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Using Cloud Data Analytics to Drive Engagement

The decisions a business makes to help drive revenue and increase customer loyalty are only as good as the available data being referenced. Given the state of consumer demands and the changing ways they prefer to interact with brands, now is a crucial time for businesses to take stock of their cloud data analytics strategies. If data in the cloud isn’t being effectively integrated into the enterprise for analysis and insights, then meaningful business decisions cannot be made accurately. 

Customers are now taking their buying experiences more seriously than ever before. Nearly three-quarters of US consumers rank their experience dealing with a business as being important to their buying decisions. A large majority (86%) say they’d even pay more if it meant getting a better customer experience (CX) out of it. 

For businesses, this means that learning as much as possible about customers and getting their buying experience right is the #1 priority. Combining the power of modern-day analytics with the flexibility offered by the cloud can help businesses generate the real-time insights needed to grow and develop CX. Understanding a customer through data requires a thoughtful approach to cloud data analytics and how to effectively harness data for maximum output.  

Cloud Data Analytics, Explained 

The phrase “cloud data analytics” can mean many things to many different businesses, but broadly speaking, it’s all about using data to make smarter decisions to retain customers and win new ones. In some cases, it may refer to the way customer information is gathered to profile a new target sector. In other cases, it may be referring to the way a business pinpoints where a customer is on their customer lifecycle and the ways in which they’ve engaged with a brand. 

However, the goal of cloud data analytics is to paint a holistic image of how to best reach the audiences that are needed to push the business forward. The use and application of these analytics has surged within the past few years. The rise of social media in the past decade has created groundswells of data on consumer feedback, with sites like Facebook and Instagram providing spaces for customers to describe their experiences with brands. Similarly, aggregator sites like G2 Crowd and Quora allow for reviews and direct questions to be asked to and about companies. 

Businesses can leverage this data for their analytics in several ways, but perhaps the most important gain is using those insights to improve marketing and advertising campaigns. Consumers want personalization more than ever when interacting with businesses, and the insights generated from existing customer data can help make that personalization a reality.  

Understanding a consumer’s buying history and behavior can better help marketers choose the messaging that will resonate best with that customer and hopefully retain them. The need for personalization here cannot be overstated – competitive businesses must know their customers.  

I’m sure that we’ve all gotten offers from companies after visiting their website or buying a product. If a consumer buys a set of sheets online from a major retailer, the next email they receive should probably be for a sale on comforters, not refrigerators. Too many irrelevant offers, and your customers will begin to view little value in your communications and unsubscribe, meaning losing opportunities to attain leads. 

Cloud data analytics can also help organizations meet consumers where they are and on the channels they prefer. As mentioned, the near-ubiquitous use of social media in recent years has ushered in a new wave of opportunities for marketers to meet consumers on these platforms. Leveraging data in the cloud for analytics can help organizations build up an effective social media strategy, one that offers insights into the way customers are using these platforms. Given the nature of today’s interconnected world, social media represents a huge chance for marketers to meet their customers on familiar ground. 

Moving up to the Cloud 

Before a cloud data analytics strategy can get off the ground, the data being used needs to be clean, secure and easily accessible in infrastructure that can scale with today’s data growth. Traditional storage setups like on-premises data warehouses may have a role in today’s enterprise tech stack, but they can’t keep pace with the explosive growth of data from marketing applications. This means businesses still using legacy data centers should consider a move to the cloud for data storage.  

By leveraging a cloud storage model, users can dynamically scale their warehouses according to their business needs. Cloud data warehouses also ensure that the datasets stored inside of them are structured, compliant with regulatory standards, accurate, and accessible to any team that needs access. Furthermore, cloud-based warehouse architectures are significantly speedier than their on-premises counterparts, so you gain access to customer data quickly and efficiently – allowing you to meet customer demands as they happen. 

Perhaps most important, by leveraging the elasticity of the cloud, you have a more cost-effective way to handle spikes in business activity. Seasonality, market changes, and changes in current events can be addressed immediately, instead of wasting time waiting on infrastructure provisioning to accommodate the change in demand. This is particularly true when that demand is unforeseen, like we witnessed during the early days of the pandemic. 

Putting it All to Use 

Now that the goals of a customer data analytics strategy have been identified and the data has been moved to the cloud, a business can then begin using the data for analysis. Here are some key ways to leverage data to connect with customers: 

Find Areas of Opportunity 

It’s important to know your customers as intimately as possible, including their likes, dislikes and needs. The more you know about your customer base, the better you can pinpoint areas of opportunity within your company. The more opportunities you find, the higher chance you have of meeting those needs and making a sale. 

Keep an Eye on Customer Sentiment 

There are more channels than ever for customers to leave product reviews, ask questions, and engage in conversations surrounding your products. A large portion of those channels will not be within your control, but by leveraging the data from these places, you can not only respond to customers where they are, but over time gain a bird’s eye view of customer sentiment as well – both good and bad. The trends you uncover will help your product teams to address areas of improvement and help your marketing teams to zero in on clear value propositions. 

Get Early Warning Signs Before They Happen 

One of the most important benefits of customer data analytics is being able to see early warning signs before shifts in behavior happen. Whether that means a dip in sales or a shift in sentiment from customers, getting ahead of changes can give you more time to react and address issues before they impact your goals. Being able to take preemptive action will allow you to do some damage control before it becomes an irreversible issue for your company. Effectively leveraging cloud data analytics can be a major influence on how businesses drive CX and secure revenue. Access to clean data that can be easily shared can unlock new insights on customers and how to keep them coming back for more. 

For more information on how the Avalanche Cloud Data platform can help you better connect your cloud data for real-time analytics, check out our blog. 

The post Using Cloud Data Analytics to Drive Engagement appeared first on Actian.


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Author: Traci Curran

Best Practices for Using Data to Optimize Your Supply Chain

When a company is data-driven, it makes strategic decisions based on data analysis and interpretation rather than mere intuition. A data-driven approach to supply chain management is the key to building a strong supply chain, one that’s efficient, resilient, and that can easily adapt to changing business conditions.  

How exactly you can best incorporate data and analytics to optimize your supply chain depends on several factors, but these best practices should help you get started:     

#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 that has 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:   

The post Best Practices for Using Data to Optimize Your Supply Chain appeared first on Actian.


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Author: Teresa Wingfield

10 Strategies to Incentivize Customer Survey Participation


You know that customer surveys can provide you with valuable data for market research. But you’ve struggled to attract enough participants for your surveys in the past.

If you’re interested in transforming your market research strategy, working with the consultants can help.

There are a few methods you can use to motivate your customers to complete your surveys, providing them with gift cards, cash rewards, or freebies for example.

Cash and Discounts

Customers will be more inclined to take surveys when you’re offering cash or coupons!

Special Prizes

Sometimes, giving your customers a prize – or a chance to win a contest – can prompt them to complete surveys.

Other Creative Incentives

Don’t be afraid to think outside the box when it comes to incentives.

Here are some innovative ideas!

You don’t want to send out surveys that customers are unlikely to fill out.

Offering exciting incentives is one of the best ways to boost survey participation rates.

By giving your customers gift cards, registering them for contests or sweepstakes, and dishing out free samples, you’ll have no trouble getting the responses you need.

Ready to take your business in a new direction? Look no further than the experts at JonesAssociates! Fill out the contact form on the website to learn more about our services.

Photo via Unsplash


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Author: Flaminio

Incentivizing Consumers to Self-Serve Zero-Party Data and Consent


Privacy remains a big deal and there are several reasons why consumers may be hesitant to allow organizations to master their personal data.

Organizations keep records on consumers for various reasons, among them, personalization, service, marketing, compliance and fraud prevention.

They may use your data to personalize your experience with their products or services; using your browsing and purchase history to recommend products that you are more likely to be interested in.

Keeping records of your interactions with customer service teams enables them to provide better support in the future and ensure that needs are met quickly and efficiently.

Marketing campaigns may be annoying but when they are personalized there may be a change in perception. Analysing behaviour and preferences, marketeers can create more relevant and targeted advertising that is more likely to result in a conversion.

Especially in financial services, organizations need to keep records on consumers to comply with legal and regulatory requirements. For example, they may need to keep records of your transactions for tax or accounting purposes but also to minimize the likelihood of money laundering or illegal use of financial instruments and infrastructure.

In exchange for goods, services or funding, they may use consumer data to prevent fraudulent activity.; monitoring behaviour, usage profiles and transactions, they can identify suspicious activity and take action to prevent fraud.

On the flipside, consumers may feel that their personal data is sensitive and should be kept private.

They may worry that if an organization masters their personal data, it could be used for nefarious purposes or sold to third-party companies without their consent.

Consumers may also be concerned that if an organization masters their personal data, it could be at risk of being hacked or stolen by cybercriminals, resulting in potential identity theft, personal financial loss, and other undesirable consequences.

One of the reasons is that consumers feel that if an organization masters their personal data, they lose control over it; worrying that the data will be used in ways they do not approve of, or that they will not be able to access or delete their data as they see fit.

In particular, consumers worry that their personal data could be used to discriminate against them based on their race, gender, religion, or other personal characteristics. Personal data that is used to make decisions about who to hire, who to offer loans to, or who to market products to are undesirable uses of personal data, for consumers at least.

Consumers have long held feelings that if an organization masters their personal data, it could also lead to unwanted intrusion into their personal lives accompanied by constantly being targeted with ads or other forms of marketing, based on their behaviour being monitored and analysed in ways that feel intrusive or uncomfortable and an invasion of privacy.

Zero party data

An opt-in approach with first-party data can help to address some of the concerns that consumers may have about their personal data being mastered.

First-party data refers to the information that consumers willingly provide through interactions with a website, a product, or a service. An opt-in approach means that organizations only collect and use the consumer’s data with the explicit consent of the consumer. This can give consumers greater control over their data, and can help to build trust between consumers and organizations.

Those privacy concerns can be addressed through opt-in meaning consumers must explicitly agree to allow the collection and use of the data in specific ways. This can give consumers greater control over their personal information and can help to ensure that their data is being used only for legitimate purposes.

By limiting the data that is collected to only what is necessary for specific purposes, the opt-in approach with first-party data helps to reduce the exposure risk associated with prospective data breaches. Organizations that collect first-party data are often also more invested in protecting that data, as it is valuable for building and maintaining the customer relationship.

An opt-in approach also gives consumers more control over the personal information allowing them to choose which data to continue to share, and supporting opt-out of specific data and its collection at any time.

To reduce the risk of discrimination, organizations are required to obtain explicit consent before collecting data on personal characteristics and though data is typically used for personalization and targeted advertising, the consumer can decide how it should be used especially in relation to important decisions about the consumer.

An opt-in approach with first-party data also helps to reduce the feeling of intrusiveness. Consumers now have control over what data is collected and how it is used, the personalization and customization can enhance the user experience rather than detracting from it.

If an organization is considering implementing a customer master data management solution, it’s important to understand how this approach can address consumers’ concerns about their personal data.

Through increased transparency the CMDM provides greater transparency into the data that an organization collects and how it is used; this in turn builds trust with consumers, as they can see exactly what information is being collected and why.

By centralizing the customer data in a CMDM and implementing robust security measures, a customer master data management solution reduces the vectors and edges that provide risk in the event of data breaches. This can also provide reassurance to consumers who are concerned about the security of their personal information.

A CMDM also enables organizations to provide more personalized experiences to customers which in turn helps to build stronger relationships with customers, increases loyalty, and ultimately drives revenue growth.

An opt-in approach gives customers more control over their data, the CMDM can demonstrate that the organization respects the privacy of its customers. This is often a important differentiator in the competitive marketplace, where consumers are increasingly concerned about their data privacy.

CMDM also helps with compliance. Organizations need to comply with data privacy regulations, such as GDPR and CCPA. CMDM’s like that offered by Pretectum, can help to avoid legal and reputational risks associated with non-compliance by providing reassurance to customers and regulators that consumer data is being handled in a responsible and compliant manner.

Overall, a customer master data management solution can help to build trust with customers, enhance data security, deliver better customer experiences, and demonstrate respect for privacy and compliance with regulations.

Communicating with customers about how their personal data is being collected, used, and protected is increasingly important in good customer relationship management.

Consumers expect organizations to be transparent about the data they collect and how it is being used. They expect clear communication on the purpose of the data collection, and what benefits the customers can expect from it. They also expect to provide customers with easy-to-understand information about their data rights and options for managing the data.

Organizations would reassure customers that their personal data is being stored and protected securely, explaining the measures they have put in place to safeguard against data breaches, such as encryption, firewalls, and access controls.

Using an opt-in approach to data collection, means that customers have control over the data that is collected and can choose to opt out at any time. The benefits of opting in are of course more personalized experiences or access to exclusive offers.

Emphasizing respect for privacy of customers and a commitment to protecting personal data go hand in hand and would also explain compliance with relevant data privacy regulations. The responsible organization also highlights any certifications or standards they have achieved in in relation to governance and compliance regulation adherence.

The benefits that customers expect from the data collection might seem obvious such as an enhanced overall experience, but providing examples of how the data is being used to personalize products and services, improve customer service, and offer tailored promotions and discounts is important communication.

Overall, effective communication with customers about the implementation of a customer master data management solution is most critical to building trust and addressing concerns.

Transparency on intent and behaviours, emphasizing data security and privacy, using an opt-in approach, highlighting customer benefits, and complying with relevant regulations, organizations can reassure their consumers that their personal data is being handled responsibly and ethically.

In response, consumers should engage in self-service zero-party data and consent inquiries because it allows them to have greater control over their personal data and the experiences they have with an organization.

By providing preferences and consent, consumers can receive more relevant and personalized experiences, products, and services.

Ecommerce sites could show recommendations based on customer stated interests and preferences, health apps could provide workout plans tailored to a user’s fitness level and selected goals.

Reduced clutter in inboxes may make interactions with an organization more efficient and enjoyable and when accompanied by the ability to decide what information is shared with an organization and how it is used, feelings of more control of personal data and confidence that it is being handled responsibly may follow.

Keeping the interest alive

If the data is collected but not used, it should be securely stored and deleted after a reasonable period of time to ensure compliance with relevant data privacy regulations and businesses can incentivize consumers to provide their data in the context of self-service zero-party data and consent inquiry by offering exclusives, discounts, rewards and previews.

Offering exclusive content, such as whitepapers, eBooks, or reports only accessible to those who provide their data can be a powerful incentive, especially for customers who are interested in a particular topic.

Personalized discounts or coupons to customers who provide their data especially in retail could encompass discounts on next purchases based on stated interests or style preferences.

A free cup of coffee, for example, is obvious at a coffee shop but consider how Waitrose did the same for loyal card holders and how other retailers do the same for their loyalty scheme members. The offer of a free drink after a certain number of visits, with additional rewards for sharing preferences and feedback is an obvious option but the others are a little more subtle.

Giving customers early access to new products, services, or features if they provide their data like AMEX customers in association with events or event tickets is a great way to build excitement and loyalty among customers. Capital One and other financial institutions incentivize in similar ways.

Game or challenge e vents that encourages customers to provide their data like Pokémon Go, a 2016 augmented reality mobile game offers participants rewards for completing certain challenges. Additional rewards for sharing preferences and data is common with many card loyalty schemes as well as social apps.

In the end, it’s important to ensure that any incentives offered are aligned with the interests and preferences of customers, and that they are relevant and valuable.

Organizations today should ensure that they are transparent about their data collection practices and are respecting the privacy of their customers at all times.

Give customers the opportunity to self serve and drive first party data into the DNA of your business.


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Author: Uli Lokshin

6 Things You Must Know About Data Modernization

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 in order 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 Avalanche Cloud Data Platform can help make your digital transformation easier with proven data integration, data management, and data analytics services. Learn more about how the Avalanche Cloud Data platform accelerates data modernization so you can deliver today while building your digital future.  

The post 6 Things You Must Know About Data Modernization appeared first on Actian.


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Author: Teresa Wingfield

Steffen Kläbe Wins Best Paper at 2023 EDBT/ICDT Conference

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. 

The post Steffen Kläbe Wins Best Paper at 2023 EDBT/ICDT Conference appeared first on Actian.


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Author: Saquondria Burris

Top Technical Requirements for 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 case 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 Avalanche Cloud Data Platform Helps

The Avalanche Cloud 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 Avalanche platform or use your preferred application for embedded analytics.

Don’t take our word for it, start your free trial today and see why the Avalanche platform is a great fit for your embedded analytics needs!

The post Top Technical Requirements for Embedded Analytics appeared first on Actian.


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Author: Teresa Wingfield

Actian’s Hamburg, Germany Team Volunteers at Local 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.

The post Actian’s Hamburg, Germany Team Volunteers at Local Food Bank appeared first on Actian.


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Author: Saquondria Burris

REAL Real-time Data Analytics: When Seconds Matter

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 Avalanche Cloud 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 integration that includes orchestration, scheduling, and data pipeline management functionality to help ensure that there is no delay in the timeliness of information.  

The Avalanche Cloud 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 Avalanche 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 Avalanche Cloud Data Platform the world’s fastest analytics platform. The Avalanche platform is up to 7.9 x faster than alternatives, according to the Enterprise Strategy Group.  

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

The post REAL Real-time Data Analytics: When Seconds Matter appeared first on Actian.


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Author: Teresa Wingfield

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

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