Data Analytics

Best Practices for Using Data to Optimize Your Supply Chain

Teresa Wingfield

April 25, 2023

plane and truck showing supply chain optimization

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 can easily adapt to changing business conditions.

How to Optimize Your Supply Chain:

1. Build a Data-Driven Culture

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 with buy-in from all levels of the organization.

2. Identify Priority Business Use Cases

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.

3. Define Success Criteria

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.

4. Invest in a Data Platform

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.

5. Use Advanced Analytics

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.

6. Collaborate With Suppliers and Partners

Sharing data and insights can help develop strategies aimed at improving supply chain efficiency and developing innovative products and services.

7. Train and Educate Employees

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.

Learn More

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:

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

6 Things You Must Know About Data Modernization

Teresa Wingfield

April 21, 2023

laptop showing a cloud for data modernization and data analytics

Data is the heart of digital transformation and the digital+ economy. Data modernization moves data from siloed legacy systems to the digital world to help organizations optimize their use of data as a strategic asset. For a successful data modernization journey, the following are some important things you need to know:

1. Data Strategy

A data strategy lays out your plan to improve how your business acquires, stores, manages, uses, and shares data. The creation of a strategy, according to McKinsey, ranks as the top reason for companies’ success in data and analytics. Your data strategy should include your vision, business objectives, use cases, goals, and ways to measure success.

2. Data Architecture and Technologies

To improve access to information that empowers “next best action” decisions, you will need to transfer your data from outdated or siloed legacy databases in your on-premises data center to a modern cloud data platform. Gartner says that more than 85% of organizations will embrace a cloud-first principle by 2025 and will not be able to fully execute their digital strategies without the use of cloud-native architectures and technologies. For successful data modernization, your cloud data platform must be a cloud-native solution to provide the scalability, elasticity, resiliency, automation, and accessibility needed to accelerate cycles of innovation and support real-time data-driven decisions.

3. Data Analytics

Another important part of data modernization is data analytics. Traditional business tools aren’t enough to support modern data needs. Advanced analytics such as predictive modeling, statistical methods, and machine learning are needed to forecast trends and predict events. Further, embedding analytics directly within applications and tools helps users better understand and use data since it’s in the context of their work.

4. Data Quality

Quality matters a lot in data modernization because users who rely on data to help them make important business decisions need to know that they can trust its integrity. Data should be accurate, complete, consistent, reliable, and up-to-date. A collaborative approach to data quality across the organization increases knowledge sharing and transparency regarding how data is stored and used.

5. Data Security

Strong data security is the foundation for protecting modern cloud data platforms. It includes safeguards and countermeasures to prevent, detect, counteract, or minimize security risks. In addition to security controls to keep your data safe, including user authentication, access control, role separation, and encryption, you’ll need to protect cloud services using isolation, a single tenant architecture, a key management service, federated identity/single sign-on, and end-to-end data encryption.

6. Data Governance

Data governance determines the appropriate storage, use, handling, and availability of data. As your data modernization initiative democratizes data, you’ll need to protect privacy, comply with regulations, and ensure ethical use. This requires fine-grained techniques to prevent inappropriate access to personally identifiable information (PII), sensitive personal information, and commercially sensitive data, while still allowing visibility to data attributes a worker needs.

Make Modernization Easier

Your modernization journey depends on a cloud data platform that eliminates internal data silos and supports cloud-native technologies. You’ll also need to choose the right data analytics tools, ensure that your data is trustworthy and implement solid data and cloud security and data governance. The Actian Data Platform can help make your digital transformation easier with proven data integration, data management, and data analytics services. Learn more about how Actian Data Platform accelerates data modernization so you can deliver today while building your digital future.

About Teresa Wingfield

Actian Life

Steffen Kläbe Wins Best Paper at 2023 EDBT/ICDT Conference

Actian Corporation

April 19, 2023

We’d like to recognize Steffen Kläbe, a Research Engineer at Actian in llmenau (Thuringia, Germany). He attended the 2023 joint conference by EDBT/ICDT in Greece, one of the top database conferences worldwide, where he presented two research papers. For his research on Patched Multi-Key Partitioning for Robust Query Performance he received an award for Best Paper. In the research community, this award is quite a success.

View The Abstract:

“Data partitioning is the key for parallel query processing in modern analytical database systems. Choosing the right partitioning key for a given dataset is a difficult task and crucial for query performance. Real world data warehouses contain a large amount of tables connected in complex schemes resulting in an overwhelming amount of partition key candidates. In this paper, we present the approach of patched multi-key partitioning, allowing to define multiple partition keys simultaneously without data replication. The key idea is to map the relational table partitioning problem to a graph partition problem in order to use existing graph partitioning algorithms to find connectivity components in the data and maintain exceptions (patches) to the partitioning separately. We show that patched multi-key partitioning offer opportunities for achieving robust query performance, i.e. reaching reasonably good performance for many queries instead of optimal performance for only a few queries.”

Kläbe’s additional paper Exploration of Approaches for In-Database ML covers the increasing role of integrating ML models with specialized frameworks for classification or prediction.

View The Abstract:

“Database systems are no longer used only for the storage of plain structured data and basic analyses. An increasing role is also played by the integration of ML models, e.g., neural networks with specialized frameworks, and their use for classification or prediction. However, using such models on data stored in a database system might require downloading the data and performing the computations outside. In this paper, we evaluate approaches for integrating the ML inference step as a special query operator – the ModelJoin. We explore several options for this integration on different abstraction levels: relational representation of the models as well as SQL queries for inference, the use of UDFs, the use of APIs to existing ML runtimes and a native implementation of the ModelJoin as a query operator supporting both CPU and GPU execution. Our evaluation results show that integrating ML runtimes over APIs perform similarly to a native operator while being generic to support arbitrary model types. The solution of relational representation and SQL queries is most portable and works well for smaller inputs without any changes needed in the database engine.”

Congratulations, Steffan! We look forward to seeing more of your wins and research in the future.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

Data Analytics

Top Technical Requirements for Embedded Analytics

Teresa Wingfield

April 14, 2023

chart showing technical requirements of embedded analytics

What is Embedded Analytics?

More employees making decisions based on data insights leads to better business outcomes. Increasingly, data analytics needs to be surfaced to users via the right medium to inform better and faster decisions. This is why embedded analytics has emerged as an important way to help organizations unlock the potential of their data. Gartner defines embedded analytics as a digital workplace capability where data analytics occurs within a user’s natural workflow, without the need to toggle to another application.

How do you embed data analytics so that users can better understand and use data? It all starts with building the right data foundation with a modern cloud data platform. While technical requirements to support embedded analytics depend on specific use cases and user needs, there are general requirements that a cloud data platform should always meet. Below is a summary of each one.

Technical Requirements

API Integration: The cloud data platform must provide flexible API choices to allow effortless application access to data.

Extract, Transform and Load (ETL) Integration: The solution should also include ETL capabilities to integrate data from diverse sources, including databases, internal and third-party applications, and cloud storage.

Data Variety: Support for different data types, including structured, semi-structured, and unstructured data, is essential as data comes in many forms, including text, video, audio, and many others.

Data Modeling: The solution should be able to model the data in a way that supports analytics use cases, such as aggregating, filtering, and visualizing data.

Data Quality: Data profiling and data quality should be built into the platform so that users have data they can trust.

Performance: REAL real-time performance is a critical need to ensure that users can access and analyze data in the moment.

Scalability: The solution should be able to handle large volumes of data, support a growing number of users and use cases, and reuse data pipelines.

Security: The solution should provide robust security measures to protect data from unauthorized access, including role-based access control, encryption, and secure connections.

Governance: Embedded analytics demands new approaches to data privacy. The cloud data platform should help organizations comply with relevant data and privacy regulations in their geography and industry while also making sure that data is useful to analysts and decision-makers.

Support for Embedded Analytics Vendors: In addition to sending data directly to applications, the cloud data platform should allow developers to leverage their embedded application of choice.

How the Actian Data Platform Helps

The Actian Data Platform, with built-in integration, including APIs, and data quality, is an ideal foundation for embedded analytics. These features combined with dynamic scaling, patented REAL real-time performance, compliance and data masking help meet the needs of even the most challenging embedded analytics use cases. In addition, you can fuel your applications with data directly from the Actian Data Platform or use your preferred application for embedded analytics.

About Teresa Wingfield

Actian Life

Actian’s Hamburg, Germany Team Volunteers at Local Food Bank

Actian Corporation

April 11, 2023

Jeevans Tafeltag surrounded by food at a food bank

At Actian, we believe that giving back to the community is an essential part of our corporate social responsibility. That’s why the local team in Hamburg, Germany, was thrilled to have the opportunity to volunteer at a local food bank.

On the volunteer day, they were greeted by Deacon Franz Sauerteig and other volunteers. They were immediately struck by the warmth and sense of community among everyone there. After introductions, they got to work, distributing non-food items and sorting through donated food to ensure that only the best, edible items made it into the hands of those who needed it most. As they worked alongside the other volunteers, they were struck by the importance of the food bank’s mission. Many of the people who rely on the food bank are struggling to make ends meet, and without the help of volunteers and donors, they might not have access to healthy and nutritious food.

Volunteering at the food bank wasn’t just about helping others—it was also a rewarding experience for us as individuals. It was a chance to break out of our daily routines, to work together towards a common goal, and to connect with others in our community.
They came away from the experience feeling grateful for the opportunity to give back and inspired to continue finding ways to make a positive impact in our community.

Actian encourages more of our employees to join in embracing corporate social responsibility and finding ways to give back to the communities in which we operate. It’s a wonderful opportunity to spread kindness and support those in need, fostering a stronger sense of community among us all.

About Actian Corporation

Data Analytics

REAL Real-Time Data Analytics: When Seconds Matter

Teresa Wingfield

April 7, 2023

According to Gartner, real-time analytics is the discipline that applies logic and mathematics to data to provide insights for making better decisions quickly. For some use cases, real-time means analytics is completed within a few seconds after the arrival of new data. Actian calls this REAL real-time data analytics.

Analytics solutions vary greatly in their real-time capabilities, with many having only “near” real-time analytics. REAL real-time analytics means that you can immediately deliver real-time data and consistently execute ultra-fast queries to inform decisions in the moment. Here’s a quick overview of how the Actian Data Platform achieves these two requirements.

Real-Time Data

Real-time data is information that is delivered immediately after collection. This requires real-time, event and embedded processing options so that you can ingest your data quickly. You will also need an integration that includes orchestration, scheduling, and data pipeline management functionality to help ensure that there is no delay in the timeliness of information.

The Actian Data Platform is noted for its fast delivery of real-time data using the above data integration features. In a recent Enterprise Strategy Group economic validation, customers reported that the Actian data platform reduced data load times up to 99% and reduced integration and conversion time up to 95%.

Real-Time Queries

A columnar database with vectorized data processing has become the de facto standard to accelerate analytical queries. While row-oriented storage and execution are designed to optimize performance for online transaction processing queries, they provide sub-optimal performance for analytical queries.

A columnar database stores data in columns instead of rows. The purpose of a columnar database is to efficiently write and read data to and from hard disk storage to speed up the time it takes to return query results.

Vectorization enables highly optimized query processing of columnar data. Vectorization is the process of converting an algorithm from operating on a single value at a time to operating on a set of values (vector) at one time. Modern CPUs support this with Single instruction, multiple data (SIMD) parallel processing.

Additional optimizations such as multi-core parallelism, query execution in CPU cores/cache, and more contribute to making the Actian Data Platform the world’s fastest analytics platform. The Actian Data Platform is up to 7.9 x faster than alternatives, according to the Enterprise Strategy Group.

The Actian Data Platform also has patented technology that allows you to continuously keep your analytics dataset up-to-date, without affecting downstream query performance. This is ideal for delivering faster analytic outcomes.

When Seconds Matter

So why does speed matter? Real-time data analytics allows businesses to act without delay so that they can seize opportunities or prevent problems before they happen. Here is a brief example of each type of benefit.

Online Insurance Quotes

Insurance comparison websites in the UK give top billing to insurers who respond fastest to online requests for quotes. Insurance uses the Actian Data Platform for real-time analytics to deliver a risk-balanced, competitive insurance quote with sub-second speed.

Proactive Equipment Maintenance

As manufacturers incorporate more IoT devices on their plant floors, they have opportunities to analyze data from them in real-time to identify and resolve potential problems with production-line equipment, before they happen, and to spot bottlenecks and quality assurance issues faster.

The Actian Data Platform is a single solution for data integration, data management, and real-time data analytics. Check out how the platform lets you integrate anytime.

About Teresa Wingfield

Data Analytics

Leveraging Segment Analysis and Predictive Analytics for CX

Actian Corporation

April 4, 2023

leveraging segment analysis and personalized customer experiences

Today’s customers expect a timely, relevant, and personalized experience across every interaction. They have high expectations for when and how companies engage with them—meaning customers want communications on their terms, through their preferred channels, and with personalized, relevant offers. With the right data and analytics capabilities, organizations can deliver an engaging and tailored customer experience (CX) along each point on the customer journey to meet, if not exceed, expectations.

Those capabilities include segment analysis, which analyzes groups of customers who have common characteristics, and predictive analytics, which utilizes data to predict future events, like what action a customer is likely to take. Organizations can improve CX using segment analysis and predictive analytics with the following steps.

Elevating Customer Experiences Starts With Seven Key Steps

Use a Scalable Data Platform

Bringing together the large volumes of data needed to create customer 360-degree profiles and truly understand customers requires a modern and scalable data platform. The platform should easily unify, transform, and orchestrate data pipelines to ensure the organization has all the data needed for accurate and comprehensive analytics—and make the data readily available to the teams that need it. In addition, the platform must be able to perform advanced analytics to deliver the insights necessary to identify and meet customer needs, leading to improved CX.

Integrate the Required Data

Unifying customer data across purchasing history, social media, demographic information, website visits, and other interactions enables the granular analytic insights needed to nurture and influence customer journeys. The insights give businesses and marketers an accurate, real-time view of customers to understand their shopping preferences, purchasing behaviors, product usage, and more to know the customer better. Unified data is essential for a complete and consistent customer experience. A customer data management solution can acquire, store, organize, and analyze customer data for CX and other uses.

Segment Customers into Groups

Customer segmentation allows organizations to optimize market strategies by delivering tailored offers to groups of customers that have specific criteria in common. Criteria can include similar demographics, number of purchases, buying behaviors, product preferences, or other commonalities. For example, a telco can make a custom offer to a customer segment based on the group’s mobile usage habits. Organizations identify the criteria for segmentation, assign customers into groups, give each group a persona, then leverage segment analysis to better understand each group. The analysis helps determine which products and services best match each persona’s needs, which then informs the most appropriate offers and messaging. A modern platform can create personalized offers to a customer segment of just one single person—or any other number of customers.

Predict What Each Segment Wants

Elevating CX requires the ability to understand what customers want or need. With predictive analytics, organizations can oftentimes know what a customer wants before the customer does. As a McKinsey article noted, “Designing great customer experiences is getting easier with the rise of predictive analytics.” Companies that know their customers in granular detail can nurture their journeys by predicting their actions, and then proactively delivering timely and relevant next best offers. Predictive analytics can entail artificial intelligence and machine learning to forecast the customer journey and predict a customer’s lifetime value. This helps better understand customer pain points, prioritize high-value customer needs, and identify the interactions that are most rewarding for customers. These details can be leveraged to enhance CX.

Craft the Right Offer

One goal of segment analysis and predictive analytics is to determine the right offer at the right time through the right channel to the right customers. The offer can be recommending a product customers want, a limited time discount on an item they’re likely to buy, giving an exclusive deal on a new product, or providing incentives to sign up for loyalty programs. It’s important to understand each customer’s appetite for offers. Too much and it’s a turn off. Too little and it may result in missed opportunities. Data analytics can help determine the optimal timing and content of offers.

Perform Customer Analytics at Scale

Once customers are segmented into groups and organizations are optimizing data and analytics to create personalized experiences, the next step is to scale analytics across the entire marketing organization. Expanding analytics can lead to hyper-personalization, which uses real-time data and advanced analytics to serve relevant offers to small groups of customers—or even individual customers. Analytics at scale can lead to tailored messaging and offers that improve CX. The analytics also helps organizations identify early indicators of customers at risk of churn so the business can take proactive actions to reengage them.

Continue Analysis for Ongoing CX Improvements

Customer needs, behaviors, and preferences can change over time, which is why continual analysis is needed. Ongoing analysis can identify customer likes and dislikes, uncover drivers of customer satisfaction, and nurture customers along their journeys. Organizations can use data analytics to continually improve CX while strengthening customer loyalty.

Make Data Easily Accessible

To improve CX with data and analytics, organizations need a platform that makes data easy to use and access for everyone. For example, the Actian Data Platform offers enterprise-proven data integration, data management, and analytics in a trusted, flexible, and easy-to-use solution.

The platform unifies all relevant data to create a single, accurate, real-time view of customers. It makes the customer data available to everyone across marketing and the business who needs it to engage customers and improve each customer experience.

Additional Resources:

About Actian Corporation

Data Intelligence

Metadata Management vs. Master Data Management

Actian Corporation

April 3, 2023

Business Information And Infographics Concept.

In order to stand out from the competition, offer an adapted customer experience, reinforce innovation, and improve internal processes or production flows, companies are strongly relying on data. Many organizations are looking for ways to better leverage this massive resource and ensure rigorous data governance. In this article, discover the differences as well as the similarities between two concepts that are essential to a data-driven approach: metadata management and master data management.

According to a study entitled “The Strategic Role of Data Governance and its Evolution“, conducted by the Enterprise Strategy Group (ESG), at the end of 2022, companies saw their data volumes double every two years. On average, the organizations that were surveyed reported managing 3 petabytes of data, about two-thirds of which is unstructured data. The survey data also shows an average annual increase of 40%. And 32% of respondents even report a yearly increase of more than 50%.

In the context of exponentially increasing data volumes, companies must face a major challenge: ensure optimal data and metadata management, at the risk of exposing themselves to an explosion of costs related to errors. According to recent Gartner estimates, poor data quality costs companies in all industries nearly $13 billion annually. Metadata management and master data management (MDM) provide organizations with critical processes to gain the knowledge they need to meet their market challenges while limiting their exposure to the risk of excess costs.

The Definitions of Metadata Management and Master Data Management

First, let’s define each term clearly. Metadata management corresponds to the set of practices and tools that enable the management of metadata of an information system in an efficient and consistent way. As such, metadata management aims to guarantee the quality, relevance, and accessibility of metadata, as well as their compliance with data norms and standards.

Master data management (MDM) brings together all the techniques and processes that enable reference data to be managed in a centralized, consistent, and reliable manner. This reference data, also known as “master data”, is critical information, absolutely essential to the company’s activity. It can be information about customers, suppliers, products, operating and production sites, or data about employees. The purpose of master data management is to build a single repository for this reference data. This repository is then used by the different applications and systems of the company and guarantees access to reliable and consistent data.

What are the Differences Between Metadata Management and Master Data Management?

Although both concepts are related to data management, metadata management and master data management (MDM) serve different company objectives and take distinct approaches.

While metadata management is primarily concerned with the management of information that describes data, its context, and its use, MDM focuses on the management of business-critical master data. These two different scopes make metadata management and master data management two complementary disciplines for your data strategy. Where metadata management focuses on the description and use of data, MDM focuses on the management and harmonization of business-critical master data.

What do Master Data Management and Metadata Management Have in Common?

The first thing master data management and metadata management have in common is that they both contribute to the efficiency and success of your data-driven projects. Both aim to guarantee the quality, relevance, and consistency of data. MDM and metadata management also both require dedicated processes and tools. Finally, both disciplines integrate and contribute to a broader data governance approach.

Combined, they allow you to be more agile, more efficient, and more responsible at the same time.

About Actian Corporation

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.

About Actian Corporation

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.

About Teresa Wingfield

Data Analytics

Are You Building Your Data Strategy to Scale?

Teresa Wingfield

March 28, 2023

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.

About Teresa Wingfield

Data Analytics

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

Traci Curran

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.

About Traci Curran

Traci Curran is Director of Product Marketing at Actian, focusing on the Actian Data Platform. With 20+ years in tech marketing, Traci has led launches at startups and established enterprises like CloudBolt Software. She specializes in communicating how digital transformation and cloud technologies drive competitive advantage. Traci's articles on the Actian blog demonstrate how to leverage the Data Platform for agile innovation. Explore her posts to accelerate your data initiatives.

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