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Built with BigQuery: How Exabeam delivers a petabyte-scale cybersecurity solution

Built with BigQuery: How Exabeam delivers a petabyte-scale cybersecurity solution

Editor’s note: The post is part of a series highlighting our awesome partners, and their solutions, that are Built with BigQuery.

Exabeam, a leader in SIEM and XDR, provides security operations teams with end-to-end Threat Detection, Investigation, and Response (TDIR) by leveraging a combination of user and entity behavioral analytics (UEBA) and security orchestration, automation, and response (SOAR) to allow organizations to quickly resolve cybersecurity threats. As the company looked to take its cybersecurity solution to the next level, Exabeam partnered with Google Cloud to unlock its ability to scale for storage, ingestion, and analysis of security data.

Harnessing the power of Google Cloud products including BigQuery, Dataflow, Looker, Spanner and Bigtable, the company is now able to ingest data from more than 500 security vendors, convert unstructured data into security events, and create a common platform to store them in a cost-effective way. The scale and power of Google Cloud enables Exabeam customers to search multi-year data and detect threats in seconds

Google Cloud provides Exabeam with three critical benefits.  

Global scale security platform. Exabeam leveraged serverless Google Cloud data products to speed up platform development. The Exabeam platform supports horizontal scale with built-in resiliency (backed by 99.99% reliability) and data backups in three other zones per region. Also, multi-tenancy with tenant data separation, data masking, and encryption in transit and at rest are backed up in the data cloud products Exabeam uses from Google Cloud.

Scale data ingestion and processing. By leveraging Google’s compute capabilities, Exabeam can differentiate itself from other security vendors that are still struggling to process large volumes of data. With Google Cloud, Exabeam can provide a path to scale data processing pipelines. This allows Exabeam to offer robust processing to model threat scenarios with data from more than 500 security and IT vendors in near-real time. 

Search and detection in seconds. Traditionally, security solutions break down data into silos to offer efficient and cost-effective search. Thanks to the speed and capacity of BigQuery, Security Operations teams can search across different tiers of data in near real time. The ability to search data more than a year old in seconds, for example, can help security teams hunt for threats simultaneously across recent and historical data. 

Exabeam joins more than 700 tech companies powering their products and businesses using data cloud products from Google, such as BigQuery, Looker, Spanner, and Vertex AI. Google Cloud announced theBuilt with BigQuery initiative at the Google Data Cloud Summit in April, which helps Independent Software Vendors like Exabeam build applications using data and machine learning products. By providing dedicated access to technology, expertise, and go-to-market programs, this initiative can help tech companies accelerate, optimize, and amplify their success. 

Google’s data cloud provides a complete platform for building data-driven applications like those from Exabeam — from simplified data ingestion, processing, and storage to powerful analytics, AI, ML, and data sharing capabilities — all integrated with the open, secure, and sustainable Google Cloud platform. With a diverse partner ecosystem and support for multi-cloud, open-source tools, and APIs, Google Cloud can help provide technology companies the portability and the extensibility they need to avoid data lock-in.   

To learn more about Exabeam on Google Cloud, visit www.exabeam.com. Click here to learn more about Google Cloud’s Built with BigQuery initiative. 

We thank the many Google Cloud team members who contributed to this ongoing security collaboration and review, including Tom Cannon and Ashish Verma in Partner Engineering.

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3 Smart Technologies Boosting Energy Efficiency Worldwide

3 Smart Technologies Boosting Energy Efficiency Worldwide

The growth of smart technology is one of the most beneficial trends brought on by advances in AI. It is projected that there will be over 77 million smart homes in the United States by 2025. Smart technology is also being used by businesses and government institutions around the world.

Many factors are driving the demand for smart technology. The quest for efficient and sustainable energy usage is one of the defining technological challenges of the modern age — especially as we find ourselves in the throes of the world’s first truly global energy crisis. Fortunately, data scalability has made smart technology more accessible.

Although this is a structural problem, it’s vital for individuals, families and businesses to optimize their energy consumption, both to mitigate the staggering prices and initiate a widespread move towards more mindful and responsible energy consumption.

Technological innovation has proved incredibly useful for this purpose, so we’ve compiled a list of smart energy inventions that are helping us all save energy costs and reduce our reliance on finite resources.

1.      Smart grids

We’re starting off big with the smart grid. This creation, which has been around for about a decade, is an electricity network (these power millions of homes at a time) that permits a two-way flow of electricity, using data-driven technology to detect and adjust energy usage.

The impact

One of their key benefits is that they can manage electrical transmissions and limit the amount of electrical losses in the distribution of energy. Not only does this improve overall efficiency, but it also reduces the risk of equipment failure and encourages the use of renewable energy by promoting the tracking and regulating of consumption. Around the world, more and more government administrations are increasing funding for grid modernization and the development of smart technologies.

The future

While the smart grid has been around for a decade or so, innovations abound. New alternatives to AC powered grids are being proposed, such as hybrid AC/DC smart microgrids. Studies have shown how these allow for greater flexibility and reliability, with research suggesting that the convergence is not only foreseeable but also necessary.

As it stands, many of the industrial converters that would be involved in a hybrid system are designed for “high active mode efficiency and low no-load power consumption, complying with the latest global energy efficiency standards”, as XP Power explains.

2.      Smart meters

We know this next innovation might not necessarily be news to you. After all, how many times have you received a letter in the post demanding that you “consider” switching to a smart meter?

The impact

On an individual level, these let individuals monitor their energy usage in real-time and automatically generate readings that can be easily communicated to consumers. Not only does this give them the option to find cheaper tariffs, but it also helps them identify the energy usage levels for different household appliances.

So far, it looks like this is working. Evidence suggests that there is a strong correlation between owning a smart meter and consistent changes in behavior in terms of attitudes towards energy usage and savings.

The future

A gradual incorporation of smart meters in households has been advocated by the EU, for similar reasons to the role of smart grids. Speaking with Smart Energy International, WiFore Consulting CTO Nick Hunn claimed that smart meters must be designed and implemented in all future energy security plans: “as we move to more renewables, distributed storage and an all-electric future, energy becomes a two-way process. That needs a completely different approach to meter design”.

3.      Battery storage

Following a similar principle to the household batteries in your TV remote, battery energy storage systems (BESS) play an increasingly valuable role in maintaining and supporting renewable energy. These storage systems accumulate and control energy generated from various sources of renewable energy, such as wind and solar, and reserve it using much larger battery technology.

The impact

Smart battery storage works by using intelligent software to coordinate the production of energy. This includes choosing when to store it to create reserves for future use, or allow it to be distributed into the grid. That way, energy can be released from the storage system when demand is at its highest, which limits total costs and removes the superfluous flow of electricity. Among other benefits, these help with peak shaving, whereby the storage systems “guarantee that no power above a predetermined threshold will be drawn from the grid during peak times”, as Ideal Energy Solar explains.

The future

Looking ahead, their wide-scale application seems inevitable. Power and Beyond argue that “with the rise of electric vehicles bringing lots more innovation in the battery space and the growth of solar power significantly driving down cost, now is the time when energy storage matters.” In the meantime, energy storage solutions could reach a new high in terms of overall market share by 2025, with the new pan-European automatic frequency restoration reserve market (aFFR) set to launch in 2022.

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10 Essential Data-Driven B2B Email Marketing Strategies

10 Essential Data-Driven B2B Email Marketing Strategies

Big data technology is leading to a lot of changes in the field of marketing. A growing number of marketers are exploring the benefits of big data as they strive to improve their branding and outreach strategies.

Email marketing is one of the disciplines that has been heavily touched by big data. If you want to make the most of your big data strategy, you should keep reading to learn how to incorporate data into email marketing.

How to Use Data to Improve Your Email Marketing Strategy

Email is one of the oldest and most reliable digital marketing tools around for good reason—it works. In fact, a recent study by the Direct Marketing Association showed that email marketing produces an average return on investment (ROI) of $44 for every dollar spent. That’s why it’s so important for businesses to get their email marketing campaigns right.

You can get even more value from email marketing if you leverage data strategically. Here are 10 essential strategies for email marketing success with data analytics.

Always Provide Value

Providing value means delivering content that educates, entertains, or both. It could be a helpful blog post, an insightful whitepaper, or just a quick tip. It should always be worth the reader’s time. If you’re not providing value, you will not get very far with your email marketing campaigns.

Yo can use big data to make this easier. One option is to use data mining tools to learn more about the challenges people are making. You can assimilate data from various polls to learn more about the pain points of your target customers and create content that addresses them.

Keep it Short and Sweet

In the age of information overload, people are bombarded with content from all channels. It’s important to ensure that your emails can be quickly scanned and digested. This means keeping your emails short, sweet, and to the point. Get straight to the point, and don’t ramble on.

Personalize Your Emails

Big data technology has led to advances in personalization. You can use AI technology to personalize content to give the best possible experience.

Personalization is key when it comes to email marketing. You want to make sure that your emails are addressed to the individual recipient by name. If you have any other relevant information about them (such as their company size, location, or pain points), be sure to include that in the email as well.

Test Different Calls-to-Action

Every email you send should have a call-to-action (CTA) included. This could be as simple as “Download our new eBook” or “Register for our upcoming webinar.” Your CTA should be relevant to the content you’re providing, and it should be clear what the reader needs to do next.

You will need to test different CTAs, which is going to require data analytics tools. Many email marketing solutions such as Hubspot and Aweber have analytics interfaces that make it easier to test different elements in your marketing funnels, such as CTAs. You want to use these analytics interfaces to optimize your CTAs for the best CTR and conversion rates.

Test Different Professional Email Signature

You should also use data analytics to test different email signatures. A professional email signature is a must-have for any B2B marketer. It’s a simple way to promote your brand and increase awareness about your company, products, or services. Plus, it can also drive traffic back to your website or landing pages. Be sure to include links to your social media profiles so people can connect with you through multiple channels.

Generate Timeless Content

Timeless content is content that will be just as relevant and valuable 6 months from now as it is today. This is the type of content you want to include in your email marketing campaigns. It’s evergreen content that will continue to generate leads and sales long after you hit “send.”

You can use data mining tools to find the best content on the web for your niche. Tools like SEMRush can make this a lot easier, since they have sophisticated data analytics tools. This will help you create the best content for your email funnels.

Use Alt Text for Images

Alt-text is the text that appears when an image fails to load. It’s also read by screen readers, which are used by visually impaired people. Including alt text with your images is a great way to make your emails more accessible and improve your deliverability rates.

You will want to use data mining tools to find the alt tags of similar images on the web. Tools like BeautifulSoup and Screaming Frog SEO use data mining algorithms to find the alt tags of different images, which you can use in your own content.

Test, Test, Test

Email marketing is all about experimentation. You must constantly test different subject lines, calls-to-action, images, and email templates to see what works best for your audience. Only through testing will you be able to optimize your campaigns and get the best results possible.

Again, you can’t possibly do this without the data analytics interface of your email marketing platform. Make the most of it!

Pay Attention to the Details

The little things matter when it comes to email marketing. From the “From” field and subject line to the design and layout of your email, all of these details can affect your open and click-through rates. Take the time to ensure that every detail is perfect before hitting “send.”

Automate Your Emails

Another huge benefit of big data is that it has made it easier to automate your email marketing funnels. With email automation, you can send triggered emails based on specific actions that your contacts take (or don’t take). For example, you can set up an automated welcome email to be sent to new subscribers or a follow-up email for people who don’t open your first email. Automating your emails can save you a lot of time and help you stay in contact with your leads and customers.

You can also hire Advertas to help you reach your email marketing objectives.

Big Data Has Made Email Marketing Easier than Ever

Email marketing is a critical part of any successful B2B marketing plan. Big data technology can help immensely. By following these strategies, you can ensure that your campaigns are successful and that you can generate the leads and sales you need.

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Fintech App Development Discover the Benefits of Using AI

Fintech App Development Discover the Benefits of Using AI

The rapid pace of digitization has caused fintech markets to boom around the world. The market for Fintech was over $112 billion last year, but is projected to be worth over $333 billion by 2028.

During this wave of disruption, successful business owners and startup founders need to understand the technologies that are driving the industry forward. Artificial intelligence is one of the most important trends pushing the envelope of what’s possible with fintech. Let’s talk about AI’s benefits in fintech, especially in terms of app development and how you can use it to maximize the success of your business.

When Fintech Meets Artificial Intelligence

AI can benefit financial technology in a number of ways. It can result in more active user engagement, secure payments, and efficient workflows. From these benefits ultimately come cost savings. Since the tasks AI can perform reduce time significantly, teams can invest more in other aspects of a project. So it is not surprising that AI use ​​in the fintech market is growing. Mordor Intelligence predicts that the technology’s value in the industry will grow at a CAGR of 23.17% until 2025.

Fintech app developers are adopting AI in their applications to solve a number of business challenges, such as providing a high level of customer support. Since AI can automate customer service to a degree with chatbots, AI assistants and other methods, teams can spend less time on repetitive questions and focus on more complex cases.

Artificial intelligence is also adept at data processing and analytics, both useful tools for financial applications. It can be leveraged even more effectively with Agile technology. Interpreting data to generate valuable insights on analytics for consumers can help users improve their financial habits and achieve their financial goals easily. It can also help businesses plan for the future and make wiser investing choices. That’s why artificial intelligence is widely used in budgeting apps, financial assistant apps and investment platforms. For example, the world’s popular money management apps such as Cleo and Fyle have AI at their core and leverage the power of this technology to provide intelligent financial tools. This gives them competitive advantages and opens additional business opportunities.

Top Use Cases of AI in Fintech

Let’s review some use cases to get a deeper insight into how AI can empower the fintech app development process.

OCR for Processing Receipts and Invoices

Document digitization is one of the most time-consuming tasks that finance teams face. In addition to this, often the concept of a fintech application includes fast and automatic recognition for receipts, invoices, and other financial documents to enable accurate spending tracking, KYC/AML processes, reporting, and so on.

Based on Optical Character Recognition (OCR), apps like Extracta, and Klippa solve these challenges by converting financial documents into accessible text in minutes. All the user has to do is take a photo of the invoice with their phone and run it through the program. Simplifying the process like this makes it easier to keep track of costs and reduces the manual effort required to do so. This can also reduce the cost of data entry for businesses, as this process becomes automated with OCR.

Using OCR for KYC processes allows you to automate the verification of documents such as an identity card, driver’s license or passport. The AI ​​should be able to determine the required data like name, photo, address, and contact details and extract that info for further validation. All this is done in a matter of minutes and significantly speeds up the identity verification process.

Natural Language Processing for Speech Recognition and Voice Assistants

One of the many challenges fintech has had to overcome in the past few years is how to automate financial assistance. With the rise of consumer assistants like Siri, Amazon Alexa, and Google Assistant, it seems possible that AI can provide financial assistance and advice as well. Many banks have already begun to utilize chatbots powered by natural language processing, also known as NLP. This technology leverages AI to communicate with humans more seamlessly.

NLP chatbots can automate the workflow and collect valuable data through these interactions. They are also sensitive to details like mood and satisfaction. With NLP, AI can also search through documents more effectively and present them to users in a streamlined form. By taking complex documents and simplifying them into a more digestible format, AI can help users understand how to improve their financial behaviors.

AI Biometrics for Authentication

Keeping financial data secure is essential to prevent fraud. That’s why AI biometrics in fintech applications are becoming more common. It’s a misconception that biometrics are impossible to trick. Many hackers aren’t using complicated console commands to hack into your system. Instead, they’re using printed-out selfies to fool facial recognition and fake fingerprints. While traditional biometrics may be fooled by these spoofing methods, artificial intelligence is more resilient. AI can detect unusual patterns in behavior to prevent threats.

Predictive Analytics

Artificial intelligence works best when paired with real-time data. With financial technology apps, predictive analytics has a number of benefits. For example, users can get forecasts on their income or expenses in the future. This is a useful feature included in many budgeting and financial assistant apps.

Predictive analytics is helpful not just for consumers. In fact, there are more profound applications of predictive analytics for fintech businesses. Being able to forecast demand and revenue, improving cash flow management, and predicting financial risks are all ways that fintech companies can remain relevant in the modern market.

Key Challenges of AI in Fintech

As exciting as the benefits of artificial intelligence for fintech app development are, to be successful, you need to understand all the challenges of implementing AI in fintech. Partnering with expert fintech developers will help you get around these complexities and create a quality AI-powered fintech app.

Bias

Many assume that artificial intelligence is the most objective way of completing tasks. However, this isn’t true. Although AI may act autonomously, it relies on our instructions. That data is provided by humans and is subject to bias. Good engineers know how to mitigate this bias. The first basic step is to compare and test different samples of training data for representativeness.

Compliance and Privacy

Regulations are another important challenge. Since AI relies on high-quality and high quantity data, staying compliant with data collection laws is crucial. A few laws that firms will need to pay close attention to are SOC2 Type II, HIPAA, GDPR, and CCPA. The full list of regulatory frameworks will depend on your region and what kind of data your application will handle.

Success Metrics

Metrics of success for one fintech firm may be different than another. Users may not check the app every day and instead have different behaviors, such as once a month or during an important financial change. Just because they aren’t using the app as often doesn’t mean the app has failed. Seeing if the app has helped change their behavior while offline may be even more important. The user should feel more in control of their finances as a result of using the application.

Wrapping Up

The development of fintech is closely related to the adoption of AI technology. Artificial intelligence helps create smarter applications that can improve the efficiency of financial management and compete with traditional financial agents.

However, implementing AI in fintech requires deep technology expertise, so don’t hesitate to enlist the support of experienced fintech developers. With the right team, you can leverage the power of AI to maintain your fintech application’s relevance for the foreseeable future.

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Use Data Analytics to Benefit from Fibonacci Retracement Levels

Use Data Analytics to Benefit from Fibonacci Retracement Levels

Data analytics has led to some major changes in the field of finance. Financial institutions invest heavily in big data technology in order to offer the highest ROI to their clients.

However, individuals are also using big data to improve their own financial strategies. One of the ways that savvy investors are leveraging big data is through the use of technical analysis. This helps them increase the ROI of their own trading strategies.

You can use data analytics to build neural networks to take advantage of Fibonacci retracement. This is one of the best ways to grow your portfolio through using data analytics as a financial trader.

Using Data Analytics to Improve Your Trading Strategy with Fibonacci Retracements

You can use data analytics to improve your technical analysis strategy with Fibonacci retracements. However, you first need to understand what these are.

How can you use Fibonacci retracements? Fibonacci retracement levels are horizontal lines that indicate where support and resistance are likely to occur. They are based on Fibonacci numbers, which are a series of numbers in which each number is the sum of the previous two.

You can use data analytics technology to make the most of these technical analysis tools. Towards Data Science has a great guide on this, which entails building neural networks to handle them.

The most popular Fibonacci ratios are 23.6%, 38.2%, and 61.8%. These ratios can be found by dividing one number in the series by the number immediately following it. For example, 21 divided by 34 equals 0.618, or 61.8%.

Traders use Fibonacci retracement levels to identify potential support and resistance levels on a price chart. They will get more value from them if they use data analytics effectively.

How to Use Fibonacci Retracement Levels?

Fibonacci retracement levels are typically used as a larger technical analysis strategy. For example, a trader may identify a stock in a long-term uptrend and then use Fibonacci retracement levels to time entries during pullbacks.

The most important thing to remember when using Fibonacci retracement levels is that they are not exact numbers but rather zones where support or resistance is likely to occur. Therefore, using them in conjunction with other technical indicators or chart patterns is often best.

It can be difficult to identify these zones on your own, which is why data analytics tools can be so helpful. They have AI algorithms that can identify important data points and help you determine the right buy and sell points to boost profit.

Fibonacci Retracements vs. Fibonacci Extensions

How to use Fibonacci retracement? While Fibonacci retracement levels identify potential support and resistance levels, Fibonacci extensions are used to predict potential price targets.

Fibonacci extensions are based on the same Fibonacci numbers as Fibonacci retracement levels. However, instead of dividing one number in the series by the number immediately following it, Fibonacci extensions use division by two numbers further down the sequence. So, for example, 23.6% is found by dividing 21 by 89 (21/89=0.236).

Fibonacci extension levels are typically used as a larger technical analysis strategy. For example, a trader may identify a stock in a long-term uptrend and then use Fibonacci extension levels to predict potential price targets.

What Do Fibonacci Extension Levels Tell You?

Fibonacci extension levels can help you predict potential price targets. However, it is important to remember that they are not exact numbers but rather zones where the price is likely to reach. Therefore, using them in conjunction with other technical indicators or chart patterns is often best.

Fibonacci Retracements vs. Fibonacci Arcs

How to use Fibonacci retracement? While Fibonacci retracement levels and Fibonacci extension levels are based on the Fibonacci numbers, Fibonacci arcs are based on the Fibonacci ratios.

Fibonacci arcs are half circles drawn from a price move’s high to the low. The most popular Fibonacci ratios used for Fibonacci arcs are 23.6%, 38.2%, and 61.8%. These ratios can be found by dividing one number in the series by the number immediately following it. For example, 21 divided by 34 equals 0.618, or 61.8%.

How to use Fibonacci retracement? Fibonacci arcs are typically used as part of a larger technical analysis strategy. For example, a trader may identify a stock in a long-term uptrend and then use Fibonacci arcs to predict potential price targets.

What Do Fibonacci Arcs Tell You?

Fibonacci arcs can help you predict potential price targets. However, it is important to remember that they are not exact numbers but rather zones where the price is likely to reach. Therefore, using them in conjunction with other technical indicators or chart patterns is often best.

The Formula for Fibonacci Retracement Levels

The Fibonacci retracement levels are based on a mathematical formula to calculate the Fibonacci numbers. The formula is as follows:

Fn = Fn-1 + Fn-2

where

Fn = the nth Fibonacci number

Fn-1 = the previous Fibonacci number

Fn-2 = the Fibonacci number before that

The first two Fibonacci numbers are 0 and 1, so the formula starts with:

F0 = 0

F1 = 1

Each subsequent Fibonacci number is the sum of the previous two. So, the next Fibonacci number would be:

F2 = F1 + F0 = 1 + 0 = 1

and the one after that would be:

F3 = F2 + F1 = 1 + 1 = 2

Why are Fibonacci Retracements Important?

How to use Fibonacci retracement? Fibonacci retracement levels are important because many traders use them to predict potential support and resistance levels.

The Fibonacci numbers are a sequence of numbers first discovered by Italian mathematician Leonardo Fibonacci in the 13th century. The sequence starts with 0 and 1, and each subsequent number is the sum of the previous two. So, the next number in the sequence would be 1+0=1, followed by 1+1=2, 2+1=3, 3+2=5, 5+3=8, 8+5=13, and so on.

The Fibonacci numbers have been found to occur naturally in many places, including in the arrangement of leaves on a stem and the spiral of a seashell.

The Fibonacci ratios are derived from the Fibonacci numbers and are used by traders to predict potential support and resistance levels. The most popular Fibonacci ratios are 23.6%, 38.2%, and 61.8%. These ratios can be found by dividing one number in the series by the number immediately following it. For example, 21 divided by 34 equals 0.618, or 61.8%.

Fibonacci retracement levels are important because many traders use them to predict potential support and resistance levels. However, it is important to remember that they are not exact numbers but rather zones where the price is likely to reach. Therefore, using them in conjunction with other technical indicators or chart patterns is often best.

Use Data Analytics Tools to Create Neural Networks to Spot Fibonacci Retracement Levels

Data analytics technology can be very effective at creating neural networks, which is invaluable for financial traders. You will want to make the most of them, especially if you are depending on Fibonacci retracement levels as indicators for your technical analysis trading strategy.

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4 IT Management Best Practices Data-Driven Businesses Must Practice

4 IT Management Best Practices Data-Driven Businesses Must Practice

Data-driven businesses are far more successful than companies that don’t utilize data to their advantage. Unfortunately, they often find that managing their data effectively can be a challenge.

Companies that rely on big data need a reliable IT department. You have to make sure that your IT infrastructure is adequately equipped to handle the volume of data your company will be processing and that it will be properly secured.

Companies that Rely on Big Data Must Have a Functional IT Department

Due to modern advancements in big data technology, the IT sector is becoming more competitive with each passing day. Companies leverage modern technologies to streamline business operations and gain a competitive edge in this competitive marketplace. According to EasyVista, more than 70% of companies worldwide will have invested in digital technologies in 2022, which will increase in the coming years. This figure will increase as big data becomes even more important.

These numbers indicate that embracing IT organizational structure best practices 2021 is the key to staying competitive, streamlining business operations, and outperforming competitors.

While businesses are constantly looking for ways to grow as a whole, sometimes they often invest in the technology that generates no results. In fact, according to Gartner, it is expected that business owners will spend $750 million on investing in ineffective features of IT tools, up from $600 million in 2019. Thus, it is wise to implement a few essential ITSM best practices to combat overspending.

You have to make sure that you invest in the right technology to make the most of big data. Keep reading to learn how to do this.

What Is the Role Of IT Management In a Data-Centric Organization?

IT management plays a crucial role in every organization. Companies that rely on big data have to use it even more. IT managers are responsible for planning, coordinating, communicating, and leading computer-based activities in an organization. Moreover, they are also responsible for researching new technologies and understanding how they can help the business grow.

In short, they help determine the department’s needs and implement the best practices to fulfill the organization’s system requirements. Besides researching new technologies and streamlining the tech operations, they also seek to mitigate IT risks, such as threats to cybersecurity and misalignment between IT professionals and business requirements.

The IT department in an organization focuses on meeting the following objectives:

Enhance the overall IT processDeveloping employees’ skills to meet the future organization’s needsMaintenance of transparency within the companyControl the financial aspect of the IT department

IT management is also responsible for organized IT business operations. They implement the best practices and regularly measure the performance to understand which areas need improvement. This will help you make the most of your data resources.

4 Best Practices For IT Management in Companies that Rely on Big Data

Companies that depend on big data will need to follow these practices to create a functional IT department.

1.      Build A Clear Strategy

Initiate your journey by defining your business goals and vision. Develop a roadmap vision for your IT service management goals, as well as the types of data that you intend to store. Then, create a systematic approach to measure your efforts, define KPIs, and the state at every stage of the implementation process.

A roadmap should cover all three essential domains, including:

Front-end IT: It includes service design, service operations, and service transition. The perfect example of front-end IT is Service Desk applications.Middle IT: It includes business frameworks and automation efforts.Back-end IT: It includes leveraging modern technologies such as AI, IoT, robotics, etc., to help executives make informed business decisions.

You have to get this part right as a company that depends on big data.

2.      Invest In The Right Technology

A business that invests in the right technology invests in its overall growth. It will also make employees more efficient at work, resulting in streamlining business operations. However, choosing the right tool can be challenging, especially for startups.

The reason is that they don’t determine their business needs, and they have a tight budget, too. However, it doesn’t mean investing in cheap tools and regretting later. Instead, it is wise to monitor your business needs and invest accordingly.

Digital transformation has taken over the corporate world like a storm. Most businesses have embraced digital transformation technologies to automate tasks and improve the employee experience. Artificial Intelligence performs human-like tasks like problem-solving, speech, and text recognition.

Moreover, it can accomplish specific tasks by analyzing vast amounts of data and recognizing recurrent patterns in these data recurrent patterns. According to Transparency Market Research, the global market for AI is estimated to gain 36.1% CAGR between 2016 and 2024. It is expected to reach a soaring height of $3,061.35 billion by 2024. Thus, investing in such technologies can benefit your business a lot in the long run.

3.      Seek Help from Top Management

Your decisions can make or break your business. To ensure business sustainability and revamp the business operations, involving senior management in the process can be a better option. They are well-experienced executives and know what’s good and bad for a business.

Without the involvement of senior management in the business decision process, revamping the IT department for business success may become an enormous challenge.

4.      Remote Work Expansion

It would be almost impossible to discuss the recent changes in the corporate world without mentioning the importance of remote working. Thanks to the pandemic; it introduced the remote work or work-from-home model across companies worldwide.

Today, companies must adapt to modern corporate world changes to boost employees’ productivity and overall business growth. However, if you believe that returning employees to the office will enhance productivity, you are mistaken. According to Flexjobs, more than 94% of companies report that their productivity has been the same (67%) or higher (27%) since employees started working remotely.

Another benefit of embracing the remote work model is that you are no longer limited to hiring local IT talent. You can boost your business efficiency by hiring talented folks across boundaries.

IT Management is Crucial for Companies that Depend on Big Data

Adapting to modern changes is challenging, even for companies that utilize big data effectively. You can’t create a successful data-driven company without a dependable IT department. You need to be clear about your goals and accordingly plan, communicate, and implement the right strategies that contribute to your business growth.

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Now in preview, BigQuery BI Engine Preferred Tables

Now in preview, BigQuery BI Engine Preferred Tables

Earlier in the quarter we had announced that BigQuery BI Engine support for all BI and custom applications was generally available. Today we are excited to announce the preview launch of Preferred Tables support in BigQuery BI Engine!  BI Engine is an in-memory analysis service that helps customers get low latency performance for their queries across all BI tools that connect to BigQuery.  With support for preferred tables,  BigQuery customers now have the ability to prioritize specific tables for acceleration, achieving predictable performance and optimized use of their BI Engine resources. 

BigQuery BI Engine is designed to help deliver freshest insights without having to sacrifice the performance of their queries by accelerating their most popular dashboards and reports.  It provides intelligent scaling and ease of configuration where customers do not have to worry about any changes to their BI tools or in the way they interact with BigQuery. They simply have to create a project level memory reservation.  BigQuery BI Engine’s smart caching algorithm ensures that the data that tends to get queried often is in memory for faster response times.  BI Engine also creates replicas of the data being queried to support concurrent access, this is based on the query patterns and does not require manual tuning from the administrator.  

However, some workloads are more latency sensitive than others.  Customers would therefore want more control of the tables to be accelerated within a project to ensure reliable performance and better utilization of their BI Engine reservations.  Before this feature,  BigQuery BI Engine customers could achieve this by using separate projects for only those tables that need acceleration. However, that requires additional configuration and not the best reason to use separate projects.

With the launch of preferred tables in BI Engine, you can now tell BI Engine which tables should be accelerated.  For example, if you have two types of tables being queried from your project.  The first being a set of pre-aggregated or dimension tables that get queried by dashboards for executive reporting and the other representing all tables used for ad hoc analysis.  You can now ensure that your reporting dashboards get predictable performance by configuring them as ‘preferred tables’ in the BigQuery project.  That way, other workloads from the same project will not consume memory required for interactive use-cases. 

Getting started

To use preferred tables, you can use cloud console, BigQuery Reservation API or a data definition language (DDL) statement in SQL.  We will show the UI experience below.  You can look at detailed documentation of the preview feature here

You can simply edit existing BI Engine configuration in the project.  You will see an optional step of specifying the preferred tables, followed by a box to specify the tables you want to set as preferred.

The next step is to confirm and submit the configuration and you will be ready to go! 

Alternatively, you can also achieve this by issuing a DDL statement in SQL editor as follows:

code_block[StructValue([(u’code’, u’ALTER BI_CAPACITY `<PROJECT_ID>.region-<REGION>.default`rnSET OPTIONS(rn size_gb = 100,rn preferred_tables = [“bienginedemo.faadata.faadata1″]);’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e6e28921450>)])]

This feature is available in all regions today and rolled out to all BigQuery customers. Please give it a spin!

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How Credit Providers and Lenders Use Data Effectively

How Credit Providers and Lenders Use Data Effectively

Big data technology has had massive implications for the financial industry. Banks, credit card companies and other financial service providers are leveraging big data in unprecedented ways.

One of the biggest benefits of big data is with loan and mortgage processing. The use of data can be crucial for credit card companies and those offering loans and mortgages. The Forbes Technology Council discussed these benefits in an article last spring.

The ability to carry out checks on credit histories, income, employment and expenses can play a huge role to learn from previous customers and determine which are the best customers to pursue and which to avoid.

We speak to two finance startup founders to get a better idea of how they use data effectively to do underwriting, grow and scale their businesses.

Initial Approval

Data is vital for the initial approval of a credit card or loan. A customer’s journey typically starts with filling in a few details via an online form, often taking around 5 to 10 minutes. From this, the lender is able to determine a customer’s profile and whether they meet the initial criteria which might be having a certain minimum age (e.g 18 or 21), having a permanent residence and regular employment.

This initial data can help siphon out any applicants who will not be eligible and who are not worth pursuing.

For Underwriting and Funding

“For underwriting and deciding who is going to be approved for a loan, the use of data is everything,” explains Richard Dent of Finger Finance. This is a great example of how big data has changed the financial industry.

“We are always looking at data, including new customers that come in and fundamentally historical data and trying to spot trends of customers who paid us on time and who have not.”

“We use this data to change our decision rules, and this could let in more customers to the final stages or stop any that are not deemed worthy. We will find examples whereby a woman over 35 in a certain location and earning a certain amount have a 8% chance of defaulting on their loans. From here, we know that we can operate profitably at a 15% default rate – we will use data to alter our decision rules and look at this type of customer more favorably.”

To Minimize Default Rates

Trying to lower default rates is of paramount importance to credit card, mortgage and loan vendors. You ideally want to find customers who repay and do not default, even though this is inevitable.

“We therefore try to find shared characteristics of customers who default,” continues Dent.

“Do they live in certain areas, have certain professions or is it the loan amount or credit limit that they cannot handle?”

“We will analyze this data constantly to improve our processes and lower the default rate wherever possible.”

To Reduce Fraud

“Fraud is a huge financial burden for us as a lender and credit provider,” explains Richard Allan of business loans provider Funding Zest.

“Whether it is through fake applicants or stolen details, fraudsters are always looking for new and innovative ways to game our system to loans with no intention of paying them back.”

“We use data to find any patterns in the fraud. There are some obvious cues when the name of the customer does not vaguely resemble their email address or if we have seen the same mobile number come up on various occasions.”

“Otherwise, we look for patterns in the time of day, number of applications and even loan amounts to spot fraud and avoid it from being a huge liability on our business.”

It is expected that big data will continue to change the trajectory of the financial industry for years to come.

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5 Huge Benefits of Financial Analytics for Your Business

5 Huge Benefits of Financial Analytics for Your Business

Data analytics technology has become a pillar in modern business. A growing number of companies are utilizing data analytics to improve their operating strategies.

One of the most important functions that data analytics is helping with is finance. Companies are projected to spend just shy of $20 billion on financial analytics services in 2030.

Are youu wondering how your company can benefit from financial analytics? Keep reading to learn more.

Financial Analytics Offers Huge Benefits for Companies of All Sizes

You can do a lot to improve your business’ financial performance by using data analytics. This can greatly impact your ability to hire the best people, maintain staff’s salaries and effectively grow your business.

So while a lot of us are busy focusing on the day-to-day tasks of running a business, there are many things that we can do to boost our business’ financial performance and make it run more effectively. The right data analytics tools can be very valuable.

Here are some financial analytics tools that are worth exploring:

TrendingView is a financial analytics tool that helps you create useful financial visualizations.FactSet Research Management is a financial analytics tool that helps companies take advantage of opportunities more quickly.eMoney is a financial analytics tool designed for financial advisors, but your business can try using it as well.

This article will list five fundamental focus areas for a business’ financial performance. You can keep reading to learn how to use financial analytics technology to make the most of them.

1.      Chase any outstanding payments

This is extremely important, regardless of whether the payments due are small or large. You always want to ensure that there are clearly stated terms and conditions with the payment due date. You need to pay attention to this whenever contracts are signed for goods and services. It is important to set reminders closer to the time to remind whoever may owe you to pay.
For some businesses, it may be worth investing in certain financial analytics software that takes payments automatically. This can remove the administrative frustrations of chasing the other party for the outstanding amount.

A lot of financial analytics tools make it easier to keep track of outstanding invoices. You can easily mark any billables and use your analytics tools to see the state of payment.

2.      Find ways to rearrange expenses

Expenses should be evaluated every month or quarter. You might have standing orders for a service you are paying for that you no longer need for the remainder of the year, so these can be eliminated.
Other expenses that could be changed include shopping around online for the best insurance deals, offering exchanges in goods or services instead of as, or making up for discounted payments.
Finally, if your company is paying for office space through renting, it may be worth adopting a hybrid working model and only using communal workspaces on the days you need to get together. This can help save on expenses, including rent, utility bills, and transport.

Data analytics tools make it easier to take a deep dive into your finances. Some budgeting tools will connect with your bank account and data mine information about your spending habits. You can use this data to make more informed decisions.

3.      Offer multiple payment options

“Offering payment options open to customers can open up your business to different markets,” explains David Soffer of financial price comparison, Proper Finance.

“This could include paying in cash, credit, PayPal, or even via stalled payment services such as Klarna. By providing more payment options, this will allow your business to cater to a greater number of potential customers, consequently resulting in more purchases being made.”

“This can help to increase sales and avoid customers going elsewhere.”

4.      Make use of Government grants where possible

The UK Government offers many financial grants to businesses, both small and large. This can be a great way to borrow money to invest in infrastructure, talent, and technology to help make your business more efficient and thus consequently improve your financial performance. The Government offers grants to companies that may be interested in pursuing the following; research and development, innovation, exporting, and expansion.

Data mining tools make it a lot easier to find government grants. Some of them can aggregate data from search engines, so you don’t have to manually look for new grants all the time. A full listing of government grants that businesses can apply for can be found here: https://www.gov.uk/business-finance-support.

5.      Keep track of your cash flow

One of the most important benefits of financial analytics tools is that they can help manage your cash flow more easily. Trovata and CashAnalytics are two of the best tools for doing this. They can help you resolve many financial issues.

“It is highly important to stay on top of your business’ cash flow,” says Richard Allan of funding startup, Capital Bean.

“This includes monitoring all ingoings and outgoings. This can help you to identify trends, as well as plan ahead for the financial year – working out where to budget, what to sell, as well as which offerings you may need to forgo.”

“Keeping track of your cash flow could simply consist of routinely checking in on expenses every month or quarter but will certainly prove to be beneficial in saving money on certain areas of the business that may not require investment right now.”

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Is it OK to Send Emails to a Database That Isn’t Yours?

Is it OK to Send Emails to a Database That Isn’t Yours?

Data privacy is more important than ever. Seventy-seven percent of customers in the United States at at least somewhat concerned about data privacy, according to a 2019 survey by Pew Research. You can’t just focus on protecting consumer data privacy because they expect it. Data protection is now a legal requirement.

The GDPR regulation which we are all too familiar with was introduced in 2016, signaling an end to bulk-sent chainmail which recipients had not consented to receiving. It is the most drastic legislation ever passed to protect data from being leaked.

Prior to this point, marketers were able to email whomever they liked, however much they liked, and it was irrelevant as to how they obtained their contact details. The new legislation means that marketers must take more stringent measures to protect their data. While some people are still debating whether data privacy is a right or a luxury, there is no disputing the fact that it is a legal expectation, at least for companies doing business in Europe.

Now, five years on from GDPR’s introduction, companies and marketers must stay firmly in line with data privacy regulation. This means that they are prohibited from emailing recipients who have not consented to receiving their mail. If marketers fail to comply with these rules, they could be levied with a fine by the ICO following an investigation. In simple terms, it’s risky (and illegal) to not comply with GDPR.

What Are The Data Privacy Rules on Email Marketing And When Can I Send Marketing Emails?

Data privacy requirements don’t usually prohibit you from messaging people that want to be contacted. As long as those you are emailing have opted in to receive contact from you, then you are well within your rights to contact them. This usually refers to emails or text messages, which could contain updates, information, offers, competitions, discount codes and much more. When customers tick that box that says ‘opt into contact’ or words to those effects, it means they want to hear from you, and you are well placed to get in touch.

However, when customers agree to hearing from you, they can change their mind at any point. For that reason, it is pivotal that marketing emails provide the option for readers to unsubscribe from your mailing list. You need a regularly updated database to meet their needs. You have to avoid data privacy complacency at all costs. Failing to honor their request can land you in trouble, and can damage your reputation as you show a lack of care for customers.

You should not seek to buy email addresses, contact lists or similar data from third parties. This is usually looked upon unfavorably, as you are essentially buying data and the ability to contact people who haven’t consented to your contact.  Even so, engaging in brand partnerships can provide you with the opportunity to contact their clientele, with genuine reason and through respectable means. When doing so, you should outline to recipients how you retrieved their details and, yet again, provide the option to unsubscribe.

How Can I Safely Send Emails?

If you are trying to contact somebody for a particular purpose, and their contact details have been made visible by them online, then you are generally safe to send them an email.

‘Contacting someone is perfectly fine,’ said Ben Sweiry of installment loans provider, Dime Alley. “As long as you are not sending endless emails their way, there is nothing wrong with finding someone’s email address if it is already online and sending someone a message.”

“Respecting customer privacy is essential to a strong business. Customers are your audience, and your audience should be treated well.”

“It is also perfectly fine to send someone a follow-up email. Where the line is drawn is where you opt them into regular contact where they have not consented. Adding them to your subscription list without their say-so is in breach of GDPR and can have serious consequences if they report you.”

Partnering with brands allows you to get in touch with a new and wide audience, and this is a safe, and customer-centric means of reaching a greater client base. However, to be safe and respectful, you should always include how you received their contact details, and how to unsubscribe.

If you are still unsure, or have further queries, you should have a look at the GDPR guidelines online.

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