According to the report published by Allied Market Research The Why to Invest in Sports Analytics Market Which Size Reach USD 6 376 Billion by 2026 The report provides an extensive analysis of changing market dynamics major segments value chain ...

Why to Invest in Sports Analytics Market Which Size Reach USD 6.376 Billion by 2026

In this week&#x27;s real-time analytics news: LinkedIn announced LakeChime, a new data trigger service to streamline data management in data lakes.

Real-time Analytics News for the Week Ending May 4 - RTInsights

Verisk Analytics (NASDAQ:VRSK – Get Free Report) and Potash America (OTCMKTS:PTAM – Get Free Report) are both business services companies, but which is the better investment? We will compare the two companies based on the strength of their profitability, institutional ownership, analyst ...

Reviewing Potash America (OTCMKTS:PTAM) & Verisk Analytics (NASDAQ:VRSK) - Defense World

Ailytics

ailytics

A look at the application architecture of wealthAPI, a data analytics provider for the financial sector that has created a highly accurate way to identify recurring payment entries. At wealthAPI, we’ve always believed financial analytics should be smarter and faster, especially when identifying recurring payments hidden in transaction data. We’ve built a solution that transforms raw transaction data into actionable insights by leveraging AI. Our system uses vector embeddings to group transactions into recurring payment patterns, ensuring accuracy even when recurring payment entries contain subtle wording differences. From subscriptions to insurance payments, our platform delivers reliable results while maintaining the speed and scalability financial companies need. Here, we’ll show how we designed our architecture to solve these challenges, from data ingestion and vector embeddings to clustering transactions into meaningful groups. We’ll also explore how AI powers advanced features like semantic search, allowing users to find and analyze financial data effortlessly. What The Application Does wealthAPI tackles a common yet challenging problem for financial companies: identifying recurring payments, such as subscriptions, in bank transaction histories. Traditional methods struggled with scaling and often relied on exact matches, missing subtle differences (e.g., “Spotify” vs. “Spotify AB”). wealthAPI addresses this problem with an AI-driven approach that delivers accuracy and speed. At the heart of this solution lies DataStax Astra DB, a database platform purpose-built for modern, scalable, and AI-integrated workflows. Architecture wealthAPI’s system takes raw bank transactions, processes them into embeddings, and groups them into recurring payment patterns — all powered by Astra DB’s vector similarity search capabilities. The architecture ensures scalability and responsiveness at each stage, even under high data volumes. Here’s a simplified flow of the process: Data ingestion - When bank transactions are received, the wealthAPI backend publishes them on a message queue for asynchronous processing.


Embedding creation - Each transaction (e.g., “Spotify, -10€, 22.10.24”) is transformed into a numeric vector (e.g., [0.12, 0.65, 0.78, ..., 0.23]) using Astra DB’s vectorize feature.


Vector storage and search in Astra DB - The embeddings are stored in Astra DB, where lightning-fast vector similarity searches allow the system to find and cluster similar transactions.


Regularity analysis - The clusters are analyzed to identify recurring payments, categorizing them as contracts like “Spotify - music service - monthly" or "Health insurance - Health - yearly." Astra DB ensures the entire process is scalable and responsive, even with high volumes of data. The process also adheres to strict data security measures to ensure that end users and their transactions remain anonymous and protected from external access. Technical Implementation Clustering transactions into contracts Grouping transactions has always been a core challenge. Previous tools depended on exact matches (e.g., vendor name or payment amount), which often failed to capture variations and were slow to scale. At wealthAPI, we tried searching for patterns among millions of transactions with traditional databases in the past, which was both slow and prone to errors. Even small variations in transaction details broke the clustering logic. Because we’re using Astra DB, we can store embeddings and efficiently search for similar transactions, even with minor variations in details. Here’s an example: A payment labeled “Spotify AB” for €10 on one day and “Spotify” for €10 the next is correctly grouped as the same recurring payment. Handling Large Data Volumes With thousands of transactions processed daily, wealthAPI required a database that could scale seamlessly while maintaining speed and accuracy. Astra DB’s foundation is Apache Cassandra, so it’s built for scalability. It also integrates with AI workflows, enabling wealthAPI to maintain fast queries without compromising precision. Transaction Search Engine Because embeddings capture the underlying meaning of transactions, wealthAPI can also implement a search feature. Users can type a keyword like “health” to retrieve all health-related transactions without relying on predefined tags or categories. The system generates an embedding from the user query and runs a simple similarity search using Astra DB; its vector search capability makes this kind of semantic search fast and accurate. A user typing “health,” for example, will see all payments for health-related services, like insurance or gym memberships, even if the vendor names differ. Wrapping Up wealthAPI’s use of Astra DB demonstrates how advanced database technology can drive innovation in financial analytics. From precise transaction clustering to enabling a cutting-edge semantic search engine, Astra DB’s vector search and scalability empower wealthAPI to deliver faster, smarter solutions to its clients. By integrating AI workflows directly into Astra DB’s architecture, wealthAPI has enhanced financial data processing and introduced a valuable new capability for contract analytics. By Belkacem Berchiche, machine learning engineer, wealthAPI, and Dieter Flick, solution engineer, DataStax Learn more about Astra DB and wealthAPI. A look at the application architecture of wealthAPI, a data analytics provider for the financial sector that has created a highly accurate way to identify recurring payment entries. A look at the application architecture of wealthAPI, a data analytics provider for the financial sector that has created a highly accurate way to identify recurring payment entries. A look at the application architecture of wealthAPI, a data analytics provider for the financial sector that has created a highly accurate way to identify recurring payment entries. At wealthAPI , we’ve always believed financial analytics should be smarter and faster, especially when identifying recurring payments hidden in transaction data. We’ve built a solution that transforms raw transaction data into actionable insights by leveraging AI. Our system uses vector embeddings to group transactions into recurring payment patterns, ensuring accuracy even when recurring payment entries contain subtle wording differences. wealthAPI From subscriptions to insurance payments, our platform delivers reliable results while maintaining the speed and scalability financial companies need. Here, we’ll show how we designed our architecture to solve these challenges, from data ingestion and vector embeddings to clustering transactions into meaningful groups. We’ll also explore how AI powers advanced features like semantic search, allowing users to find and analyze financial data effortlessly. What The Application Does wealthAPI tackles a common yet challenging problem for financial companies: identifying recurring payments, such as subscriptions, in bank transaction histories. Traditional methods struggled with scaling and often relied on exact matches, missing subtle differences (e.g., “Spotify” vs. “Spotify AB”). wealthAPI addresses this problem with an AI-driven approach that delivers accuracy and speed. At the heart of this solution lies DataStax Astra DB , a database platform purpose-built for modern, scalable, and AI-integrated workflows. DataStax Astra DB Architecture wealthAPI’s system takes raw bank transactions, processes them into embeddings, and groups them into recurring payment patterns — all powered by Astra DB’s vector similarity search capabilities. The architecture ensures scalability and responsiveness at each stage, even under high data volumes. Here’s a simplified flow of the process: Data ingestion - When bank transactions are received, the wealthAPI backend publishes them on a message queue for asynchronous processing. Embedding creation - Each transaction (e.g., “Spotify, -10€, 22.10.24”) is transformed into a numeric vector (e.g., [0.12, 0.65, 0.78, ..., 0.23]) using Astra DB’s vectorize feature. Vector storage and search in Astra DB - The embeddings are stored in Astra DB, where lightning-fast vector similarity searches allow the system to find and cluster similar transactions. Regularity analysis - The clusters are analyzed to identify recurring payments, categorizing them as contracts like “Spotify - music service - monthly" or "Health insurance - Health - yearly." Data ingestion - When bank transactions are received, the wealthAPI backend publishes them on a message queue for asynchronous processing. Data ingestion - When bank transactions are received, the wealthAPI backend publishes them on a message queue for asynchronous processing. Data ingestion - Embedding creation - Each transaction (e.g., “Spotify, -10€, 22.10.24”) is transformed into a numeric vector (e.g., [0.12, 0.65, 0.78, ..., 0.23]) using Astra DB’s vectorize feature. Embedding creation - Each transaction (e.g., “Spotify, -10€, 22.10.24”) is transformed into a numeric vector (e.g., [0.12, 0.65, 0.78, ..., 0.23]) using Astra DB’s vectorize feature. Embedding creation - Vector storage and search in Astra DB - The embeddings are stored in Astra DB, where lightning-fast vector similarity searches allow the system to find and cluster similar transactions. Vector storage and search in Astra DB - The embeddings are stored in Astra DB, where lightning-fast vector similarity searches allow the system to find and cluster similar transactions. Vector storage and search in Astra DB - Regularity analysis - The clusters are analyzed to identify recurring payments, categorizing them as contracts like “Spotify - music service - monthly" or "Health insurance - Health - yearly." Regularity analysis - The clusters are analyzed to identify recurring payments, categorizing them as contracts like “Spotify - music service - monthly" or "Health insurance - Health - yearly." Regularity analysis - Astra DB ensures the entire process is scalable and responsive, even with high volumes of data. The process also adheres to strict data security measures to ensure that end users and their transactions remain anonymous and protected from external access. Technical Implementation Clustering transactions into contracts Grouping transactions has always been a core challenge. Previous tools depended on exact matches (e.g., vendor name or payment amount), which often failed to capture variations and were slow to scale. At wealthAPI, we tried searching for patterns among millions of transactions with traditional databases in the past, which was both slow and prone to errors. Even small variations in transaction details broke the clustering logic. Because we’re using Astra DB, we can store embeddings and efficiently search for similar transactions, even with minor variations in details. Here’s an example: A payment labeled “Spotify AB” for €10 on one day and “Spotify” for €10 the next is correctly grouped as the same recurring payment. Handling Large Data Volumes With thousands of transactions processed daily, wealthAPI required a database that could scale seamlessly while maintaining speed and accuracy. Astra DB’s foundation is Apache Cassandra, so it’s built for scalability. It also integrates with AI workflows, enabling wealthAPI to maintain fast queries without compromising precision. Transaction Search Engine Because embeddings capture the underlying meaning of transactions, wealthAPI can also implement a search feature. Users can type a keyword like “health” to retrieve all health-related transactions without relying on predefined tags or categories. The system generates an embedding from the user query and runs a simple similarity search using Astra DB; its vector search capability makes this kind of semantic search fast and accurate. A user typing “health,” for example, will see all payments for health-related services, like insurance or gym memberships, even if the vendor names differ. Wrapping Up wealthAPI’s use of Astra DB demonstrates how advanced database technology can drive innovation in financial analytics. From precise transaction clustering to enabling a cutting-edge semantic search engine, Astra DB’s vector search and scalability empower wealthAPI to deliver faster, smarter solutions to its clients. By integrating AI workflows directly into Astra DB’s architecture, wealthAPI has enhanced financial data processing and introduced a valuable new capability for contract analytics. By Belkacem Berchiche, machine learning engineer, wealthAPI, and Dieter Flick, solution engineer, DataStax By Belkacem Berchiche, machine learning engineer, wealthAPI, and Dieter Flick, solution engineer, DataStax Learn more about Astra DB and wealthAPI. Learn more about Astra DB and wealthAPI. Learn more about Astra DB and wealthAPI . Astra DB Astra DB wealthAPI wealthAPI

How Vector Search Cracks the Code on Contract Analytics 

Как Vector Search разбива кода на Contract Analytics 

Com la cerca vectorial trenca el codi a l'anàlisi de contractes 

Cómo Vector Search descifra el código del análisis de contratos

Kuinka vektorihaku murtaa koodin sopimusanalyysissä 

Como a busca vectorial descifra o código en analítica de contratos 

כיצד חיפוש וקטור מפצח את הקוד בניתוח חוזים 

Come la ricerca vettoriale decifra il codice dell'analisi contrattuale

Vector Search が契約分析のコード解読に成功

Ndenge nini Vector Search Ezali ko craquer Code na Analytique ya Contrat 

Kaip vektorinė paieška nulaužia sutarčių analizės kodą 

Cara Carian Vektor Memecahkan Kod pada Analitis Kontrak 

Ukuseshwa KweVector Kuyiqhekeza Kanjani Ikhodi Kuzibalo Zenkontileka 

The TechBeat: Building a Data Analytics Platform to Streamline the Temporary Labor Sector (12/8/2024)

The TechBeat: Building a Data Analytics Platform to Streamline the Temporary Labor Sector (12/6/2024)

How to Master Real-Time Analytics With AWS: Timestream and Beyond

Cómo dominar el análisis en tiempo real con AWS: Timestream y más allá

Kuinka hallita reaaliaikaista analytiikkaa AWS:n avulla: Timestream and Beyond

Como dominar a análise en tempo real con AWS: Timestream e máis aló

AWS でリアルタイム分析をマスターする方法: Timestream とその先

AWS로 실시간 분석을 마스터하는 방법: Timestream 및 그 이상

AWS менен реалдуу убакыттагы аналитиканы кантип өздөштүрүү керек: Timestream жана андан тышкары

AWS ашиглан бодит цагийн аналитикийг хэрхэн эзэмших вэ: Timestream ба түүнээс дээш

AWS को साथ वास्तविक-समय एनालिटिक्स कसरी मास्टर गर्ने: टाइमस्ट्रीम र परे

Hoe u realtime-analyses onder de knie krijgt met AWS: Timestream en verder

Tsela ya go Master Ditshekatsheko tša Nako ya Kgonthe Ka AWS: Nako ya Moela le ka Ntle ga yona

Imayna chiqa pacha t'aqwiyta AWS kaqwan kamachiy: Pacha puriy chaymanta aswan karu

วิธีการเชี่ยวชาญการวิเคราะห์แบบเรียลไทม์ด้วย AWS: Timestream และ Beyond

Data analysis is the foundation of strategic success in digital marketing. It is the direct route to smarter decisions, improving ROI, and staying ahead of the competition. With the rapid advancement of AI-driven analytics, marketing has entered a new phase of precision and actionable insights. Transforming Data Into Customer Insights Uncovering golden nuggets in vast datasets is central to understanding customers better. AI technology allows marketing teams to process and interpret huge streams of data in real time – a task that has always been out of reach. 📊 Tapping in faster and deeper into customer patterns, trends, and motivations is a clear advantage in outpacing competitors. Social Discovery Group (SDG), the world’s largest group of social discovery companies, is a prime example of how AI-driven data analytics is impacting marketing results. 🌐 Understanding the most effective technologies and methods is critical to unlocking this potential. The Basics of AI in Data Analysis In the contemporary business landscape, artificial intelligence represents a pivotal breakthrough in data analysis. AI enables organizations to process and interpret complex datasets with unprecedented speed, precision, and depth, transcending traditional analytical limitations. Key Technologies 💡 in AI-Driven Analytics AI-powered analytics offers a suite of tools to extract meaningful insights from data: Predictive Analytics: Tools that use historical data to forecast future trends and anticipate customer behavior and trends.
Machine Learning (ML): Algorithms that learn from historical data to identify patterns, predict behaviors, and improve decisions.
AI-Powered Data Collection: Aggregates and organizes data from diverse sources such as the web, clickstreams and surveys.
Advanced Data Processing: Processes large datasets to analyze dynamically, uncovering nuanced insights at scale. Enhancing the Customer Experience Data analysis must always be handled with careful consideration to achieve a positive impact on the customer experience. 🎯 At Social Discovery Group, data is drawn from a variety of behavioural sources: Website interactions
Online activity
Email engagement
Paid ad performance With faster AI-driven analysis across its platforms, SDG is able to better match users based on a deeper range of data. The results gained offer versatility in boosting connections and raising the level of virtual intimacy. Responsive Advertising Performance One of AI’s most transformative applications is in digital advertising. 🧩 By continuously adapting to consumer and market patterns, AI enables real-time adjustments that maximize campaign effectiveness. Companies like Amazon use powerful AI tools to quickly understand their customers and serve up the right solutions. For instance, this is very potent when dealing with A/B testing where AI offers real-time analysis and other time-saving benefits. Social Discovery Group has found this to be very helpful when creating landing pages for acquiring users. SDG has developed an in-house method for real-time A/B testing to establish the customer profile and alter the landing page accordingly. For example, an office-based professional might see a different page than a remote worker, improving engagement and conversion rates. Maximizing ROI With AI Analytics The ultimate advantage of AI-powered analytics lies in its impact on the bottom line. By identifying high-performing campaigns and reallocating resources from underperforming ones, businesses can achieve maximum efficiency and ROI. As global markets become increasingly complex and data-driven, AI-powered analytics has transitioned from a competitive advantage to a critical strategic necessity. Organizations that effectively integrate AI-driven insights into their decision-making processes will be able to anticipate market shifts, optimize strategies, and achieve more precise business outcomes. ✨The future of intelligent business strategy is powered by adaptive, insights-driven artificial intelligence that transforms raw data into strategic competitive advantages. Written by Alex Kudos, CMO at Social Discovery Group 20+ years of marketing expertise. Alex is passionate about digital transformation and holds a dual Master’s degree in Computer Science and Business Administration. Data analysis is the foundation of strategic success in digital marketing. It is the direct route to smarter decisions, improving ROI, and staying ahead of the competition. With the rapid advancement of AI-driven analytics, marketing has entered a new phase of precision and actionable insights. Transforming Data Into Customer Insights Transforming Data Into Customer Insights Uncovering golden nuggets in vast datasets is central to understanding customers better. AI technology allows marketing teams to process and interpret huge streams of data in real time – a task that has always been out of reach. 📊 Tapping in faster and deeper into customer patterns, trends, and motivations is a clear advantage in outpacing competitors. Social Discovery Group (SDG), the world’s largest group of social discovery companies, is a prime example of how AI-driven data analytics is impacting marketing results. 🌐 Understanding the most effective technologies and methods is critical to unlocking this potential. Social Discovery Group The Basics of AI in Data Analysis The Basics of AI in Data Analysis In the contemporary business landscape, artificial intelligence represents a pivotal breakthrough in data analysis. AI enables organizations to process and interpret complex datasets with unprecedented speed, precision, and depth, transcending traditional analytical limitations. Key Technologies 💡 in AI-Driven Analytics Key Technologies in AI-Driven Analytics AI-powered analytics offers a suite of tools to extract meaningful insights from data: Predictive Analytics: Tools that use historical data to forecast future trends and anticipate customer behavior and trends. Machine Learning (ML): Algorithms that learn from historical data to identify patterns, predict behaviors, and improve decisions. AI-Powered Data Collection: Aggregates and organizes data from diverse sources such as the web, clickstreams and surveys. Advanced Data Processing: Processes large datasets to analyze dynamically, uncovering nuanced insights at scale. Predictive Analytics: Tools that use historical data to forecast future trends and anticipate customer behavior and trends. Predictive Analytics: Machine Learning (ML): Algorithms that learn from historical data to identify patterns, predict behaviors, and improve decisions. Machine Learning (ML): AI-Powered Data Collection: Aggregates and organizes data from diverse sources such as the web, clickstreams and surveys. AI-Powered Data Collection: Advanced Data Processing: Processes large datasets to analyze dynamically, uncovering nuanced insights at scale. Advanced Data Processing: Enhancing the Customer Experience Data analysis must always be handled with careful consideration to achieve a positive impact on the customer experience. 🎯 At Social Discovery Group, data is drawn from a variety of behavioural sources: Website interactions Online activity Email engagement Paid ad performance Website interactions Online activity Email engagement Paid ad performance With faster AI-driven analysis across its platforms, SDG is able to better match users based on a deeper range of data. The results gained offer versatility in boosting connections and raising the level of virtual intimacy. With faster AI-driven analysis across its platforms, SDG is able to better match users based on a deeper range of data. The results gained offer versatility in boosting connections and raising the level of virtual intimacy. Responsive Advertising Performance One of AI’s most transformative applications is in digital advertising. 🧩 By continuously adapting to consumer and market patterns, AI enables real-time adjustments that maximize campaign effectiveness. Companies like Amazon use powerful AI tools to quickly understand their customers and serve up the right solutions. For instance, this is very potent when dealing with A/B testing where AI offers real-time analysis and other time-saving benefits. Social Discovery Group has found this to be very helpful when creating landing pages for acquiring users. SDG has developed an in-house method for real-time A/B testing to establish the customer profile and alter the landing page accordingly. For example, an office-based professional might see a different page than a remote worker, improving engagement and conversion rates. Social Discovery Group Maximizing ROI With AI Analytics The ultimate advantage of AI-powered analytics lies in its impact on the bottom line. By identifying high-performing campaigns and reallocating resources from underperforming ones, businesses can achieve maximum efficiency and ROI. As global markets become increasingly complex and data-driven, AI-powered analytics has transitioned from a competitive advantage to a critical strategic necessity. Organizations that effectively integrate AI-driven insights into their decision-making processes will be able to anticipate market shifts, optimize strategies, and achieve more precise business outcomes. ✨The future of intelligent business strategy is powered by adaptive, insights-driven artificial intelligence that transforms raw data into strategic competitive advantages. Written by Alex Kudos, CMO at Social Discovery Group 20+ years of marketing expertise. Alex is passionate about digital transformation and holds a dual Master’s degree in Computer Science and Business Administration. Written by Alex Kudos, CMO at Social Discovery Group 20+ years of marketing expertise. Alex is passionate about digital transformation and holds a dual Master’s degree in Computer Science and Business Administration. Written by Alex Kudos, CMO at Social Discovery Group 20+ years of marketing expertise. Alex is passionate about digital transformation and holds a dual Master’s degree in Computer Science and Business Administration. Alex Kudos, Alex Kudos, Social Discovery Group Social Discovery Group

Digital Marketing: Empowering Decision-Making with AI-Driven Data Analytics

التسويق الرقمي: تمكين اتخاذ القرار باستخدام تحليلات البيانات المدعومة بالذكاء الاصطناعي

Лічбавы маркетынг: пашырэнне магчымасцей прыняцця рашэнняў з дапамогай аналізу даных на аснове штучнага інтэлекту

Digitalni marketing: osnaživanje donošenja odluka uz analitiku podataka vođenu umjetnom inteligencijom

Marketing digital: cómo potenciar la toma de decisiones con análisis de datos basados en inteligencia artificial

Digitalni marketing: Osnaživanje donošenja odluka pomoću analize podataka vođene umjetnom inteligencijom

デジタルマーケティング: AI 主導のデータ分析による意思決定の強化

Цифрлық маркетинг: AI негізіндегі деректерді талдау арқылы шешім қабылдау мүмкіндігін кеңейту

Marketing nomerika: Fanapahan-kevitra amin'ny alàlan'ny AI-Driven Data Analytics

Digitale marketing: besluitvorming versterken met AI-gestuurde data-analyse

Digital marknadsföring: Styrkande av beslutsfattande med AI-driven dataanalys

ዲጂታል ማርኬቲንግ፡ ብAI-Driven Data Analytics ምሕያል ውሳነ ምሃብ

Dijital Pazarlama: Yapay Zeka Destekli Veri Analitiğiyle Karar Vermeyi Güçlendirme

Building a Data Analytics Platform to Streamline the Temporary Labor Sector

Стварэнне платформы аналітыкі даных для ўпарадкавання сектара часовай працы

Creación de una plataforma de análisis de datos para optimizar el sector del trabajo temporal

Création d'une plateforme d'analyse de données pour rationaliser le secteur du travail temporaire

अस्थायी श्रम क्षेत्र को कारगर बनाने के लिए डेटा एनालिटिक्स प्लेटफ़ॉर्म का निर्माण

派遣労働部門の効率化に向けたデータ分析プラットフォームの構築

ការកសាងវេទិកាវិភាគទិន្នន័យ ដើម្បីពង្រឹងវិស័យការងារបណ្តោះអាសន្ន

임시 노동 부문을 간소화하기 위한 데이터 분석 플랫폼 구축

Construindo uma plataforma de análise de dados para otimizar o setor de trabalho temporário

තාවකාලික කම්කරු අංශය විධිමත් කිරීම සඳහා දත්ත විශ්ලේෂණ වේදිකාවක් ගොඩනැගීම

Kuunda Jukwaa la Uchanganuzi wa Data ili Kuhuisha Sekta ya Kazi ya Muda

การสร้างแพลตฟอร์มการวิเคราะห์ข้อมูลเพื่อปรับปรุงภาคส่วนแรงงานชั่วคราว

Створення платформи аналізу даних для оптимізації сектору тимчасової праці

The data analytics landscape is changing very quickly. For many businesses, the ability to gather, analyze, and interpret data effectively helps them to understand their customers, improve internal processes, and remain competitive. There are five trends that we think will shape up how businesses analyze and use data in the future. Let’s take a look at each of them and understand what they are. 1. Data Sources – Synthetic Data Any kind of data analytics needs source data, and in today’s world this can come from nearly anywhere. Traditional data sources like customer databases, sales records, and website analytics are now joined by newer sources, such as social media feeds, IoT devices (e.g. drones), and third-party databases. But amongst all these newer sources, there is one in particular that we think is going to shape the future - Synthetic data. This is where Artificial Intelligence (‘AI’) is being used to create a simulation of data based on real-world information. While we are in the early stages of using synthetic data, it is becoming an increasingly important tool in areas where it is hard to source original data. 2. Data Infrastructure - Interoperability With a growing number of data feeds and sources, it has been a constant challenge for many businesses to implement the right infrastructure. This includes both the software and hardware required to capture, store and process data across an organization. On the hardware front, one trend that has emerged over the past 10 years is cloud computing. This refers to the storage and processing of data in third-party data centres. Amazon was a first mover in this space with introduction of AWS. Nower days, the industry has matured with increased competition from other tech giants like Microsoft. Another trend within cloud computing has been to set up data lakes and warehouses. However, the trend that we are focused on is around software that can connect data from different sources in a standardized way - This is referred to as data interoperability. Having interoperability between different systems, such as data from the Customer Relationship Management system and the Enterprise Resource Planning system is critical for the flow of information. While many businesses struggle with interoperability, those that achieve it will have a real advantage by being able to leverage their data to gain deeper insights. 3. AI and Machine Learning (‘ML’) – Natural Language Processing (‘NLP’) AI and ML shook the world in late 2022 with the release of ChatGPT 3.5, which was an information chatbot free for the public to use. Since then, AI and ML have become the most powerful tools in data analytics. This was initially focused on using structured data within companies to gain new insights and forecast with increased accuracy. The use of AI has moved at a very quick pace and now there are all sorts of new tools that can help organizations leverage their structured data better. However, the trend that we really think is worth keeping an eye on is NLP, which is using AI to analyze and provide insights from unstructured data. This includes extracting insights from data sources like video, images and audio, which has traditionally been a very time consuming exercise, until now. 4. Data Visualization - Storytelling Data visualization historically refers to using graphics to present data. For most of us, these are the charts and graphs used in presentations and reports. However, we think that it’s becoming more about transforming data into a story that can be easily acted on. As the number of data sources increase, using data visualization as a tool to actually tell the story is becoming more important. Especially when it comes to informing people within an organization and having the right decisions made on time. Storytelling is a growing trend that simply combines data visualizations with the narrative. It makes it easier to explain the findings from complex data to a non-technical audience, and drive informed decision-making across all levels of the organization. 5. Talent – New Roles The demand for data savvy talent is growing as analytics becomes more central to business strategy. From data scientists to analysts, organizations need skilled professionals who can make sense of the data and communicate it effectively. While there’s a push to make data skills more accessible across roles, there is also the creation of new data-related roles, such as “analytics translators”. This particular role is focused on bridging the gap between technical and non-technical teams. These professionals help communicate insights effectively, ensuring that business leaders can act on data insights without needing a strong technical background. This is one example of some of the new roles that are being created, in parallel with increasing demand for traditional data roles, such as data scientists and engineers. Conclusion The world of data analytics and insights is moving quickly. It is being driven by new sources of data, robust infrastructure, advanced AI and ML capabilities, improved visualization tools, and growing demand for data-savvy talent. While data analytics is typically focused on internal data, benchmarking is another tool that can uncover competitive trends and insights using external data. Organizations who stay on top of these emerging trends are well-positioned to continue winning into the future. The data analytics landscape is changing very quickly. For many businesses, the ability to gather, analyze, and interpret data effectively helps them to understand their customers, improve internal processes, and remain competitive. There are five trends that we think will shape up how businesses analyze and use data in the future. Let’s take a look at each of them and understand what they are. 1. Data Sources – Synthetic Data Any kind of data analytics needs source data, and in today’s world this can come from nearly anywhere. Traditional data sources like customer databases, sales records, and website analytics are now joined by newer sources, such as social media feeds, IoT devices (e.g. drones ), and third-party databases. But amongst all these newer sources, there is one in particular that we think is going to shape the future - Synthetic data. This is where Artificial Intelligence (‘AI’) is being used to create a simulation of data based on real-world information. While we are in the early stages of using synthetic data, it is becoming an increasingly important tool in areas where it is hard to source original data. drones drones 2. Data Infrastructure - Interoperability With a growing number of data feeds and sources, it has been a constant challenge for many businesses to implement the right infrastructure. This includes both the software and hardware required to capture, store and process data across an organization. On the hardware front, one trend that has emerged over the past 10 years is cloud computing. This refers to the storage and processing of data in third-party data centres. Amazon was a first mover in this space with introduction of AWS. Nower days, the industry has matured with increased competition from other tech giants like Microsoft. Another trend within cloud computing has been to set up data lakes and warehouses . However, the trend that we are focused on is around software that can connect data from different sources in a standardized way - This is referred to as data interoperability. Having interoperability between different systems, such as data from the Customer Relationship Management system and the Enterprise Resource Planning system is critical for the flow of information. While many businesses struggle with interoperability, those that achieve it will have a real advantage by being able to leverage their data to gain deeper insights. data lakes and warehouses data lakes and warehouses 3. AI and Machine Learning (‘ML’) – Natural Language Processing (‘NLP’) AI and ML shook the world in late 2022 with the release of ChatGPT 3.5, which was an information chatbot free for the public to use. Since then, AI and ML have become the most powerful tools in data analytics. This was initially focused on using structured data within companies to gain new insights and forecast with increased accuracy. The use of AI has moved at a very quick pace and now there are all sorts of new tools that can help organizations leverage their structured data better. However, the trend that we really think is worth keeping an eye on is NLP , which is using AI to analyze and provide insights from unstructured data. This includes extracting insights from data sources like video, images and audio, which has traditionally been a very time consuming exercise, until now. NLP NLP 4. Data Visualization - Storytelling Data visualization historically refers to using graphics to present data. For most of us, these are the charts and graphs used in presentations and reports. However, we think that it’s becoming more about transforming data into a story that can be easily acted on. As the number of data sources increase, using data visualization as a tool to actually tell the story is becoming more important. Especially when it comes to informing people within an organization and having the right decisions made on time. Storytelling is a growing trend that simply combines data visualizations with the narrative. It makes it easier to explain the findings from complex data to a non-technical audience, and drive informed decision-making across all levels of the organization. Data visualization Data visualization 5. Talent – New Roles The demand for data savvy talent is growing as analytics becomes more central to business strategy. From data scientists to analysts, organizations need skilled professionals who can make sense of the data and communicate it effectively. While there’s a push to make data skills more accessible across roles, there is also the creation of new data-related roles, such as “ analytics translators ”. This particular role is focused on bridging the gap between technical and non-technical teams. These professionals help communicate insights effectively, ensuring that business leaders can act on data insights without needing a strong technical background. This is one example of some of the new roles that are being created, in parallel with increasing demand for traditional data roles, such as data scientists and engineers. analytics translators Conclusion The world of data analytics and insights is moving quickly. It is being driven by new sources of data, robust infrastructure, advanced AI and ML capabilities, improved visualization tools, and growing demand for data-savvy talent . While data analytics is typically focused on internal data, benchmarking is another tool that can uncover competitive trends and insights using external data. Organizations who stay on top of these emerging trends are well-positioned to continue winning into the future. growing demand for data-savvy talent benchmarking

5 Trends Shaping the Future of Data Analytics and Insights

5 প্রবণতা ডেটা বিশ্লেষণ এবং অন্তর্দৃষ্টির ভবিষ্যত গঠন করে

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