastic platform, which can easily embed search insights into data-driven applications.
MongoDB is launching new templates to significantly help accelerate customers’ ability to move data between Atlas and BigQuery. This will also open up new use cases for customers to apply Google Cloud AI and ML capabilities to MongoDB using Vertex AI.
Palantir is certifying BigQuery as an engine for Foundry Ontology which connects underlying data models to business objects, predictive models, and actions which can enable customers to turn data into intelligent operations.
ServiceNow plans to work with mutual customers and build use case specific integrations with BigQuery to help customers aggregate diverse, external data with data residing in their ServiceNow instance. The integration will help customers create greater insights and value from data residing in the ServiceNow instance, like IT service management data, customer service records, or order management data and move data to BigQuery where the customers can use Google’s analytics capabilities to process and analyze data from these multiple sources.
Sisu Data will collaborate with Google Cloud’s business intelligence solutions to help automate finding root causes 80% faster than traditional approaches to provide augmented analytics for more customers.
Reltio’s integration with BigQuery can improve the customer experience by consolidating, cleansing, and enriching data in real-time with master data management capabilities, and then enable intelligent action with Vertex AI.
Striim’s managed service for BigQuery can reduce time to insight, allowing customers to replicate data from a variety of operational sources with automatic schema creation, coordinated initial load, and built-in parallel processing for sub-second latency. With faster insights can come faster decision-making across the organization.
As consumers, we all benefit from unprecedented access to data in everything we do, from finding answers on the web to navigating a new city to picking the best place to eat dinner. But at work it’s not that easy. Instead of having answers to questions at our fingertips, getting those answers is a costly IT project away—and when we get the answers, they only raise new questions that you then need to get back into the IT queue to answer.
Just as Google’s mission is to organize the world’s information and make it universally accessible and useful, Looker aims to do the same for your business data, making it easy for users to get insights, and for you to build insight-powered applications. That vision is our north star for business intelligence at Google Cloud, which is why we acquired Looker in 2020, and why we have big plans for the next few years.
Today, we are unifying our business intelligence product family under the Looker umbrella. Looker is the name you’ll hear us use when talking about our Google Cloud business intelligence products, as we bring together Looker, Data Studio, and core Google technologies like artificial intelligence (AI) and machine learning (ML). And starting today, Data Studio is now Looker Studio. With this complete enterprise business intelligence suite, we will help you go beyond dashboards and infuse your workflows and applications with the intelligence needed to help make data-driven decisions.
Expanding the power and reach of Looker
Looker Studio helps make it easy for everyone to do self-service analytics. It currently supports more than 800 data sources with a catalog surpassing 600 connectors, making it simple to explore data from different sources with just a few clicks, and without ever needing IT. In addition to the functionality customers already know and love, Looker Studio plans to evolve to include a complete user interface for working with data modeled in Looker.
As a first step on this journey, we are happy to announce that access to Looker data models from Looker Studio is available in preview today. This capability allows customers to explore trusted data via the Looker modeling layer. For the first time, customers can easily combine both self-service analytics from ad-hoc data sources and trusted data that has already been vetted and modeled in Looker.
We love what our users have accomplished with Looker Studio, from tracking NBA MVP Award votes and the location of the International Space Station, to breaking down the pumpkin spice economy and the video gaming industry. To support these vast and diverse use cases, we will continue to make Looker Studio available at no cost. At the same time, many of our customers require enterprise-focused features to use Looker Studio as part of their data stack while also addressing their governance and compliance requirements. To meet this customer demand, we are pleased to announce the availability of Looker Studio Pro.
Customers who upgrade to Looker Studio Pro will get new enterprise management features, team collaboration capabilities, and SLAs. This is only the first release, and we’ve developed a roadmap of capabilities, starting with Dataplex integration for data lineage and metadata visibility, that our enterprise customers have been asking for.
When Looker joined Google Cloud, one of the primary goals was to bring business intelligence closer to core Google Cloud technologies. As a major step on that journey, we are pleased to announce Looker (Google Cloud core) in preview today. This new version of Looker will be available in the Google Cloud Console and is deeply integrated with core cloud infrastructure services, such as key security and management services.
Open data means open business intelligence, too
Insights everywhere doesn’t just mean everywhere in Looker. Our customers want an open data cloud that breaks down silos, whether that’s analytics, business intelligence, or machine learning. Just as we’ve already done with our BigQuery data warehouse, we are committed to integrating Looker with as many Google and partner products as our customers need.
For example, we are deeply integrating Looker and Google Workspace so insights are available in the familiar productivity tools you use every day. This will provide easy access, via spreadsheets and other documents, to consistent, trusted answers from curated data sources across your organization. Looker integration with Google Sheets is in preview now, with plans for full release in the first half of 2023.
To continue to meet our customers where they are in their open data cloud journey, we are working to connect other popular business intelligence offerings as well. That connection can allow visualization tools, including Tableau and Power BI, to easily analyze trusted data from Looker. Additionally, Looker continues to be an open platform, and we are expanding our partnerships from the modeling layer right into the user experience.
To demonstrate that, we are also pleased to announce a new partnership with Sisu Data. Often, your data will tell you when something unusual has happened, but finding out why can take hours or days, even for skilled data scientists. Our partnership with Sisu Data will help automate finding root causes 80% faster than traditional approaches. This deep integration will enable a smooth experience for more customers.
Real data, real intelligence, real impact
Our customers use Looker for both internal business intelligence and to create embedded data products. Mercado Libre, a retailer in Latin America with a geographically diverse workforce, uses Looker and BigQuery to enable accelerated fulfillment. In the first half of 2022 alone, they were able to deliver 79% of shipments in less than 48 hours.
Another customer, Wpromote, is one of Adweek’s fastest growing digital marketing agencies. They rebuilt the infrastructure they use to run every aspect of their business using Looker and BigQuery. “We wanted to build a system with no ceiling, where the only limitation would be our vision and our ability to execute,” Wpromote Chief Technology Officer Paul Dumais said. “Everything that you can do from the Looker UI can be done via API.”
So much of our everyday lives revolve around data, and that’s especially true when it comes to the way we work. Data provides us with a wealth of knowledge, but without the right tools, sometimes it can be overwhelming, expensive, and can take far too long. The new, unified Looker product family helps you make the most of your data, delivering a better experience for your teams and, ultimately, a better experience for your customers — today and long into the future.
From creating world-famous games like Tertis to being recognized by Google and Facebook with some of the most prestigious programmer awards, Russian developers have come a long way to becoming global leaders in programming.
The country is known for its large pool of skilled and expert programmers who are capable of accomplishing the most challenging tasks. All thanks to the country’s immense focus on promoting Science and Technology, Russia is known for its top-notch programming and IT services all around the world.
Naturally, the scope of the IT service sector is tremendously increasing in Russia, and so is its contribution to the country’s economy. The following section will throw light on the increasing scope and potential of Russia’s IT service sector in detail.
Have you ever wondered how cloud technology is changing the scope of the software development profession? There are clearly many benefits of the cloud, but they are especially significant in the computer science profession. Software engineers are obviously highly involved in the development of new cloud applications. However, they also use the cloud to do their jobs more effectively.
A number of Russian programmers are using cloud technology to expand their reach. As a result, they have become some of the most desired developers in the world.
How is the Cloud Allowing Russian Developers to Grow their Reach?
There are a number of ways that cloud technology helps programmers around the world do their job more effectively. Russian developers are no exception.
The cloud has helped Russian software engineers and programmers work remotely, which allows them to find clients in any part of the world. A growing number of cloud-based apps allow developers to work remotely. Microsoft Teams, Skype and Trello are just a few of the cloud tools that they use to connect with people all over the world.
Cloud technology has boosted the satisfaction of employees all over the world. One survey showed that 75% of developers are happiest working from home, which would not have been possible without modern advances in the cloud. However, the opportunity to connect with clients and employers around the globe is arguably an even bigger benefit. This is one of the biggest benefits for Russian developers.
The cloud also helps Russian developers store and access data more easily. This is also important, because data-driven software development is the future of the software engineering profession.
Russian Developers Become Some of the World’s Most Coveted Experts
At present, Russia is the global leader in the tech sector. With more than 400,000 programmers, the country has excelled in its IT services for a long time to become the top choice for entrepreneurs looking to build digital products for their businesses. None of this would have been possible without advances in cloud technology.
Russia has been persistently focusing on STEM (Science, Technology, Engineering, and Mathematics) education to boost its IT service sector and create an unparallel position in the global IT service market.
The programmers in Russia are well-versed with the latest technologies and tech advancements and utilize their skills to curate top-class digital products. As a result, the global demand for Russian programmers has been on a surge lately.
Apart from high-tech expertise, there are numerous other factors that make Russian programmers prominent players in the IT service sector. The following section will thoroughly discuss how Russian programmers are changing the paradigm of the tech industry with their top-notch skills.
6 Reasons to Choose Russian Programmers
Low Development Rate
Programmers in Russia are known to have some of the lowest development rates across the globe. To explain, Russian developers generally charge $20-$30 per hour for their services. On the other hand, the development rates in countries like the USA and Canada can be as high as $150-$2000 per hour.
As a result, choosing Russian programmers makes it economically viable for businesses to create a digital platform for their business. Moreover, choosing Russian programmers is the most optimal option for businesses working on a limited budget. These programmers ensure that you receive high-quality results, while also maintaining the economic viability of the project.
High Technology Expertise
Earlier, we discussed how Russia is persistently focusing on promoting STEM education. Resulting from this, the programmers from this country hold tremendous technology expertise, a factor that is essential for the optimum quality of a digital product.
The developers from Russia are known to be programming-smart and are highly qualified to work on your project. Moreover, the programmers are familiar with consumer trends and ongoing technology trends, which is an important factor in ensuring that the digital product is at par with the market’s needs.
As a result, businesses that hire Russian developers to work on their projects benefit from a seamless development process and are certain to get top-tier results.
Ease in Communication
Seamless communication is essential for efficient and timely project completion. However, choosing programmers from a different country often results in a communication gap, primarily due to the language barrier.
On the contrary, choosing a developer from Russia reduces the communication gap to a great extent. This is because the Russian programmers have realized the scope of international business and have made themself familiar with some of the most prominent languages across the globe to grab these business opportunities.
Today, most Russian programmers are fluent in major foreign languages, especially English. As a result, hiring Russian developers ensures seamless and hurdle-free communication throughout the development lifecycle.
Flexible Working Hours
Businesses that hire development teams from other countries are often concerned about time zone differences. This is because working in different time zones makes it challenging between the business owner and the developer to communicate with each other. This is especially the case for businesses choosing developers from countries with totally opposite time zones.
However, you must know that Russian programmers have efficiently overcome this challenge. Today, most Russian programmers work flexible hours, and in many cases, even in their client’s time zone. This allows the businesses to easily communicate with the development team, further increasing the efficiency of the development process.
International Trust
Russia is a globally recognized location for its excellent-quality outsourcing services. You must know that the IAOP (International Association of Outsourcing Professionals) published a list of the top 100 outsourcing agencies in 2021, most of which were located in Russia.
Moreover, in the past few years, the Russian IT service sector has grown tremendously and has become a global leader in the international IT service market. Today, a large number of businesses are choosing Russia-based programmers to build their digital platforms, making Russia a global hub for the IT service industry.
Diverse Programmer Community
We mentioned before that Russia is one of the biggest IT hubs in the world. Programmers from Russia are known for their top-of-the-class skills and vast expertise in different technologies, languages, and frameworks.
There is a vast pool of talent in Russia, and therefore, choosing programmers from this country is an excellent idea. To explain, Stack Overflow, which is a popular portal among the programmer’s community, is commonly used to ask complex programming questions. Here, Russian programmers are known to have a solution to the most difficult challenges, exemplifying their programming skills and expertise.
Now that you are aware of the benefits of hiring Russian programmers for your project, you must be wondering, what are the ways to hire Russian programmers, right? The upcoming section has covered this topic in detail.
How to Benefit from Russian Developer’s Expertise?
In order to hire Russian programmers, you have to outsource your design and development requirements to either a Russia-based freelancer or a development agency. Either way, you must hire an external programmer/ team of programmers to work on your project.
Nowadays, businesses outsource development for websites, mobile apps, software, and numerous other technologies. Moving further, we will thoroughly discuss what exactly it means to hire a Russian freelancer and a development agency, and their impact on the project.
Hire Russian Freelance Developers
Freelancers are remote workers that are hired to work on project-specific requirements. Businesses usually hire freelance programmers from Russia to cut down on the overall cost of product development.
However, it is commonly known that hiring freelance programmers, even though it can reduce the project’s financial investment, can largely impact the end quality of the platform. Freelancers often focus on quantitative work over qualitative work, and therefore, hiring them can hinder the overall objective of developing a digital platform.
Moreover, it is commonly known that freelancers often take on multiple projects at once, and therefore, aim to finish the assigned task as early as possible, further resulting in compromised platform quality. Therefore, even though the initial investment in hiring a freelancer/ team of freelance programmers is low, you will greatly compromise on your platform’s efficiency, performance, and, most importantly, quality.
Hire Russian Development Agencies
Businesses hire development outsourcing agencies to get top-quality digital platforms within less time and cost investment. These development agencies have a complete team of project managers, designers, programmers, testing engineers, and business analysts to work on your business requirements.
As a result, hiring a development agency ensures that your project receives optimum attention and undergoes a complete development lifecycle. Moreover, it is worth noting that outsourcing your design and development requirements to an agency will guarantee that each phase of the development lifecycle will be handled by a qualified and experienced expert in that field.
Overall, outsourcing development to Russian agencies ensures that your digital product is developed within less time and cost investment and with optimal quality. This makes outsourcing to a development agency the most optimum option for digital platform development.
Moreover, most businesses looking to outsource design and development prefer to outsource their requirements to an agency over a freelancer. Regardless of whether you want to hire a freelancer or an agency, you will need to follow a systematic process to find the right development partner for your project. The upcoming section will cover these steps in detail and will provide clarity on this topic.
6-Step Process to Hire Russian Developers
Step 1: Search for Development Partner
Depending upon whether you are looking to hire a freelancer or an agency, you can use digital platforms to search for relevant digital partners.
In case you are looking to hire freelance programmers from Russia, you can use portals like Freelancer, Fiverr, and Upwork and get a list of freelance programmers from the location of your choice. These platforms are highly competitive, and therefore, you can find Russian freelance programmers at a low price here.
On the other hand, if you are looking to hire a Russian design and development agency, you can use platforms like Clutch and GoodFirms to look for relevant agencies. All you need to do is visit these websites and search for services and locations, and you will get a list of all the agencies offering the services you are looking for.
Alternatively, you can also use search engines like Google to look for freelancers and agencies. All you need to do is fire relevant search queries, like ‘top freelance programmers in Russia’ or ‘leading design and development company in Russia’, and you are good to go.
Step 2: Check Profile
Once you have made a list of all the leading freelance programmers/ development agencies, you can review their profiles and check their experience. Generally, businesses look for factors like portfolios, client testimonials, and services offered to determine whether or not the development partner is eligible for the role.
You must know that if you are looking to hire freelance programmers, you will have to request them to share their portfolios. However, for development agencies, you can simply visit their website and get all the information you need in one place.
Moreover, it is commonly known that the information provided by agencies is more reliable and authentic, which may or may not be the case with freelancers.
Step 3: Shortlist Developers
Once you have reviewed the portfolios, client testimonials, and services and have received other necessary information on the potential development partners (freelancer/ agency), you must shortlist the development partner based on their capabilities.
This step ensures that all the under-qualified development partners are eliminated from your list. Once you have shortlisted the potential development partners, you will get the best of the best developers to outsource your project’s development.
Step 4: Send Requirements
Once you have shortlisted the agencies/ freelancers, you will be left with some of the best development partners to choose from. Here, you have to send your requirements to these development partners and get their input on your projects. Generally, the agencies/ freelancers provide a price quotation and estimated development timeline at this point, along with their ideas for the project.
Signing a mutual NDA is a good idea at this stage to maintain the integrity of your business idea. This way, you can ensure that your idea’s sole rights remain with you and are not being copied.
Step 5: Receive Feedback & Select a Development Partner
In this stage, you must review the proposals given by agencies and freelancers in the previous stage and choose one development partner.
Generally speaking, businesses consider development cost as the key factor in determining whether or not to choose a development partner. However, let us tell you that the ROI in digital product development is high. Therefore, even though the initial investment might seem to be high for your budget, you are likely to recover the investment in a short time span.
Moreover, if you want to cut down on time and cost investment, you can go for MVP development and launch a digital platform with just the essential features. This idea is also great for testing the waters in the market before investing in a full-scale platform.
Step 6: Finalize Contract
Once you have reviewed the feedback, you have to finalize and select one development partner and close the deal. Usually, the development agencies/ freelancers and business owners sign a contract here mentioning the terms and conditions of the project, along with numerous other factors.
This ensures a smooth and seamless development process. Once you have signed the contract, the development partner will immediately start working on the project.
This was it for the steps of hiring a development partner from Russia. Regardless of whether you want to outsource your development requirements to Russia or any other country, you can follow the same process and find the top programmers in the country.
Cloud Technology Has Helped Russia Gain Prominence in the Software Development Sector
Russia is a rising technology hub and is a globally renowned destination to outsource digital platform design and development. Many of these benefits can be attributed to advances in cloud technology. Owing to the high-tech expertise of Russian programmers and the country’s focus on developing its IT service sector, more and more businesses are choosing Russia to outsource their digital product development requirements. They can use cloud-based tools to connect with clients more easily than ever.
By understanding the different outsourcing approaches and following a systematic and well-defined selection process, businesses can easily find the right development team, benefit from Russian programmers’ tech expertise, and get a high-end product for their business.
Google Cloud Next kicks off tomorrow, and we’ve prepared a wealth of content — keynotes, customer panels, technical breakout sessions — designed for data professionals. If you haven’t already, now is the perfect time to register, and build out your schedule. Here’s a sampling of data-focused breakout sessions:
Join this session to learn how Google’s Data Cloud can transform your decision making and turn data into action by operationalizing Data Analytics and AI. Google Cloud brings together Google’s most advanced Data and AI technology to help you train, deploy, and manage ML faster at scale. You will learn about the latest product innovations for BigQuery and Vertex AI to bring intelligence everywhere to analyze and activate your data. You will also hear from industry leading organizations who have realized tangible value with data analytics and AI using Google Cloud.
Organizations are facing increased pressure to deliver new, transformative user experiences in an always-on, global economy. Learn how Google’s data cloud unifies your data across analytical and transactional systems for increased agility and simplicity. You’ll also hear about the latest product innovations across Spanner, AlloyDB, Cloud SQL and BigQuery.
In the new digital-first era, data analytics continues to be at the core of driving differentiation and innovation for businesses. In this session, you’ll learn how BigQuery is fueling transformations and helping organizations build data ecosystems. You’ll hear about the latest product announcements, upcoming innovations, and strategic roadmap.
Business intelligence (BI) is more than dashboards and reports, and we make it easy to deliver insights to your users and customers in the places where it’ll make the most difference. In this session, we’ll discuss the future of our BI products, as well as go through recent launches and the roadmap for Looker and Google Data Studio. Hear how you can use both products — today and in the future — to get insights from your data, including self-service visualization, modeling of data, and embedded analytics.
Data often ends up in distributed environments like on-premises data centers and cloud service providers, making it incredibly difficult to get 360-degree business insights. In this session, we’ll share how organizations can get a complete view of their data across environments through a single pane of glass without building huge data pipelines. You’ll learn directly from Accenture and L’Oréal about their cross-cloud analytics journeys and how they overcame challenges like data silos and duplication.
Learn how leading aerospace company Boeing transformed its data operations by migrating hundreds of applications across multiple business groups and aerospace products to Google Cloud. This session will explore the use of data analytics, AI, and machine learning to design a data operating system that addresses the complexity and challenges of traditional on-premises implementations to take advantage of the scalability and flexibility of the cloud.
Open source is no longer a separate corner of the data infrastructure. Instead, it needs to be integrated into the rest of your data platform. Join this session to learn how Walmart uses data to drive innovation and has built one of the largest hybrid clouds in the world, leveraging the best of cloud-native and open source technologies. Hear from Anil Madan, Corporate Vice President of Data Platform at Walmart, about the key principles behind their platform architecture and his advice to others looking to undertake a similar journey.
Build your data playlist today
One of the coolest things about the Next ‘22 website is the ability to create your own playlist, and share it with people. To explore the full catalog of breakout sessions and labs designed for data scientists and engineers, check out the Analyze and Design tracks in the Next ‘22 Catalog.
Google Cloud Next starts this week, and features over a dozen sessions dedicated to helping organizations innovate with machine learning (ML) and inject artificial intelligence (AI) into their workflows. Whether you’re a data scientist looking for cutting-edge ML tools, a developer aiming to more easily build AI-powered apps, or a non-technical worker who wants to leverage AI for greater productivity, here are some can’t miss AI and ML sessions to add to your playlist.
Developing ML models faster and turning data into action
For data scientists and ML experts, we’re offering a variety of sessions to help accelerate the training and deployment of models to production, as well to bridge the gap between data and AI. Top sessions include:
Join this session to learn how Google Cloud can help your organization turn data into action, including overviews of the latest announcements and best practices for BigQuery and Vertex AI.
What does the end-to-end journey from raw data to AI look like on Google Cloud? In this session, learn how Vertex AI can help you decrease time to production, track data lineage, catalog ML models for production, support governance, and more — including step-by-step instructions for integrating your data warehouse, modeling, and MLOps with BigQuery and Vertex AI.
Google Cloud’s BigQuery ML accelerates the data-to-AI journey by letting practitioners build and execute ML models using standard SQL queries. Join this session to learn about the latest BigQuery ML innovations and how to apply them.
Building AI-powered apps
Developers building AI into their apps will also find lots to love, including the following:
Accurately classifying content at scale across domains and languages ranks among the most challenging natural language problems. Featuring Erik Bursch, Senior Vice President of Digital Consumer Product and Engineering at Gannett, this session explores how Google Cloud can help identify content for ad targeting, taxonomize product listings, serve the most relevant content, and generate actionable insights.
Featuring Ryan Wheeler, Vice President of Machine Learning at Toyota Connected North American, this session dives into the ways organizations use Google’s automatic speech recognition (ASR) and speech synthesis products to unlock new use cases and interfaces.
Applying AI to core business processes
Employees without technical expertise are innovating with AI and ML as well, infusing it into business processes so they can get more done. To learn more, be sure to check out these sessions:
An estimated 500 billion words are translated daily but most translation processes for enterprises are manual, time-consuming, and expensive. Join this session — featuring Murali Nathan, Senior Director, Digital Experience and Transformation at Avery Dennison — to find out how Google Cloud’s AI-powered translation services are addressing these challenges, helping businesses to drive more inclusive consumer experiences, save millions of dollars, and localize messages across the world in minutes.
Google’s Document AI family of solutions help organizations capture data at scale by extracting structured data from unstructured documents, reducing processing costs and improving business speed and efficiency. Featuring Andreas Vollmer, Managing Director, Head of Document Lifecycle at Commerzbank, this session investigates how Google is expanding the capabilities of our Document AI suite to solve document workflow challenges.
Google Cloud Contact Center AI brings the power of AI to large contact centers, helping them to deliver world-class customer experiences while reducing costs. Join this session — featuring Stephen Chauvin, Business Technology Executive, Voice & Chat Automation/Contact Center Delivery at KeyBank — to learn about the newest Contact Center AI capabilities and how they can help your business.
Companies have been urged to shift their IT assets over to the cloud for years now, and there are lots of benefits that come with taking the leap.
However, it’s also wise to be aware of the struggles that are also involved, so let’s look at the main problems that could come your way during cloud adoption.
Security
The benefit of keeping mission-critical data on-site is that it puts you in the driving seat when it comes to security.
Switching to the cloud means giving a third-party provider responsibility for sensitive info, and also for handling matters like compliance.
This is why a hybrid cloud setup is often preferred, giving you a way to leverage the cloud while still maintaining the required levels of security.
Cost
It’s possible to make savings by migrating to the cloud, but there are upfront expenses to bear as well. These come from the need to train employees in the use of your new resources, the need to upgrade network connectivity to encompass the increased traffic, and the ongoing expense of subscription to third-party services.
The interoperability of different hardware and software configurations has always been a talking point, and in the cloud you can’t just assume that every tool, platform, and service will be compatible with the rest of your stack.
Cloud migration can make your IT setup more complex, especially if internal and remote resources are involved.
It pays to have local experts on hand to help, such as IT support based in San Diego. That way you can call for assistance and advice if something goes awry post-migration.
Commitment
The cloud is praised for its flexibility, but that can come crashing down if you realize that you’re locked into a deal with a single vendor that you can’t wriggle out of even if it becomes obvious the package isn’t the right fit for your business.
Checking terms and conditions carefully before committing is key if you don’t want to fall foul of overly restrictive cloud service agreements.
Outages
When you become reliant on the cloud, you’re beholden to more forms of productivity-decimating unplanned downtime. This can occur if the provider has issues at their end, or if your own network connection is compromised.
You need to plan for such outages and look for minimum guaranteed levels of uptime advertised by reputable providers to ensure you’re happy with what’s on offer.
Reluctance to adopt
This is more of an ideological issue than a technical one, but nevertheless you need to consider it when you broach the topic of cloud migration.
From top-level decision makers to entry-level employees, you can expect that there will be a degree of resistance to adopting cloud services. This is part of human nature, as we prefer the familiar to the unusual, even if the latter is far better for us.
Making sure that you pitch cloud products and the benefits of migrating clearly will earn you the support you need.
The bottom line
Making a major change to any of your business systems is never a walk in the park, and the cloud has its downs as well as its ups. But being empowered by understanding what obstacles lie ahead is better than not looking for them in the first place, until it’s too late.
The SAT remains the premier college entrance exam in the United States. It’s taken by more than one million students each year and is considered one of the most important factors in whether a student is accepted at their college of choice.
In conjunction with the ACT exam, GPA, and extracurricular resume, the SAT can dramatically hurt or help your chances of getting into a good school.
The SAT isn’t something you just wake up and take. It requires ample preparation, studying, and practice. If you want a high score, you’ll want to plan ahead.
Here are several helpful tips:
1. Understand the Format
The SAT is a 3 hour and 50-minute exam. (This includes a 50-minute optional essay.) It consists of five individual sections: reading, writing, math with a calculator, math without a calculator, and the optional essay.
The SAT exam is a paper-based test that’s administered at hundreds of schools and sites around the country (and throughout the year).
According to LA Tutors 123, “Students are allowed to take the test as many times as they want; most universities will only look at the highest score or the super-score (a combination of the highest sections). However, since the scoring format changed in 2016, schools may tweak their policies accordingly. For more accurate information, please contact individual universities to confirm their score acceptance policy.”
Total SAT scores range from 400 to 1600. The total score is the sum of the reading, writing, and math sections.
2. Create a Schedule
When studying for the SAT, you’ll want to create a schedule. This will help you stay on track and keep you consistent with your effort. While many people choose to take their first SAT exam with very little preparation (as a way of setting a “baseline” score and measuring their current levels), you’ll certainly want to study for the second and third time.
At a minimum, we recommend a study schedule of five to six hours per week for at least three to four weeks prior to the exam. Preparing for the SAT is like running a marathon. You want to pace yourself. In doing so, you don’t have to sprint/cram in the hours leading up to the exam.
3. Boost Your Reading Score
For many students, the reading section presents the best opportunity to elevate their scores. It makes up 50 percent of the reading and writing portion of the exam. There aren’t any formulas or crazy comma rules to memorize. You just need to learn how to properly read and dissect a passage.
“You’ll have 65 minutes to read five passages (taken from literature, history, social studies, and the natural sciences) and answer a total of 52 questions,” The Princeton Review explains. “The questions will ask you to do everything from determining the meaning of words in context, deciding why an author included a certain detail, finding the main idea of a whole passage, comparing two passages, or even pinpointing information on a graph.”
You don’t have to read the entire passage to score well. Part of preparing for the reading portion of the exam is learning how to scan the passage and pull out the important concepts. By learning to focus on what matters most, you can avoid getting overwhelmed by time-consuming details.
4. Use the Right Prep Materials
The good news is that there are plenty of study materials you can use for the SAT. This includes everything from full-length online practice exams to books in your local library. You’ll also find apps, online prep courses, private tutors, and more. It’s worth spending time and money on SAT prep, as a good score can literally save you tens of thousands of dollars on college tuition, loans, and interest.
Be sure to read and review all SAT directions for each section prior to sitting for the exam. This allows you to use the allotted time for taking the exam (not getting familiar with instructions).
There’s no penalty for wrong answers, so it’s okay to guess! Just make sure they’re educated guesses. Start by eliminating any answers that are obviously wrong. If you can whittle the possibilities down to just two answers, you increase your odds from 25 percent to 50 percent.
Be neat and organized. Don’t get sloppy filling in answers (and avoid stray marks on your test). Tests are machine-scored, which means you must be precise.
6. Take the SAT Seriously
You’ll never get into a school based solely on an SAT score. You can, however, ruin your chances of getting into the school of your choice with a bad SAT score. Thus, it’s important to take your time, study well, and be strategic in your approach.
Hopefully, this article has given you some valuable tips and resources to do so.
