Cloud Data Warehouse – 🔴 Red Button Data

Mar 22 2024

Strategies for Midsize Enterprises to Overcome Cloud Adoption Challenges

While moving to the cloud is transformative for businesses, the reality is that midsize enterprise CIOs and CDOs must consider a number of challenges associated with cloud adoption. Here are the three most pressing challenges we hear about – and how you can work to solve them.

Leveraging existing data infrastructure investments
Closing technical skills gap
Cloud cost visibility and control

Recommendations

Innovate with secure hybrid cloud solutions
Choose managed services that align with the technical ability of your data team
Maintain cost control with a more streamlined data stack

Innovate With Secure Hybrid Cloud Solutions

There is no denying that cloud is cheaper in the long run. The elimination of CapExcosts enables CIOs to allocate resources strategically, enhance financial predictability, and align IT spending with business goals. This shift toward OpEx-based models is integral to modernizing IT operations and supporting organizational growth and agility in today’s digital economy.

But migrating all workloads to the cloud in a single step carries inherent risks including potential disruptions. Moreover, companies with strict data sovereignty requirements or regulatory obligations may need to retain certain data on-premises due to legal, security, or privacy considerations. Hybrid cloud mitigates these risks by enabling companies to migrate gradually, validate deployments, and address issues iteratively, without impacting critical business operations. It offers a pragmatic approach for midsize enterprises seeking to migrate to the cloud while leveraging their existing data infrastructure investments.

How Actian Hybrid Data Integration Can Help

The Actian Data Platform combines the benefits of on-premises infrastructure with the scalability and elasticity of the cloud for analytic workloads. Facilitating seamless integration between on-premises data sources and the cloud data warehouse, the platform enables companies to build hybrid cloud data pipelines that span both environments. This integration simplifies data movement, storage and analysis, enabling organizations to extend the lifespan of existing assets and deliver a cohesive, unified and resilient data infrastructure. To learn more read the ebook 8 Key Reasons to Consider a Hybrid Data Integration Solution

Choose Managed Services That Align With the Technical Ability of Your Data Team

Cloud brings an array of new opportunities to the table, but the cloud skills gap remains a problem. High demand means there’s fierce market competition for skilled technical workers. Midsize enterprises across industries and geos are struggling to hire and retain top talent in the areas of cloud architecture, operations, security, and governance, which in turn severely delays their cloud adoption, migration, and maturity. This carries the potential greater risk of falling behind competitors.

Bridging this skills gap requires strategic investments in HR and Learning and Development (L&D), but the long-term solution has to go simply beyond upskilling employees. One such answer is managed services that are typically low- or no-code, thus enabling even non-IT users to automate key BI, reporting, and analytic workloads with proper oversight and accountability. Managed solutions are typically designed to handle large volumes of data and scale seamlessly as data volumes grow—perfect for midsize enterprises. They often leverage distributed processing frameworks and cloud infrastructure to ensure high performance and reliability, even with complex data pipelines.

Actian’s Low-Code Solutions

The Actian Data Platform was built for collaboration and governance midsize enterprises demand. The platform comes with more than 200 fully managed pre-built connectors to popular data sources such as databases, cloud storage, APIs, and applications. These connectors eliminate the need for manual coding to interact with different data systems, speeding up the integration process and reducing the likelihood of errors. The platform also includes built-in tools for data transformation, cleansing, and enrichment. Citizen integrators and business analysts can apply various transformations to the data as it flows through the pipeline, such as filtering, aggregating and cleansing, ensuring data quality and reliability—all without code.

Maintain Cost Control with a More Streamlined Data Stack

Midsize enterprises are rethinking their data landscape to reduce cloud modernization complexity and drive clear accountability for costs across their technology stack. This complexity arises due to various factors, including the need to refactor legacy applications, integrate with existing on-premises systems, manage hybrid cloud environments, address security and compliance requirements, and ensure minimal disruption to business operations.

Point solutions, while helpful for specific problems, can lead to increased operational overhead, reduced data quality, and potential points of failure, increasing the risk of data breaches and regulatory violations. Although the cost of entry is low, the ongoing support, maintenance, and interoperability cost of these solutions are almost always high.

A successful journey to cloud requires organizations to adopt a more holistic approach to data management, with a focus on leveraging data across the entire organization’s ecosystem. Data platforms can simplify data infrastructure, thus enabling organizations to migrate and modernize their data systems faster and more effectively in cloud-native environments all while reducing licensing costs and streamlining maintenance and support.

How Actian’s Unified Platform Can Help

The Actian Data Platform can unlock the full potential of the cloud and offers several advantages over multiple point solutions with its centralized and unified environment for managing all aspects of the data journey from collection through to analysis. The platform reduces the learning curve for users, enabling them to derive greater value from their data assets while reducing complexity, improving governance, and driving efficiency and cost savings.

Getting Started

The best way for data teams to get started is with a free trial of the Actian Data Platform. From there, you can load your own data and explore what’s possible within the platform. Alternatively, book a demo to see how Actian can accelerate your journey to the cloud in a governed, scalable, and price-performant way.

The post Strategies for Midsize Enterprises to Overcome Cloud Adoption Challenges appeared first on Actian.

How Your Peers are Experiencing their Journeys to the Cloud

According to new customer research from Actian, “Data Analytics Journey to the Cloud,” over 70% of companies are mandating that all new data analytics applications must use cloud-based platforms. Our research reveals many good reasons why the rush to the cloud is on. It also shows that organizations can run into cloud migration roadblocks, that prevent them from realizing the full potential of running their data analytics in the cloud.

Read our eBook to get insights from 450 business and technical leaders across industries and company sizes to improve your chances of a smoother cloud journey. Here are a few highlights of what these leaders shared on their cloud migration:

Over 60% of companies measure the impact of data analytics on their business.
Data privacy is the top challenge facing companies transitioning to the cloud.
More than half of companies say that scaling their business growth is a major challenge and are using cloud-based data analytics to address this.
Customer 360 customer analytics is the leading use case for companies.
Over 50% of companies are using cloud-based analytics to measure and improve customer experience key performance indicators (KPIs).
More than half of companies use data analytics to address their talent challenges.
Over 50% of companies use cloud-based data analytics to impact their employee experience and talent management KPIs.

Making your Cloud Migration Easier

Our research provides additional details that can help you become more confident in your cloud migration, improve planning, and better leverage cloud resources by understanding how other organizations approach their migration. If you’re already in a cloud, multi-cloud, or hybrid environment, you can use insights in our eBook to modernize applications, business processes, and data analytics in the cloud.

Leading Drivers of Cloud Transitions
Data Analytics Challenges and Cloud Migration Friction Points
Top Cloud-Native Technologies in Operation
Most Common Real-World Analytics Use Cases
How to Deliver New Capabilities.

You might also want to sign up for a free trial of the Avalanche Cloud Data Platform. You’ll discover how this modern platform simplifies how you connect, manage, and analyze your data.

The post How Your Peers are Experiencing their Journeys to the Cloud appeared first on Actian.

7 Steps to Leveraging Segment Analysis and Predictive Analytics to Improve CX

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 Avalanche Cloud 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.

Related resources:

6 Predictive Analytics Steps to Reduce Customer Churn

7 Ways Market Basket Analysis Can Make You More Money

How Application Analytics Can Optimize Your Customer Experience Strategy

The post 7 Steps to Leveraging Segment Analysis and Predictive Analytics to Improve CX appeared first on Actian.

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.

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:

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