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Mar 30 2023
Deciphering the Data Story Behind Supply Chain Analytics

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 to 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 Avalanche Cloud 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 Avalanche 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.

The post Deciphering the Data Story Behind Supply Chain Analytics appeared first on Actian.


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Author: Teresa Wingfield

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Mar 28 2023
Are you Building Your Data Strategy to Scale?

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 Avalanche Cloud Data Platform

The Avalanche Cloud 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.

Contact our enterprise data management experts for a free trial of the Avalanche Cloud Data Platform.

The post Are you Building Your Data Strategy to Scale? appeared first on Actian.


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Author: Teresa Wingfield

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Mar 23 2023
Discover the Top 5 Data Quality Issues – And How to Fix Them!

‍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

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.

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.

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

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Mar 20 2023
What Makes a Great Machine Learning Platform?

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.

The Avalanche Platform and Machine Learning

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:

  1. Breaking down silos: The Avalanche 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
  2. Blazing fast database: Modeling big datasets can be time-consuming. The Avalanche 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 Avalanche platform is up to 9 x faster than alternatives, according to the Enterprise Strategy Group.
  3. 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 Avalanche 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.
  4. 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 Avalanche platform’s database, machine learning models will run extremely fast.
  5. 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.

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

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Mar 17 2023
How Banks Can Use Analytics to Stay Out of the Headlines

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.  

 

 

 

 

 

The post How Banks Can Use Analytics to Stay Out of the Headlines appeared first on Actian.


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Author: Traci Curran

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Mar 17 2023
How to Use Data to Get More Visibility into Your Supply Chain

Supply chains have undergone—and continue to experience—major changes and disruption. 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.   

Supply Chain Analytics Keep Modern Supply Chains Running 

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 sentiment.  

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. According to “Gartner Predicts 2023: Supply Chain Technology,” by 2026, 95% of companies will have failed to enable end-to-end resiliency in their supply chains. “Due to the last few years of major and minor supply chain disruptions, many companies are looking to drive more resiliency into their supply chains,” according to Gartner. “They see this as a key means to help them buffer against the impacts of these ongoing disruptions more effectively.” 

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. 

The Avalanche Cloud Data Platform makes data easy. It simplifies how people connect, manage, and analyze their data to modernize and transform their supply chain. With Avalanche’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.  

Related resources you may find useful: 

The post How to Use Data to Get More Visibility into Your Supply Chain appeared first on Actian.


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Author: Brett Martin

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Mar 17 2023
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 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:

  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 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

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Mar 14 2023
How To Scale Your Data Team’s Impact Without Scaling Costs 


Photo by Lukas As you increase your analytical processes and abilities, you’ll unavoidably increase costs. But there are definite ways to avoid having your costs grow at an unsustainable rate. This is the topic of a panel at the Modern Data Stack Conference featuring Maura Church, ex-director of data science and data engineering from Patreon.…
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The post How To Scale Your Data Team’s Impact Without Scaling Costs  appeared first on Seattle Data Guy.


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Author: research@theseattledataguy.com

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Mar 13 2023
Is data governance the right role for you?


Data governance is a critical function in any organization that deals with data. It involves creating and maintaining policies, procedures, and standards that govern how data is collected, stored, used, and shared. In a data-driven world, the role of data governance becomes increasingly important. But is data governance the right role for you? Table of […]

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Author: George Firican

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