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Author: Kartik Patel
Companies that are data-driven demonstrate improved business performance. McKinsey says that data and analytics can provide EBITDA (earnings before interest, taxes, depreciation, and amortization) increases of up to 25% [1]. According to MIT, digitally mature firms are 26% more profitable than their peers [2]. Forrester research found that organizations using data are three times more […]
The post Harnessing Data: From Resource to Asset to Product appeared first on DATAVERSITY.
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Author: Prashanth Southekal
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Author: John Wills
The rise of artificial intelligence (AI) has sparked a heated debate about the future of jobs across various industries. Data analysts, in particular, find themselves at the heart of this conversation. Will AI render human data analysts obsolete?
Contrary to the doomsayers’ predictions, the future is not bleak for data analysts. In fact, AI will empower data analysts to thrive, enhancing their ability to provide more insightful and impactful business decisions. Let’s explore how AI, and specifically large language models (LLMs), can work in tandem with data analysts to unlock new levels of value in data and analytics.
First, it’s essential to understand that the role of a data analyst extends far beyond mere number crunching. Data analysts are storytellers, translating complex data into actionable insights that all decision makers can easily understand. They possess the critical thinking skills to ask the right questions, interpret results within the context of business objectives, and communicate findings effectively to stakeholders. While AI excels at processing vast amounts of data and identifying patterns, it lacks the nuanced understanding of business context and the ability to interpret data that are essential capabilities unique to human analysts.
AI can automate many routine and repetitive tasks that occupy a significant portion of a data analyst’s time. Data cleaning, integration, and basic statistical analysis can be streamlined using AI, freeing analysts to focus on more complex and value-added activities. For example, AI-powered tools can quickly identify and correct data inconsistencies, handle missing values, and perform preliminary data exploration. This automation increases efficiency and allows analysts to delve deeper into data interpretation and strategic analysis.
AI and machine learning algorithms can augment the analytical capabilities of data analysts. These technologies can uncover hidden patterns, detect anomalies, and predict future trends with greater accuracy and speed than legacy approaches. Analysts can use these advanced insights as a foundation for their analysis, adding their expertise and business acumen to provide context and relevance. For instance, AI can identify a subtle trend in customer behavior, which an analyst can then explore further to understand underlying causes and implications for marketing strategies.
Large language models (LLMs), such as GPT-4, can democratize access to data insights by enabling non-technical stakeholders to interact with data in natural language. LLMs can interpret complex queries and generate understandable explanations very quickly, making data insights more accessible to everyone within an organization. This capability enhances collaboration between data analysts and business teams, fostering a data-driven culture where decisions are informed by insights derived from both human and AI analysis.
LLMs can simplify data querying through natural language processing (NLP). Instead of writing complex SQL queries, analysts and business users can ask questions in plain English. For example, a user might ask, “What were our top-selling products last quarter?” and the LLM can translate this query into the necessary database commands and retrieve the relevant data. This capability lowers the barrier to entry for data analysis, making it more accessible and efficient.
LLMs can assist in generating reports by summarizing key insights from data and creating narratives around them. Analysts can use these auto generated reports as a starting point, refining and adding their insights to produce comprehensive and insightful business reports. This collaboration between AI and analysts ensures that reports are both data-rich and contextually relevant.
LLMs can enhance data visualization by interpreting data and providing textual explanations. For instance, when presenting a complex graph or chart, the LLM can generate accompanying text that explains the key takeaways and trends in the data. This feature helps bridge the gap between data visualization and interpretation, making it easier for stakeholders to understand and act on the insights.
Despite the advancements in AI, the human element remains irreplaceable in data analysis. Analysts bring context, ethical considerations, and nuanced interpretation to the table. They understand the business environment, can ask probing questions, and can foresee the potential impact of data-driven decisions on various areas of the business. Moreover, analysts are crucial in ensuring that data usage adheres to ethical standards and regulatory requirements, areas where AI still has limitations.
AI might identify a correlation, but it takes a human analyst to understand whether the correlation is meaningful and relevant to the business. Analysts can discern whether a trend is due to a seasonal pattern, a market anomaly, or a fundamental change in consumer behavior, providing depth to the analysis that AI alone cannot achieve.
AI systems can inadvertently perpetuate biases present in the data they are trained on. Data analysts play a vital role in identifying and mitigating these biases, ensuring that the insights generated are fair and ethical. They can scrutinize AI-generated models and results, applying their judgment to avoid unintended consequences.
Ultimately, data analysts are instrumental in strategic decision-making. They can synthesize insights from multiple data sources, apply their industry knowledge, and recommend actionable strategies. This strategic input is crucial for aligning data insights with business goals and driving impactful decisions.
The future of data analysis is not a zero-sum game between AI and human analysts. Instead, it is a symbiotic relationship where each complements the other. AI, with its ability to process and analyze data at unprecedented scale, enhances the capabilities of data analysts. Analysts, with their contextual understanding, critical thinking, and ethical oversight, ensure that AI-driven insights are relevant, accurate, and actionable.
By embracing AI as a tool rather than a threat, data analysts can unlock new levels of productivity and insight, driving smarter business decisions and better outcomes. In this collaborative future, data analysts will not only survive but thrive, leveraging AI to amplify their impact and solidify their role as indispensable assets in the data-driven business landscape.
The post Will AI Take Data Analyst Jobs? appeared first on Actian.
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Author: Dee Radh
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Author: Kartik Patel
Manufacturing today is far from the straightforward assembly lines of the past; it is chaos incarnate. Each stage in the manufacturing process comes with its own set of data points. Raw materials, production schedules, machine operations, quality control, and logistics all generate vast amounts of data, and managing this data effectively can be the difference between smooth operations and a breakdown in the process.
Data integration is a powerful way to conquer the chaos of modern manufacturing. It’s the process of combining data from diverse sources into a unified view, providing a holistic picture of the entire manufacturing process. This involves collecting data from various systems, such as Enterprise Resource Planning (ERP) systems, Manufacturing Execution Systems (MES), and Internet of Things (IoT) devices. When this data is integrated and analyzed cohesively, it can lead to significant improvements in efficiency, decision-making, and overall productivity.
A robust data platform is essential for effective data integration and should encompass analytics, data warehousing, and seamless integration capabilities. Let’s break down these components and see how they contribute to conquering the manufacturing chaos.
Data without analysis is like raw material without a blueprint. Advanced analytics tools can sift through the vast amounts of data generated in manufacturing, identifying patterns and trends that might otherwise go unnoticed. Predictive analytics, for example, can forecast equipment failures before they happen, allowing for proactive maintenance and reducing downtime.
Analytics can also optimize production schedules by analyzing historical data and predicting future demand. This ensures that resources are allocated efficiently, minimizing waste and maximizing output. Additionally, quality control can be enhanced by analyzing data from different stages of the production process, identifying defects early, and implementing corrective measures.
A data warehouse serves as a central repository where integrated data is stored. This centralized approach ensures that all relevant data is easily accessible, enabling comprehensive analysis and reporting. In manufacturing, a data warehouse can consolidate information from various departments, providing a single source of truth.
For instance, production data, inventory levels, and sales forecasts can be stored in the data warehouse. This unified view allows manufacturers to make informed decisions based on real-time data. If there’s a sudden spike in demand, the data warehouse can provide insights into inventory levels, production capacity, and lead times, enabling quick adjustments to meet the demand.
Integration is the linchpin that holds everything together. It involves connecting various data sources and ensuring data flows seamlessly between them. In a manufacturing setting, integration can connect systems like ERP, MES, and Customer Relationship Management (CRM), creating a cohesive data ecosystem.
For example, integrating ERP and MES systems can provide a real-time view of production status, inventory levels, and order fulfillment. This integration eliminates data silos, ensuring that everyone in the organization has access to the same accurate information. It also streamlines workflows, as data doesn’t need to be manually transferred between systems, reducing the risk of errors and saving time.
Aeriz is a national aeroponic cannabis brand that provides patients and enthusiasts with the purest tasting, burning, and feeling cultivated cannabis. They needed to be able to connect, manage, and analyze data from several systems, both on-premises and in the cloud, and access data that was not easy to gather from their primary tracking system.
By leveraging the Actian Data Platform, Aeriz was able to access data that wasn’t part of the canned reports provided by their third-party vendors. They were able to easily aggregate this data with Salesforce to improve inventory visibility and accelerate their order-to-cash timeline.
The result was an 80%-time savings of a full-time employee responsible for locating and aggregating data for business reporting. Aeriz can now focus resources on analyzing data to find improvements and efficiencies to accommodate rapid growth.
Imagine having the ability to foresee equipment failures before they happen? Or being able to adjust production lines based on live demand forecasts? Enter the Actian Data Platform, a powerhouse designed to tackle the complexities of manufacturing data head-on. The Actian Data Platform transforms your raw data into actionable intelligence, empowering manufacturers to make smarter, faster decisions.
But it doesn’t stop there. The Actian Data Platform’s robust data warehousing capabilities ensure that all your critical data is centralized, accessible, and ready for deep analysis. Coupled with seamless integration features, this platform breaks down data silos and ensures a cohesive flow of information across all your systems. From the shop floor to the executive suite, everyone operates with the same up-to-date information, fostering collaboration and efficiency like never before. With Actian, chaos turns to clarity and complexity becomes a competitive advantage.
Imagine analytics that predict the future, a data warehouse that’s your lone source of truth, and integration that connects it all seamlessly. This isn’t just about managing chaos—it’s about turning data into a well-choreographed dance of efficiency and productivity. By embracing the power of data, you can watch your manufacturing operations transform into a precision machine that’s ready to conquer any challenge!
The post Streamlining the Chaos: Conquering Manufacturing With Data appeared first on Actian.
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Author: Kasey Nolan
You may not be an analytics expert and you may find terms like PMML integration somewhat daunting. But, in reality, the concept is not complex, and the value is outstanding. So, what is PMML integration? PMML stands for “predictive model markup language.” It is an interchange format that provides a method by which analytical applications and […]
The post What Is PMML and Why Is It Important? appeared first on DATAVERSITY.
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Author: Kartik Patel
Data extraction is a cornerstone in data analytics, enabling organizations to extract valuable insights from raw data. While basic extraction techniques are fundamental, understanding advanced strategies is crucial for maximizing efficiency and accuracy. This article will explore advanced tips for effective data extraction, shedding light on automation tools, leveraging APIs and web scraping techniques, enhancing […]
The post Beyond the Basics: Advanced Tips for Effective Data Extraction appeared first on DATAVERSITY.
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Author: Irfan Gowani
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Author: Myles Suer
Consumers and citizens are accustomed to getting instant answers and results from businesses. They expect the same lightning-fast responses from the public sector, too. Likewise, employees at public sector organizations need the ability to quickly access and utilize data—including employees without advanced technical or analytics skills—to identify and address citizens’ needs.
Giving employees the information to meet citizen demand and answer their questions requires public sector organizations to capture and analyze data in real time. Real-time data supports intelligent decision making, automation, and other business-critical functions.
Easily accessible and trusted data can also increase operational effectiveness, predict risk with greater accuracy, and ultimately increase satisfaction for citizens. That data must be secure while still enabling frictionless sharing between departments for collaboration and use cases.
This naturally leads to a pressing question—How can your organization achieve real-time analytics to benefit citizens and staff alike? The answer, at a foundational level, is to implement a modern, high-performance data platform.
Achieving a digital transformation in the public sector involves more than upgrading technology. It entails rethinking how services are delivered, how data is shared, and how your infrastructure handles current and future workloads. Too often in public service organizations, just like with their counterparts in the private sector, legacy systems are limiting the effectiveness of data.
These systems lack the scalability and integration needed to support digital transformation efforts. They also face limitations making trusted data available when and where it’s needed, including availability for real-time data analytics. Providing the data, analytics, and IT capabilities required by modern organizations is only possible with a modern and scalable data platform. This type of platform is designed to integrate systems and operations, capture and share all relevant data to predict and respond quickly to changes, and improve service delivery to citizens.
At the same time, modernization efforts that include a cloud migration can be complex. This is often due to the vast amounts of data that need to be moved to the cloud and the legacy systems entrenched in organizational processes. That’s why you need a clear and proven strategy and to work with an experienced vendor to make the transition seamless while ensuring data quality.
Hybrid cloud data platforms have emerged as a proven solution for integrating and sharing data in the public sector. By combining on-premises infrastructure with cloud-based services, these platforms offer the flexibility, scalability, and capability to manage, integrate, and share large data volumes.
Another benefit of hybrid solutions is that they allow organizations to optimize their on-premises investments while keeping costs from spiraling out of control in the cloud—unlimited scaling in the cloud can have costs associated with it. Public sector organizations can use a hybrid platform to deliver uninterrupted service, even during peak times or critical events, while making data available in real time for analytics, apps, or other needs.
Smart decision-making demands accurate, trustworthy, and integrated data. This means that upstream, you need a platform capable of seamlessly integrating data and adding new data pipelines—without relying on IT or advanced coding.
Likewise, manual processes and IT intervention will quickly bog down an organization. For example, when a social housing team needs data from multiple systems to ensure buildings meet safety regulations, accessing and analyzing the information might take days or weeks—with no guarantee the data is trustworthy. Automating the pipelines reduces time to insights and ensures data quality measures are in place to catch errors and duplication.
Data integration is essential to breaking down data silos, providing deeper context and relevancy to data, and ensuring the most informed decisions possible. For example, central government agencies can use the data to drive national policies while identifying issues and needs, and strategically allocating resources.
Moving from legacy systems to a modern platform and migrating to the cloud at a pace your organization is comfortable with enables a range of benefits:
With a solution like the Actian Data Platform, you can do even more. For example, the platform lets you easily connect, transform, and manage data. The data platform enables real-time data access at scale along with real-time analytics. Public sector organizations can benefit, for instance, by using the data to craft employee benefits programs, housing policies, tax guidelines, and other government programs.
The Actian Data Platform can integrate into your existing infrastructure and easily scale to meet changing needs. The platform makes data easy to use so you can better predict citizen needs, provide more personalized services, identify potential problems, and automate operations.
Taking a modern approach to data management, integration, and quality, along with having the ability to process, store, and analyze even large and complex data sets, allows you to digitally transform faster and be better positioned for intelligent decision making. As the public sector strives to effectively serve the needs of the public in a cost-effective, sustainable, and responsible way, data-driven decision-making will play a greater role for all stakeholders.
The path toward an effective and responsive public sector lies in the power of data and a modern data platform. Our new eBook “Accelerate a Digital Transformation in the UK Public Sector” explains why a shift from legacy technologies to a modern infrastructure is essential for today’s organizations. The eBook shares how local councils and central government organizations can balance the need to modernize with maximizing investments in current on-prem systems, meeting the changing needs of the public, and making decisions with confidence.
The post Leverage Real-Time Analytics for Smarter Decision-Making in Public Services appeared first on Actian.
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Author: Tim Williams
Businesses today are drowning in data. The sheer volume and complexity of information available have made it increasingly difficult for organizations to extract meaningful insights using traditional business intelligence (BI) tools and the expertise of specialized data scientists. This is where augmented analytics comes in. This game-changing technology combines the power of artificial intelligence (AI) […]
The post The Rise of Augmented Analytics: Combining AI with BI for Enhanced Data Insights appeared first on DATAVERSITY.
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Author: Nahla Davies
I am often asked about the role of intercepts in linear regression models – especially the negative intercepts. Here is my blog post on that topic in simple words with minimal statistical terms.  Regression models are used to make predictions. The coefficients in the equation define the relationship between each independent variable and the dependent variable. […]
The post Understanding Linear Regression Intercepts in Plain Language appeared first on DATAVERSITY.
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Author: Prashanth Southekal
In today’s global landscape, organizations worldwide are increasingly turning to data analytics to enhance their business performance. Research conducted by McKinsey Consulting revealed that data-driven companies not only experience above-market growth but also witness EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) increases of up to 25% [1]. Additionally, Forrester’s findings indicate that organizations utilizing […]
The post Demystifying Data Analytics Models appeared first on DATAVERSITY.
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Author: Prashanth Southekal
“If I can’t picture it, I can’t understand it.” —Albert Einstein Research has found that 65% of the general population are visual learners, meaning they need to see information as images to understand it. The business world confirms this: Visualization is essential in driving success. Take, for instance, data visualization, or, the art of translating data into […]
The post 5 Key Strategies for Making Data Visualization Accessible appeared first on DATAVERSITY.
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Author: Daniel Jebaraj
In the rapidly evolving landscape of the manufacturing industry, data has become a cornerstone of innovation. From robotics and connected factories to operational efficiency, the potential for leveraging data is boundless. However, to harness the full power of data, manufacturers must ensure that their data management strategies are not only current but also future-ready. For this reason, organizations must consider critical needs when creating a robust data management strategy. They must ensure that this strategy aligns with manufacturing priorities and carefully consider the value of digital transformation.
A data management strategy is the backbone of successful data utilization in manufacturing. It encompasses the integration, standardization, and secure data storage, ensuring it is governed and trusted. In the context of the future of manufacturing, this strategy must align seamlessly with industry priorities, such as enhancing efficiency, maintaining quality control, predicting delays, and fostering innovation while simultaneously reducing costs.
A forward-thinking data management strategy is indispensable for any manufacturer looking to embark on a digital transformation journey. As the manufacturing landscape becomes increasingly digital and automated, selecting the right platform is crucial. A well-crafted data strategy, as often stated, is at the center of every successful digital transformation. This ensures not just immediate gains but also future-proofs the business against evolving technological landscapes.
Technology is a catalyst for digital transformation in manufacturing, enhancing efficiency, agility, and innovation. Integrating advanced technologies empowers manufacturers to optimize processes, improve product quality, and respond more effectively to market demands. By leveraging technology, manufacturers can not only optimize operations but also get ahead of any disruptions to suppliers or supply chains.
Measuring the success of digital transformation in manufacturing requires defined metrics that should be an integral part of any data management strategy. These metrics serve as benchmarks, allowing manufacturers to gauge the impact of their digital initiatives. According to Gartner, “36% of manufacturing enterprises realize above-average business value from IT spending in digitalization at a reasonable cost compared with peers.”
Other metrics to consider include:
Customer Engagement
Track metrics such as website traffic, social media interactions, and customer feedback to assess the level of engagement with digital platforms.
Customer Satisfaction (CSAT) Scores:
Use surveys and feedback mechanisms to measure customer satisfaction with digital services, products, and overall experiences.
Operational Efficiency
Assess improvements in operational efficiency through metrics like reduced process cycle times, decreased manual intervention, and streamlined workflows.
Employee Productivity:
Monitor changes in employee productivity resulting from digital tools and automation. This can include metrics like tasks completed per hour or efficiency gains in specific processes.
Cost Reduction:
Measure the cost savings achieved through digital optimization, such as reduced manual processes, lower maintenance costs, and improved resource utilization.
Data Quality and Accuracy:
Evaluate the quality and accuracy of data, ensuring that digital transformation initiatives contribute to improved data integrity.
Customer Lifetime Value (CLV):
Evaluate the long-term value generated from each customer, factoring in repeat business, upsells, and customer loyalty influenced by digital initiatives.
Net Promoter Score (NPS):
Measure the likelihood of customers recommending your products or services as an indicator of overall satisfaction and loyalty.
Industry 4.0 represents a paradigm shift in manufacturing, characterized by integrating advanced technologies, digitalization, and data-driven decision-making. Entering the era of Industry 4.0 necessitates manufacturers to have clear, concise, and contextualized data.
Real-time decision-making is a cornerstone of Industry 4.0, and clear data ensures that manufacturers can swiftly respond to dynamic conditions, optimize processes, and troubleshoot issues in real-time. Predictive maintenance, a key aspect of this industrial revolution, relies on contextualized data to anticipate equipment needs and minimize downtime. By harnessing clear and contextualized data, manufacturers can optimize production processes, implement robust quality control measures, and achieve end-to-end visibility in the supply chain. This level of data clarity facilitates customization and personalization in production, enhances energy efficiency, and supports the integration of connected ecosystems within the manufacturing environment.
Additionally, manufacturers can identify potential risks through clear data insights and implement strategies to mitigate uncertainties. Clear data is crucial for ensuring compliance with regulatory standards, a necessity in Industry 4.0, given the increasing focus on stringent regulations.
Actian has decades of experience helping manufacturers create and implement robust data management strategies. Actian’s solutions enable data-driven decision-making processes, ensuring manufacturers not only stay competitive in the present but also remain agile and prepared for the future.
In the dynamic landscape of manufacturing, a well-crafted data management strategy is not just a necessity, it’s a roadmap to success. As the industry hurdles towards an era of unprecedented technological advancement, manufacturers must ensure their strategies are not only current but also forward-looking. It’s time to embrace the future of manufacturing by putting data at the forefront of operations, and Actian is here to guide that transformative journey. Start a free trial now.
The post Is Your Data Management Strategy Ready for the Future of Manufacturing? appeared first on Actian.
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Author: Traci Curran
As data science professionals, we are often viewed as people who draw conclusions based only on data and minimize other factors. This perception usually becomes contentious when the insights and evidence from the data are inconsistent with somebody else’s “hypothesis.” Or we are confused and maybe frustrated when “qualitative” analysis trumps quantitative analysis. The next time […]
The post Four Perspectives on the Art of Data Analytics appeared first on DATAVERSITY.
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Author: Jason Jue
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Author: Myles Suer
Analytics at the core is using data to derive insights for measuring and improving business performance [1]. To enable effective management, governance, and utilization of data and analytics, an increasing number of enterprises today are looking at deploying the data catalog, semantic layer, and data warehouse. But what exactly are these data and analytics tools […]
The post Data Catalog, Semantic Layer, and Data Warehouse: The Three Key Pillars of Enterprise Analytics appeared first on DATAVERSITY.
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Author: Prashanth Southekal and Inna Tokarev Sela
Let’s talk about an inconvenient truth: For the typical business, data reporting has a tendency to fall short of producing the desired outcomes. Despite the significant resources that organizations often invest in producing data reports – and in the data collection, governance, and analytics processes that happen prior to reporting – the people who actually […]
The post Data Activation: The Key to Taking Data Reports to the Next Level appeared first on DATAVERSITY.
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Author: Daniel Zagales
Environmental, social, and governance (ESG) initiatives assess and measure the sustainability and societal impact of a company or investment. The number of countries and even within the United States that are implementing mandatory ESG reporting is rapidly expanding. One of the most far-reaching laws is the European Union’s Corporate Sustainability Reporting Directive (CSRD), which requires companies to publish reports on the social and environmental risks they face, and on how their activities impact the rights of people and the environment. According to the Wall Street Journal, more than 50,000 EU-based companies and approximately 10,400 non-EU enterprises are subject to CSRD compliance and some of these companies will need to disclose as many as 1,000 discrete items.
Companies using manual processes for data collection will find it difficult to keep up with the breadth and depth of these mandates. This is why generative AI will begin to play a significant role to streamline data collection, automate reporting, improve accuracy and transparency, identify risks, and resolve compliance gaps.
Data Integration:  Generative AI can help address various integration challenges and streamline processes such as data mapping and transformation, data conversion, data cleansing, data standardization, data enrichment, data validation, and more. This assistance allows companies to consider a wider range of data and criteria, which can lead to more accurate assessments of a company’s ESG performance and compliance.
Natural Language Processing (NLP): Generative AI models based on NLP can extract and analyze information from regulatory texts, legal documents, and compliance guidelines. This can be valuable for understanding and adhering to complex compliance requirements.
ESG Reporting Automation: Generative AI can automate compiling ESG compliance reports, reducing the time and resources required to gather, analyze, and present data.
Data Analysis: Generative AI can process and analyze vast amounts of data to provide insights related to ESG performance and compliance. It can identify trends, patterns, and areas to help a company improve its ESG practices.
Regulatory Change Analysis: Generative AI can monitor and analyze changes in regulatory requirements. By processing and generating summaries of new regulations and regulation updates, it helps organizations stay informed and adapt their compliance practices to changes.
Compliance Chatbots: Chatbots powered by generative AI can answer compliance-related questions, guide employees and customers through compliance processes, and provide real-time compliance information. Compliance chatbots can be particularly useful in industries with strict regulatory requirements, such as banking and healthcare.
Risk Assessment: Generative AI can analyze ESG data to identify potential risks that can lead to non-compliance, such as supply chain vulnerabilities, pollution, emissions, resource usage, and improper waste disposal, helping companies proactively address these issues.
ESG Investment: Generative AI can assist in creating investment strategies that help fill ESG compliance gaps by identifying companies or assets that meet ESG criteria.
You may have clear and comprehensive ESG policies, but inadequate data collection, reporting, analytics, and risk assessment can lead to non-compliance and dramatically increase the time and resources needed for meeting extensive and demanding reporting mandates. The Actian Data Platform makes it simple to connect, manage, and analyze your compliance-related data. With the unified Actian platform, you can easily integrate, transform, orchestrate, store, and analyze your data. It delivers superior price performance as demonstrated by a recent GigaOm Benchmark, enabling REAL real-time analytics with split-second response times.
The post Gen AI for ESG Reporting and Compliance appeared first on Actian.
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Author: Teresa Wingfield
By now, all marketers know that they need data to successfully engage customers over the course of their entire customer journey. But, with customers sometimes having needs and expectations that are very different from others—and even very different from their own previous wants and needs—nurturing each long-term relationship can be difficult. Yet, with the right data and strategy, it can be done.
Building and sustaining relationships requires an in-depth understanding of each customer at an individual level. This includes knowing their past behaviors, what motivates them to take action, and also having the ability to predict what they will do next. Predicting and meeting changing needs and preferences are instrumental to creating customers for life.
Here are some key, data-driven approaches that can help you engage customers and sustain long-term relationships that improve sales and build loyalty.
Any customer initiative will entail using all relevant data to create comprehensive profiles, which is commonly known as building 360-degree customer views. This critical step involves integrating data on a single platform, then making it easily accessible to everyone who needs it. Profiles typically include transactional, demographic, web visits, social media, and behavioral data, as well as data from a myriad of other sources. Gathering this information may require you to build data pipelines to new sources.
Profiles allow you to truly know your customer, such as their buying habits, preferred shopping and delivery channels, and interests. The profiles ultimately give you the insights needed to engage each person with relevant, targeted offers, based on their behaviors and preferences to ensure effective campaigns and deepen customer relationships.
Keeping profiles current and accurate is essential to identify, predict, and meet customer expectations. Preferences and habits can change quickly and without warning, which is why continually integrating data is essential to understanding customers’ current and future needs, and ensuring their profiles are up-to-date. Having insights into what customers want next—and being able to deliver that product or service—is the key to successfully nurturing customers.
Predictive analytics is one of your most important capabilities to gain an understanding of how customer needs are changing. This type of analytics can help you make informed decisions about delivering the next best offer to customers, enabling you to be proactive rather than reactive when meeting and exceeding customer expectations.
A proactive approach allows you to guide customers on their journeys and improve customer retention. It also helps you nudge, or motivate, customers who are not progressing on their journeys in order to reengage them and reduce the risk of churn.
The analysis looks at past behaviors to predict future actions. In addition to helping you identify shifting customer preferences, the analytics can help you uncover any emerging industry or consumer trends that could impact business or marketing decisions.
Another benefit of predicting actions is improving customer satisfaction by understanding their ongoing needs, which supports customer-for-life strategies. Likewise, performing predictive analytics on customer data can help you identify the most opportune moments to reach out to customers with a relevant offer—and determine what that offer should be.
Nurturing customers requires you to create a perfectly tailored experience for every single engagement. Today’s customers expect businesses to know and understand their individual needs, and then meet those needs with personalized offers. Customers are accustomed to companies providing targeted communications and recommendations based on their habits and preferences, which is why personalization is now tables stakes for interacting with customers.
Going beyond personalized offers to hyper-personalized or ultra-personalized experiences lets you separate yourself from competitors. Hyper-personalization involves more than using the customer’s first name in communications and lumping the person into a customer segment.
Hyper-personalization involves delivering highly customized offers, products, or services that are relevant and timely to the customer. With the right data platform, you can analyze large data volumes to truly know your customer and deliver the right offer at the right time. You can even personalize offers to small customer segments—even curating unique offers to a customer segment of just one person.
Turning leads into customers is a great success. The next goal is to continually stay ahead of customer needs to sustain long-term relationships. Some churn is inevitable, but using data can improve customer retention and drive higher sales.
To build trust with your customers and nurture relationships, you must be able to gather, analyze, and trust your data. The Actian Data Platform makes it easy for everyone across your organization to access, share, and trust data with complete confidence. This allows you to take a truly data-driven approach to customer engagement, to help you better understand each customer, and make predictions with a high degree of accuracy.
The Actian platform can help you transform your customer relationships and accelerate your marketing goals. Take a free trial and experience the platform for yourself.
The post Using Data to Nurture Long-Term Customer Relationships appeared first on Actian.
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Author: Becky Staker
Modern automotive customers expect engaged and superb user experiences. Automotive companies can collect, store, and analyze data across a spectrum of assets. By architecting better customer experiences (CX), automotive companies will reduce customer churn and increase new vehicle sales.
Connected cars, beginning with the GM OnStar service, provided the world an early glimpse into the future of automotive innovation. The GM OnStar service relied primarily on CDMA phone technology. Cellular providers and technology added support to transmit data, and this ushered in the era of GPS vehicle connectivity.
Fast forward twenty years and the connected car is no longer sufficient. Modern automotive consumers require not only connected but also intelligent vehicles that provide a host of Customer Experience services. Modern-day intelligent vehicle services can include hands-free driving, navigating traffic, fastest route navigation, weather and road condition navigation, and accident prevention. Additional complimentary services can include vehicle health reports, preventative maintenance, automatic parking, automatic vehicle system updates, remote vehicle start and stop, in-car hotspots, in-vehicle entertainment systems, stolen vehicle tracking, and mobile application support. And with the replacement of mechanical parts and combustion engines with electronic ones, intelligent vehicle capabilities further increase.
The CX services and features mentioned above have the inherited requirement to collect and analyze data both in real-time and in historical batches. The modern intelligent vehicle must be able to access, query, analyze, and predict data and model scores in real time. Modern intelligent vehicles will need to easily transmit and receive ever-increasing volumes of data to provide this portfolio of customer experiences. This combination of macro events (i.e. weather, quickest route) coupled with micro-events (i.e. tire pressure, road conditions, driverless) lays the foundation for quickly moving and processing data across a variety of cloud and in-vehicle environments. In effect, the modern intelligent vehicle is becoming a mobile data generator and processing unit.
Data processing and model scoring tasks will need to be done in vehicle progressively more into the future as vehicles continue to get smarter with regard to their immediate surroundings. Customers will expect all the above-mentioned experiences and services with a new vehicle purchase. Automotive manufacturers will continue to invest in edge and hybrid cloud data processing architectures for product development.
Actian provides a hybrid cloud data platform and data portfolio that includes edge data processing technologies. Customers can easily process and store data on the edge while easily moving data up and across a variety of cloud data processing environments. Our hybrid cloud data platform includes built-in features to reduce the total cost of ownership (TCO). This makes common tasks such as data integration, management, and analytics easy with compelling price performance. The demands of modern intelligent vehicles have arrived and Actian is here to help. Take the next and start a free trial today!
The post Winning in the Automotive Industry with CX appeared first on Actian.
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Author: Derek Comingore
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Author: EDM Council
As the Vice President of Engineering at Actian, I have been very involved in the recent launch of our Actian Data Platform. My role in this major upgrade has been twofold—to ensure our easy-to-use platform offers rewarding user experiences, and to deliver the technology updates needed to meet our customers’ diverse data needs. Â
On a personal level, I’m most excited about the fact that we put in place the building blocks to bring additional products onto this robust data platform. That means, over time, you can continue to seamlessly add new capabilities to meet your business and IT needs. Â
This goes beyond traditional future-proofing. We have provided an ecosystem foundation for the entire Actian product suite, including products that are available now and those that will be available in the coming years. This allows you to bring the innovative Actian products you need onto our hybrid platform, giving you powerful data and analytics capabilities in the environment of your choice—in the cloud, on-premises, or both. Â
One of the Actian Data Platform’s greatest strengths is its extreme performance. It performs query optimization and provides analytics at the best price performance when compared to other solutions. In fact, it offers a nine times faster speed advantage and 16 times cost savings over alternative platforms. Â
This exceptional price performance, coupled with the platform’s ability to optimize resource usage, means you don’t have to choose between speed and cost savings. And regardless of which of our pricing plans you choose—a base option or enterprise-ready custom offering—you only pay for what you use. Â
Our platform also offers other modern capabilities your business needs. For example, as a fully-managed cloud data platform, it provides data monitoring, security, backups, management, authentication, patching, usage tracking, alerts, and maintenance, freeing you to focus on your business rather than spending time handling data processes.  Â
Plus, the platform’s flexible and scalable architecture lets you integrate data from new and existing sources, then make the data available wherever you need it. By unifying data integration, data management, and analytics, the Actian Data Platform reduces complexity and costs while giving you fast, reliable insights.Â
Another goal we achieved with our platform is making it even simpler to use. The user experience is intuitive and friendly, making it easy to benefit from data access, data management, data analytics, and integrations.Â
We also rolled out several important updates with our launch. One focuses on integration. For example, we are providing stronger integration for DataConnect and Link customers to make it easier than ever to optimize these platforms’ capabilities. Â
We have also strengthened the integration and data capabilities that are available directly within the Actian Data Platform. In addition to using our pre-built connectors, you can now easily connect data and applications using REST- and SOAP-based APIs that can be configured with just a few clicks. To address data quality issues, the Actian Data Platform now provides the ability to create codeless transformations using a simple drag-and-drop canvas. Â
The platform offers the best mix of integration, quality, and transformation tools. It’s one of the reasons why our integration as a service and data quality as a service are significant differentiators for our platform. Â
With our data integration and data quality upgrades, along with other updates, we’ve made it easy for you to configure and manage integrations in a single, unified platform. Plus, with our native integration capabilities, you can connect to various data sources and bring that data into the data warehouse, which in turn feeds analytics. Actian makes it easy to build pipelines to new and emerging data sources so you can access all the data you need. Â
We paid close attention to the feedback we received from customers, companies that experienced our free trial offer, and our partners about our platform. The feedback helped drive many of our updates, such as an improved user experience and making it easy to onboard onto the platform.Â
I am a big proponent of quality being perceptive and tangible. With our updates, users will immediately realize that this is a high-quality, modern platform that can handle all of their data and data management needs.Â
Many organizations are interested in optimizing AI and machine learning (ML) use cases, such as bringing generative AI into business processes. The Actian Data Platform lends itself well to these projects. The foundation for any AI and ML project, including generative AI, is to have confidence in your data. We meet that need by making data quality tooling natively available on our platform. Â
We also have an early access program for databases as a service that’s been kickstarted with this platform. In addition, we’ve added scalability features such as auto-scaling. This enables your data warehouse to scale automatically to meet your needs, whether it’s for generative AI or any other project. Â
The Actian Data Platform monitors and drives the entire data journey, from integrations to data warehousing to real-time analytics. Our platform has several differentiators that can directly benefit your business:Â Â
These capabilities make our platform more than a tool. More than a cloud-only data warehouse or transactional database. More than an integration platform as a service (iPaas). Our platform is a trusted, flexible, easy-to-use offering that gives you unmatched performance at a fraction of the cost of other platforms. Â
Can you imagine how your business would benefit if everyone who needed data could easily access and use it—without relying on IT help? What if you could leverage your integrated data for more use cases? And quickly build pipelines to new and emerging data sources for more contextual insights, again without asking IT? All of this is possible with the Actian platform.Â
Data scientists, analysts, and business users at any skill level can run BI queries, create reports, and perform advanced analytics with our platform with little or no IT intervention. We ensure quality, trusted data for any type of analytics use case. In addition, low-code and no-code integration and transformational capabilities make the Actian Data Platform user friendly and applicable to more analysts and more use cases, including those involving generative AI. Â
Our patented technology continuously keeps your datasets up to date without affecting downstream query performance. With its modern approach to connecting, managing, and analyzing data, the Actian platform can save you time and money. You can be confident that data meets your needs to gain deep and rich insights that truly drive business results at scale. Â
Our Actian platform offers the advantages your business needs—ease of use, high performance, scalability, cost effectiveness, and integrated data. We’ve listened to feedback to deliver a more user-friendly experience with more capabilities, such as an easy-to-understand dashboard that shows you what’s happening with consumption, along with additional metering and monitoring capabilities.  Â
It’s important to note that we’ve undertaken a major upgrade to our platform. This is not simply a rebranding—it’s adding new features and capabilities to give you confidence in your data to grow your business. We’ve been planning this strategic launch for a long time, and I am extremely proud of being able to offer a modern data platform that meets the needs of data-driven businesses and puts in place the framework to bring additional products onto the platform over time. Â
I’d like you to try the platform for yourself so you can experience its intuitive capabilities and ultra-fast performance. Try it free for 30 days. You can be up and running in just a few minutes. I think you’ll be impressed.  Â
Related resources you may find useful:Â
The post Introducing The Actian Data Platform: Redefining Speed and Price Performance appeared first on Actian.
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Author: Vamshi Ramarapu
Today, corporate boards and executives understand the importance of data and analytics for improved business performance. However, most of the data in enterprises is of poor quality, hence the majority of the data and analytics fail. To improve the quality of data, more than 80% of the work in data analytics projects is on data […]
The post Managing Missing Data in Analytics appeared first on DATAVERSITY.
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Author: Prashanth Southekal
