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Predictive Maintenance with Industrial Big Data: Reactive to Proactive Strategies

Predictive Maintenance with Industrial Big Data
Explore the benefits of using industrial big data for predictive maintenance strategies. Learn how businesses can shift from reactive to proactive maintenance approaches and optimize operations with the power of predictive analytics.

Contents

1  Importance of Predictive Maintenance
2  Challenges of Traditional Reactive Maintenance for Enterprises
3  Emergence of Proactive Strategies for Predictive Maintenance
4  Reactive vs. Proactive Strategies
5  Industrial Big Data Analytics for Predictive Maintenance: Importance and Applications
6  Navigating Implementation Challenges
7  Leverage Predictive Maintenance for Optimal Operations
8  Final Thoughts

1.  Importance of Predictive Maintenance

Predictive maintenance (PdM) is a proactive maintenance approach that employs advanced downtime tracking software to evaluate data and predict when maintenance on equipment should be conducted. With PdM constantly monitoring equipment performance and health using sensors, maintenance teams can be alerted when equipment is nearing a breakdown, allowing them to take mitigation measures before any unscheduled downtime occurs.

The global predictive maintenance market is expected to expand at a 25.5% CAGR to reach USD 23 billion in 2025 during the forecast period.

(Market Research Future)

Organizations often prefer PdM as a maintenance management method as it reduces costs with an upfront investment compared to preventive and reactive maintenance. Furthermore, maintenance has become crucial to ensuring smooth system functioning in today's complex industrial environment. Therefore, predictive maintenance is an essential strategy for industrial organizations, as it improves safety and productivity and reduces costs.

As industrial equipment becomes more automated and diagnostic tools become more advanced and affordable, more and more plants are taking a proactive approach to maintenance. The immediate goal is to identify and fix problems before they result in a breakdown, while the long-term goal is to reduce unexpected outages and extend asset life.

Plants that implement predictive maintenance processes see a 30% increase in equipment mean time between failures (MTBF), on average. This means your equipment is 30% more reliable and 30% more likely to meet performance standards with a predictive maintenance strategy.

(Source: FMX)


2.  Challenges of Traditional Reactive Maintenance for Enterprises

The waning popularity of reactive maintenance is attributed to several inherent limitations, such as exorbitant costs and a heightened likelihood of equipment failure and safety hazards. At the same time, the pursuit of maintaining industrial plants at maximum efficiency with minimal unplanned downtime is an indispensable objective for all maintenance teams.

However, the traditional reactive approach, which involves repairing equipment only when it malfunctions, can result in substantial expenses associated with equipment downtime, product waste, and increased equipment replacement and labor costs. To overcome these challenges, organizations can move towards proactive maintenance strategies, which leverage advanced downtime tracking software to anticipate maintenance needs and forestall potential breakdowns.


3.  Emergence of Proactive Strategies for Predictive Maintenance

The constraints of reactive maintenance have instigated the emergence of proactive approaches, including predictive analytics. It employs real-time data gathered from equipment to predict maintenance needs and employs algorithms to recognize potential issues before they result in debilitating breakdowns. The data collected through sensors and analytics facilitates the establishment of a more thorough and precise assessment of the general well-being of the operation.

With such proactive strategies, organizations can:
  • Arrange maintenance undertakings in advance,
  • Curtail downtime,
  • Cut expenses, and
  • Augment equipment reliability and safety


4.  Reactive vs. Proactive Strategies

As of 2020, 76% of the respondents in the manufacturing sector reported following a proactive maintenance strategy, while 56% used reactive maintenance (run-to-failure).

(Source: Statista)

Proactive maintenance strategies, such as predictive maintenance, offer many benefits over reactive maintenance, which can be costly and time-consuming. By collecting baseline data and analyzing trends, proactive maintenance strategies can help organizations perform maintenance only when necessary, based on real-world information.

However, establishing a proactive maintenance program can be challenging, as limited maintenance resources must be directed to address the most critical equipment failures. Analyzing data from both healthy and faulty equipment can help organizations determine which failures pose the biggest risk to their operation.

A proactive maintenance approach may assist in avoiding the fundamental causes of machine failure, addressing issues before they trigger failure, and extending machine life, making it a crucial strategy for any industrial operation.


5.  Industrial Big Data Analytics for Predictive Maintenance: Importance and Applications

Big data analytics is a key enabler of predictive maintenance strategies. Its capability to process vast amounts of data provides valuable insights into equipment health and performance, making predictive maintenance possible. With their wide-ranging applications, industrial big data analytics tools can predict maintenance needs, optimize schedules, and detect potential problems before they escalate into significant problems. It can also monitor equipment performance, identify areas for improvement, and refine processes to increase equipment reliability and safety.

Industrial big data is indispensable in realizing the shift from reactive to proactive predictive maintenance, which is accomplished through the optimal utilization of available datasets. Industrial big data can glean insights into equipment condition, including patterns of maintenance that may not be readily apparent. Moreover, it has the capacity to attain actionable intelligence capable of effecting a closed loop back to the plant floor.

Integration of big data technologies with industrial automation is key to this accomplishment. Nevertheless, this transition will necessitate investment in supplementary assets, such as new maintenance processes and employee training.


6.  Navigating Implementation Challenges


6.1  Overcoming Data Collection and Pre-processing Challenges

One of the primary challenges in implementing industrial big data analytics for predictive maintenance is the collection and pre-processing of data. The voluminous industrial data, which comes in various formats and from multiple sources, makes it necessary for organizations to develop robust data collection and pre-processing strategies to ensure data accuracy and integrity.

To achieve this, organizations need to establish sensor and data collection systems and ensure that the data undergoes appropriate cleaning, formatting, and pre-processing to obtain accurate and meaningful results.


6.2  Addressing Data Integration Challenges

Integrating data from heterogeneous sources is a daunting challenge that organizations must overcome when implementing industrial big data analytics for predictive maintenance. It involves processing multiple datasets from different sensors and maintenance detection modalities, such as vibration analysis, oil analysis, thermal imaging, and acoustics.

While utilizing data from various sources leads to more stable and accurate predictions, it requires additional investments in sensors and data collection, which is generally very hard to achieve in most maintenance systems.

A well-crafted data architecture is critical to managing the copious amounts of data that come from different sources, including various equipment, sensors, and systems. Organizations must devise a comprehensive data integration strategy that incorporates relevant data sources to ensure data integrity and completeness.


6.3  Model Selection and Implementation Solutions

Selecting appropriate predictive models and implementing them effectively is another significant challenge. To overcome this, organizations need to have an in-depth understanding of the various models available, their strengths and limitations, and their applicability to specific maintenance tasks.

They must also possess the necessary expertise to implement the models and seamlessly integrate them into their existing maintenance workflows to achieve timely and accurate results. Furthermore, it is crucial to align the selected models with the organization's business objectives and ensure their ability to deliver the desired outcomes.


6.4  Staffing and Training Solutions

In order to ensure successful implementation, organizations must allocate resources toward staffing and training solutions. This entails hiring proficient data scientists and analysts and then providing them with continual training and professional development opportunities. Moreover, it is imperative to have personnel with the requisite technical expertise to manage and maintain the system.

Equally crucial is providing training to employees on the system's usage and equipping them with the necessary skills to interpret and analyze data.


7.  Leverage Predictive Maintenance for Optimal Operations

Predictive maintenance is widely acknowledged among plant operators as the quintessential maintenance vision due to its manifold advantages, such as higher overall equipment effectiveness (OEE) owing to a reduced frequency of repairs. Furthermore, predictive maintenance data analytics facilitate cost savings by enabling optimal scheduling of repairs and minimizing planned downtimes.

It also enhances employees' productivity by providing valuable insights on the appropriate time for component replacement. Additionally, timely monitoring and addressing potential problems can augment workplace safety, which is paramount for ensuring employee well-being.

In a survey of 500 plants that implemented a predictive maintenance program, there was an average increase in equipment availability of 30%. Simply implementing predictive maintenance will ensure your equipment is running when you need it to run.

(Source: FMX)

By synchronizing real-time equipment data with the maintenance management system, organizations can proactively prevent equipment breakdowns. Successful implementation of predictive maintenance data analytic strategies can substantially reduce the time and effort spent on maintaining equipment, as well as the consumption of spare parts and supplies for unplanned maintenance.

Consequently, there will be fewer instances of breakdowns and equipment failures, ultimately leading to significant cost savings.

On average, predictive maintenance reduced normal operating costs by 50%.

(Source: FMX)


8.  Final Thoughts

Traditional reactive maintenance approaches need to be revised in today's industrial landscape. Proactive strategies, such as predictive maintenance, are necessary to maintain equipment health and performance. Real-time predictive maintenance using big data collected from equipment can help prevent costly downtime, waste, equipment replacement, and labor expenses, thus enhancing safety and productivity. The shift from reactive to proactive maintenance is crucial for organizations, and industrial big data analytics is vital for realizing this transition. Although big data analytics applications for predictive maintenance pose challenges, they can be overcome with the right measures.

Ultimately, the effective implementation of big data analytics solutions is a vital enabler of big data predictive maintenance strategies and an essential tool for any industrial plant seeking to optimize its maintenance approach. By embracing predictive maintenance strategies and leveraging the power of industrial big data and analytics, organizations can ensure the longevity and reliability of their equipment, enhancing productivity and profitability.

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Bny Mellon | Pershing

BNY Mellon’s Pershing and its affiliates provide global financial business solutions to advisors, broker-dealers, family offices, hedge fund and ’40 Act fund managers, registered investment advisor firms and wealth managers. Pershing helps clients improve profitability and drive growth, create capacity and efficiency, attract and retain talent, and manage risk and regulation. With a network of 23 offices worldwide, Pershing provides business-to-business solutions to clients representing more than 6 million investor accounts globally.

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7 Top Data Analytics Trends

Article | April 13, 2023

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Re-prioritizing how you spend your time, how you build out your team and the resources you invest in channels and efforts are critical steps to achieving marketing team success" } },{ "@type": "Question", "name": "What is the use of marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics is used to measure how well your marketing efforts are performing and to determine what can be done differently to get better results across marketing channels." } },{ "@type": "Question", "name": "Which companies use marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics enables you to improve your overall marketing program performance by identifying channel deficiencies, adjusting strategies and tactics as needed, optimizing processes, etc. Companies like Netflix, Sephora, EasyJet, and Spotify use marketing analytics to improve their markeitng performance as well." } }] }

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Article | December 23, 2020

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Article | April 13, 2023

The COVID-19 compelled organizations utilizing traditional analytics methods to accept digital data analytics platforms. The pandemic has also accelerated the digital revolution, and as we already know, data and analytics with technologies like AI, NLP, and ML have become the heart of this digital revolution. Therefore, this is the perfect time to break through data, analytics, and AI to make the most of it and stay a step ahead of competitors. Besides that, Techjury says that by 2023, the big data analytics market is expected to be worth $103 billion. This shows how quickly the field of data analytics is growing. Today, the data analytics market has numerous tools and strategies evolving rapidly to keep up with the ever-increasing volume of data gathered and used by businesses. Considering the swift pace and increasing use of data analytics, it is crucial to keep upgrading to stay ahead of the curve. But before we explore the leading data analytics trends, let's check out some data analytics use cases. Data Analytics Use Cases Customer Relationship Analytics One of the biggest challenges is recognizing clients who will spend money continuously for a long period purchasing their products. This insight will assist businesses in attracting customers who will add long-term value to their business. Product Propensity Product propensity analytics combines data on buying actions and behaviors with online behavioral indicators from social media and e-commerce to give insight into the performance of various campaigns and social media platforms promoting the products and services of your company. This enables your business to forecast which clients are most likely to purchase your products and services and which channels are most likely to reach those customers. This lets you focus on the channels that have the best chance of making a lot of money. Recommendation Engines There are recommendations on YouTube, Spotify, Amazon Prime Videos, or other media sites, "recommendations for you." These customized recommendations help users save time and improve their entire customer experience. Top Data Analytics Trends That Will Shape 2022 1. Data Fabrics Architecture The goal of data fabric is to design an exemplary architecture and advise on when data should be delivered or changed. Since data technology designs majorly rely on the ability to use, reuse, and mix numerous data integration techniques, the data fabric reduces integration data technology design time by 30%, deployment time by 30%, and maintenance time by 70%. "The data fabric is the next middleware." -ex-CTO of Splunk, Todd Papaioannou, 2. Decision Intelligence Decision intelligence directly incorporates data analytics into the decision process, with feedback loops to refine and fine-tune the process further. Decision intelligence can be utilized to assist in making decisions, but it also employs techniques like digital twin simulations, reinforcement learning, and artificial intelligence to automate decisions where necessary. 3. XOps With artificial intelligence (AI) and data analytics throughout any firm, XOps has become an essential aspect of business transformation operations. XOps uses DevOps best practices to improve corporate operations, efficiency, and customer experience. In addition, it wants to make sure that the process is reliable, reusable, and repeatable and that there is less technology and process duplication. 4. Graph Analytics Gartner predicts that by 2025, 80% of data and analytics innovation will be developed with the help of graphs. Graph analytics uses engaging algorithms to correlate multiple data points scattered across numerous data assets by exploring relationships. The AI graph is the backbone of modern data and analytics with the help of its expandable features and capability to increase user collaboration and machine learning models. 5. Augmented Analytics Augmented Analytics is another data-trend technology that is gaining prominence. Machine learning, AI, and natural language processing (NLP) are used in augmented analytics to automate data insights for business intelligence, data preparation, discovery, and sharing. The insights provided through augmented analytics help businesses make better decisions. According to Allied Market Research, the worldwide augmented analytics market is expected to reach $29,856 million by 2025. 6. Self-Service Analytics-Low-code and no-code AI Low-code and no-code digital platforms are speeding up the transition to self-service analytics. Non-technical business users can now access data, get insights, and make faster choices because of these platforms. As a result, self-service analytics boosts response times, business agility, speed-to-market, and decision-making in today's modern world. 7. Privacy-Enhancing Computation With the amount of sensitive and personal data being gathered, saved, and processed, it has become imperative to protect consumers' privacy. As regulations become strict and customers become more concerned, new ways to protect their privacy are becoming more important. Privacy-enhancing computing makes sure that value can be extracted from the data with the help of big data analytics without breaking the rules of the game. 3 Ways in Which the C-Suite Can Ensure Enhanced Use of Data Analytics There are many businesses that fail to realize the benefits of data analytics. Here are some ways the C-suite can ensure enhanced use of data analytics. Use Data Analytics for Recommendations Often, the deployment of data analytics is considered a one-time mission instead of an ongoing, interactive process. According to recent McKinsey research, employees are considerably more inclined to data analytics if their leaders actively commit. If the C-suite starts using analytics for decision-making, it will set an example and establish a reliability factor. This shows that when leaders rely on the suggestions and insights of data analytics platforms, rest of the company will follow the C-suite. This will result in broad usage, better success, and higher adoption rates of data analytics. Establish Data Analytics Mind-Sets Senior management starting on this path should learn about data analytics to comprehend what's fast becoming possible. Then they can use the question, "Where might data analytics bring quantum leaps in performance?" to promote lasting behavioral changes throughout the business. A senior executive should lead this exercise with the power and influence to encourage action throughout each critical business unit or function. Use Machine Learning to Automate Decisions The C-suite is introducing machine learning as they are recognizing its value for various departments and processes in an organization either processing or fraud monitoring. 79% of the executives believe that AI will make their jobs more efficient and manageable. Therefore, C-level executives would make an effort to ensure the rest of the organization follows that mentality. They will have to start by using machine learning to automate time-consuming and repeatable tasks. Conclusion From the above-mentioned data analytics trends one can infer that it is no longer only a means to achieve corporate success. In 2022 and beyond, businesses will need to prioritize it as a critical business function, accurately recognizing it as a must-have for long-term success. The future of data analytics will have quality data and technologies like AI at its center. FAQ 1. What is the difference between data analytics and data analysis? Scalability is the key distinguishing factor between analytics and analysis. Data analytics is a broad phrase that encompasses all types of data analysis. The evaluation of data is known as data analysis. Data analysis includes data gathering, organization, storage, and analysis techniques and technologies. 2. When is the right time to deploy an analytics strategy? Data analytics is not a one-time-only activity; it is a continuous process. Companies should not shift their attention from analytics and should utilize it regularly. Usually, once companies realize the potential of analytics to address concerns, they start applying it to various processes. 3. What is platform modernization? Modernization of legacy platforms refers to leveraging and expanding flexibility by preserving consistency across platforms and tackling IT issues. Modernization of legacy platforms also includes rewriting a legacy system for software development.

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Data Visualization

15 Data Visualization Tools for Unbelievably Better Decisions

Article | March 15, 2024

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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Predictive Analytics

Empowering Strategic Decision Making Through Predictive Analytics

Article | June 28, 2024

When it comes to marketing today, big data analytics has become a powerful being. The raw material marketers need to make sense of the information they are presented with so they can do their jobs with accuracy and excellence. Big data is what empowers marketers to understand their customers based on any online action they take. Thanks to the boom of big data, marketers have learned more about new marketing trends and preferences, and behaviors of the consumer. For example, marketers know what their customers are streaming to what groceries they are ordering, thanks to big data. Data is readily available in abundance due to digital technology. Data is created through mobile phones, social media, digital ads, weblogs, electronic devices, and sensors attached through the internet of things (IoT). Data analytics helps organizations discover newer markets, learn how new customers interact with online ads, and draw conclusions and effects of new strategies. Newer sophisticated marketing analytics software and analytics tools are now being used to determine consumers’ buying patterns and key influencers in decision-making and validate data marketing approaches that yield the best results. With the integration of product management with data science, real-time data capture, and analytics, big data analytics is helping companies increase sales and improve the customer experience. In this article, we will examine how big data analytics are transforming the marketing industry. Personalized Marketing Personalized Marketing has taken an essential place in direct marketing to the consumers. Greeting consumers with their first name whenever they visit the website, sending them promotional emails of their favorite products, or notifying them with personalized recipes based on their grocery shopping are some of the examples of data-driven marketing. When marketers collect critical data marketing pieces about customers at different marketing touchpoints such as their interests, their name, what they like to listen to, what they order most, what they’d like to hear about, and who they want to hear from, this enables marketers to plan their campaigns strategically. Marketers aim for churn prevention and onboarding new customers. With customer’s marketing touchpoints, these insights can be used to improve acquisition rates, drive brand loyalty, increase revenue per customer, and improve the effectiveness of products and services. With these data marketing touchpoints, marketers can build an ideal customer profile. Furthermore, these customer profiles can help them strategize and execute personalized campaigns accordingly. Predictive Analytics Customer behavior can be traced by historical data, which is the best way to predict how customers would behave in the future. It allows companies to correctly predict which customers are interested in their products at the right time and place. Predictive analytics applies data mining, statistical techniques, machine learning, and artificial intelligence for data analysis and predict the customer’s future behavior and activities. Take an example of an online grocery store. If a customer tends to buy healthy and sugar-free snacks from the store now, they will keep buying it in the future too. This predictable behavior from the customer makes it easy for brands to capitalize on that and has been made easy by analytics tools. They can automate their sales and target the said customer. What they would be doing gives the customer chances to make “repeat purchases” based on their predictive behavior. Marketers can also suggest customers purchase products related to those repeat purchases to get them on board with new products. Customer Segmentation Customer segmentation means dividing your customers into strata to identify a specific pattern. For example, customers from a particular city may buy your products more than others, or customers from a certain age demographic prefer some products more than other age demographics. Specific marketing analytics software can help you segment your audience. For example, you can gather data like specific interests, how many times they have visited a place, unique preferences, and demographics such as age, gender, work, and home location. These insights are a golden opportunity for marketers to create bold campaigns optimizing their return on investment. They can cluster customers into specific groups and target these segments with highly relevant data marketing campaigns. The main goal of customer segmentation is to identify any interesting information that can help them increase revenue and meet their goals. Effective customer segmentation can help marketers with: • Identifying most profitable and least profitable customers • Building loyal relationships • Predicting customer patterns • Pricing products accordingly • Developing products based on their interests Businesses continue to invest in collecting high-quality data for perfect customer segmentation, which results in successful efforts. Optimized Ad Campaigns Customers’ social media data like Facebook, LinkedIn, and Twitter makes it easier for marketers to create customized ad campaigns on a larger scale. This means that they can create specific ad campaigns for particular groups and successfully execute an ad campaign. Big data also makes it easier for marketers to run ‘remarketing’ campaigns. Remarketing campaigns ads follow your customers online, wherever they browse, once they have visited your website. Execution of an online ad campaign makes all the difference in its success. Chasing customers with paid ads can work as an effective strategy if executed well. According to the rule 7, prospective customers need to be exposed to an ad minimum of seven times before they make any move on it. When creating online ad campaigns, do keep one thing in mind. Your customers should not feel as if they are being stalked when you make any remarketing campaigns. Space out your ads and their exposure, so they appear naturally rather than coming on as pushy. Consumer Impact Advancements in data science have vastly impacted consumers. Every move they make online is saved and measured. In addition, websites now use cookies to store consumer data, so whenever these consumers visit these websites, product lists based on their shopping habits pop up on the site. Search engines and social media data enhance this. This data can be used to analyze their behavior patterns and market to them accordingly. The information gained from search engines and social media can be used to influence consumers into staying loyal and help their businesses benefit from the same. These implications can be frightening, like seeing personalized ads crop up on their Facebook page or search engine. However, when consumer data is so openly available to marketers, they need to use it wisely and safeguard it from falling into the wrong hands. Fortunately, businesses are taking note and making sure that this information remains secure. Conclusion The future of marketing because of big data and analytics seems bright and optimistic. Businesses are collecting high-quality data in real-time and analyzing it with the help of machine learning and AI; the marketing world seems to be up for massive changes. Analytics are transforming marketing industry to a different level. And with sophisticated marketers behind the wheel, the sky is the only limit. Frequently Asked Questions Why is marketing analytics so important these days? Marketing analytics helps us see how everything plays off each other, and decide how we might want to invest moving forward. Re-prioritizing how you spend your time, how you build out your team, and the resources you invest in channels and efforts are critical steps to achieving marketing team success. What is the use of marketing analytics? Marketing analytics is used to measure how well your marketing efforts are performing and to determine what can be done differently to get better results across marketing channels. Which companies use marketing analytics? Marketing analytics enables you to improve your overall marketing program performance by identifying channel deficiencies, adjusting strategies and tactics as needed, optimizing processes, etc. Companies like Netflix, Sephora, EasyJet, and Spotify use marketing analytics to improve their markeitng performance as well. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Why is marketing analytics so important these days?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics helps us see how everything plays off each other, and decide how we might want to invest moving forward. Re-prioritizing how you spend your time, how you build out your team and the resources you invest in channels and efforts are critical steps to achieving marketing team success" } },{ "@type": "Question", "name": "What is the use of marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics is used to measure how well your marketing efforts are performing and to determine what can be done differently to get better results across marketing channels." } },{ "@type": "Question", "name": "Which companies use marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics enables you to improve your overall marketing program performance by identifying channel deficiencies, adjusting strategies and tactics as needed, optimizing processes, etc. Companies like Netflix, Sephora, EasyJet, and Spotify use marketing analytics to improve their markeitng performance as well." } }] }

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Data Science

Thinking Like a Data Scientist

Article | December 23, 2020

Introduction Nowadays, everyone with some technical expertise and a data science bootcamp under their belt calls themselves a data scientist. Also, most managers don't know enough about the field to distinguish an actual data scientist from a make-believe one someone who calls themselves a data science professional today but may work as a cab driver next year. As data science is a very responsible field dealing with complex problems that require serious attention and work, the data scientist role has never been more significant. So, perhaps instead of arguing about which programming language or which all-in-one solution is the best one, we should focus on something more fundamental. More specifically, the thinking process of a data scientist. The challenges of the Data Science professional Any data science professional, regardless of his specialization, faces certain challenges in his day-to-day work. The most important of these involves decisions regarding how he goes about his work. He may have planned to use a particular model for his predictions or that model may not yield adequate performance (e.g., not high enough accuracy or too high computational cost, among other issues). What should he do then? Also, it could be that the data doesn't have a strong enough signal, and last time I checked, there wasn't a fool-proof method on any data science programming library that provided a clear-cut view on this matter. These are calls that the data scientist has to make and shoulder all the responsibility that goes with them. Why Data Science automation often fails Then there is the matter of automation of data science tasks. Although the idea sounds promising, it's probably the most challenging task in a data science pipeline. It's not unfeasible, but it takes a lot of work and a lot of expertise that's usually impossible to find in a single data scientist. Often, you need to combine the work of data engineers, software developers, data scientists, and even data modelers. Since most organizations don't have all that expertise or don't know how to manage it effectively, automation doesn't happen as they envision, resulting in a large part of the data science pipeline needing to be done manually. The Data Science mindset overall The data science mindset is the thinking process of the data scientist, the operating system of her mind. Without it, she can't do her work properly, in the large variety of circumstances she may find herself in. It's her mindset that organizes her know-how and helps her find solutions to the complex problems she encounters, whether it is wrangling data, building and testing a model or deploying the model on the cloud. This mindset is her strategy potential, the think tank within, which enables her to make the tough calls she often needs to make for the data science projects to move forward. Specific aspects of the Data Science mindset Of course, the data science mindset is more than a general thing. It involves specific components, such as specialized know-how, tools that are compatible with each other and relevant to the task at hand, a deep understanding of the methodologies used in data science work, problem-solving skills, and most importantly, communication abilities. The latter involves both the data scientist expressing himself clearly and also him understanding what the stakeholders need and expect of him. Naturally, the data science mindset also includes organizational skills (project management), the ability to work well with other professionals (even those not directly related to data science), and the ability to come up with creative approaches to the problem at hand. The Data Science process The data science process/pipeline is a distillation of data science work in a comprehensible manner. It's particularly useful for understanding the various stages of a data science project and help plan accordingly. You can view one version of it in Fig. 1 below. If the data science mindset is one's ability to navigate the data science landscape, the data science process is a map of that landscape. It's not 100% accurate but good enough to help you gain perspective if you feel overwhelmed or need to get a better grip on the bigger picture. Learning more about the topic Naturally, it's impossible to exhaust this topic in a single article (or even a series of articles). The material I've gathered on it can fill a book! If you are interested in such a book, feel free to check out the one I put together a few years back; it's called Data Science Mindset, Methodologies, and Misconceptions and it's geared both towards data scientist, data science learners, and people involved in data science work in some way (e.g. project leaders or data analysts). Check it out when you have a moment. Cheers!

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Spotlight

Bny Mellon | Pershing

BNY Mellon’s Pershing and its affiliates provide global financial business solutions to advisors, broker-dealers, family offices, hedge fund and ’40 Act fund managers, registered investment advisor firms and wealth managers. Pershing helps clients improve profitability and drive growth, create capacity and efficiency, attract and retain talent, and manage risk and regulation. With a network of 23 offices worldwide, Pershing provides business-to-business solutions to clients representing more than 6 million investor accounts globally.

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Big Data

Airbyte Racks Up Awards from InfoWorld, BigDATAwire, Built In; Builds Largest and Fastest-Growing User Community

Airbyte | January 30, 2024

Airbyte, creators of the leading open-source data movement infrastructure, today announced a series of accomplishments and awards reinforcing its standing as the largest and fastest-growing data movement community. With a focus on innovation, community engagement, and performance enhancement, Airbyte continues to revolutionize the way data is handled and processed across industries. “Airbyte proudly stands as the front-runner in the data movement landscape with the largest community of more than 5,000 daily users and over 125,000 deployments, with monthly data synchronizations of over 2 petabytes,” said Michel Tricot, co-founder and CEO, Airbyte. “This unparalleled growth is a testament to Airbyte's widespread adoption by users and the trust placed in its capabilities.” The Airbyte community has more than 800 code contributors and 12,000 stars on GitHub. Recently, the company held its second annual virtual conference called move(data), which attracted over 5,000 attendees. Airbyte was named an InfoWorld Technology of the Year Award finalist: Data Management – Integration (in October) for cutting-edge products that are changing how IT organizations work and how companies do business. And, at the start of this year, was named to the Built In 2024 Best Places To Work Award in San Francisco – Best Startups to Work For, recognizing the company's commitment to fostering a positive work environment, remote and flexible work opportunities, and programs for diversity, equity, and inclusion. Today, the company received the BigDATAwire Readers/Editors Choice Award – Big Data and AI Startup, which recognizes companies and products that have made a difference. Other key milestones in 2023 include the following. Availability of more than 350 data connectors, making Airbyte the platform with the most connectors in the industry. The company aims to increase that to 500 high-quality connectors supported by the end of this year. More than 2,000 custom connectors were created with the Airbyte No-Code Connector Builder, which enables data connectors to be made in minutes. Significant performance improvement with database replication speed increased by 10 times to support larger datasets. Added support for five vector databases, in addition to unstructured data sources, as the first company to build a bridge between data movement platforms and artificial intelligence (AI). Looking ahead, Airbyte will introduce data lakehouse destinations, as well as a new Publish feature to push data to API destinations. About Airbyte Airbyte is the open-source data movement infrastructure leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Self-Managed, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.com.

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Big Data Management

The Modern Data Company Recognized in Gartner's Magic Quadrant for Data Integration

The Modern Data Company | January 23, 2024

The Modern Data Company, recognized for its expertise in developing and managing advanced data products, is delighted to announce its distinction as an honorable mention in Gartner's 'Magic Quadrant for Data Integration Tools,' powered by our leading product, DataOS. “This accolade underscores our commitment to productizing data and revolutionizing data management technologies. Our focus extends beyond traditional data management, guiding companies on their journey to effectively utilize data, realize tangible ROI on their data investments, and harness advanced technologies such as AI, ML, and Large Language Models (LLMs). This recognition is a testament to Modern Data’s alignment with the latest industry trends and our dedication to setting new standards in data integration and utilization.” – Srujan Akula, CEO of The Modern Data Company The inclusion in the Gartner report highlights The Modern Data Company's pivotal role in shaping the future of data integration. Our innovative approach, embodied in DataOS, enables businesses to navigate the complexities of data management, transforming data into a strategic asset. By simplifying data access and integration, we empower organizations to unlock the full potential of their data, driving insights and innovation without disruption. "Modern Data's recognition as an Honorable Mention in the Gartner MQ for Data Integration is a testament to the transformative impact their solutions have on businesses like ours. DataOS has been pivotal in allowing us to integrate multiple data sources, enabling our teams to have access to the data needed to make data driven decisions." – Emma Spight, SVP Technology, MIND 24-7 The Modern Data Company simplifies how organizations manage, access, and interact with data using its DataOS (data operating system) that unifies data silos, at scale. It provides ontology support, graph modeling, and a virtual data tier (e.g. a customer 360 model). From a technical point of view, it closes the gap from conceptual to physical data model. Users can define conceptually what they want and its software traverses and integrates data. DataOS provides a structured, repeatable approach to data integration that enhances agility and ensures high-quality outputs. This shift from traditional pipeline management to data products allows for more efficient data operations, as each 'product' is designed with a specific purpose and standardized interfaces, ensuring consistency across different uses and applications. With DataOS, businesses can expect a transformative impact on their data strategies, marked by increased efficiency and a robust framework for handling complex data ecosystems, allowing for more and faster iterations of conceptual models. About The Modern Data Company The Modern Data Company, with its flagship product DataOS, revolutionizes the creation of data products. DataOS® is engineered to build and manage comprehensive data products to foster data mesh adoption, propelling organizations towards a data-driven future. DataOS directly addresses key AI/ML and LLM challenges: ensuring quality data, scaling computational resources, and integrating seamlessly into business processes. In our commitment to provide open systems, we have created an open data developer platform specification that is gaining wide industry support.

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Big Data Management

data.world Integrates with Snowflake Data Quality Metrics to Bolster Data Trust

data.world | January 24, 2024

data.world, the data catalog platform company, today announced an integration with Snowflake, the Data Cloud company, that brings new data quality metrics and measurement capabilities to enterprises. The data.world Snowflake Collector now empowers enterprise data teams to measure data quality across their organization on-demand, unifying data quality and analytics. Customers can now achieve greater trust in their data quality and downstream analytics to support mission-critical applications, confident data-driven decision-making, and AI initiatives. Data quality remains one of the top concerns for chief data officers and a critical barrier to creating a data-driven culture. Traditionally, data quality assurance has relied on manual oversight – a process that’s tedious and fraught with inefficacy. The data.world Data Catalog Platform now delivers Snowflake data quality metrics directly to customers, streamlining quality assurance timelines and accelerating data-first initiatives. Data consumers can access contextual information in the catalog or directly within tools such as Tableau and PowerBI via Hoots – data.world’s embedded trust badges – that broadcast data health status and catalog context, bolstering transparency and trust. Additionally, teams can link certification and DataOps workflows to Snowflake's data quality metrics to automate manual workflows and quality alerts. Backed by a knowledge graph architecture, data.world provides greater insight into data quality scores via intelligence on data provenance, usage, and context – all of which support DataOps and governance workflows. “Data trust is increasingly crucial to every facet of business and data teams are struggling to verify the quality of their data, facing increased scrutiny from developers and decision-makers alike on the downstream impacts of their work, including analytics – and soon enough, AI applications,” said Jeff Hollan, Director, Product Management at Snowflake. “Our collaboration with data.world enables data teams and decision-makers to verify and trust their data’s quality to use in mission-critical applications and analytics across their business.” “High-quality data has always been a priority among enterprise data teams and decision-makers. As enterprise AI ambitions grow, the number one priority is ensuring the data powering generative AI is clean, consistent, and contextual,” said Bryon Jacob, CTO at data.world. “Alongside Snowflake, we’re taking steps to ensure data scientists, analysts, and leaders can confidently feed AI and analytics applications data that delivers high-quality insights, and supports the type of decision-making that drives their business forward.” The integration builds on the robust collaboration between data.world and Snowflake. Most recently, the companies announced an exclusive offering for joint customers, streamlining adoption timelines and offering a new attractive price point. The data.world's knowledge graph-powered data catalog already offers unique benefits for Snowflake customers, including support for Snowpark. This offering is now available to all data.world enterprise customers using the Snowflake Collector, as well as customers taking advantage of the Snowflake-only offering. To learn more about the data quality integration or the data.world data catalog platform, visit data.world. About data.world data.world is the data catalog platform built for your AI future. Its cloud-native SaaS (software-as-a-service) platform combines a consumer-grade user experience with a powerful Knowledge Graph to deliver enhanced data discovery, agile data governance, and actionable insights. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500. Our company has 76 patents and has been named one of Austin’s Best Places to Work seven years in a row.

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Big Data

Airbyte Racks Up Awards from InfoWorld, BigDATAwire, Built In; Builds Largest and Fastest-Growing User Community

Airbyte | January 30, 2024

Airbyte, creators of the leading open-source data movement infrastructure, today announced a series of accomplishments and awards reinforcing its standing as the largest and fastest-growing data movement community. With a focus on innovation, community engagement, and performance enhancement, Airbyte continues to revolutionize the way data is handled and processed across industries. “Airbyte proudly stands as the front-runner in the data movement landscape with the largest community of more than 5,000 daily users and over 125,000 deployments, with monthly data synchronizations of over 2 petabytes,” said Michel Tricot, co-founder and CEO, Airbyte. “This unparalleled growth is a testament to Airbyte's widespread adoption by users and the trust placed in its capabilities.” The Airbyte community has more than 800 code contributors and 12,000 stars on GitHub. Recently, the company held its second annual virtual conference called move(data), which attracted over 5,000 attendees. Airbyte was named an InfoWorld Technology of the Year Award finalist: Data Management – Integration (in October) for cutting-edge products that are changing how IT organizations work and how companies do business. And, at the start of this year, was named to the Built In 2024 Best Places To Work Award in San Francisco – Best Startups to Work For, recognizing the company's commitment to fostering a positive work environment, remote and flexible work opportunities, and programs for diversity, equity, and inclusion. Today, the company received the BigDATAwire Readers/Editors Choice Award – Big Data and AI Startup, which recognizes companies and products that have made a difference. Other key milestones in 2023 include the following. Availability of more than 350 data connectors, making Airbyte the platform with the most connectors in the industry. The company aims to increase that to 500 high-quality connectors supported by the end of this year. More than 2,000 custom connectors were created with the Airbyte No-Code Connector Builder, which enables data connectors to be made in minutes. Significant performance improvement with database replication speed increased by 10 times to support larger datasets. Added support for five vector databases, in addition to unstructured data sources, as the first company to build a bridge between data movement platforms and artificial intelligence (AI). Looking ahead, Airbyte will introduce data lakehouse destinations, as well as a new Publish feature to push data to API destinations. About Airbyte Airbyte is the open-source data movement infrastructure leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Self-Managed, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.com.

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Big Data Management

The Modern Data Company Recognized in Gartner's Magic Quadrant for Data Integration

The Modern Data Company | January 23, 2024

The Modern Data Company, recognized for its expertise in developing and managing advanced data products, is delighted to announce its distinction as an honorable mention in Gartner's 'Magic Quadrant for Data Integration Tools,' powered by our leading product, DataOS. “This accolade underscores our commitment to productizing data and revolutionizing data management technologies. Our focus extends beyond traditional data management, guiding companies on their journey to effectively utilize data, realize tangible ROI on their data investments, and harness advanced technologies such as AI, ML, and Large Language Models (LLMs). This recognition is a testament to Modern Data’s alignment with the latest industry trends and our dedication to setting new standards in data integration and utilization.” – Srujan Akula, CEO of The Modern Data Company The inclusion in the Gartner report highlights The Modern Data Company's pivotal role in shaping the future of data integration. Our innovative approach, embodied in DataOS, enables businesses to navigate the complexities of data management, transforming data into a strategic asset. By simplifying data access and integration, we empower organizations to unlock the full potential of their data, driving insights and innovation without disruption. "Modern Data's recognition as an Honorable Mention in the Gartner MQ for Data Integration is a testament to the transformative impact their solutions have on businesses like ours. DataOS has been pivotal in allowing us to integrate multiple data sources, enabling our teams to have access to the data needed to make data driven decisions." – Emma Spight, SVP Technology, MIND 24-7 The Modern Data Company simplifies how organizations manage, access, and interact with data using its DataOS (data operating system) that unifies data silos, at scale. It provides ontology support, graph modeling, and a virtual data tier (e.g. a customer 360 model). From a technical point of view, it closes the gap from conceptual to physical data model. Users can define conceptually what they want and its software traverses and integrates data. DataOS provides a structured, repeatable approach to data integration that enhances agility and ensures high-quality outputs. This shift from traditional pipeline management to data products allows for more efficient data operations, as each 'product' is designed with a specific purpose and standardized interfaces, ensuring consistency across different uses and applications. With DataOS, businesses can expect a transformative impact on their data strategies, marked by increased efficiency and a robust framework for handling complex data ecosystems, allowing for more and faster iterations of conceptual models. About The Modern Data Company The Modern Data Company, with its flagship product DataOS, revolutionizes the creation of data products. DataOS® is engineered to build and manage comprehensive data products to foster data mesh adoption, propelling organizations towards a data-driven future. DataOS directly addresses key AI/ML and LLM challenges: ensuring quality data, scaling computational resources, and integrating seamlessly into business processes. In our commitment to provide open systems, we have created an open data developer platform specification that is gaining wide industry support.

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Big Data Management

data.world Integrates with Snowflake Data Quality Metrics to Bolster Data Trust

data.world | January 24, 2024

data.world, the data catalog platform company, today announced an integration with Snowflake, the Data Cloud company, that brings new data quality metrics and measurement capabilities to enterprises. The data.world Snowflake Collector now empowers enterprise data teams to measure data quality across their organization on-demand, unifying data quality and analytics. Customers can now achieve greater trust in their data quality and downstream analytics to support mission-critical applications, confident data-driven decision-making, and AI initiatives. Data quality remains one of the top concerns for chief data officers and a critical barrier to creating a data-driven culture. Traditionally, data quality assurance has relied on manual oversight – a process that’s tedious and fraught with inefficacy. The data.world Data Catalog Platform now delivers Snowflake data quality metrics directly to customers, streamlining quality assurance timelines and accelerating data-first initiatives. Data consumers can access contextual information in the catalog or directly within tools such as Tableau and PowerBI via Hoots – data.world’s embedded trust badges – that broadcast data health status and catalog context, bolstering transparency and trust. Additionally, teams can link certification and DataOps workflows to Snowflake's data quality metrics to automate manual workflows and quality alerts. Backed by a knowledge graph architecture, data.world provides greater insight into data quality scores via intelligence on data provenance, usage, and context – all of which support DataOps and governance workflows. “Data trust is increasingly crucial to every facet of business and data teams are struggling to verify the quality of their data, facing increased scrutiny from developers and decision-makers alike on the downstream impacts of their work, including analytics – and soon enough, AI applications,” said Jeff Hollan, Director, Product Management at Snowflake. “Our collaboration with data.world enables data teams and decision-makers to verify and trust their data’s quality to use in mission-critical applications and analytics across their business.” “High-quality data has always been a priority among enterprise data teams and decision-makers. As enterprise AI ambitions grow, the number one priority is ensuring the data powering generative AI is clean, consistent, and contextual,” said Bryon Jacob, CTO at data.world. “Alongside Snowflake, we’re taking steps to ensure data scientists, analysts, and leaders can confidently feed AI and analytics applications data that delivers high-quality insights, and supports the type of decision-making that drives their business forward.” The integration builds on the robust collaboration between data.world and Snowflake. Most recently, the companies announced an exclusive offering for joint customers, streamlining adoption timelines and offering a new attractive price point. The data.world's knowledge graph-powered data catalog already offers unique benefits for Snowflake customers, including support for Snowpark. This offering is now available to all data.world enterprise customers using the Snowflake Collector, as well as customers taking advantage of the Snowflake-only offering. To learn more about the data quality integration or the data.world data catalog platform, visit data.world. About data.world data.world is the data catalog platform built for your AI future. Its cloud-native SaaS (software-as-a-service) platform combines a consumer-grade user experience with a powerful Knowledge Graph to deliver enhanced data discovery, agile data governance, and actionable insights. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500. Our company has 76 patents and has been named one of Austin’s Best Places to Work seven years in a row.

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