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Feb 27 2024
The Link Between Trusted Data and Expanded Innovation

One highlight of my job is being able to talk to customers and prospective customers throughout the year at various events. What I keep hearing is that data is hard, and this holds true for companies of all sizes. And they’re right. Data can be hard. It can be hard to integrate, manage, govern, secure, and analyze. Building pipelines to new data sources can also be hard.

Business and IT both need data to be accessible to all users and applications, cost-effective to store, and deliver real-time insights. Any data challenges will limit these capabilities and present major barriers to innovation. That’s why we’ve made it our mission to make data easy and trustworthy.

Actian exists to provide the most trusted, flexible, and easy-to-use data platform on the market. We know that’s a bold promise and requires solving a lot of your data pain points. Yet we also know that to be truly data driven, you must have uninterrupted access to trusted data.

Overcoming the Trust Barrier

At Actian, we’ve been saying for a long time that you need to be able to trust your data. For too many companies, that’s not happening or it’s not happening in a timely manner. For example, nearly half—48%—of CEOs worry about data accuracy, according to IBM, while Gartner found that less than half of data and analytics teams—just 44%—are effectively providing value to their organization.

These numbers are unacceptable, especially in the age of technology. Everyone who uses data should be able to trust it to deliver ongoing value. So, we have to pause and ask ourselves why this isn’t happening. The answer is that common barriers often get in the way of reaching data goals, such as:

  • Silos that create isolated, outdated, and untrustworthy data.
  • Quality issues, such as incomplete, inaccurate, and inconsistent data.
  • Users do not have the skills needed to connect and analyze data, so they rely on IT.
  • Latency issues prevent real-time data access, which limits timely insights.
  • Data management problems that existed on-premises were migrated to the cloud.

Organizations know they have some or all of these problems, but they often don’t know what steps are needed to resolve them. Actian can help. We have the technology and expertise to enable data confidence—regardless of where you are on your data journey.

Innovation Starts with Trustworthy Data

What if you could swiftly go from data to decision with full confidence and ease? It doesn’t have to be a pipe dream. The solution is readily available now. It ensures you’re using high-quality, accurate data so you have full confidence in your decision-making. It simplifies data transformations, empowering you to get the data you want, when and how you want it, regardless of your skill level, and without relying on IT. Plus, you won’t have to wait for data because it gets delivered in real-time.

The Actian Data Platform makes data easy-to-use, allowing you to meet the needs of more business users, analysts, and data-intensive applications. You can collect, manage, and analyze data in real-time with our transactional database, data integration, data quality, and data warehouse capabilities working together in a single, easy-to-use platform.

The platform lets you manage data from any public cloud, multi- or hybrid cloud, and on-premises environment through a single pane of glass. The platform’s self-service data integration lowers costs while enabling you to perform more use cases without needing multiple data products.

What does all of this mean for your business? It means that data integration, access, and quality are easier than ever. It also means that you can trust your data to make confident decisions that accelerate your organization’s growth, foster new levels of innovation, support your digital transformation, and deliver other business value.

Enabling a Data-Driven Culture

With data volumes becoming more robust, having immediate access to high-quality data is essential, but challenging. Any problems with quality, latency, or integration will compound as data volumes grow, leading to potentially misinformed decision-making and mistrust in the data. Establishing data quality standards, making integration and access easy, and putting data in the hands of everyone who needs it advances the business, promotes a data-driven culture, and drives innovation. And this is where Actian can play a critical role.

What makes the Actian Data Platform unique, at a high level, is its ability to consolidate various data functions into a single platform, making data readily available and easy to use across your organization.

The platform handles extract, transform, and load (ETL), data transformation, data quality checks, and data analytics all in one place. Bringing everything and everyone together on a single platform lowers costs and reduces the resources needed to manage your data system. You benefit from real-time, trustworthy data across the entire organization, giving you full confidence in your data.

When you trust your data, you have the ability—and the confidence—to explore more use cases, increase revenues, reduce costs, fast-track innovation, win market share, and more for a strategic edge in your industry. Our customers are using data to drive new successes everyday!

Related resources you may find useful:

Top Capabilities to Look for in Database Management Tools

The Actian Data Platform’s Superior Price-Performance

How to Build an Effective Data Management Project Plan

The post The Link Between Trusted Data and Expanded Innovation appeared first on Actian.


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Author: Actian Corporation

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Jan 2 2024
Top Capabilities to Look for in Database Management Tools

As businesses continue to tap into ever-expanding data sources and integrate growing volumes of data, they need a solid data management strategy that keeps pace with their needs. Similarly, they need database management tools that meet their current and emerging data requirements.

The various tools can serve different user groups, including database administrators (DBAs), business users, data analysts, and data scientists. They can serve a range of uses too, such as allowing organizations to integrate, store, and use their data, while following governance policies and best practices. The tools can be grouped into categories based on their role, capabilities, or proprietary status.

For example, one category is open-source tools, such as PostgreSQL or pgAdmin. Another category is tools that manage an SQL infrastructure, such as Microsoft’s SQL Server Management Studio, while another is tools that manage extract, transform, and load (ETL) and extract, load, and transform (ELT) processes, such as those natively available from Actian.

Using a broad description, database management tools can ultimately include any tool that touches the data. This covers any tool that moves, ingests, or transforms data, or performs business intelligence or data analytics.

Data Management Tools for Modern Use Cases

Today’s data users require tools that meet a variety of needs. Some of the more common needs that are foundational to optimizing data and necessitate modern capabilities include:

  • Data management: This administrative and governance process allows you to acquire, validate, store, protect, and process data.
  • Data integration: Integration is the strategic practice of bringing together internal and external data from disparate sources into a unified platform.
  • Data migration: This entails moving data from its current or storage location to a new location, such as moving data between apps or from on-premises to the cloud.
  • Data transformation: Transformative processes change data from one format or structure into another for usage and ensure it’s cleansed, validated, and properly formatted.
  • Data modeling: Modeling encompasses creating conceptual, logical, and physical representations of data to ensure coherence, integrity, and efficiency in data management and utilization.
  • Data governance: Effective governance covers the policies, processes, and roles used to ensure data security, integrity, quality, and availability in a controlled, responsible way.
  • Data replication: Replicating data is the process of creating and storing multiple copies of data to ensure availability and protect the database against failures.
  • Data visualization: Visualizing data turns it into patterns and visual stories to show insights quickly and make them easily understandable.
  • Data analytics and business intelligence: These are the comprehensive and sophisticated processes that turn data into actionable insights.

It’s important to realize that needs can change over time as business priorities, data usage, and technologies evolve. That means a cutting-edge tool from 2020, for example, that offered new capabilities and reduced time to value may already be outdated by 2024. When using an existing tool, it’s important to implement new versions and upgrades as they become available.

You also want to ensure you continue to see a strong return on investment in your tools. If you’re not, it may make more sense from a productivity and cost perspective to switch to a new tool that better meets your needs.

Ease-of-Use and Integration Are Key

The mark of a good database management tool—and a good data platform—is the ability to ensure data is easy-to-use and readily accessible to everyone in the organization who needs it. Tools that make data processes, including analytics and business intelligence, more ubiquitous offer a much-needed benefit to data-driven organizations that want to encourage data usage for everyone, regardless of their skill level.

All database management tools should enable a broad set of users—allowing them to utilize data without relying on IT help. Another consideration is how well a tool integrates with your existing database, data platform, or data analytics ecosystem.

Many database management tool vendors and independent software vendors (ISVs) may have 20 to 30 developers and engineers on staff. These companies may provide only a single tool. Granted, that tool is probably very good at what it does, with the vendor offering professional services and various features for it. The downside is that the tool is not natively part of a data platform or larger data ecosystem, so integration is a must.

By contrast, tools that are provided by the database or platform vendor ensure seamless integration and streamline the number of vendors that are being used. You also want to use tools from vendors that regularly offer updates and new releases to deliver new or enhanced capabilities.

If you have a single data platform that offers the tools and interfaces you need, you can mitigate the potential friction that oftentimes exists when several different vendor technologies are brought together, but don’t easily integrate or share data. There’s also the danger of a small company going out of business and being unable to provide ongoing support, which is why using tools from large, established vendors can be a plus.

Expanding Data Management Use Cases

The goal of database management tools is to solve data problems and simplify data management, ideally with high performance and at a favorable cost. Some database management tools can perform several tasks by offering multiple capabilities, such as enabling data integration and data quality. Other tools have a single function.

Tools that can serve multiple use cases have an advantage over those that don’t, but that’s not the entire story. A tool that can perform a job faster than others, automate processes, and eliminate steps in a job that previously required manual intervention or IT help offers a clear advantage, even if it only handles a single use case. Stakeholders have to decide if the cost, performance, and usability of a single-purpose tool delivers a value that makes it a better choice than a multi-purpose tool.

Business users and data analysts often prefer the tools they’re familiar with and are sometimes reluctant to change, especially if there’s a long learning curve. Switching tools is a big decision that involves both cost and learning how to optimize the tool.

If you put yourself in the shoes of a chief data officer, you want to make sure the tool delivers strong value, integrates into and expands the current environment, meets the needs of internal users, and offers a compelling reason to make a change. You also should put yourself in the shoes of DBAs—does the tool help them do their job better and faster?

Delivering Data and Analytics Capabilities for Today’s Users

Tool choices can be influenced by no-code, low-code, and pro-code environments. For example, some data leaders may choose no- or low-code tools because they have small teams that don’t have the time or skill set needed to work with pro-code tools. Others may prefer the customization and flexibility options offered by pro-code tools.

A benefit of using the Actian Data Platform is that we offer database management tools to meet the needs of all types of users at all skill levels. We make it easy to integrate tools and access data. The Actian Platform offers no-code, low-code, and pro-code integration and transformation options. Plus, the unified platform’s native integration capabilities and data quality services feature a robust set of tools essential for data management and data preparation.

Plus, Actian has a robust partner ecosystem to deliver extended value with additional products, tools, and technologies. This gives customers flexibility in choosing tools and capabilities because Actian is not a single product company. Instead, we offer products and services to meet a growing range of data and analytics use cases for modern organizations.

Experience the Actian Data Platform for yourself. Take a free 30-day trial.

Related resources you may find useful:

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Author: Derek Comingore

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Dec 27 2023
The Actian Data Platform’s Superior Price-Performance

When it comes to choosing a technology partner, price and performance should be top of mind. “Price-performance” refers to the measure of how efficiently a database management system (DBMS) utilizes system resources, such as processing power, memory, and storage, in relation to its cost. It is a crucial factor for organizations to consider when selecting a DBMS, as it directly impacts the overall performance and cost-effectiveness of their data management operations. The Actian Data Platform can provide the price-performance you’re looking for and more.

Getting the most value out of any product or service has always been a key objective of any smart customer. This is especially true of those who lean on database management systems to help their businesses compete and grow in their respective markets, even more so when you consider the exponential growth in both data sources and use cases in any given industry or vertical. This might apply if you’re an insurance agency that needs real-time policy quote information, or if you’re in logistics and need the most accurate, up-to-date information about the location of certain shipments. Addressing use cases like these as cost-effectively as possible is key in today’s fast-moving world.

The Importance of Prioritizing Optimal Price-Performance

Today, CFOs and technical users alike are trying to find ways to get the best price-performance possible from their DBMS systems. Not only are CFOs interested in up-front acquisition and implementation costs, but also all costs downstream that are associated with the utilization and maintenance of whichever system they choose.

Technical users of various DBMS offerings are also looking for alternative ways to utilize their systems to save costs. In the back alleys of the internet (places like Reddit and other forums), users of various DBMS platforms are discussing how to effectively “game” their DBMS platforms to get the best price-performance possible, sometimes leading to the development of shadow database solutions just to try and save costs.

According to a December 2022 survey by Actian, 56% of businesses struggle to maintain costs as data volumes and workloads increase. These types of increases affect total cost of ownership and related infrastructure maintenance, support, query complexity, the number of concurrent users, and management overhead, which have a significant impact on the costs involved in using a database management system.

Superior Price-Performance

Having been established over 50 years ago, Actian was in the delivery room when enterprise data management was born. Since then, we’ve had our fingers on the pulse of the market’s requirements, developing various products that meet various use cases from various industries worldwide.

The latest version of the Actian Data Platform includes native data integrations with 300+ out-of-the-box connectors and scalable data warehousing and analytics that produce REAL real-time insights to more confident support decision-making. The Actian Data Platform can be used on-premises, in the cloud across multiple clouds, and in a hybrid model. The Actian Platform also provides no-code, low-code, and pro-code solutions to enable a multitude of users, both technical and non-technical.

The 2023 Gigaom TCP-H Benchmark Test

At Actian, we’re really curious about how our platform compared with other major players and whether or not it could help deliver the price-performance being sought after in the market. In June of 2023, we commissioned a TCP-H Benchmark test with GigaOm, pitting the Actian Data Platform against both Google Big Query and Snowflake. This test involved running 22 queries against a 30TB TCP-H data set. Actian’s response times were better than the competition in 20 of those 22 requests. Furthermore, the benchmark report revealed that:

  • In a test of five concurrent users, Actian was overall 3x faster than Snowflake and 9x faster than Big Query.

  • In terms of price-performance, the Actian Data Platform produced even greater advantages when running the five concurrent user TPC-H queries. Actian proved roughly 4x less expensive to operate than Snowflake, based on cost per query per hour, and 16x less costly than BigQuery.

These were compelling results. Overall, the GigaOm TCP-H benchmark shows that the Actian Data Platform is a high-performance cloud data warehouse that is well-suited for organizations that need to analyze large datasets quickly and cost-effectively.

Actian customer, the Automobile Association (AA), located in the United Kingdom, was able to reduce their quote response time to 400 milliseconds. Without the speed provided by the Actian Platform, they wouldn’t have been able to provide prospective customers the convenience of viewing insurance quotes on their various comparison pages, which allows them to gain and maintain a clear advantage over their competitors.

Let Actian Help

If price-performance is a key factor for you, and you’re looking for a complete data platform that will provide superior capabilities and ultimately lower your TCO, do these three things:

  1. Download a copy of the Gigaom TCP-H Benchmark report and read the results for yourself.
  2. Take the Actian Data Platform for a test-drive!
  3. Contact us! One of our friendly, knowledgeable representatives will be in touch with you to discuss the benefits of the Actian Data Platform and how we can help you have more confidence in your data-driven decisions that keep your business growing.

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Author: Phil Ostroff

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Dec 7 2023
The Future of Automation in Manufacturing

As manufacturers know, automation enables a range of high-value benefits, such as cost and time savings. The Outlook of Automation in 2023 from Thomas Insights captures these advantages succinctly by noting that “automation promises lower operating costs, improved worker safety, a higher return on investment (ROI), better product quality, operational efficiencies, and competitive advantage.”

While automation isn’t new, manufacturers have been automating processes for decades as opportunities to expand it into new areas of the factory floor continue to emerge. Meanwhile, customizing and modernizing automation to fit a manufacturer’s unique needs can bring additional benefits, such as filling the gap caused by a labor shortage, making manufacturing processes more efficient, and meeting the changing needs of contract and original equipment manufacturing.

As automation continues to shape the future of manufacturing, automating data-driven processes will likewise make growing volumes of data readily available to support manufacturing use cases. The data can also make existing manufacturing processes more efficient and potentially more sustainable.

Automation in Modern Factories Comes in Many Varieties

Manufacturers see automation as a priority area for investing. According to a Deloitte survey, 62% of large companies plan to invest in robotics and automation, making it the top focus. The next highest area of investment is data analytics at 60%.

Digital transformations, which have swept through almost every industry, have helped lay the groundwork for the future of automation. In fact, according to a survey by McKinsey, 94% of respondents said digital solutions will be important to their future automation efforts. Other key technologies that are enabling the future of automation, according to McKinsey, include soft programmable logic controllers, digital twins, and teach-less robotics.

Most people probably immediately think of robotics when they think of automation in manufacturing. While the use of robotics has certainly advanced the industry, automation also extends into areas that many people don’t see.

For example, I’ve worked on projects that were as straightforward as transitioning from paper-based processes and manual entries on a computer to automating digital workflows that didn’t require human intervention. This type of project delivers time and money savings, and transparency into processes, even though it’s not as visible as a robotic arm on a factory floor.

Automating Both Data and Manufacturing Processes

Traditionally, automation has played a key role in manufacturers’ process controls. This includes supporting quality assurance processes, identifying risks, and predicting outcomes. The driving force for all of this automation at an enterprise level, not surprisingly, is data. However, getting a consolidated and normalized view of data is challenging. It requires a modern data platform that offers data warehousing and integration capabilities that bring together data from all needed sources and automates data pipelines.

The more disparate that the application landscape, ecosystem, and infrastructure become for manufacturers, the more they are going to need efficient and scalable data preparation and management capabilities. Legacy technologies and outdated processes that still require a lot of manual intervention will delay insights and are not scalable.

One proven way to solve this challenge is to use a small footprint, low maintenance, high performance database management system like Actian Zen. It can be embedded as part of an Internet of Things (IoT) strategy to advance manufacturing operations, including automation. With Actian Zen, manufacturers can also reap the benefits of edge applications and devices, which enable data-driven improvements all the way down to the process controller level.

Performing analytics at the edge and transmitting the results, rather than moving the entire data set to a data warehouse or platform for analysis, avoids the task of transferring data. This is certainly a big advantage, especially when manufacturers are faced with large data volumes, limited bandwidth, and latency issues.

For example, Actian is currently setting up a proof of concept to intercept data streams from a satellite that was shot up by a space organization that tracks GPS data from endangered animals. There’s a big problem with poaching for these animals, but if we can monitor their GPS movements, we can detect and then alert authorities when there are anomalies. This type of capability can help manufacturers pinpoint potential problems in automation by recognizing patterns or behaviors that deviate from a baseline.

A lot of IT applications require 5G or Global System for Mobile Communications (GSM), but these options have limited bandwidth. That’s why smart driving vehicles have not taken off—the bandwidth doesn’t support the vehicles’ massive data needs. Once the bandwidth improves to move data at the speed required for data-intensive applications, companies across all industries can find new use cases for automation in everything from manufacturing to the automotive industry.

Keeping Assembly Line Belts Moving Efficiently

Automation and digital transformations often go hand in hand to drive process and operational improvements across manufacturing. “Organizations are now utilizing automation as their most up-to-date approach for innovating and operating,” according to Smartbridge. “Companies are putting automation at the forefront of their digital strategies, making it a core priority for the entire enterprise.”

Similarly, Boston Consulting Group calls digitization and automation core elements of the “factory of the future.” Part of the reason is because manual processes are not designed for automation. Digital processes are, so they lend themselves to automating key aspects of supply chains, manufacturing tasks, and other operations. For example, manufacturers need to ensure they have enough supplies on-premises to keep their assembly line belts moving efficiently, but without incurring bloated inventory that increases storage costs. This is all in the interest of keeping production moving while minimizing costs, and nowadays meeting sustainability goals.

Accurately predicting and meeting rolling forecasts is the holy grail in manufacturing. Rolling forecasts are continuously updated based on past performance, current trends and operations, and other factors. Automating data processes to feed these forecasts gives stakeholders the real-time insights needed to make informed decisions that can impact all aspects of manufacturing.

Our customer Aeriz is a good example. The company unifies and analyzes data to inform a wide range of decisions. Aeriz is a national aeroponic cannabis brand, but it runs manufacturing processes that are reminiscent of those used by pharmaceutical companies. The organization’s leaders put a lot of thought into processes and automation controls, such as controlling the humidity and temperature for growing cannabis as well as the speed of conveyor belts for manufacturing processes. Like other companies, Aeriz relies on data to tell a comprehensive story about the state of the business and what is expected to happen next.

What this demonstrates is that the more opportunities there are to automate, from data processing to assembly line interactions, the more companies benefit from accuracy and time savings, which can transform standard operating procedures. Every step that can be automated provides value.

Improving Product Lifecycle Management

Bringing automation into manufacturing can solve new and ongoing challenges. This includes expanding the use of automation to optimize efficiencies, encourage sustainable operations, and make processes less complex. When the International Society of Automation (ISA) published a blog on the four biggest manufacturing automation trends of 2023, it called out connecting automation to sustainability goals, using automation to address skills shortages, leveraging automation as a competitive differentiator, and implementing more accessible forms of automation such as turnkey robotics.

These trends can certainly bring welcome advantages to manufacturing. Yet, from a big-picture view, one key benefit of automation is how it advances overall operations. When we think of manufacturing, whether it’s a mid-sized custom manufacturer or a large global enterprise, we oftentimes think of automating repetitive tasks. Once tasks are automated, it doesn’t mean the job is done. There may be opportunities to make changes, even minor enhancements, to improve individual processes or large-scale operations.

For example, manufacturers may find that they can further optimize the movement of a robotic arm to be faster or more efficient. Plus, connecting data from automated robotics with other sources across a factory floor may uncover ways to minimize waste, identify any silos or duplicated processes, and inform planning strategies. All of this ultimately plays a role in improving product lifecycle management, which can include everything from product design to testing and development. Improvements made to product lifecycle management can trickle down to improvements made on the factory floor.

Optimizing automation to drive the future of manufacturing requires not only an accurate overview of everything going on inside the factory walls, but also insight into what’s going on outside. This includes understanding supply chain operations and tier one, tier two, and tier three vendors. This helps ensure the manufacturer doesn’t run out of an essential item that can shut down production and bring automated processes to a halt.

The Future of Automation will Rely on Data

One aspect of modernization that’s been consistent over the decades—and is positioned to be the driving force into the future—is the use of data. As new use cases emerge, all available data will be needed to inform decisions and enable precision automation.

Manufacturers will need the ability to go from data source to decision with confidence. At Actian, we deliver by making data easy. We enable manufacturers and others to access unified, trusted data in real-time. The Actian Data Platform provides data integration, quality, and superior performance, along with native integration and codeless transformations that allow more users to access data to drive business goals.

With new capabilities such as integration as a service and database as a service, the Actian Data Platform meets the current and future needs of manufacturers. Find out what it can do for your business with a free 30-day trial.

Related resources you may find useful:

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Author: Robert Gorsuch

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Jul 24 2023
De-Risking The Road to Cloud: 6 Questions to Ask Along the Way

In my career, I’ve had first-hand experience as both a user and a chooser of data analytics technology, and have also had the chance to talk with countless customers about their data analytics journey to the cloud. With some reflection, I’ve distilled the learnings down to 6 key questions that every technology and business leader should ask themselves to avoid pitfalls along the way to the cloud so they can achieve its full promise.

1. What is my use case?

Identifying your starting point is the critical first step of any cloud migration. The most successful cloud migrations within our customer base are associated with a specific use case. This focused approach puts boundaries around the migration, articulates the desired output, and enables you to know what success looks like. Once a single use case has been migrated to the cloud, the next one is easier and often relies on data that has already been moved.

2. How will we scale over time?

Once you’ve identified the use case, you’ll need to determine what scaling looks like for your company. The beauty of the cloud is that it’s limitless in its scalability; however, businesses do have limits. Without planning for scale, businesses run the risk of exceeding resources and timelines.

To scale quickly and maximize value, I always recommend customers evaluate use cases based on level of effort and business value: plotting each use case in a 2Ă—2 matrix will help you identify the low effort, high value areas to focus on. By planning ahead for scale, you de-risk the move to the cloud because you understand what lies ahead.

3. What moves, what doesn’t, and what’s the cost of not planning for a hybrid multi-cloud implementation?

We hear from our customers, especially those in Europe, that there is a need to be deliberate and methodical in selecting the data that moves to the cloud. Despite the availability of data masking, encryption, and other protective measures available, concerns about GDPR and privacy are still very real. These factors need to be considered as the cloud migration roadmap is developed.

Multi-cloud architectures create resiliency, address regulatory requirements, and help avoid the risk of vendor lock-in. The benefits of multi-cloud environments were emphasized in a recent meeting with one of our EMEA-based retail customers. They experienced significant lost revenue and reputation damage after an outage of one of the largest global cloud service providers. The severe impact of this singular outage made them rethink a single cloud strategy and move to multi-cloud as part of their recovery plan.

4. How do I control costs?

In our research on customers’ move to the cloud, we found that half of organizations today are demanding better cost transparency, visibility, and planning capabilities. Businesses want a simple interface or console to determine which workloads are running and which need to be stopped – the easier this is to see and control, the better. Beyond visibility in the control console, our customers also use features such as idle stop, idle sleep, auto-scaling, and warehouse scheduling to manage costs. Every company should evaluate product performance and features carefully to drive the best cost model for the business. In fact, we’ve seen our health insurance customers leverage performance to control costs and increase revenue.

5. What skills gaps will I need to plan for, and how will I address them?

Our customers are battling skills gaps in key areas, including cloud, data engineering, and data science. Fifty percent of organizations lack the cloud skills to migrate effectively to the cloud, and 45 percent of organizations struggle with data integration capacity and challenges, according to our research. Instead of upskilling a team, which can often be a slow and painful process, lean on the technology and take advantage of as-a-service offerings. We’ve seen customers that engage in services agreements take advantage of platform co-management arrangements, fully managed platform services, and outsourcing to help offset skills gap challenges.

6. How will I measure success?

Look beyond cost and measure success based on the performance for the business. Ask yourself: is your cloud solution solving the problem you set out to solve? One of our customers, Met Eireann, the meteorological service for Ireland, determined that query speed was a critical KPI to measure. They found after moving to the cloud that performance improved 60-600 times and reduced query result time down to less than a second. Every customer measures success differently, whether it’s operational KPIs, customer experience, or data monetization. But whatever the measure, make sure you define success early and measure it often.

Making the move to the cloud is a journey, not a single step. Following a deliberate path, guided by these key questions, can help you maximize the value of cloud, while minimizing risk and disruption. With the right technology partner and planning, you can pave a smooth road to the cloud for your organization and realize true business value from your data.

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Author: Jennifer Jackson

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Jul 6 2023
Algorithmic Bias: The Dark Side of Artificial Intelligence

The growth of social media and the advancement of mobile technology has created exponentially more ways to create and share information. Advanced data tools, such as AI and data science are being employed more often as a solution for processing and analyzing this data. Artificial Intelligence (AI), combines computer science with robust datasets and models to facilitate automated problem-solving. Machine Learning (ML) models, a subfield of AI that uses statistical techniques that enables computers to learn without explicit programming, use data inputs to train actions and responses for users. This data is being leveraged to make critical decisions surrounding governmental strategy, public assistance eligibility, medical care, employment, insurance, and credit scoring.  

As one of the largest technology companies in the world, Amazon Web Services (AWS) relies heavily on AI and ML as the solution they need for storing, processing, and analyzing data. But, in 2015, even with their size and technical sophistication, they discovered bias in their hiring algorithm. It was biased to favor men because the data set it referenced was based on past applicants over the previous 10 years, which contained a much larger sample of men than women. 

Bias was found in an algorithm COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), which is used by US court systems to predict offender recidivism. The data used, the chosen model, and the algorithm employed overall, showed that it produced false positives for almost half (45%) of African American offenders in comparison to Caucasian American offenders (23%). 

Without protocols and regulations to enforce checks and balances for the responsible use of AI and ML, society will be on a slippery slope of issues related to bias based on socioeconomic class, gender, race, and even access to technology. Without clean data, algorithms can intrinsically create bias, simply due to the use of inaccurate, incomplete, or poorly structured data sets. To avoid bias, it starts with accurately assessing the quality of the dataset, which should be: 

  • Accurate 
  • Clean and consistent 
  • Representative of a balanced data sample 
  • Clearly structured and defined by fair governance rules and enforcement 

Defining AI Data Bias 

The problem that exists with applying Artificial Intelligence to make major decisions is the presence and opportunity for bias to cause significant disparities in vulnerable groups and underserved communities. A part of the problem is volume and processing methods of Big Data, but there is also the potential for data to be used intentionally to perpetuate discrimination, bias, and unfair outcomes 

“What starts as a human bias turns into an algorithmic bias,” states Gartner. In 2019, Algorithmic bias was defined by Harvard researchers as the application of an algorithm that compounds existing inequities in socioeconomic status, race, ethnic background, religion, gender, disability, or sexual orientation and amplifies inequities in health systems. Gartner also explained four types of algorithmic bias: 

  • Amplified Bias: systemic or unintentional bias in processing data used in training machine learning algorithms. 
  • Algorithm Opacity: end-user data black boxes, whether intrinsic or intentional, cause concern about levels of integrity during decision-making. 
  • Dehumanized Processes: views on replacing human intelligence with ML and AI are highly polarized, especially when used to make critical, life-changing decisions. 
  • Decision Accountability: there exists a lack of sufficient reporting and accountability from organizations using Data Science to develop strategies to mitigate bias and discrimination. 

A study by Pew Research found that “at a broad level,” 58% of Americans feel that computer programs will always reflect some level of human bias – although 40% think these programs can be designed in a way that is bias-free. This may be true when you’re looking at data about shipments in a supply chain or inventory predicting when your car needs an oil change, but human demographic, behaviors, and preferences can be fluid and subject to change based on data points that may not be reflected in the data sets being analyzed.  

Chief data and analytics officers and decision-makers must challenge themselves by ingraining bias prevention throughout their data processing algorithms. This can be easier said than done, considering the volume of data that many organizations process to achieve business goals. 

The Big Cost of Bias  

The discovery of data disparities and algorithmic manipulation to favor certain groups and reject others has severe consequences. Due to the severity of the impact of bias in Big Data, more organizations are prioritizing bias mitigation in their operations. InformationWeek conducted a survey on the impact of AI bias on companies using bad algorithms.  It revealed that bias was found to be related to gender, age, race, sexual orientation, and religion. In terms of damages to the businesses themselves, they included: 

  • Lost Revenue (62%) 
  • Lost Customers (61%) 
  • Lost Employees (43%) 
  • Paying legal fees due to lawsuits and legal actions against them (35%) 
  • Damage to their brand reputation and media backlash (6%) 

Solving Bias in Big Data 

Regulation of bias and other issues created by using AI, or having poor-quality data are in different stages of development, depending on where you are in the world. For example, in the EU, an Artificial Intelligence Act is in the works that will identify, analyze, and regulate AI bias. 

However, the true change starts with business leaders who are willing to do the leg work of ensuring diversity and responsible usage and governance remain at the forefront of their data usage and policies “Data and analytics leaders must understand responsible AI and the measurable elements of that hierarchy — bias detection and mitigation, explainability, and interpretability,” Gartner states. Attention to these elements supports a well-rounded approach to finding, solving, and preventing issues surrounding bias in data analytics.  

Lack of attention to building public trust and confidence can be highly detrimental to data-dependent organizations. Implement these strategies across your organization as a foundation for the responsible use of Data Science tools: 

  • Educate stakeholders, employees, and customers on the ethical use of data including limitations, opportunities, and responsible AI.  
  • Establish a process of continuous bias auditing using interdisciplinary review teams that discover potential biases and ethical issues with the algorithmic model. 
  • Mandate human interventions along the decision-making path in processing critical data. 
  • Encourage collaboration with governmental, private, and public entities, thought leaders and associations related to current and future regulatory compliance and planning and furthering education around areas where bias is frequently present. 

Minimizing bias in big data requires taking a step back to discover how it happens and preventive measures and strategies that are effective and scalable. The solution may need to be as big as big data to successfully surmount the shortcomings present today and certainly increasing in the future. These strategies are an effective way to stay informed, measure success, and connect with the right resources to align with current and future algorithmic and analytics-based bias mitigation. 

Related Resources 

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Author: Saquondria Burris

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Jun 27 2023
13 Churn Prevention Strategies to Improve CX

Happy customers can be advocates for your brand, make repeat purchases, and influence others to buy from your business. This type of success is the result of using data to holistically understand each customer and developing a customer-centric business strategy that engages and rewards each individual when they interact with your company.

Elevating the customer experience (CX) is a proven way to connect with customers and prevent churn. It also helps with profitability, as it’s much more expensive to acquire new customers than to keep existing ones.

How to Retain Customers and Enhance CX

1. Simplify customer onboarding.

Ensuring a fast and painless onboarding experience is essential since it’s often your first opportunity to make an impression on the customer—and first impressions shape CX. An intuitive and positive first experience sets the tone for the customer journey. Whether onboarding entails filling out an online form, activating a new product, or hands-on training on a product, delivering an engaging experience gives customers the confidence that they’re making a good decision by working with your business.

2. Deliver timely, truly meaningful CX.

One of the best ways to prevent churn is to continually provide relevant and authentic customer experiences. These experiences nurture customers by delivering the next best action at the most opportune time. With the right cloud data platform and analytics, you can accurately predict what customers want and when they want it, then delight them with the right offer at the right time and at the right price point. This is where a comprehensive CX strategy that optimizes customer data delivers ongoing value.

3. Personalize all interactions.

Personalized CX is now table stakes for companies. According to McKinsey & Company, 71% of consumers expect organizations to deliver personalized interactions, and 76% are frustrated when this doesn’t happen. Personalization improves customer outcomes—and drives more revenue. Product and service offers must be customized, too. Once you’ve built 360-degree profiles, you can segment customers for special offers. You can even personalize offers to a single customer for a truly customized experience.

4. Engage customers at all touchpoints.

CX is an ongoing journey that requires support and nurturing at every step. Customers typically have several interactions with a company before making a purchase. Understanding each touchpoint and ensuring a positive experience is essential—or the customer could abruptly end the journey. These touchpoints, such as website visits, downloading an app, or social media views, shape the way customers view your brand, company, and offerings. This is why each touchpoint is an opportunity to impress customers and guide their journey.

5. Respond promptly to complaints or concerns.

Customer journeys are not always smooth or linear. Shipping delays, product glitches, and user errors all impact CX. Unhappy customers have a higher likelihood of churn, which brings the challenges of identifying these customers and addressing their concerns. This is especially important when it’s a high-value customer. Sometimes feedback is direct, such as a call or email to a customer service desk or sales rep. Other times, you need to identify negative sentiment indirectly, like through social media. And sometimes customers won’t proactively share at all, which is where surveys and post-sales follow-up provide value. Simply connecting with a customer is sometimes enough to make a difference and make them feel valued.

6. Reward loyalty.

Loyalty programs are a great way to know and recognize your best customers. You can use the programs to gather information about customers, then reward loyalty with special offers, like free merchandise, a discount, or a chance to buy a product before it goes on sale to the public. While these programs improve CX, they also encourage customers to engage with the brand more often to accumulate points. Another benefit is loyalty programs, which can turn customers into authentic advocates for your brand. In addition, studies have shown that consumers are more likely to spend—and spend more—with companies offering a loyalty program. Gartner predicts that one in three businesses without a loyalty program today will establish one by 2027.

7. Build excitement.

When you can build customer excitement, then you know your CX strategy is excelling. This excitement can organically inspire conversations, posts, and comments on social media about your brand. Effective ways to build this excitement include giving loyal customers a sneak peek at upcoming product releases, offering “behind the scenes” content, and creating customer contests on social media that award prizes.

8. Foster trust.

People want to do business with companies they trust and that share their values. Meeting or exceeding customer expectations and resolving problems before they occur builds trust. So does making an emotional connection with customers through your content. Other ways to foster trust include demonstrating that you protect their data, showing concern for the environment through sustainable business practices, and delivering products and services when and how the customer expects.

9. Listen to customers.

Your customers have a lot to say, even if they don’t tell you directly. They might be sharing their thoughts on social media or through their browsing history. Integrating customer data from all relevant sources allows you to understand each customer. You can then listen based on their behaviors and feedback before, during, and after a sale. This can help you determine which features and price points are most effective. Also, addressing any changes in behaviors and responding to complaints quickly can help mitigate churn.

10. Find out why customers are leaving.

Understanding why customers are ending subscriptions, switching to a competitor, or no longer purchasing from your company allows you to identify churn patterns. This can help you evaluate if you’re experiencing a level of churn the business is comfortable with—some amount of churn is to be expected—or if there’s a sudden spike or ongoing problem. Churn analysis offers insights into why customers are leaving, such as products that don’t meet expectations, prices that are higher than competitors, poor customer service, or other reasons.

11. Be proactive.

It’s important to identify customers at risk of churning, then engage them before they leave. Measuring customer sentiment helps to determine areas needing improvement and creates a consistent channel for feedback. Proactively addressing customers’ concerns before they spiral into full-blown problems can encourage them to stay. Being proactive requires a robust customer retention strategy and the ability to perform granular customer analytics for insights into the early stages of churn.

12. Know what your competitors are doing.

Knowing your business and your customers is not enough. You must also know what your competitors are doing. This allows you to better understand the competitive landscape and have insights into potential market changes—or major market disruptions. A competitive analysis can help you understand key differences between your products and competitors’ offerings. This can help you update your product design and marketing strategy, and even be an opportunity to poach customers.

13. Stay relevant.

Growing the business and staying relevant are ongoing challenges. It requires continually delivering innovative products and services, regularly connecting with customers, staying ahead of changing customer preferences, and updating the brand as needed. You also need to evaluate if you have gaps in your product or service offerings, and if so, plan how to address them. As customer wants and needs change, your brand also needs to change in ways that are relevant to your customers.

Let’s Get Started

Your ability to tackle customer attrition while enhancing customer experiences starts with data. You need the right data, and you need the ability to integrate it using a single, scalable platform for analytics. The Avalanche Cloud Data Platform can help you transform your churn and CX strategies by bringing together all of the customer data you need on an easy-to-use platform. Our advanced capabilities for data integration, data management, and analytics give you the insights and confidence needed to retain and engage customers.

Related Resources:

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Author: Brett Martin

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May 9 2023
Embedded Databases Everywhere: Top 3 IoT Use Cases

The rise of edge computing is fueling demand for embedded devices for Internet of Things (IoT). IoT describes physical objects with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks. Diverse technologies such as real-time data analytics, machine learning, and automation tie in with IoT to provide insights across various edge to cloud use cases. 

It is not surprising that embedded databases are widely used for IoT given its explosive growth. International Data Corporation (IDC) estimates there will be 55.7 billion connected IoT devices (or “things”) by 2025, generating almost 80B zettabytes (ZB) of data. 

Our research reveals the top six use cases for embedded databases for IoT. Here, we will discuss the first 3: manufacturing, mobile and isolated environments, and medical devices. You can read our Embedded Databases Use Cases Solution Brief if you would like to learn more about the other three use cases.  

Manufacturing  

In fiercely competitive global markets, IoT-enabled manufacturers can get better visibility into their assets, processes, resources, and products. For example, connected machines used in smart manufacturing at factories help streamline operations, optimize productivity, and improve return on investment. Warehouse and inventory management can leverage real-time data analytics to source missing production inputs from an alternative supplier or to resolve a transportation bottleneck by using another shipper. Predictive maintenance using IoT can help identify and resolve potential problems with production-line equipment before they happen and spot bottlenecks and quality assurance issues faster.  

Mobile/Isolated Environments 

IoT is driving the shift towards connected logistics, infrastructure, transportation, and other mobile/isolated use cases. In logistics, businesses use edge computing for route optimization and tracking vehicles and shipping containers. Gas and oil companies take advantage of IoT to monitor remote infrastructure such as pipelines and offshore rigs. In the transportation industry, aviation and automotive companies use IoT to improve the passenger experience and to improve safety and maintenance.  

Medical Devices 

Healthcare is one of the industries that will benefit the most from IoT, given its direct connection with improving lives. IoT is recognized as one of the most promising technological advancements in healthcare analytics. Medical IoT devices are simultaneously improving patient outcomes and providers’ return on investment. The processing of medical images and laboratory equipment maintenance are particularly important use cases. Data from MRIs, CTs, ultrasounds, X-Rays, and other imaging machines help medical experts diagnose diseases at earlier stages and provide faster and more accurate results. Edge analytics enables predictive maintenance of laboratory equipment to reduce maintenance costs, but more importantly, to help prevent the failure of critical equipment that is often in short supply.  

What is possible today with IoT in healthcare was inconceivable a decade ago: tracking medications, their temperature, and safe transportation at any point in time. 

Learn More 

Read our solution brief for more information on additional embedded database for IoT use cases as well as Actian’s Edge to Cloud capabilities for these. 

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Author: Teresa Wingfield

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Apr 25 2023
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:   

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Author: Teresa Wingfield

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Apr 21 2023
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.  

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Author: Teresa Wingfield

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Apr 14 2023
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!

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Author: Teresa Wingfield

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Apr 4 2023
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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Author: Brett Martin

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Mar 30 2023
Deciphering the Data Story Behind Supply Chain Analytics

When it comes to supply chain data, there’s an intriguing story to be told. If businesses have access to accurate data in real time about their supply chain operations, they have tremendous opportunities to increase efficiency, reduce costs, and grow revenue. Here’s a look at some of the types of supply chain data and the data story that supply chain analytics can reveal.

Procurement Data

This includes information about the type, quality, quantity, and cost of raw materials and components used in the production process. Analyzing spend can help businesses identify areas where they can reduce costs and make data driven decisions about how to best allocate their budget. For example, real-time comparisons of supplier pricing can help sourcing teams negotiate more favorable prices.

Supplier Data

This includes data about suppliers, such as their performance history, delivery times, and product quality. Supplier data is key to reducing order fulfillment issues and to identifying and proactively planning for supply chain disruption. Companies are increasingly leveraging supplier data in real-time to enhance their environmental, social and governance efforts.

Production Data

This includes data about manufacturing processes, including production schedules, output levels, and equipment utilization and performance. Faster insights into production data can help optimize material availability, workforce and processes needed to keep production lines running. Businesses can also more quickly spot quality control issues and equipment problems before they lead to costly downtime.

Inventory Data

This includes data about the quantity and location of inventory, inventory turnover and safety stock requirements. Demand forecasting using predictive analytics helps to determine the right level of inventory. Real-time visibility is essential to dynamically adjust production up or down as demand fluctuates and to offer promotions and sales for slow-moving inventory.

Transportation Data

This includes data about the movement of goods from one location to another such as shipment tracking, transit conditions and times, and transportation costs. Predictive analytics can estimate transit times to determine the best possible routes. What’s possible today was inconceivable a decade ago: using sensors to track things such as temperature and safe transportation at any point in time to protect goods and improve driving habits.

Customer Data

This includes customer data such as order history, purchase behavior, and preferences. Companies can meet customer expectations and increase sales when they understand and anticipate what their customers need – and when they are able to create personalized experiences and quickly adjust the supply change based on constantly changing customer behavior.

Sales Data

This includes sales data such as revenue, profit margins and customer satisfaction. Companies use demand forecasting based on past sales to help them adjust production, inventory levels, and improve sales and operations planning processes.

Create Your Data Story

What’s your supply chain data story going to be? It all depends on the data platform you choose to process your supply chain analytics. The platform will need to be highly scalable to accommodate what can be massive amounts of supply chain data and must support real-time insights into supply chain events as they happen so decision makers can form next-best actions in the moment.

The Avalanche Cloud Data Platform provides data integration, data management, and data analytics services in a single platform that offers customers the full scalability benefits of cloud- native technologies. The Avalanche platform provides REAL, real-time analytics by taking full advantage of the CPU, RAM, and disk to store, compress, and access data with unmatched performance.

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Author: Teresa Wingfield

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

A data strategy is a long-term plan that defines the infrastructure, people, tools, organization, and processes to manage information assets. The goal of a data strategy is to help a business leverage its data to support decision making. To make the plan a reality, the data strategy must scale. Here are a few pointers on how to achieve this:

Infrastructure

The right infrastructure is necessary to give an organization the foundation it needs to scale and manage data and analytics across the enterprise. A modern cloud data platform will make it easy to scale with data volumes, reuse data pipelines and ensure privacy and regulations are met while also making sure that data is accessible to analysts and business users. The platform should use cloud native technologies that allow an organization to build and run scalable data analytics in public, private, and hybrid clouds.

People

The talent shortage for analysts and data scientists, particularly for advanced analytics requiring knowledge of artificial intelligence, is a big challenge. With the U.S. Bureau of Labor Statistics projecting a growth rate of nearly 28% in the number of jobs requiring data science skills by 2026, the shortage will continue to grow.

To cope with the shortage, businesses will need to invest more in training and education. The more teams know about advanced data analytics techniques and how to use and interpret data, the more value an organization can derive from its data. Also, with demand for analytics skills far exceeding supply, organizations will need to make of the talent pool they already have.

Tools

A cost-optimal solution should not only process data analytics workloads cost effectively, but also include data integration, data quality, and data management that add more costs, and complexity when sourced from multiple vendors. However, there is no such thing as a one-size fits-all tool when it comes to analytics. Increasingly, organizations are adding many types of advanced analytics such as machine learning to their analytics tool portfolio to identify patterns and trends in data that help optimize various aspects of the business.

Businesses will also need to devise strategies for users to easily access data on their own so that limited technical staff doesn’t bottleneck data analytics. Embedded analytics and self-service help support the needs of data democratization. Self-service gives users insights faster so businesses can realize the value of data faster. Analytics embedded within day-to-day tools and applications deliver data in the right context, allowing users to make better decisions faster

Organization

For a data strategy to scale, an organization needs to build a data driven culture. Transitioning to a data driven approach requires a corporate cultural change where leadership views data as valuable, creates greater awareness of what it means to be data driven and develops and communicates a well-defined strategy.

Processes

There are many processes involved in a scalable data strategy. Data governance is particularly critical to democratizing data while protecting privacy, complying with regulations, and ensuring ethical use. Data governance establishes and enforces policies and processes for collecting, storing, using, and sharing information. These include assigning responsibility for managing data, defining who has access to data and establishing rules for usage and protection.

Get Started with the Avalanche Cloud Data Platform

The Avalanche Cloud Data Platform provides data integration, data management, and data analytics services in a single platform that offers customers the full benefits of cloud native technologies. It can quickly shrink or grow CPU capacity, memory, and storage resources as workload demands change. As user load increases, containerized servers are provisioned to match demand. Storage is provisioned independently from compute resources to support compute or storage-centric analytic workloads. Integration services can be scaled in line with the number of data sources and data volumes.

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Author: Teresa Wingfield

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