Do you have a lot of BigQuery tables? Do you find it hard to keep track of which ones are partitioned and clustered, and which ones could be? If so, we have good news. We’re launching a partitioning and clustering recommender that will do the work for you! The recommender analyzes all your organization’s workloads and tables and identifies potential cost optimization opportunities. And the best part is, it’s completely free!
“The BigQuery partitioning and clustering recommendations are awesome! They have helped our customers identify areas where they can reduce costs, improve performance, and optimize our BigQuery usage.” Sky, one of Europe leading media and communications companies
How does the recommender work?
Partitioning divides a table into segments, while clustering sorts the table based on user-defined columns. Both methods can improve the performance of certain types of queries, such as queries that use filter clauses and queries that aggregate data.
BigQuery’s partitioning and clustering recommender analyzes each project’s workload execution over the past 30 days to look for suboptimal scans of the table data. The recommender then uses machine learning to estimate the potential savings and generate final recommendations. The process has four key steps: Candidate Generation, Read Pattern Analyzer, Write Pattern Analyzer, and Generate Recommendations.
Candidate Generation is the first step in the process, where tables and columns are selected based on specific criteria. For Partitioning, tables larger than 100 Gb are chosen, and for Clustering tables larger than 10 Gb are chosen. The reason for filtering out the smaller tables is because the optimization benefit is smaller and less predictable. Then we identify columns that meet BigQuery’s partitioning and clustering requirements.
In the Read Pattern Analyzer step, the recommender analyzes the logs of queries that filter on the selected columns to determine their potential for cost savings through partitioning or clustering. Several metrics, such as filter selectivity, potential file pruning, and runtime, are considered, and machine learning is used to estimate the potential slot time saved if partitioning or clustering is applied.
The Write Pattern Analyzer step is then used to estimate the cost that partitioning or clustering may introduce during write time. Write patterns and table schema are analyzed to determine the net savings from partitioning or clustering for each column.
Finally, in Generate Recommendations, the output from both the Read Pattern Analyzer and Write Pattern Analyzer is used to determine the net savings from partitioning or clustering for each column. If the net savings are positive and meaningful, the recommendations are uploaded to the Recommender API with proper IAM permissions.
Discovering BigQuery partitioning and clustering recommendations
You can access these recommendations via a few different channels:
We hope you use BigQuery partitioning and clustering recommendations to optimize your BigQuery tables, and can’t wait to hear your feedback and thoughts about this feature. Please feel free to reach us at active-assist-feedback@google.com.
Discovering new drugs is at the heart of modern medicine, yet finding a “needle in the haystack” is immensely challenging due to the enormous number of possible drug-like compounds (estimated at 10^60 or more). To increase our chances of finding breakthrough medicines for patients with unmet medical needs, we need to explore the vast universe of chemical compounds and use predictive in silico methods to select the best compounds for lab-based experiments. Enter reaction-based enumeration, a powerful technique that generates novel, synthetically accessible molecules. Our team at Psivant has been pushing the boundaries of this process to an unprecedented scale, implementing reaction-based enumeration on Google Cloud. By tapping into Google Cloud’s robust infrastructure and scalability, we’re unlocking the potential of this technique to uncover new chemical entities, leading to groundbreaking advancements and life-altering therapeutics.
Our journey began with a Python-based prototype, leveraging RDKit for chemistry and Ray for distributed computing. Despite initial progress, we encountered a roadblock: our on-premises computing resources were limited, holding back our prototype’s potential. While we could explore millions of compounds, our ambition was to explore billions and beyond. To address this limitation, we sought a solution that offered greater flexibility and scalability, leading us to the powerful ecosystem provided by Google Cloud.
Leveraging Google Cloud infrastructure
Google Cloud’s technologies allowed us to supercharge our pipelines and conduct chemical compound exploration at scale. By integrating Dataflow, Google Workflows, and Compute Engine, we built a sophisticated, high-performance system that is both flexible and resilient.
Dataflow is a managed batch and streaming system that provides real-time, fault-tolerant, and parallel processing capabilities to manage and manipulate massive datasets effectively. Google Workflows orchestrates the complex, multi-stage processes involved in enumeration, ensuring smooth transitions and error handling across various tasks. Finally, Compute Engine provides us with scalable, customizable infrastructure to run our demanding computational workloads, ensuring optimal performance and cost-effectiveness. Together, these technologies laid the foundation for our cutting-edge solution to explore the endless possibilities of reaction-based enumeration.
We built a cloud-native solution to achieve the scalability we sought, taking advantage of Dataflow, which relies on Apache Beam, a versatile programming model with its own data structures, such as the PCollection — a distributed dictionary designed to handle computation efficiently.
Enter Dataflow
Balancing performance and cost-efficiency was crucial during pipeline development. That is where Dataflow came in, allowing us to optimize resource utilization without compromising performance, paving the way for optimal resource allocation and cost control.
Our pipeline required a deep understanding of the chemistry libraries and Google Cloud ecosystem. We built a simple, highly distributed enumeration pipeline, then added various chemistry operations while ensuring scalability and performance at every step. Google Cloud’s team played a pivotal role in our success, providing expert guidance and troubleshooting support.
To 100 billion and beyond
Our journey implementing reaction-based enumeration at scale on Google Cloud has been an inspiring testament to the collaborative spirit, relentless innovation, and unwavering pursuit of excellence. With smart cloud-native engineering and cutting-edge technologies, our workflow achieves rapid scalability, capable of deploying thousands of workers within minutes, enabling us to explore an astounding 100 billion compounds in under a day. Looking ahead, we’re excited to integrate Vertex AI into our workflow as our go-to MLOps solution, and to supercharge our high-throughput virtual screening experiments with the robust capabilities of Batch, further enhancing our capacity to innovate.
We’d like to extend our heartfelt thanks to Javier Tordable for his guidance in distributed computing, enriching our understanding of building a massively scalable pipeline.
As we persistently push the boundaries of computational chemistry and drug discovery, we are continuously motivated by the immense potential of reaction-based enumeration. This potential is driven by the powerful and flexible infrastructure of Google Cloud, combined with the comprehensive capabilities of Psivant’s QUAISAR platform. Together, they empower us to design the next generation of groundbreaking medicines to combat the most challenging diseases.
Today, the vast majority of data that gets generated in the world is unstructured (text, audio, images), but only a fraction of it ever gets analyzed. The AI pipelines required to unlock the value of this data are siloed from mainstream analytic systems, requiring engineers to build custom data infrastructure to integrate structured and unstructured data insights.
Our goal is to help you realize the potential of all your data, whatever its type and format. To make this easier, we launched the preview of BigQuery object tables at Google Cloud Next 2022. Powered by BigLake, object tables provide BigQuery users a structured record interface for unstructured data stored in Cloud Storage. With it, you can use existing BigQuery frameworks to process and manage this data using object tables in a secure and governed manner.
Since we launched the preview, we have seen customers use object tables for many use cases and are excited to announce that object tables are now generally available.
Analyzing unstructured data with BigQuery object tables
Object tables let you leverage the simplicity of SQL to run a wide range of AI models on your unstructured data. There are three key mechanisms for using AI models; all enabled through the BigQuery Inference engine.
First, you can import your models and run queries on the object table to process the data within BigQuery. This approach works well for customers looking for an integrated BigQuery solution that allows them to utilize their existing BigQuery resources. Since the preview, we’ve expanded support beyond TensorFlow models with TF-Lite and ONNX models and introduced new scalar functions to pre-process images. We also added support for saving pre-processed tensors to allow for efficient multi-model use of tensors to help you reduce slot usage.
Second, you can choose from various pre-trained Google models such as Cloud Vision API, Cloud Natural Language API, and Cloud Translation API, for which we have added pre-defined SQL table valued functions that invoke when querying an object table. The results of the inference are stored as a BigQuery table.
Third, you can integrate customer-hosted AI models or custom models built through Vertex AI using remote functions. You can call these remote functions from BigQuery SQL to serve objects to models, and the results are returned as BigQuery tables. This option is well suited if you run your own model infrastructure such as GPUs, or have externally maintained models.
During the preview, customers used a mix of these integration mechanisms to unify their AI workloads with data already present in BigQuery. For example, Semios, an agro-tech company, uses imported and remote image processing models to serve precision agriculture use cases.
“With the new imported model capability with object table, we are able to import state-of-the-art Pytorch vision models to process image data and improve in-orchard temperature prediction using BigQuery. And with the new remote model capability, we can greatly simplify our pipelines and improve maintainability.” – Semios
Storage insights, fine-grained security, sharing and more
Beyond processing with AI models, customers extending existing data management frameworks to unstructured data, resulting in several novel use cases such as:
Cloud Storage insights – Objects tables provide an SQL interface to Cloud Storage metadata (e.g., storage class), making it easy to build analytics on Cloud Storage usage, understand growth, optimize costs, and inform decisions to better manage data.
Fine-grained access control at scale – Object tables are built on BigLake’s unified lakehouse infrastructure and support row- and column-level access controls. You can use it to secure specific objects with governed signed URLs. Fine-grained access control has broad applicability for augmenting unstructured data use cases, for example securing specific documents or images based on PII inferences returned by the AI model.
Sharing with Analytics Hub – You can share object tables, similar to BigLake tables, via Analytics Hub, expanding the set of sharing use cases for unstructured data. Instead of sharing buckets, you now get finer control over the objects you wish to share with partners, customers, or suppliers.
Run generative AI workloads using object tables (Preview)
Members of Google Cloud AI’s trusted tester program can use a wide range of generative AI models available in Model Garden to run on the object table. You can use Generative AI studio to decide on a foundation model of your choice or fine-tune it to deploy a custom API endpoint. You can then call this API using BigQuery using the remote function integration to pass prompts/inputs and return the text results from Language Learning Models (LLM) in a BigQuery table. In the coming months, we will enable SQL functions through the BigQuery Inference engine to call LLMs directly, further simplifying these workloads.
Getting started
To get started, follow along with a guided lab or tutorials to run your first unstructured data analysis in BigQuery. Learn more by referring to our documentation.
Acknowledgments: Abhinav Khushraj, Amir Hormati, Anoop Johnson, Bo Yang, Eric Hao, Gaurangi Saxena, Jeff Nelson, Jian Guo, Jiashang Liu, Justin Levandoski, Mingge Deng, Mujie Zhang, Oliver Zhuang, Yuri Volobuev and rest of the BigQuery engineering team who contributed to this launch.
Great customer support plays a central role in a company’s success and profitability. Businesses with an excellent reputation for customer service tend to do better overall and stay in business longer. Companies that miss the mark often lose customers and generate negative reviews online. Unfortunately, bad reviews can deter new customers.
What makes some businesses fail to provide outstanding customer support? Usually, it’s because they haven’t employed the right technology. The reasons for this vary. Some business owners try to get by with the bare minimum, don’t know what they need, or can’t afford to hire programmers. This is where AI can help; with the right tools, there’s no need to hire developers.
AI greatly enhances customer support
Of all the advances in technology, AI has the power to enhance customer support like never before. Conversational AI, for example, completely transforms the interactions between customers and the support team.
The bottom line is that the majority of issues get resolved quickly, without involving a human agent. The business benefits by reducing agent workloads for repetitive issues that can be resolved with simple self-help solutions.
Through a conversational AI platform, users can engage with a chatbot and have a humanlike conversation instead of getting robotic, pre-programmed responses that don’t always help. Powered by generative AI and natural language processing (NLP), this technology can create customized, personalized responses that are relevant to the nuances of a user’s inquiry.
This technology isn’t just for customers – it can also be used to power internal help desk solutions, like the conversational AI platform from Aisera. When a business uses this technology for employees, they get answers and solutions fast, which makes them far more productive. When paired with ChatGPT, this technology gives automated support systems an even bigger boost.
Machine learning continually improves performance
Perhaps the best part of AI is machine learning. Companies that provide AI customer support solutions generally analyze mass amounts of user input, including tickets, to train the algorithm to respond appropriately.
This is an ongoing process, which means the effectiveness of an AI customer service tool is constantly improving. Compared to traditional tools, like rule-based chatbots that don’t improve unless they’re reprogrammed, conversational AI won’t fall behind.
Here are just some of the benefits businesses can get from using this automated AI technology:
Better customer service. Customers will always have questions, and AI technology helps them get the answers they seek. Self-service options. A conversational AI chatbot is ideal for providing users with self-service options, which happens to be what people want. According to data sourced by Customer Gauge, 67% of people prefer self-help over speaking to a human for support, and 40% of people who call in have already searched for answers on their own. Fast response times. Responses from an AI system are almost always faster than engaging a human. Cost-effective. Great customer service is priceless because it’s what keeps a business going. Using AI reduces expenses, like wasted payroll dollars for agents who spend all day answering simple questions users can handle on their own with the right resource. Less stress on agents. Since AI chatbots can resolve most customer issues automatically, support agents will have fewer tickets to manage. A reduced workload results in less stress and more time to focus on urgent matters. 24/7 support without live agents. Customers won’t have to wait to get support when you have a round-the-clock AI-powered chatbot available.
AI enhances customer service
Some people are worried that AI is going to obliterate the need for customer support agents, but that’s not true. There will always be some elements that require a human touch. This technology will transform jobs, but it’s only going to enhance – not replace – customer service teams.
Completely automating your entire customer service team won’t turn out well. However, you can use AI to support your teams by making them more productive and relieving them of the repetitive tasks that lead to frustration and job dissatisfaction.
The ideal solution is to use a blend of humans and AI-powered technology for customer service solutions.
AI chatbots empower businesses to succeed
When great customer service is the goal, AI delivers outstanding results through a variety of automated tools powered by NLP, machine learning, generative AI, and conversational AI. It’s even used in omnichannel marketing, which further supports this goal. For businesses that want to continuously improve customer service and reduce the strain on their agents, using AI technology is a must.
Aviation and accommodation play a big role in impacting the tourism economy, but analysis of recent data also highlights tourism’s impact on other sectors, from financial services to healthcare, to retail and transportation.
With travel recovery in full swing post pandemic, Google search queries related to “travel insurance” and “medical tourism” in Thailand have increased by more than 900% and 500% respectively. Financial institutions and healthcare providers must therefore find ways to deliver tailored offerings to travelers who are seeking peace of mind from unexpected changes or visiting the country to receive specialized medical treatment.
Interest in visiting Thailand for “gastronomy tourism” is also growing, with online searches increasing by more than 110% year-on-year. Players in the food and beverage industry should therefore be looking at ways to better engage tourists keen on authentic Thai cuisine.
Most importantly, digital services will play an integral role in travel recovery. More than one in two consumers in Thailand are already using online travel services, with this category expected to grow 22% year-on-year and contribute US$9 billion to Thailand’s digital economy by 2025. To seize growth opportunities amidst the country’s tourism rebound, businesses cannot afford to overlook the importance of offering always-on, simple, personalized, and secure digital services.
With Thailand reopening its borders, there has been an upturn in both inbound and outbound air travel. To accommodate these spikes in passenger traffic across its six international airports, AOT migrated its entire IT footprint to Google Cloud, which offers an open, scalable, and secure data platform, with implementation support from its partner SKY, an aviation technology solutions provider.
Tapping on Google Cloud’s dynamic autoscaling capabilities, the IT systems underpinning AOT’s ground aviation services and the SAWASDEE by AOT app can now accommodate up to 10 times their usual workloads. AOT can also automatically scale down its resources to reduce costs when they are no longer in use. Using the database management services of Google Cloud to eliminate data silos, the organization is able to enhance its capacity to deliver real-time airport and flight information to millions of passengers. As a result, travelers enjoy a smoother passenger experience, from check-in to baggage collection.
At the same time, SKY uses Google Kubernetes Engine (GKE) to transform SAWASDEE by AOT into an essential, all-in-one travel app that offers a full range of tourism-related services. GKE allows AOT to automate application deployment and upgrades without causing downtime. This frees up time for the tech team to accelerate the launch of new in-app features, such as a baggage tracker service, airport loyalty programs, curated travel recommendations, an e-payment system, and more.
EVme drives sustainable travel with data
Being able to travel more efficiently is only one part of the future of travel. More than ever, sustainability is becoming a priority for consumers when they plan their travel itineraries. For instance, search queries related to “sustainable tourism” in Thailand have increased by more than 200% in the past year, with close to four in 10 consumers sharing that they are willing to pay more for a sustainable product or service.
To meet this increasing demand and support Thailand’s national efforts to become a low-carbon society, EVme, a subsidiary of PTT Group, is building its electric vehicle lifestyle app on Google Cloud, the industry’s cleanest cloud. It has also deployed the advanced analytics and business intelligence tools of Google Cloud to offer its employees improved access to data-driven insights, which helps them better understand customer needs and deliver personalized interactions. These insights have helped EVme determine the range of electric vehicle models it offers for rental via its app, so as to cater to different preferences. At the same time, the app can also share crucial information, such as the availability of public electric vehicle charging stations, while providing timely support and 24-hour emergency assistance to customers.
As we empower organizations across industries with intelligent, data-driven capabilities to make smarter business decisions and be part of an integrated ecosystem that delivers world-class visitor experiences, our collaborations with AOT, SKY, and EVme will enhance their ability to serve travelers with personalized, digital-first offerings powered by our secure and scalable open data cloud.
Few industries have been untouched by changes in artificial intelligence technology. However, the financial industry has been affected more than most others. Therefore, it should not be surprising to hear that the global market for AI in the financial services sector was worth $9.45 billion in 2021 and is growing at a rate of 16.5% a year.
AI is revolutionizing the financial industry by automating many processes and providing new, meaningful insights that were once impossible. From automated trading to fraud detection, AI has become a powerful tool for financial institutions trying to increase efficiency and improve their bottom lines. There are also many benefits for customers since AI helps financial institutions lower their fees, improve their product and service offerings, and offer their services to a broader range of consumers, such as approving a higher percentage of loans to reliable borrowers by improving actuarial decision-making.
AI also allows credit card companies to take advantage of predictive analytics capabilities, which can help make better decisions and identify trends in the market. With its ability to quickly process large amounts of data, AI is becoming increasingly important in the financial industry. It can help banks reduce costs while improving customer service and accuracy. As such, it is changing the way we interact with our finances daily.
The credit card industry is one of the financial sectors most affected by advances in artificial intelligence. AI technology has significantly improved analytics capabilities, which has helped solve many problems in the credit card sector. It can lead to lower interest rates and unique product offerings, such as great new student credit cards.
Ai Creates New Analytics Capabilities for Credit Card Providers and Customers
Disha Singha of Analytics Insights reports that AI technology has significantly changed the state of the credit card industry. She reports that AI technology can improve credit systems and increase the number of people using them. Banks worldwide offer credit cards with varying interests, deals, and rewards and do everything that they can to make it easy for customers to their balances. Credit cards are becoming the most common payment method for many products and services due to their convenience and the fact that they are more secure than cash.
Singha reports that marketing analysts believe the global credit card market will be worth $103.06 billion in 2021, which means it is growing at an annual rate of 3%. Meanwhile, the global market for artificial intelligence technology is expected to be worth S$228.3 billion in 2026, growing at a yearly rate of 32.7%. Therefore, leveraging AI technology can help credit card companies offer higher-quality services and increase their growth targets.
AI has been used by banking and fintech firms to improve fraud detection on credit and debit cards. It also analyzes patterns of defaulters and cautions users from overspending. Predictive analytics is now being used to enhance how credit and debit cards are used in real-time. Some companies have started using this technology
This has many incredible benefits for both credit card providers and their consumers. Credit card fraud is a growing problem that can be costly for both consumers and businesses. BankRate reports that credit card fraud costs nearly $6 billion a year, which requires card providers to charge higher interest rates for customers.
However, advancements in technology have enabled companies to reduce the amount of credit card fraud, which can help lower interest rates for consumers. By using AI to detect fraudulent activity, businesses can protect their customers from being victims of scams and other fraudulent activities. This will result in lower interest rates for consumers since credit card providers will no longer need to cover the costs associated with fraudulent activity. Additionally, companies can use AI to create better customer experiences by providing personalized services that reduce the risk of fraud.
While AI analytics is most useful for dealing with fraud prevention, it can be utilized by credit card companies to solve various other business challenges. For example, AI software can be used by credit card companies and financial institutions to enhance customer service and create targeted marketing campaigns for customers. One example of using AI for customer service is the use of AI chatbots, which are integral to many modern marketing strategies.
What Credit Card Companies Are Going to Use Ai Technology to Offer Better Service and Improve Profitability
In recent years, the credit card industry has become much more reliant on artificial intelligence. Credit card companies are using AI to offer better service and improve profitability. Customers benefit from lower rates as well. Overall, high technology has been highly beneficial for the sector, which means credit card companies are likely to dedicate even more resources to it in the near future.
At the third annual Google Data Cloud and AI Summit, we shared how data analytics and insights continue to be a key focus area for our customers and how we’re accelerating their data journeys through new product innovations and partner offerings.
A big part of that is helping customers turn their data into insights faster using differentiated datasets from partners and integrating them into their AI/ML workflows. We recently announced our partnership with Crux to add over 1,000 new datasets on Analytics Hubto provide customers with access to a rich ecosystem of data to enrich first-party data assets and accelerate time to value and scalability with real time insights. There will be an initial focus on Financial Services, ESG, and Supply Chain, but we plan to increase this to 2,000 datasets later this year. These datasets are critical to our customers who execute highly process-intensive analytics workloads for trading, planning, and risk calculations.
An industry leader, Dun & Bradstreet, will also make much of its catalog available on Analytics Hub and listed on the Google Cloud Marketplace. This will enable customers to achieve the same benefits they receive for SaaS purchases in the Marketplace, including simplified procurement, consolidated billing, and financial incentives.
“We are excited to build upon our ten-year relationship with Google Cloud and both companies’ commitments to deliver innovative opportunities to our mutual customers,” said Ginny Gomez, President, Dun & Bradstreet North America. “By making D&B datasets and products available in the Google Cloud Marketplace, we are making it easier for our customers to access and utilize this critical data, while also helping to provide a frictionless procurement process for customers to use their committed Google Cloud spend.”
When you purchase and subscribe to a dataset in the Google Cloud Marketplace, the data is immediately accessible via your BigQuery environment via Analytics Hub, without ingress, storage charges, or wait times. This allows your project teams to leverage Google Cloud AI/ML, BigQuery, and other third-party innovations to get valuable insights from datasets with ease. This is a commercial expansion on the hundreds of public and free datasets already listed in the Google Cloud Marketplace.
Analytics Hub is built on a decade of data sharing in BigQuery. Since 2010, BigQuery has supported always-live, in-place data sharing within an organization’s security perimeter, as well as data sharing across boundaries to external organizations. Analytics Hub makes the administration of sharing assets across boundaries even easier and more scalable, while retaining access to key capabilities of BigQuery like its time-tested sharing infrastructure, and built-in ML, real-time and geospatial analytics.
These datasets on Marketplace also benefit from BigQuery’s advantages:
Scale: BigQuery is an exabyte-scale data warehouse that can handle even the most demanding data sharing needs. It grows with your data needs including auto scaling capabilities.
Security: BigQuery is built on Google’s secure infrastructure and offers various security features to protect your data. Data is always encrypted and PII data discovery services can be directly used to improve the security of the data.
Freshness: BigQuery data can be shared without moving it, this means you can join shared data with your own data with no need to implement expensive ETLs to bring the data from the providers
Cost-effectiveness: BigQuery provides different billing models so each workload can make use of the data providing the best price/performance.
At Google Cloud, we believe data and AI have the power to transform businesses and unlock the next wave of innovation. We are excited to share that customers can now procure new data assets on the Google Cloud Marketplace to accelerate their business decisions and drive new innovations. Customers interested in these datasets, can request a custom quote, or more information by clicking Contact Sales on the Marketplace product page and completing the inquiry form.
AI has been the latest controversy, as it may seem to eliminate the need for human working positions in most professions. Artists, especially, were vocal about their concerns about the technology, considering the system’s merging way of creating art. ChatGPT doesn’t seem to think about the importance of artists after it tells a writer they don’t exist but after citing their previous work.
In most industries, AI is hyped, especially by companies looking to cut costs and minimise risks linked with occupational hazards. And although artificial intelligence is a useful tool that can extend one’s work and capabilities, it must not be included too much in certain sectors, such as marketing.
AI should be used with caution and consideration for people’s hard work. At the same time, it can be more powerful next to people’s contribution, which is why we’ll discuss its pros and cons and analyse the hype.
What can AI bring to marketing?
Marketing is always changing in a matter of strategies and methods in relationship with current world trends. Marketing is a mix of technology and human-based interactions, with customer support being the most important part of the advertisement and promotion process. During the steps of this development, marketers can recognise the number of repetitive actions that are time-consuming and don’t offer enough space for innovation and creativity.
Here comes AI technology, whose programmable system is capable of automating these tasks regarding the stage in the marketing process, be it data analysis or campaign personalisation. Artificial intelligence can indeed minimise human errors, such as grammar mistakes or incorrect data entry.
However, AI has some limitations in marketing
Of course, you can choose AI to do your whole marketing campaign, but customers will notice this change more likely and might not like it. In the marketing world, there’s nothing like human interaction to boost the company’s brand into fame, for which services like Savanta in Europe offer personalised research and consulting for B2B and B2C markets to create custom plans for each type of business.
Customers require human connection
Despite clients appreciating customer service chatbots for rapid inquiries, their disadvantage is the lack of emotions and empathy for people’s concerns. And it seems like customers are avoiding using the services or products of a company whose chatbot experience was unpleasant. Of course, that doesn’t mean not using AI in customer support, but your business shouldn’t rely on it fully because any kind of technology cannot overtake human interaction.
Even AI can be wrong sometimes
Although AI is believed to be always right, fast and efficient in providing answers and guidance, the truth gives us another perspective on its performance. For example, AI cannot fully notice different tones in people’s emotions, which is why performing a reliable sentiment analysis is challenging for the system. Intentions are also difficult to seize by technology, and this lack of human features can lead to inaccurate predictions and calculations, affecting data marketing.
AI requires huge sets of data for proper intervention
AI needs a lot of human intervention and considerable sets of data in order to contain all the knowledge required to perform on its own. The tool can only carry out tasks as long as it has the proper set of data with which it can make decisions ―otherwise, its efficiency is little to nothing. Therefore, your company needs someone who is trained in guiding AI by uploading the required data sets, which can also count as a tedious process.
AI can’t compete with human creativity
No matter how advanced this technology is, it can’t beat human creations. AI is merging human-made music and art to create unique pieces, so its activity is still possible with the help of previous human genius outcomes. If you base your marketing strategy on AI too much, the content exposed may seem disconnected from reality, which customers won’t appreciate. After all, your target audience includes only human customers, so why have a fully robotic approach?
How to blend AI and Human resources
Your business can be successful by implementing a fusion between artificial intelligence and employees. This will ensure enough support for your marketing campaign while keeping up with trends and customer demands.
The first thing you can do is conduct an audit and find the areas in your business where AI can bring the most benefits. You can involve your employees or team in this process to brainstorm ideas on the possible contribution of AI to your company. AI can do many repetitive and tiring tasks, so pinpoint them and then focus your attention on the creative process your team can do.
The following step requires you to collect relevant data because AI needs a lot of it to assure you of efficiency. Introduce your AI system to the information you’ve achieved to manage, but be prepared to work continuously in this process because AI and human tasks need to be optimised
Your staff shouldn’t worry about being replaced because AI can’t be reliable without human sustaining and training. AI can be a big help when it comes to automating tasks and collecting data, which are more or less the best things you can use this technology for.
AI shouldn’t be used to make a profit but to enhance people’s creativity resources. In marketing and similar sectors where employees are demanded to use their capabilities, this technological advancement is necessary to help a company flourish with all its employees contributing.
Final take: what’s your opinion on using AI in marketing?
AI is a pretty controversial topic for all professionals out there, and few of them had the courage to take control of the tool and use it solely to leverage creative outcomes. Still, others feel threatened, but technological advancements seem to eliminate the need for human services in most domains. However, AI is best used next to people’s contributions, which is why companies should adopt an inclusive approach and blend these two elements together to develop the economy and bring a new era to life.
According to the Identity Theft Resource Center’s 2022 Data Breach Report, the number of data breaches decreased slightly in 2022 from the number reported in 2021. However, this was due to a lag in the beginning of the year and the total number of Americans affected actually surged.
The number of Americans affected increased by 42% compared to the previous year. Reports were significantly lower in the first half of 2022, possibly due to the war in Ukraine and volatile cryptocurrency prices. However, the number increased in the second half of the year. Most of the compromises in both years were classified as data breaches, but in a few cases, data was exposed in other ways that didn’t involve a breach of computer systems.
All organizations need to take the right steps to protect against data breaches. These attacks will become even more common now that many hackers are using AI to deploy more cyberattacks. This entails shielding applications from cyberattacks.
Since so many companies use Office 365, they need to make sure the documents stored on it are safe from hackers.
Office 365 Products Need to Be Protected from Hackers
Office 365 has become the go-to productivity suite for businesses of all sizes. Moreover, cloud-based productivity software is defining the way modern companies do business. Not only does this software digitize the workplace, but it improves collaboration, productivity, and connectivity with customers and coworkers.
But like any other cloud-based service, users must learn to protect Office 365 to prevent data breaches and loss. Luckily, Office 365 offers a myriad of data protection features. But to fully understand the protection capabilities of Office 365, we must discuss the available data and threat protection features.
This guide will offer a deep dive into the best Office 365 protection tips to give you an overview of the suite’s capabilities and why implementation is essential for business continuity. But first, does Office 365 offer enough protection?
Does Office 365 Do Enough Protection?
While Office 365 offers built-in security features, it is essential to understand that these measures alone may not be sufficient to safeguard your business data. Microsoft provides a secure foundation, but it’s up to individual organizations to implement additional security measures to ensure comprehensive protection.
Because of that, and despite the attention to detail and professionalism, the native features aren’t enough for full-scale protection. That’s what forces many organizations to seek third-party Office 365 protection software specializing in data security.
With that said the tips in our guide will enhance the security of your Office 365 environment and minimize the risk of data breaches or unauthorized access.
6 Tips for Office 365 Security
Below are six tips we recommend for securing the Office 365 suite against threat actors, accidental deletion, and data loss prevention.
Enable Multi-Factor Authentication (MFA)
An efficient and easy way to enhance the security of each Office 365 tenant is to enable MFA. MFA adds an additional layer of protection that requires users to use an additional verification form. This is usually a code sent to their mobile device and their password.
By implementing MFA, even if an attacker obtains a user’s password, they would still need physical access to the user’s secondary authentication device, significantly reducing the risk of unauthorized access. As a result, MFA is a standout feature of most modern cloud services and one that Microsoft actively recommends you enable.
Log in to the Microsoft 365 admin center to disable legacy authentication and enable MFA.
Regularly Update and Patch Applications
Keeping your Office 365 applications up to date is crucial for maintaining security. Unfortunately, hackers will often target employees with outdated software in hopes of breaching the Office 365 suite. Luckily, Microsoft frequently releases security patches and updates to address vulnerabilities and protect against emerging threats.
Unfortunately, many employees don’t go ahead with updates and simply click the “remind me later” button whenever they log in to Office 365. That’s why you must encourage them to update when a new patch is available.
On the other hand, Office administrators can schedule these updates and eliminate employees needing to update Office 365 and its many applications manually. Establish a process to regularly review and apply these updates to protect your environment with the latest security fixes.
Implement Data Loss Prevention (DLP) Policies
Office 365 offers powerful data loss prevention features that allow you to define policies to prevent sensitive information from being shared or leaked outside your organization. These policies will alert users whenever sending confidential or sensitive data through email or other communication channels.
By configuring DLP policies, you can detect and mitigate potential data breaches, ensuring that confidential data remains secure. Subsequently, you could pair these policies with the powerful encryption features in Office 365. For example, one way to prevent sensitive data from being stolen or lost through email is to encrypt email communications.
Enforce Identity and Access Management
Office 365 Identity and Access Management (IAM) is crucial to securing and controlling access to Microsoft Office 365 resources and services. It encompasses a set of tools, policies, and practices that enable organizations to manage user identities, control access, and authorization processes, and enforce security policies within their Office 365 environment through Azure Active Directory.
The primary goal of Office 365 IAM is to ensure that the right individuals have appropriate access to the right resources while maintaining data confidentiality, integrity, and availability. In addition, IAM helps organizations prevent unauthorized access, protect sensitive information, and manage user identities efficiently to strengthen security capabilities and management.
Monitor and Analyze User Activity
Similarly to IAM, we can analyze and monitor user activity within Office 365 to see first-hand which data employees access. The monitoring features of Office 365 will allow you to track user actions, detect suspicious activity, and identify potential security incidents.
Similarly, you can also enable conditional access to specific types of data and apps and prevent unauthorized users from accessing them. Monitoring user activity gives you the benefit of limiting security threats and taking appropriate action to mitigate risks.
Backup Your Office 365 Data
Lastly, Office 365 offers several data backup capabilities for your entire digital workspace from accidental deletion, malicious attacks, or software errors. Unfortunately, any of these events can result in permanent data loss, which could have far greater consequences for your business.
However, Office 365 offers few automation tools for backing up sensitive data. This forces companies to go with dedicated third-party tools for all of their backup needs. On the other hand, these tools are expensive, and the features Office 365 offers are free of charge.
Office 365 Applications Need to Be Secured Against Data Breaches
Securing your Office 365 environment is paramount to protecting your digital workplace and valuable business data against the growing the threat of data breaches. While Office 365 provides a solid foundation, it is crucial to implement additional security measures tailored to your organization’s needs. By following the tips mentioned in our guide, you can significantly enhance the security of each Office 365 tenant and minimize the risk of data breaches or unauthorized access.
AI technology has become crucial for modern businesses trying to maintain a competitive edge in the 21st Century. Around 35% of businesses are already using AI and another 42% plan to use it in the future. The rapid proliferation of ChatGPT has gained a lot of attention over the last few months, but it is not the only form of AI software impacting business.
Businesses are also using AI-driven ERP software. This software is a gamechanger for large businesses trying to bolster efficiency.
AI-Driven ERP Tools Are Becoming More Important than Ever
AI tools are becoming more common in enterprise software. This post discusses the types of AI tools, how they are used in ERP systems to improve business processes, and the challenges of finding a supplier that offers what you need.
ERP systems have been stagnant for decades in managing and processing enterprise data. People input data into the system and move it along to support business operations. Using ERP software that utilizes AI technology will change this process. People will no longer need to code every detail of a transaction or wait to approve reports. Reports will no longer need to be manually analyzed for business insights.
Whether business leaders are looking into new technologies to improve efficiency or they’re already familiar with enterprise resource planning (ERP) software but feel that it’s time to find a new vendor, the choice of which company to trust is difficult to make. There are hundreds of products available, each with different advantages, drawbacks, and price points.
They will want to look for an ERP vendor that has used AI technology to improve the quality of its products. AI has a number of benefits for ERP software:
AI technology can improve how ERP software handles and analyzes data. It can analyze larger data sets than ever before, leading to better real-time and accurate data insights. With AI, enterprises can analyze the purchasing behavior of different client categories and tailor their inventories to their needs. This is especially important for manufacturers.
AI also helps with automation. AI technology can improve the performance of an ERP through process automation. The AI can learn and make intelligent decisions, simplifying tasks like accounting and payroll administration. When integrated with an ERP system, AI can identify inefficient processes and suggest cost-cutting solutions. Predictive diagnostics are also possible with AI, reducing resource waste.
AI technology integrated into an ERP system can also be beneficial for UX optimization. It can learn from users and simplify the software, similar to how it improves smartphone usage. AI technology can also enhance the user experience by facilitating interaction with complex systems like ERPs.
Once you understand the importance of making sure an ERP solution uses AI, it is important to find a vendor that offers the solutions that you are looking for. Simply separating the wheat from the chaff is hard enough, but even among the top-performing ERP systems there will still be key differences to consider. When choosing an ERP, business leaders should be sure to take the criteria described in this article into account.
1. Business Requirements and Functional Fit
Some ERP systems are highly generalized and can be used by companies in almost any industry. This SAP ERP and Infor M3 comparison provides a good example. By comparing SAP ERP, a software suite that is suitable for just about any large business with Infor M3, which is much more specialized, the report makes it clear whether a generalized ERP will be sufficient for many businesses.
2. Key Features
Starting by determining industry and specific company requirements makes it easier to evaluate each ERP system’s key features. However, there are some modules that every business needs. Business intelligence, data analysis, and information security are important in all industries. Similarly, every company can benefit from an ERP with simple UI and UX design and easy-to-access end-user reporting tools.
3. Total Cost of Ownership
The cost of ownership for ERP systems varies based on the deployment model, as does the return on investment (ROI) that can be expected from them. On-premises ERPs require businesses to pay up-front for the hardware and software required to run the system. Companies will also be responsible for paying to implement, customize, upgrade, and support the system and keep it secure. Cloud-based ERPs have a much lower cost of ownership and tend to provide a better return on investment.
4. Initial Scope and Scalability
Mid-sized enterprise-level businesses shouldn’t have to pay for features that are tailored exclusively to the needs of their much larger peers. However, opting for an ERP with limited functionality and scope can also come at a price, as scaling up the business will mean going through the entire process of choosing an ERP vendor all over again. The ideal solution is one that can be scaled up with the business as it expands to new markets or adopts additional technologies.
5. Vendor Reputation
Business leaders should only consider ERP solutions from vendors that have been in business for long enough to have developed a positive reputation. They should expect to be working with the company during the initial deployment phase and for upgrades, updates, and expansions. A vendor that has a reputation for inadequate customer service or one that simply hasn’t been around long enough to guarantee it will be there to help when the business is ready to scale up won’t cut it. Choose a well-established, reputable provider.
Don’t Be Afraid to Ask for Help when Choosing an AI-Driven ERP
AI technology has made ERPs more effective than ever. However, it can difficult to choose the right one. There are a lot of ERP solutions available, even if they don’t all use AI technology. Unfortunately, not all companies’ business and IT leaders have the hours of extra time required to compare every vendor. If that’s the case, the most practical and cost-effective solution is to work with a business software consultant, so don’t be afraid to ask for help.
