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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
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Author: David Cox
The rise of edge computing is fueling demand for embedded devices for Internet of Things (IoT). IoT describes physical objects with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks. Diverse technologies such as real-time data analytics, machine learning, and automation tie in with IoT to provide insights across various edge to cloud use cases.Â
It is not surprising that embedded databases are widely used for IoT given its explosive growth. International Data Corporation (IDC) estimates there will be 55.7 billion connected IoT devices (or “things”) by 2025, generating almost 80B zettabytes (ZB) of data.Â
Our research reveals the top six use cases for embedded databases for IoT. Here, we will discuss the first 3: manufacturing, mobile and isolated environments, and medical devices. You can read our Embedded Databases Use Cases Solution Brief if you would like to learn more about the other three use cases. Â
In fiercely competitive global markets, IoT-enabled manufacturers can get better visibility into their assets, processes, resources, and products. For example, connected machines used in smart manufacturing at factories help streamline operations, optimize productivity, and improve return on investment. Warehouse and inventory management can leverage real-time data analytics to source missing production inputs from an alternative supplier or to resolve a transportation bottleneck by using another shipper. Predictive maintenance using IoT can help identify and resolve potential problems with production-line equipment before they happen and spot bottlenecks and quality assurance issues faster. Â
IoT is driving the shift towards connected logistics, infrastructure, transportation, and other mobile/isolated use cases. In logistics, businesses use edge computing for route optimization and tracking vehicles and shipping containers. Gas and oil companies take advantage of IoT to monitor remote infrastructure such as pipelines and offshore rigs. In the transportation industry, aviation and automotive companies use IoT to improve the passenger experience and to improve safety and maintenance. Â
Healthcare is one of the industries that will benefit the most from IoT, given its direct connection with improving lives. IoT is recognized as one of the most promising technological advancements in healthcare analytics. Medical IoT devices are simultaneously improving patient outcomes and providers’ return on investment. The processing of medical images and laboratory equipment maintenance are particularly important use cases. Data from MRIs, CTs, ultrasounds, X-Rays, and other imaging machines help medical experts diagnose diseases at earlier stages and provide faster and more accurate results. Edge analytics enables predictive maintenance of laboratory equipment to reduce maintenance costs, but more importantly, to help prevent the failure of critical equipment that is often in short supply. Â
What is possible today with IoT in healthcare was inconceivable a decade ago: tracking medications, their temperature, and safe transportation at any point in time.Â
Read our solution brief for more information on additional embedded database for IoT use cases as well as Actian’s Edge to Cloud capabilities for these.Â
The post Embedded Databases Everywhere: Top 3 IoT Use Cases appeared first on Actian.
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Author: Teresa Wingfield
When a company is data-driven, it makes strategic decisions based on data analysis and interpretation rather than mere intuition. A data-driven approach to supply chain management is the key to building a strong supply chain, one that’s efficient, resilient, and that can easily adapt to changing business conditions. Â
How exactly you can best incorporate data and analytics to optimize your supply chain depends on several factors, but these best practices should help you get started:Â Â Â Â Â
Transitioning to a data-driven approach requires a 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 that has buy-in from all levels of the organization. Â
The good news is that there are a lot of opportunities to use supply chain analytics to optimize your supply chain across sourcing, processing, and distribution of goods. But you’ll have to start somewhere and should prioritize opportunities that will generate the greatest benefits for your business and that are solvable with the types of data and skills available in your organization. Â
After you’ve decided which use cases will add the most value, you’ll need to define what your business hopes to achieve and the key performance indicators (KPIs) you’ll use to continuously measure your progress. Your KPIs might track things such as manufacturing downtime, labor costs, and on-time delivery. Â
You’ll need a solution that includes integration, management, and analytics and that supports real-time insights into what’s happening across your supply chain. The platform will also need to be highly scalable to accommodate what can be massive amounts of supply chain data. Â
Artificial intelligence techniques such as machine learning power predictive analytics to identify patterns and trends in data. Insights help manufacturers optimize various aspects of the supply chain, including inventory levels, procurement, transportation routes, and many other activities. Artificial intelligence uncovers insights that can allow manufacturers to improve their bottom line and provide better customer service. Â
Sharing data and insights can help develop strategies aimed at improving supply chain efficiency and developing innovative products and services. Â
The more your teams know about advanced analytics techniques, especially artificial intelligence, and how to use and interpret data, the more value you can derive from your supply chain data. Plus, with demand for analytics skills far exceeding supply, manufacturers will need to make full use of the talent pool they already have. Â
Hopefully, you’ve found these best practices for using data to optimize your supply chain useful and actionable. Here’s my recommended reading list if you’d like to learn more about data-driven business and technologies:  Â
The post Best Practices for Using Data to Optimize Your Supply Chain appeared first on Actian.
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Author: Teresa Wingfield
‍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 costs organizations millions of dollars annually in lost revenue opportunities and resource productivity.
Thankfully, these pitfalls are well known, and easy to fix!
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 leave your organization with a fractured view of a single customer.
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.
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.
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.
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.
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:
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.
The post Discover the Top 5 Data Quality Issues – And How to Fix Them! appeared first on Actian.
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Author: Traci Curran
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 Avalanche Cloud 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 Avalanche Cloud 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.
Let’s take a closer look at some of the Avalanche platform’s most impactful capabilities for making machine learning simpler, faster, accurate, and accessible:
Don’t Take Our Word for It
Try our Avalanche Cloud Data Platform Free Trial to see for yourself how it can help you simplify machine learning deployment. You can also read more about the Avalanche platform.
The post What Makes a Great Machine Learning Platform? appeared first on Actian.
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Author: Teresa Wingfield
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 Avalanche Cloud 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 Avalanche Cloud 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 Avalanche 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 Avalanche Cloud 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 Avalanche Cloud 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 Avalanche Cloud 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 Avalanche Cloud Data Platform is ideally suited for AI with the following capabilities:
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 Avalanche Cloud Data Platform, contact our data analytics experts. Here’s some additional material if you would like to learn more:
·     The Power of Real-time Supply Chain Analytics
·     Actian for Manufacturing
·     Embedded Database Use Cases
The post Data Analytics for Supply Chain Managers appeared first on Actian.
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Author: Teresa Wingfield