Big Data Storage and Data Virtualization

| April 1, 2019

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The big data concept has been adopted by all kinds of organizations. Data can be "big" because of its enormous volume, because it's not structured in the traditional way, or because it's streaming in with enormous quantities. The business advantages of big data systems are clear, they can improve, deepen, and strengthen an organization's analytical capabilities with relatively attractive infrastructure economics.Until now the data virtualization servers have focused on making big data processing easy. They can hide the complex and technical interfaces of big data storage technologies, such as Hadoop and NoSQL, and they can present big data as if it's stored in traditional SQL systems. This allows developers to use their existing skills and to deploy their traditional ETL, reporting, and analytical tools that all support SQL. Additionally, the products and existing skills can extend the data security mechanisms for accessing and processing big data across multiple big data systems. Butcwith scale and performance rising, making big data processing easy is not enough anymore. As such, the next challenge for data virtualization is parallel big data processing.

Spotlight

Mode

Mode was founded in 2013 by Derek Steer, Benn Stancil, and Josh Ferguson. While working in analytics at Yammer, they saw Amazon Redshift and other cloud-based data warehouses making it possible for companies of all sizes—not just tech giants—to gather and analyze data.

OTHER ARTICLES

HOW THE CORONAVIRUS (COVID-19) MIGHT BE STOPPED BY DATA SCIENCE

Article | March 16, 2020

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How can machine learning detect money laundering?

Article | December 16, 2020

In this article, we will explore different techniques to detect money laundering activities. Notwithstanding, regardless of various expected applications inside the financial services sector, explicitly inside the Anti-Money Laundering (AML) appropriation of Artificial Intelligence and Machine Learning (ML) has been generally moderate. What is Money Laundering, Anti Money Laundering? Money Laundering is where someone unlawfully obtains money and moves it to cover up their crimes. Anti-Money Laundering can be characterized as an activity that forestalls or aims to forestall money laundering from occurring. It is assessed by UNO that, money-laundering exchanges account in one year is 2–5% of worldwide GDP or $800 billion — $3 trillion in USD. In 2019, regulators and governmental offices exacted fines of more than $8.14 billion. Indeed, even with these stunning numbers, gauges are that just about 1 % of unlawful worldwide money related streams are ever seized by the specialists. AML activities in banks expend an over the top measure of manpower, assets, and cash flow to deal with the process and comply with the guidelines. What are the punishments for money laundering? In 2019, Celent evaluated that spending came to $8.3 billion and $23.4 billion for technology and operations, individually. This speculation is designated toward guaranteeing anti-money laundering. As we have seen much of the time, reputational costs can likewise convey a hefty price. In 2012, HSBC laundering of an expected £5.57 billion over at least seven years.   What is the current situation of the banks applying ML to stop money laundering? Given the plenty of new instruments the banks have accessible, the potential feature risk, the measure of capital involved, and the gigantic expenses as a form of fines and punishments, this should not be the situation. A solid impact by nations to curb illicit cash movement has brought about a huge yet amazingly little part of money laundering being recognized — a triumph rate of about 2% average. Dutch banks — ABN Amro, Rabobank, ING, Triodos Bank, and Volksbank announced in September 2019 to work toward a joint transaction monitoring to stand-up fight against Money Laundering. A typical challenge in transaction monitoring, for instance, is the generation of a countless number of alerts, which thusly requires operation teams to triage and process the alarms. ML models can identify and perceive dubious conduct and besides they can classify alerts into different classes such as critical, high, medium, or low risk. Critical or High alerts may be directed to senior experts on a high need to quickly explore the issue. Today is the immense number of false positives, gauges show that the normal, of false positives being produced, is the range of 95 and 99%, and this puts extraordinary weight on banks. The examination of false positives is tedious and costs money. An ongoing report found that banks were spending near 3.01€ billion every year exploring false positives. Establishments are looking for increasing productive ways to deal with crime and, in this specific situation, Machine Learning can end up being a significant tool. Financial activities become productive, the gigantic sum and speed of money related exchanges require a viable monitoring framework that can process exchanges rapidly, ideally in real-time.   What are the types of machine learning algorithms which can identify money laundering transactions? Supervised Machine Learning, it is essential to have historical information with events precisely assigned and input variables appropriately captured. If biases or errors are left in the data without being dealt with, they will get passed on to the model, bringing about erroneous models. It is smarter to utilize Unsupervised Machine Learning to have historical data with events accurately assigned. It sees an obscure pattern and results. It recognizes suspicious activity without earlier information of exactly what a money-laundering scheme resembles. What are the different techniques to detect money laundering? K-means Sequence Miner algorithm: Entering banking transactions, at that point running frequent pattern mining algorithms and mining transactions to distinguish money laundering. Clustering transactions and dubious activities to money laundering lastly show them on a chart. Time Series Euclidean distance: Presenting a sequence matching algorithm to distinguish money laundering detection, utilizing sequential detection of suspicious transactions. This method exploits the two references to recognize dubious transactions: a history of every individual’s account and exchange data with different accounts. Bayesian networks: It makes a model of the user’s previous activities, and this model will be a measure of future customer activities. In the event that the exchange or user financial transactions have. Cluster-based local outlier factor algorithm: The money laundering detection utilizing clustering techniques combination and Outliers.   Conclusion For banks, now is the ideal opportunity to deploy ML models into their ecosystem. Despite this opportunity, increased knowledge and the number of ML implementations prompted a discussion about the feasibility of these solutions and the degree to which ML should be trusted and potentially replace human analysis and decision-making. In order to further exploit and achieve ML promise, banks need to continue to expand on its awareness of ML strengths, risks, and limitations and, most critically, to create an ethical system by which the production and use of ML can be controlled and the feasibility and effect of these emerging models proven and eventually trusted.

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6 Best SaaS Marketing Metrics for Business Growth

Article | July 22, 2021

The software-as-a-service industry is rapidly growing with an estimate to reach $219.5 billion by 2027. SaaS marketing strategies is highly different from other industries; thus, tracking the right metrics for marketing is necessary. SaaS kpis or metrics measure an enterprise’s performance, growth, and momentum. These saas marketing metrics are have been designed to evaluate the health of a business by tracking sales, marketing, and customer success. Direct access to data will help you develop your business and show whether there is any room for development. SaaS KPIs: What Are They and Why Do They Matter? Marketing metrics for SaaS indicate growth in different ways. SaaS KPIs, just like regular KPIs, helps business to evaluate their business models and strategies. These key metrics for SaaS companies give a deep insight into which sectors perform well and require reassessment. To optimize any company’s exposure, SaaS metrics for marketing are highly essential. They measure the performance of sales, marketing, and customer retention. SaaS companies believe in the entire life cycle of the customer, while traditional web-based companies focus on immediate sales. The overall goal of SaaS companies is to build long-lasting customer relationships since most revenue is generated through their recurring payments. SaaS marketing technology are SaaS marketers’ greatest asset if they take the time and effort to understand and implement them. There are essential and unimportant metrics. Knowing which metrics to pay attention to is a challenge. Once you get these metrics right, they will help you to detect your company’s strengths and weaknesses and help you understand whether they are working or not. There are more than fifteen metrics one can track but make you lose sight of what matters. In this article, we have identified the critical metrics every SaaS should track: Unique Visitors This metric measures the number of visitors your website or page sees in a specific time period. If someone visits your website four to five times in that given time period, it will be counted as one unique visitor. Recording this metric is crucial as it shows you what type of visitors your site receives and from what channels they arrive. When the number of unique visitors is high, it indicates to the SaaS marketers that their content resonates with the target customers. It is vital to note, however, which channels these unique visitors reach your website. These channels can be: Organic traffic Social media Paid ads SaaS marketers should, at this point, identify which channels are working and double down on those. Once you know these channels, you can allocate budgets and optimize these channels for better performance. Google Analytics is the best free tool to track unique visitors. The tool enables you to refine by dates and compare time periods and generate a report. Leads Leads is a broad term that can be broken down into two sub-categories: Sales Qualified Leads (SQL) and Marketing Qualified Leads (MQL). Defining SQL and MQL is important as they can be different for every business. So, let us break down the definitions for the two: MQL MQLs are those leads that have moved past the visitor phase in the customer lifecycle. They have taken steps to move ahead and become qualified to become potential customers. They have engaged with your website multiple times. For example, they have visited your website to check out prices, case studies or have downloaded your whitepapers more than two times. SQL SQLs actively engage with your site and are more qualified than MQLs. This lead is what you have deemed as the ideal sales candidate. They are way past the initial search stage, evaluating vendors, and are ready for a direct sales pitch. The most crucial distinction between the two is that your sales team has deemed them sales-worthy. After distinguishing between the two leads, you need to take the next appropriate steps. The best way to measure these leads is through closed-loop automation tools like HubSpot, Marketo, or Pardot. These automation tools will help you set up the criteria that automatically set up an individual as lead based on your website's SQL and MQL actions. Next, track the website traffic to ensure these unique visitors turn into potential leads. Churn The churn rate, in short, refers to the number of customers lost in a given time frame. It is the number of revenue SaaS customers who cancel their recurring revenue services. Since SaaS is a subscription-based service, losing customers directly correlates to losing money. The churn rate also indicates that your customers aren’t getting what they want from your service. Like most of your saas KPIs, you will be reporting on the churn rate every month. To calculate the churn rate, take the total number of customers you lost in the month you’re reporting on. Next, divide that by the number of customers you had at the beginning of the reporting month. Then, multiply that number by 100 to get the percentage. A churn is natural for any business. However, a high churn rate is an indicator that your business is in trouble. Therefore, it is an essential metric to track for your SaaS company. Customer Lifetime Value Customer lifetime value (CLV) measures how valuable a customer is to your business. It is the average amount of money your customers pay during their involvement with your SaaS company. You measure not only their value based on purchases but also the overall relationship. Keeping an existing client is more important than acquiring a new one which makes this metric important. Measuring CLV is a bit complicated than measuring other metrics. First, calculate the average customer lifetime by taking the number one divided by the customer churn rate. As an example, let’s say your monthly churn rate is 1%. Your average customer lifetime would be 1/0.01 = 100 months. Then take the average customer lifetime and multiply it by the average revenue per account (ARPA) over a given time period. If your company, for example, brought in $100,000 in revenue last month off of 100 customers, that would be $1,000 in revenue per account. Finally, this brings us to CLV. You’ll now need to multiply customer lifetime (100 months) by your ARPA ($1,000). That brings us to 100 x $1,000, or $100,000 CLV. CLV is crucial as it indicates whether or not there is a proper strategy in place for business growth. It also shows investors the value of your company. Customer Acquisition Cost Customer acquisition cost (CAC) tells you how much you should spend on acquiring a new customer. The two main factors that determine the CAC are: Lead generation costs Cost of converting that lead into a client The CAC predicts the resources needed to acquire new customers. It is vital to understand this metric if you want to grow your customer base and make a profit. To calculate your CAC for any given period, divide your marketing and sales spend over that time period by the number of customers gained during the same time. It might cost more to acquire a new customer, but what if that customer ends up spending more than most? That’s where the CLV to CAC ratio comes into play. CLV: CAC Ratio CLV: CAC ratio go hand in hand. Comparing the two will help you understand the impact of your business. The CLV: CAC ratio shows the lifetime value of your customers and the amount you spend to gain new ones in a single metric. The ultimate goal of your company should be to have a high CLV: CAC ratio. According to SaaS analytics, a healthy business should have a CLV three times greater than its CAC. Just divide your calculated CLV by CAC to get the ratio. Some top-performing companies even have a ratio of 5:1. SaaS companies use this number to measure the health of marketing programs to invest in campaigns that work well or divert the resources to those campaigns that work well. Conclusion Always remember to set healthy marketing KPIs. Reporting on these numbers is never enough. Ensure that everything you do in marketing ties up to all the goals you have set for your company. Goal-driven SaaS marketing strategies always pay off and empower you and your company to be successful. Frequently Asked Questions What are the 5 most important metrics for SaaS companies? The five most important metrics for SaaS companies are Unique Visitors, Churn, Customer Lifetime Value, Customer Acquisition Cost, and Lead to Customer Conversion Rate. Why should we measure SaaS marketing metrics? Measuring marketing metrics are critically important because they help brands determine whether campaigns are successful, and provide insights to adjust future campaigns accordingly. They help marketers understand how their campaigns are driving towards their business goals, and inform decisions for optimizing their campaigns and marketing channels. How to measure the success of your SaaS marketing? The success of SaaS marketing can be measured by identifying the metrics that help them succeed. Some examples of those metrics are: Unique Visitors, Churn, Customer Lifetime Value, Customer Acquisition Cost, and Lead to Customer Conversion Rate. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are the 5 most important metrics for SaaS companies?", "acceptedAnswer": { "@type": "Answer", "text": "The five most important metrics for SaaS companies are Unique Visitors, Churn, Customer Lifetime Value, Customer Acquisition Cost, and Lead to Customer Conversion Rate." } },{ "@type": "Question", "name": "Why should we measure SaaS marketing metrics?", "acceptedAnswer": { "@type": "Answer", "text": "Measuring marketing metrics are critically important because they help brands determine whether campaigns are successful, and provide insights to adjust future campaigns accordingly. 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Article | February 17, 2020

The continuous advancements in technology and the increasing use of smart devices are leading tremendous growth in data. Considering reports, more than 2.5 Quintilian bytes of data are generated on a daily basis and it is expected that 1.7 Mb of data will be produced every second in the near future. This is where data scientists play an influential role in analyzing these immense amounts of data to convert into meaningful insights. Data science is an overriding method today that will remain the same for the future. This drives the need for skilled talent across industries to meet the challenges of data analytics and assist delivering innovation in products, services and society.

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Spotlight

Mode

Mode was founded in 2013 by Derek Steer, Benn Stancil, and Josh Ferguson. While working in analytics at Yammer, they saw Amazon Redshift and other cloud-based data warehouses making it possible for companies of all sizes—not just tech giants—to gather and analyze data.

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