Data Intelligence

What is a Data Lakehouse?

Actian Corporation

April 3, 2023

Server Room Center Exchanging Cyber Datas And Connections 3d Rendering

For organizations seeking to go further in their data collection, storage, and use, a data lakehouse is a perfect solution. While data lakes and data warehouses are commonly used architectures for storing and analyzing data, a data lakehouse is a third way of unifying the two architectures and revealing their full potential.

In this article, we’ll explain all you need to know about data lakehouses.

A data lakehouse is the best of both worlds. The best of information storage and the best of data exploitation. The main promise of a data lakehouse is to store large amounts of data from different sources in a single source of truth. However, a data lakehouse does not limit itself to the storage of information. It also provides a wide variety of advanced functionalities to ensure different data exploitation tasks, such as the transformation, analysis, and modeling of this data.

Indeed, a data lakehouse is defined as a data architecture that combines the advantages of a data lake and a data warehouse in a single platform. As such, it can be illustrated schematically as an extension of the data lake concept that is enriched with advanced data processing functions. In a data lakehouse, data is most often stored as raw or semi-structured. The transformation into structured data for analysis and business purposes takes place at a later stage.

What are the Functionalities of a Data Lakehouse?

The primary function of a data lakehouse is to store large amounts of data in a single platform. A centralizing approach that promotes easy and efficient access to information and data management. Unlike a data warehouse, a data lakehouse can store raw data and semi-structured data without distinction. This means that your data teams can easily extract information from unaltered data.

A data lakehouse can also facilitate real-time data processing. This means that decisions can be made more quickly and accurately because they are based on real-time data analysis. Among the advanced functionalities available in a data lakehouse, there are also query functionalities that allow your teams to extract value-added information from your data.

Finally, the data lakehouse can be easily integrated with data analysis tools, such as data visualization and machine learning tools, to go even further in the analysis, exploitation, and valorization of your data.

What are the Benefits of a Data Lakehouse?

There are many advantages of a data lakehouse, but the main advantage is that of scalability. Indeed, the size of a data lakehouse can easily be adjusted to store large amounts of data. Like many companies, you are probably faced with the explosion of the volumes of data you generate and exploit. With a data lakehouse, you’ll never be left behind!

Because they leverage open-source technologies and cloud services, data lakehouses are also extremely competitive in terms of deployment and operating costs.

Last but not least, in terms of security and compliance, the data stored in a data lakehouse is natively secure and complies with current security standards. Therefore, using a data lakehouse is a guarantee that your data is protected against cyber threats and data breaches.

Data Lakehouse vs. Data Lakes vs. Data Warehouse

A data lake is used to store raw or semi-structured data in its unaltered format. As for the data warehouse, it stores structured data in a predefined format. The data lakehouse opens a third way by allowing at the same time to store raw, semi-structured, and structured data in their raw or preprocessed format.

The data lakehouse also distinguishes itself from the data lake and the data warehouse by allowing the processing of data in real-time and the analysis of historical data – whereas data lakes are designed to process data in real-time, and data warehouses are limited to the analysis of historical data.

actian avatar logo

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.
Data Analytics

Deciphering the Data Story Behind Supply Chain Analytics

Teresa Wingfield

March 30, 2023

Increase efficiency, reduce costs, and grow revenue with real-time data

When it comes to supply chain data, there’s an intriguing story to be told. If businesses have access to accurate data in real-time about their supply chain operations, they have tremendous opportunities to increase efficiency, reduce costs, and grow revenue. Here’s a look at some of the types of supply chain data and the data story that supply chain analytics can reveal.

Procurement Data

This includes information about the type, quality, quantity, and cost of raw materials and components used in the production process. Analyzing spend can help businesses identify areas where they can reduce costs and make data-driven decisions about how to best allocate their budget. For example, real-time comparisons of supplier pricing can help sourcing teams negotiate more favorable prices.

Supplier Data

This includes data about suppliers, such as their performance history, delivery times, and product quality. Supplier data is key to reducing order fulfillment issues and identifying and proactively planning for supply chain disruption. Companies are increasingly leveraging supplier data in real-time to enhance their environmental, social, and governance efforts.

Production Data

This includes data about manufacturing processes, including production schedules, output levels, and equipment utilization and performance. Faster insights into production data can help optimize material availability, workforce, and processes needed to keep production lines running. Businesses can also more quickly spot quality control issues and equipment problems before they lead to costly downtime.

Inventory Data

This includes data about the quantity and location of inventory, inventory turnover and safety stock requirements. Demand forecasting using predictive analytics helps to determine the right level of inventory. Real-time visibility is essential to dynamically adjust production up or down as demand fluctuates and to offer promotions and sales for slow-moving inventory.

Transportation Data

This includes data about the movement of goods from one location to another such as shipment tracking, transit conditions and times, and transportation costs. Predictive analytics can estimate transit times to determine the best possible routes. What’s possible today was inconceivable a decade ago: using sensors to track things such as temperature and safe transportation at any point in time to protect goods and improve driving habits.

Customer Data

This includes customer data such as order history, purchase behavior, and preferences. Companies can meet customer expectations and increase sales when they understand and anticipate what their customers need – and when they are able to create personalized experiences and quickly adjust the supply change based on constantly changing customer behavior.

Sales Data

This includes sales data such as revenue, profit margins and customer satisfaction. Companies use demand forecasting based on past sales to help them adjust production, inventory levels, and improve sales and operations planning processes.

Create Your Data Story

What’s your supply chain data story going to be? It all depends on the data platform you choose to process your supply chain analytics. The platform will need to be highly scalable to accommodate what can be massive amounts of supply chain data and must support real-time insights into supply chain events as they happen so decision makers can form next-best actions in the moment.

The Actian Data Platform provides data integration, data management, and data analytics services in a single platform that offers customers the full scalability benefits of cloud- native technologies. The Actian platform provides REAL, real-time analytics by taking full advantage of the CPU, RAM, and disk to store, compress, and access data with unmatched performance.

teresa user avatar

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.
Data Analytics

Are You Building Your Data Strategy to Scale?

Teresa Wingfield

March 28, 2023

scale and build your data strategy with the right infrastructure

A data strategy is a long-term plan that defines the infrastructure, people, tools, organization, and processes to manage information assets. The goal of a data strategy is to help a business leverage its data to support decision-making. To make the plan a reality, the data strategy must scale. Here are a few pointers on how to achieve this:

Infrastructure

The right infrastructure is necessary to give an organization the foundation it needs to scale and manage data and analytics across the enterprise. A modern cloud data platform will make it easy to scale with data volumes, reuse data pipelines and ensure privacy and regulations are met while also making sure that data is accessible to analysts and business users. The platform should use cloud-native technologies that allow an organization to build and run scalable data analytics in public, private, and hybrid clouds.

People

The talent shortage for analysts and data scientists, particularly for advanced analytics requiring knowledge of artificial intelligence, is a big challenge. With the U.S. Bureau of Labor Statistics projecting a growth rate of nearly 28% in the number of jobs requiring data science skills by 2026, the shortage will continue to grow.

To cope with the shortage, businesses will need to invest more in training and education. The more teams know about advanced data analytics techniques and how to use and interpret data, the more value an organization can derive from its data. Also, with demand for analytics skills far exceeding supply, organizations will need to make of the talent pool they already have.

Tools

A cost-optimal solution should not only process data analytics workloads cost-effectively, but also include data integration, data quality, and data management that add more costs, and complexity when sourced from multiple vendors. However, there is no such thing as a one-size-fits-all tool when it comes to analytics. Increasingly, organizations are adding many types of advanced analytics such as machine learning to their analytics tool portfolio to identify patterns and trends in data that help optimize various aspects of the business.

Businesses will also need to devise strategies for users to easily access data on their own so that limited technical staff doesn’t bottleneck data analytics. Embedded analytics and self-service help support the needs of data democratization. Self-service gives users insights faster so businesses can realize the value of data faster. Analytics embedded within day-to-day tools and applications deliver data in the right context, allowing users to make better decisions faster

Organization

For a data strategy to scale, an organization needs to build a data driven culture. Transitioning to a data driven approach requires a corporate cultural change where leadership views data as valuable, creates greater awareness of what it means to be data driven and develops and communicates a well-defined strategy.

Processes

There are many processes involved in a scalable data strategy. Data governance is particularly critical to democratizing data while protecting privacy, complying with regulations, and ensuring ethical use. Data governance establishes and enforces policies and processes for collecting, storing, using, and sharing information. These include assigning responsibility for managing data, defining who has access to data and establishing rules for usage and protection.

Get Started With the Actian Data Platform

The Actian Data Platform provides data integration, data management, and data analytics services in a single platform that offers customers the full benefits of cloud native technologies. It can quickly shrink or grow CPU capacity, memory, and storage resources as workload demands change. As user load increases, containerized servers are provisioned to match demand. Storage is provisioned independently from compute resources to support compute or storage-centric analytic workloads. Integration services can be scaled in line with the number of data sources and data volumes.

teresa user avatar

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.
Data Analytics

Discover the Top 5 Data Quality Issues – And How to Fix Them!

Actian Corporation

March 23, 2023

two people combatting top enterprise data quality issues

‍Poor quality data can lead to inaccurate insights, wasted resources, and decreased customer satisfaction. It is essential to ensure that all of your data is accurate and up-to-date to make the best decisions. Still, common issues and mistakes cost organizations millions of dollars annually in lost revenue opportunities and resource productivity.

Thankfully, these pitfalls are well-known and easy to fix!

Duplicate Data

Duplicate data occurs when the same information is entered into the same system multiple times. This can lead to confusion and inaccurate insights. For example, if you have two records for the same customer in your CRM system, notes, support cases, and even purchase data can be captured on different records and leaving your organization with a fractured view of a single customer.

Missing Data

Perhaps worse than having duplicate data is having incomplete data. Missing data occurs when some of the necessary information is missing from the system and can lead to incomplete insights. Many systems allow application owners to determine required data fields to prevent missing data.

Outdated Data

While capturing and retaining historical data can be very beneficial, especially regarding customer data, it’s critical that data is kept current. It’s essential to have a regular process to ensure that your organization purges information that is no longer relevant or up-to-date.

Inconsistent Data

Date formats, salutations, spelling mistakes, number formats. If you work with data, you know that the struggle is real. It’s also probably one of the trickier problems to address. Data integration platforms like DataConnect can allow data teams to establish rules that ensure data is standardized. A simple pass/fail ensures that all your data follows the established formatting standards.

Data Timeliness

Imagine buying a house without having the most current interest rate information. It could mean the difference of hundreds of dollars on a mortgage. But many companies are making decisions using days, weeks, or months old data. This may be fine for specific scenarios, but as the pace of life continues to increase, it’s essential to ensure you’re getting accurate information to decision makers as fast as possible.

Tips for Improving Data Quality

Data quality is an ongoing practice that must become part of an organization’s data DNA. Here are a few tips to help improve the quality of your data:

  • Ensure data is entered correctly and consistently.
  • Automate data entry and validation processes.
  • Develop a data governance strategy to ensure accuracy.
  • Regularly review and audit data for accuracy.
  • Utilize data cleansing tools to remove outdated or incorrect information.

Data quality is an important factor for any organization. Poor quality data can lead to inaccurate insights, wasted resources, and decreased customer satisfaction. To make the best decisions, it is essential to ensure that all your data is accurate and timely.

Ready to take your data quality to the next level? Contact us today to learn more about how DataConnect can help you start addressing these common quality challenges.

actian avatar logo

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.
Data Analytics

What Makes a Great Machine Learning Platform?

Teresa Wingfield

March 20, 2023

leveraging machine learning to enhance enterprise data analytics

Machine learning is a type of artificial intelligence that provides machines the ability to automatically learn from historical data to identify patterns and make predictions. Machine learning implementation can be complex and success hinges on using the right integration, management, and analytics foundation.

The Actian Data Platform is an excellent choice for deploying machine learning, enabling collaboration across the full data lifecycle with immediate access to data pipelines, scalable compute resources, and preferred tools. In addition, the Actian Data Platform streamlines the process of getting analytic workloads into production and intelligently managing machine learning use cases from the edge to the cloud.

With built-in data integration and data preparation for streaming, edge, and enterprise data sources, aggregation of model data has never been easier. Combined with direct support for model training, systems, and tools, and the ability to execute models directly within the data platform alongside the data, can capitalize on dynamic cloud scaling of analytics computing and storage resources.

The Actian Data Platform and Machine Learning

Let’s take a closer look at some of the Actian platform’s most impactful capabilities for making machine learning simpler, faster, more accurate, and accessible:

Breaking Down Silos

The Actian platform supports batch integration and real-time streaming data. Capturing and understanding real-time data streams is necessary for many of today’s machine learning use cases, such as fraud detection, high-frequency trading, e-commerce, delivering personalized customer experiences, and more. Over 200 connectors and templates make it easy to source data at scale. You can load structured and semi-structured data, including event-based messages and streaming data without coding.

Blazing Fast Database

Modeling big datasets can be time-consuming. The Actian platform supports rapid machine learning model training and retraining on fresh data. Its columnar database with vectorized data processing is combined with optimizations such as multi-core parallelism, making it one of the world’s fastest analytics platforms. The Actian platform is up to 9 x faster than alternatives, according to the Enterprise Strategy Group.

Granular Data

One of the main keys to machine learning success is model accuracy. Large amounts of detailed data help machine learning produce more accurate results. The Actian Data Platform scales to several hundred terabytes of data to analyze large data sets instead of just using data samples or subsets of data like some solutions.

High-Speed Execution

User Defined Functions (UDFs) support scoring data on your database at break-neck speed. Having the model and data in the same place reduces the time and effort that data movement would require. And with all operations running on the Actian platform’s database, machine learning models will run extremely fast.

Flexible Tool Support

Multiple machine learning tools and libraries are supported so that data scientists can choose the best tool(s) for their machine learning challenges, including DataFlow, KNIME, DataRobot, Jupyter, H2O.ai, TensorFlow, and others.

teresa user avatar

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.
Data Intelligence

What are Industry Cloud Platforms?

Actian Corporation

March 19, 2023

Cloud Computing Concept. Communication Network.

With 60% of enterprise data now stored in the Cloud, and companies around the world turning to Cloud solutions to manage their data, many are finding that general-purpose Cloud platforms are not always able to meet the specific needs of their industry. They must then turn to an Industry Cloud Platform.

In this article, find out everything you need to know about Industry Cloud Platforms.

All industries have specific requirements for managing and securing their data. Industry Cloud Platforms are Cloud platforms designed to meet the specific requirements of a given industry or sector.

Unlike general-purpose Cloud platforms, such as Amazon Web Services (AWS) or Microsoft Azure, Industry Cloud Platforms offer features and services tailored to industries such as healthcare, finance, logistics, retail, energy, agriculture, and many others. The popularity of these platforms continues to grow.

According to Gartner, nearly 40% of companies have already considered adopting an Industry Cloud Platform. 15% of them are already engaged in a pilot project. Even better, about 15% more are considering deployment by 2026. As a result, Gartner predicts that by 2027, companies will use Industry Cloud Platforms to accelerate more than 50% of their critical business initiatives, up from less than 10% in 2021.

How Does an Industry Cloud Platform Work?

Industry Cloud Platforms provide robust and scalable cloud infrastructure, along with features and services that are ideally suited to the specific needs of companies in each industry. This can include features such as data analytics tools, supply chain management platforms, industry-specific security solutions, and custom business applications.

Industry Cloud Platforms can help companies improve operational efficiency, reduce costs, and innovate faster by providing easy access to specialized cloud services for their industry. In addition, these platforms can help companies better manage risk and comply with industry-specific regulations.

What are the Advantages and Benefits of Industry Cloud Platforms?

Using a specialized Industry Cloud Platform for your sector provides you with data analysis tools and customized business applications. The primary benefit of being able to rely on tailored tools and services is that you gain operational efficiency and productivity.

But that’s not all. Industry Cloud Platforms help reduce the cost of purchasing, maintaining, and upgrading your IT infrastructure by taking an industry-specific approach. The “hyper-specialization” of these Cloud Platforms and the services they contain means that you only have the solutions you really need, and you don’t have to invest in expensive infrastructure that you rarely use to its full potential. This is a “best of need” rather than a “best of breed” perspective.

Moreover, since Industry Cloud Platforms are designed to be scalable and flexible, they will enable you to adapt quickly to the growth of your business. You can easily add or remove Cloud resources as needed to quickly adapt to market fluctuations.

Finally, the use of an Industry Cloud platform increases your capacity to innovate by giving you access to data analysis technologies, adapted to your activity.

Examples of Industry Cloud Platforms for Different Industries

There are many major players in the Industry Cloud Platforms market, each offering specific solutions and services for a particular industry or sector. Here are some examples of major players:

  • Salesforce is one of the leading Industry Cloud Platform players in the sales and marketing industries, with its Salesforce Customer 360 platform.
  • Microsoft offers a range of cloud solutions for different industries, such as Dynamics 365 for Finance and Operations for the finance and manufacturing sector, and Azure IoT Suite for the Internet of Things.
  • IBM is positioning itself in this market segment with a dedicated cloud platform for several industries, including healthcare, financial services, and supply chain, with its Watson Health, IBM Cloud for Financial Services, and IBM Sterling Supply Chain Suite solutions.
  • Amazon Web Services (AWS) offers a range of cloud services for different industries, including AWS Healthcare for healthcare and AWS Retail for retail. These offerings are distinct from Amazon Web Services’ general-purpose offerings.
  • SAP has developed a cloud platform for several industries, including manufacturing, retail, financial services, and healthcare, with its SAP S/4HANA, SAP Commerce Cloud, and SAP Health solutions.
actian avatar logo

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.
Data Analytics

How Banks Can Use Analytics to Stay Out of the Headlines

Actian Corporation

March 17, 2023

skyline of big banks

Financial institutions are making headlines around the world. There’s no shortage of press coverage on the recent collapse of Silicon Valley Bank and Signature Bank in New York, and there seem to be mounting fears about the overall health of the banking industry. While it is too early to know how these failures will impact the broader economy, regional banks are certainly coming under the spotlight.   

In times of uncertainty, meeting the hunger for quantitative data analytics becomes increasingly important. Financial institutions face various challenges, including economic uncertainty, changing customer behavior, and regulatory pressures. These changing conditions require banks to have trusted data and make decisions in real-time – before changing conditions can cause existential harm. By using data analytics, banks of all sizes can gain better insights into their customers, markets, and operations – and, most importantly – respond to changing conditions and understand their risk. 

Data Analytics Provide Insights Into Fast-Changing Market Conditions

Economic conditions can change rapidly, and banks need to be able to adapt quickly to stay competitive. Analytics can help banks to better understand economic trends and to make more informed decisions about lending and risk management. 

For example, banks can use predictive analytics to identify borrowers who are at high risk of default and give banks the insights needed to adjust their lending practices to maintain a risk-balanced portfolio. Banks can identify patterns and develop more accurate risk models and lending rates by analyzing customer data, such as credit scores, payment histories, and employment histories. This type of insight can help reduce exposure to high-risk borrowers. 

Understanding Evolving Customer Behaviors

Another challenge that banks face in uncertain times is changing customer behavior and sentiment. Many factors can influence customer behavior, including economic conditions, technological advancements, and changing consumer preferences. Banks need to understand these changes, then adapt their products and services to meet the evolving needs of their customers.  

Analytics can help banks to gain insights into customer behavior by analyzing customer data, such as transaction histories, account balances, and demographic information. By identifying patterns in customer behavior, banks can develop more targeted marketing campaigns, offer personalized products and services, and improve customer retention rates. They can also identify when customers may be in trouble due to a change in finances, such as a job loss, that could impact their ability to repay their loans.

Banks can also use customer segmentation to group customers based on their behavior and preferences. This allows banks to offer targeted products and services to specific customer groups, such as retirees, small business owners, or millennials. By tailoring their products and services to the needs of specific customer segments, banks can improve customer satisfaction and loyalty. Retaining loyal and low-risk customers can help offset losses caused by unexpected economic and geo-political changes. 

Managing Risk Requires Analytic Insights

In the wake of the collapse of two mid-tier banks, there is a lot of discussion around new regulations that may be needed to prevent future failures. There is an expectation that banks, especially those with under $200 billion in assets, will face increased regulatory requirements. Any new regulations will likely increase complexity and costs for banks and their customers. Strengthening operation analytics can help banks to comply with regulatory requirements by providing insights into their operations and risk management practices. 

Using analytics to manage risk, understand customer behavior, and comply with regulatory requirements can help banks of any size get in front of unforeseen market conditions. Mid-tier banking institutions need to learn from the Silicon Valley Bank experience by implementing robust risk management frameworks and increasing loyalty with their best customers. Having a data-driven approach to things like creditworthiness, liquidity, market volatility, and operational risks will allow both banks, and our economy, to weather unpredictable conditions.   

actian avatar logo

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.
Data Architecture

How to Use Data to Get More Visibility into Your Supply Chain

Jennifer Jackson

March 17, 2023

two people looking at and using data

Supply chains have undergone—and continue to experience—major changes and disruptions. Worker shortages, rapidly changing customer demands, logistics problems, transportation bottlenecks, and other factors have all contributed to challenges. Even sales patterns that used to be easy to predict, such as those based on holidays and seasonal buying, have become much harder to understand, amplifying the need for visibility across the entire supply chain. 

Business and consumer needs change faster than ever, which has a ripple effect across supply chains that are trying to keep up. On top of this, global supply chains have become increasingly complex, making them more susceptible to delays caused by everything from inclement weather to shipping problems to raw materials shortages.  

Keeping the supply chain moving without interruption places new demands for data and analytics to provide visibility and insights. A supply chain that’s driven by a modern approach to data and analytics enables new benefits, such as improved operations, enhanced demand forecasting, increased efficiencies, reduced costs, and better customer experiences.   

Building a Resilient Supply Chain With Data Analytics

Data that can provide visibility into supply chains is coming from traditional, new, and emerging sources. This includes enterprise resource planning and point-of-sale systems, a growing number of Internet of Things (IoT) devices, inventory and procurement solutions, and more.  

Customer-centric supply chains integrate additional data to better understand the products and services consumers want. This entails data across social media, purchasing histories, and customer journeys to have insights into customer behaviors and sentiments.  

Supply chain analytics and enterprise data management capabilities are needed for organizations to know where their products and materials are at any moment and identify ways to optimize processes. These capabilities, for example, allow companies to track and trace products—from parts to sub-assemblies to final builds—as they move from one location to another through the supply chain until they arrive at their final destination. That destination could be a retail store or a customer’s front doorstep.  

Supply chain visibility helps organizations minimize risk while identifying opportunities, such as improving planning to avoid higher-cost next-day shipping to meet tight timeframes. Better planning allows companies to use less expensive shipping options without causing unexpected downtimes in factories.  

Visibility is also essential for building resilience and agility into the supply chain, allowing the business to pivot quickly as customer needs change or new trends emerge. The enabler of visibility, and for insights delivered at every point across the end-to-end supply chain, is data. When all relevant data is brought together on a single platform and readily available to all stakeholders, businesses not only know where their parts, components, and products are, but they can proactively identify and address potential challenges before they cause delays or other problems.   

A Growing Need for Supply Chain Resilience

Although companies need a resilient supply chain, most are not achieving it. Improving resiliency requires the business to move from analysis on basic forecasting data to connecting and analyzing all data for real-time insights that produce more accurate and robust forecasts, uncover opportunities to improve sustainability, and meet other supply chain goals. The insights help organizations identify macro- and micro-level issues that could impact the supply chain—and predict issues with enough time for the business to proactively respond.  

Manual processes and outdated legacy systems that won’t scale to handle the data volumes needed for end-to-end insights will not give organizations the resiliency or visibility they need. By contrast, a modern cloud data platform breaks down silos to integrate all data and can quickly scale to solve data challenges.  

This type of platform can deliver the supply chain analytics and enterprise data management needed to reach supply chain priorities faster. For example, manufacturers can know where raw materials are in the supply chain, when they’re due to arrive at a facility, and how a change in transportation methods or routes can impact both operations and profitability. Retailers can know when items will be available in warehouses to meet customer demand, fill orders, and nurture customer journeys.  

Easily Connect, Manage, and Analyze Supply Chain Data

Organizations that have the ability to bring together data from all sources along the supply chain and perform analytics at scale can gain the visibility needed to inform decision-making and automate processes. With the right approach and technology, organizations can turn their supply chain into a competitive advantage. 

Actian Data Platform makes data easy. It simplifies how people connect, manage, and analyze their data to modernize and transform their supply chain. With Actian’s built-in data integration, businesses can quickly build pipelines to ingest data from any source. Anyone in the organization who needs the data can easily access it to make informed decisions, gain insights, expand automation, and optimize it for other supply chain needs.  

Additional Resources:

Jennifer Jackson headshot

About Jennifer Jackson

Jennifer"JJ" Jackson is CMO of Actian, leading global marketing strategy with a data-driven approach. With 25 years of branding and digital marketing experience and a background in chemical engineering, JJ understands the power of analytics from both a user and marketer perspective. She's spearheaded SaaS transitions, partner ecosystem expansions, and web modernization efforts at companies like Teradata. On the Actian blog, she discusses brand strategy, digital transformation, and customer experience. Explore her recent articles for real-world lessons in data-driven marketing.
Data Management

Data Analytics for Supply Chain Managers

Teresa Wingfield

March 17, 2023

streaks of blue light showing data analytics for supply chain managers

If you haven’t already seen Astrid Eira’s article in FinancesOnline, “14 Supply Chain Trends for 2022/2023: New Predictions To Watch Out For”, I highly recommend it for insights into current supply chain developments and challenges. Eira identifies analytics as the top technology priority in the supply chain industry, with 62% of organizations reporting limited visibility. Here are some of Eira’s trends related to supply chain analytics use cases and how the Actian Data Platform provides the modern foundation needed to make it easier to support complex supply chain analytics requirements.

Supply Chain Sustainability

According to Eira, companies are expected to make their supply chains more eco-friendly. This means that companies will need to leverage supplier data and transportation data, and more in real-time to enhance their environmental, social and governance (ESG) efforts. With better visibility into buildings, transportation, and production equipment, not only can businesses build a more sustainable chain, but they can also realize significant cost savings through greater efficiency.

With built-in integration, management and analytics, the Actian Data Platform helps companies easily aggregate and analyze massive amounts of supply chain data to gain data-driven insights for optimizing their ESG initiatives.

The Supply Chain Control Tower

Eira believes that the supply chain control tower will become more important as companies adopt Supply Chain as a Service (SCaaS) and outsource more supply chain functions. As a result, smaller in-house teams will need the assistance of a supply chain control tower to provide an end-to-end view of the supply chain. A control tower captures real-time operational data from across the supply chain to improve decision-making.

The Actian Data Platform helps deliver this end-to-end visibility. It can serve as a single source of truth from sourcing to delivery for all supply chain partners. Users can see and adapt to changing demand and supply scenarios across the world and resolve critical issues in real-time. In addition to fast information delivery using the cloud, the Actian Data Platform can embed analytics within day-to-day supply chain management tools and applications to deliver data in the right context, allowing the supply chain management team to make better decisions faster.

Edge-to-Cloud

Eira also points out the increasing use of Internet of Things (IoT) technology in the supply chain to track shipments and deliveries, provide visibility into production and maintenance, and spot equipment problems faster. These IoT trends indicate the need for edge to cloud where data is generated at the edge, stored, processed, and analyzed in the cloud.

The Actian Data Platform is uniquely capable of delivering comprehensive edge to cloud capabilities in a single solution. It includes Zen, an embedded database suited to applications that run on edge devices, with zero administration and small footprint requirements. The Actian Data Platform transforms, orchestrates, and stores Zen data for analysis.

Artificial Intelligence

Another trend Eira discusses is the growing use of Artificial Intelligence (AI) for supply chain automation. For example, companies use predictive analytics to forecast demand based on historical data. This helps them adjust production, inventory levels, and improve sales and operations planning processes.

The Actian Data Platform is ideally suited for AI with the following capabilities:

  1. Supports rapid machine learning model training and retraining on fresh data.
  2. Scales to several hundred terabytes of data to analyze large data sets instead of just using data samples or subsets of data.
  3. Allows a model and scoring data to be in the same database, reducing the time and effort that data movement would require.
  4. Gives data scientists a wide range of tools and libraries to solve their challenges.

This discussion of supply chain sustainability, the supply chain control tower, edge to cloud, and AI just scratch the surface of what’s possible with supply chain analytics. To learn more about how the Actian Data Platform, contact our data analytics experts.

Additional Resources:

teresa user avatar

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.
Data Analytics

How Application Analytics Can Optimize Your CX Strategy

Teresa Wingfield

March 15, 2023

data and graphs showing application analytics

With growing concerns of a recession, many application providers are turning their attention to developing a better customer experience strategy for their existing customers. This strategy often includes application analytics to improve customer retention and growth. Here’s a quick look at what application analytics is and ways to use it to measure and improve customer satisfaction.

What is Application Analytics?

Application analytics refers to the process of collecting, analyzing, and interpreting data from applications to gain insights into usage patterns, user satisfaction, application performance, and user sentiment. Application providers can leverage these insights to enhance their customer experience strategy by using Application Analytics to:

  1. Evaluate Usage: By understanding how users are really interacting with your software, application providers can identify opportunities to increase user satisfaction. With real-time analytics, providers can more easily spot areas that need improvements such as bugs and missing features. User drop-off points reveal where users get stuck so you can fix the usability issue to increase application adoption. Average session length (how much time the user spends in the application) and sessions per user (how frequently users return to your application) help you understand if the application is delivering user value.
  2. Measure User Satisfaction: In addition to customer usage, there are a plethora of key performance indicators (KPIs) and metrics that high-tech companies should keep an eye on to determine how satisfied users are with their applications. Active users, retention rate, churn rate, user lifetime value, monthly and annual recurring revenue, and average revenue per user help gauge if users are seeing the value and determine ways to grow the subscription base. Customer service KPIs are also especially important since they let you know if you are living up to your customer’s expectations. You should analyze KPIs such as the number of support tickets, first response time (how long it takes to provide an initial response), first contact resolution (whether the issue was resolved in one interaction), and average handle time (how long it takes to resolve an issue). If KPIs aren’t what they should be, high-tech companies should immediately prioritize customer service improvements that will build user loyalty.
  3. Analyze Application Performance: Another way application analytics can improve the customer experience is by providing real-time insights into application performance. Proactively monitoring KPIs such as memory, CPU and disk usage, response time, latency, uptime and error rates, and many others as well as quickly responding to issues helps prevent negative customer experiences. Also, remember to track on-premises and cloud infrastructure metrics that impact application performance.
  4. Uncover User Sentiment: Sentiment analysis helps application providers discover and measure how users feel about their products. Sentiment analysis with natural language processing (NLP) determines the emotional tone behind a body of text or a conversation so that companies can better understand their users’ opinions and attitudes towards them, including products and services. Support tickets, emails, online chats, and phone calls are all useful information sources that applications analytics can exploit to reveal user sentiment.

Application Analytics for CX

Application analytics plays a crucial role in improving the customer experience by providing insights into usage patterns, user satisfaction, application performance, and user sentiment. By leveraging these insights, application providers can make data-driven decisions that retain customers and drive greater usage.

To be successful, you’ll need the right data platform for your application analytics. The Actian Data Platform, with data integration, data management, and real-time data analytics, makes it easy to execute application analytics for optimizing your customer experience strategy.

teresa user avatar

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.
Actian Life

Leadership at Actian: Expert Advice for Women in STEM

Actian Corporation

March 8, 2023

Crowd of women of diverse age, races and occupation

Empowering women in science, technology, engineering, and math (STEM) is important year-round and can reap significant rewards for businesses and individual employees. These efforts are especially important today—International Women’s Day—and throughout March, which is Women’s History Month and Gender Equality Month.

Actian has a rich history of supporting women in the many types of jobs related to STEM. Actian celebrates women in STEM, including the women who contribute to Actian’s ongoing success. In honor of women in STEM—and those who will be stepping into these roles in the near future—female leaders at Actian offer advice to women to help them advance their careers.

Know That Leadership Entails Feedback and Teamwork

Successful leaders surround themselves with highly talented people who complement, challenge, and encourage each other. In STEM and in business, strong teams are essential to ongoing success and innovation.

“As you grow in your career, there will be many opportunities for 360° feedback. Take that feedback seriously but know that you do not have to be good at everything to be successful,” says Jennifer Jackson, Chief Marketing Officer. “A great strategy for success is to focus on doing more of the things that are your strengths and surround yourself with people who can fill the gaps that you know you have. In other words, when you start to manage and lead–hire well! Hire people who will challenge you, who do things differently than you do, and who are strongest where you are not.”

Pamela Fowler, SVP – Customer Success, agrees that leaders should surround themselves with inspiring people. Strong, successful coworkers help employees and teams do their best work.

“Stand tall and drive towards your aspirations and dreams and you can get there. Roadblocks may sometimes stand in your way but look for ways to build around them. Roadblocks are sometimes the building blocks to your success,” Fowler says. “As a leader always ensure you surround yourself with people you can inspire to be their best and who will help you be your best. It is the people we lead and the inspiration we gain from them that helps us be better leaders. Always work to build strong networks of people around you and people who inspire you to be and do your best! And always believe that you can because YOU can!”

Be True to Yourself—That’s the Best Foundation

Leaders say that being yourself is essential to success. This is true in STEM fields too.  Allowing people to be their authentic selves while doing inspiring work fosters innovation.

“My advice is to be yourself. I know it’s simple, but it’s the core of what I see from women in leadership who I admire and aspire to be,” says Kimmah Lewis, Senior Director, Digital and Demand Generation. “The intersectionality of being both black and a woman in tech can be either empowering or oppressive. But, like all things, I view life as a series of choices. In this, I chose to be empowered by my “otherness.” I embrace what makes me different and show up fully, truly, wholly, and authentically me. I spend zero time trying to “fit in” to ensure I can “stand out.” In doing so, I can bring my best self to everything I do and am a part of.”

Taking inspiration from others can help women build their career paths and set goals, but no two paths will be the same. That’s why it’s important for women in STEM to be comfortable and confident forging their own opportunities and achieving unique successes.

“You will be at your best when you are yourself–keep in mind you are shaping your own path,” says Romy Mager-Omphalius, SVP – Renewals Sales. “You are allowed to dream big, make mistakes, fail, stand up, try again, and learn. Always stay curious and surround yourself with people who challenge you.”

Realize That STEM is Competitive—and Mentors Can Help

STEM jobs are very competitive, yet they encourage innovation and allow people to utilize their skill sets. Emma McGrattan, SVP – Engineering, encourages women looking at a STEM career to have a mentor who can help navigate obstacles and opportunities.

“It has been my experience that the STEM world is a meritocracy and highly competitive. Being female will not be a hindrance but will also not be an advantage. Be prepared to have your ideas challenged, as this happens to everyone engaging in an innovative environment,” McGrattan points out. “View it as an opportunity to improve upon your original ideas or to reinforce your original thinking, not as a personal attack. Set career goals, build a plan to get yourself there, and work your plan to completion. Find a mentor and an ally willing to help execute your plan. Finally, never underestimate the power of networking and getting to know a diverse group of people. This will help you gain focus and perspective, which can be invaluable in the world of STEM.”

Becky Staker, VP of Customer Experience, also advises women to engage with mentors. She finds that helping women advance in their careers is personally rewarding.

“One of the greatest career joys for me is to lift up the women in my organization and help them progress through their careers. Women face unique challenges in the workplace and need our support to seek out and go for those opportunities with confidence,” Staker says. “Early in my career, I was fortunate to have many wonderful women mentors and managers trust me with challenging assignments that would allow me the visibility and opportunity to show my impact and potential. I have never forgotten about the path they helped me blaze and continue to be a champion for women’s career development. I would encourage women to find those who want to lift others and make the time for mentorship.”

As XuanThu Pham, Senior Director of Product Marketing, notes, helping others even outside of mentorships is important.

“Seek to help those, especially from underrepresented communities, who you can connect with to share what has been given to us in our own careers—uplifting others along the way. Reach out. Remain humble in our own abilities, and share back without expectation of anything in return—whether it’s through sharing of your time or your experiences. The intention of what we do matters to make fundamental shifts in uplifting communities, culture, and mindsets,” Pham says. “The impact you help create will not happen overnight or be in big spurts—it’s the small moments that can compound over time that we celebrate—and what a feeling to know that we can change at least one person’s career or life trajectory.”

Career Opportunities at Actian

Interested in joining the data revolution and becoming part of a diverse, collaborative environment? Actian was named a top workplace and offers opportunities for people to contribute in an environment that values employees, a supportive culture, and the chance to thrive. Internship opportunities are also available at Actian.

 

actian avatar logo

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.
Data Intelligence

Everything You Need to Know About Platform Engineering

Actian Corporation

March 8, 2023

Smart Male It Programer Working On Desktop Green Mock Up Screen Computer In Data Center System Control Room. Team Of Young Professionals Programming Sophisticated Code

To meet the challenges of your business, are you searching for a solution that enables a more available, scalable infrastructure at controlled costs? Would you like to increase your capacity to innovate? Then you need to get into Platform Engineering.

In this article, discover what Platform Engineering is and how it differs from adjacent concepts – including DevOps and SRE – as well as its benefits for your organization.

Designated by Gartner as one of the key trends of 2023, Platform Engineering is a little-known discipline. Yet, it is a crucial solution as companies increasingly move to the cloud. Platform Engineering aims to improve software development and delivery by streamlining and optimizing the process of planning and implementing tool chains such as CI/CD pipelines, test environment deployment, and infrastructure-as-code (IaC) configuration to automate cloud resource provisioning.

What is Platform Engineering?

Platform Engineering is a discipline that focuses on the design, development, and management of various technical platforms. It delivers a set of services and tools that enable developers to build, deploy, and manage applications and services efficiently and cost-effectively. Its mission? To build a robust, flexible, and automated IT infrastructure capable of meeting the needs of a wide range of applications and services.

The Platform Engineers in charge of building these infrastructures have the objective of delivering a high level of availability, scalability, and resilience, in order to absorb the ever-increasing traffic and data flows. There is a fine line between the teams in charge of platform engineering and the development and operational (DevOps) teams. They often work closely together to provide tools and services designed to accelerate development cycles, improve application quality, and facilitate continuous deployment.

What do Platform Engineering Teams do and how Does Platform Engineering Work?

Most commonly, Platform Engineering teams are responsible for the design, implementation, and management of the technical platforms that support an organization’s applications and services. To do this, they ensure in particular:

  • Developing and maintaining the platform infrastructure by managing the installation, and configuration of servers, storage, networks, and other components.
  • Automating the processes of deployment, configuration management, and system monitoring.
  • Platform security, identity, and access management, as well as certificate management, security audits, etc.
  • Technical support to the development and operations teams to solve platform-related issues.
  • Optimizing platform performance by monitoring performance metrics, identifying bottlenecks, and making improvements.
  • Platform capacity management by monitoring resource utilization trends and forecasting future needs.

What are the Benefits of Platform Engineering?

Platform Engineering improves the productivity of the development teams by providing tools and services that accelerate development and deployment cycles. This optimized productivity also contributes to cost control through more efficient use of IT resources. If Platform Engineering improves the availability of the infrastructure, it also enables scalability and adaptability for the current (and future) needs of the company.

Finally, Platform Engineering helps to strengthen the security of the IT infrastructure by providing tools for identity and access management, security monitoring, and security incident response.

What are the Differences Between Platform Engineering and DevOps?

Platform Engineering and DevOps are two different but complementary approaches. To fully understand the differences between the two disciplines, note that DevOps encourages close collaboration between development and operations teams (Dev and Ops) to accelerate development cycles, improve code quality, and reduce deployment times.

So while DevOps aims to create a culture of collaboration and shared responsibility between Dev and Ops teams, Platform Engineering focuses on the design, construction, and management of technical platforms. While the two approaches share common company objectives, they focus on different aspects of managing an organization’s IT infrastructure.

What are the Differences Between Platform Engineering and SRE

Platform Engineering and Site Reliability Engineering (SRE) are two related fields. Both focus on the management of an organization’s IT infrastructure. SRE relies on engineering practices to maintain the availability, resiliency, scalability, and performance of services and applications. The mission of SRE teams is to ensure the availability of IT systems, monitor and measure the quality of service, resolve incidents, and provide long-term solutions for recurring problems. They, therefore, work hand in hand with the DevOps teams and the Platform Engineer.

The main difference between SRE and Platform Engineering is that SRE focuses on managing software products to ensure availability and quality of service, while Platform Engineering focuses on creating and managing a robust, flexible, and scalable IT infrastructure for applications and services.

actian avatar logo

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.