Data Platform

Advantages of Implementing a Cloud Data Platform

Traci Curran

December 22, 2022

Man in front of a laptop representing the coverage of a data warehouse

A current data explosion is being fueled by cheaper data storage and advanced analytics technologies. However, a difficult task still remains: how do we aggregate that data into a single place where you can easily analyze it?

Teams struggle to access accurate, consistent data from the multiple analytics and extract, transform, load process (ETL) tools being used across the organization. The existence of data silos is nothing new, but some options can help your business become data-driven without forcing your staff to move away from the tools that they need to be productive.

A cloud data platform allows you to abstract the complexities of the underlying tools used in an organization and gets usable and accurate data into a single source of truth.

The Crucial Role of the Cloud Data Platform and Data Warehouse

A data warehouse collects clean and structured data from many sources to assist in boosting organizational performance, making smarter choices, and finding competitive advantages.

It’s important to understand the difference between a data warehouse and a database.

A database stores current transactions and provides quick access to specific transactions for ongoing business processes using online transaction processing (OLTP).
Data warehouses store large quantities of historical data and support fast, complex queries across all data using online analytical processing (OLAP). A data lake, on the flip side, provides unstructured or semi-structured data for exploration.

A cloud data platform allows you to use all of these through a single platform that can be utilized by all data teams.

Still, it’s Important to Have a Data Warehouse. Here are a Few Reasons:

Consistency is Key

Typically, data warehousing involves converting data from multiple sources and formats into one standard format, thereby making it easier for users to analyze and share insights on the entire collection of data.

A Data Warehouse Maintains Data You Can Trust

Many organizations require data from multiple subsystems built on different platforms to perform valuable business intelligence. Data warehousing solves this issue by collecting an organization’s data into one repository, allowing it to be accessed from a central location.

A Data Warehouse Enables Self-Service

Business users and decision-makers frequently need to log into multiple departmental systems or request reports through IT personnel to access the information they require. A data warehouse allows them to generate reports and queries on their own. Having easier access to data allows for less time spent on data retrieval and more time on data analysis, resulting in more productive work sessions.

Data Warehouses Improve Data Quality

Having a data warehouse allows businesses to ensure that their stored data is compliant and can mask sensitive data to protect them from exposure to data breaches or unauthorized access. Data warehouses also remove poor-quality information from the data repository and enrich data to make it more useful for insights.

Common Use Cases for a Cloud Data Platform

A cloud data platform takes all the advantages of a cloud data warehouse and combines native integration, transformation, orchestration, and data quality into a single, easy-to-use platform.

Improving Marketing Performance

It’s common for marketing data to be spread across several systems in a company, such as a customer relationship management (CRM) system and marketing automation system. When teams assemble scattered data into spreadsheets to gauge critical measurements, the information may become outdated. Isolating discrepancies becomes cumbersome, and teams can often concede to making decisions based on the limited data that they have available.

A marketing data warehouse allows the marketing team to operate off a single source of data. You may combine data from internal systems such as web analytics platforms, advertising channels, and CRM platforms with data from external systems. A data warehouse allows marketers to execute faster, more efficient initiatives by giving them access to the same standardized data. Teams can generate more granular insights and track performance metrics such as ROI, lead attribution, and customer acquisition costs more effectively.

Real-time processing of data warehouses can enable marketers to build campaigns based on the most recent data, generating more leads and business opportunities. Users can create customized dashboards or reports that evaluate marketing team performance.

Embracing Legacy

Many enterprises still depend on mainframe environments and other legacy application systems, which makes accessing and processing legacy data difficult. Unfortunately, technological advancements in platforms, architectures, and tools have not supplanted legacy application systems.

The difficulty of migrating business knowledge and rules to newer platforms and applications over the years is one reason why legacy systems still have a strong footprint in nearly all enterprises. However, information stored in legacy systems can be a valuable data resource for analytical systems.

Legacy systems were not built to analyze data; they were built to perform functions. Because of this, companies that rely on legacy software such as mainframes for critical operations are unable to obtain real-time information from their transactions. Solving business problems and gaining access to data locked away in legacy systems can be pivotal if you are working with legacy data. Built-in integration enables cloud data platforms to connect to legacy systems for better data analytics. Data can be transformed from legacy systems into a format that newer applications can use using processes such as extract, transform, load (ETL) or extract, load, transform (ELT). Using legacy data to inform new applications can help provide a clearer picture of historical trends, resulting in more accurate business decisions.

Improving Operational Performance

Customer service, customer success, sales, and marketing teams can be evaluated using metrics derived from the data warehouse, such as usage patterns, customer lifetime value, and acquisition sources. Teams’ contributions to overall business performance and objectives can also be highlighted by combining data sets from other business areas and generating stronger data analytics insights.

Business information can be collected and stored in relational formats to support historical and real-time analysis. Data can then be analyzed to discover real-time anomalies or predict events and trends from historical data. Performance improvements are far more effective when they can combine past performance and future predictions.

Conclusion

Cloud data platforms make it possible for organizations to access data from multiple sources in real-time, automate business processes, and speed up the time to insights. A data platform is more than a database or data warehouse, it’s the center of gravity for your business’ digital future. It offers a combination of services, tools, and components that helps you build a data warehouse, data lakes, and supportive analytic data hubs.

Cloud data platforms help you leverage your data assets through a single solution that has integrated data management (not just ingestion), data analytics, and data quality and automation.

About Traci Curran

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

Data Management

What is a Data Fabric and Why do You Need It?

Teresa Wingfield

December 20, 2022

Abstract image of a digital network connected by lines and dots representing what a data fabric is

What is a Data Fabric, Anyway?

The data fabric has become a rising trend in technology over the last several years. Gartner defines a data fabric as:

“a design concept that serves as an integrated layer (fabric) of data and connecting processes. A data fabric utilizes continuous analytics over existing, discoverable, and inferenced metadata assets to support the design, deployment, and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms.”

A data fabric provides a consistent set of data services and capabilities across on-premises and cloud environments. It allows users to abstract data from logically and physically different systems into a common set of data objects, so you can treat them as a unified enterprise data set.

What is a Data Fabric Used For?

Data fabrics are not new; they originated in the mid-2000s when computing began expanding from data centers into the cloud. Over time, data fabrics increased in popularity as businesses embraced hybrid clouds. Lately, data fabrics have garnered significant attention as organizations move processing to multi-cloud environments and the edge. Companies are looking for a framework that can move and manage these new loads and securely integrate them into their systems.

Data fabrics are crucial in supporting digital transformation initiatives, as companies must leverage a variety of systems that may be spread across multiple clouds, on-premises, or remote deployments. Companies can share data across systems more efficiently by using a data fabric, resulting in better business insights and agility.

The data fabric design concept helps solve an age-old data problem – making things that are fundamentally different look and act similar enough to treat them as though they are the same. More significant challenges arise as analytic environments grow and develop, creating more urgency for the best possible solution.

Data fabrics provide a framework for teams to better handle complexities such as:

Diverse data sources and types.
Demand for real-time data for fast-paced decision-making.
Data siloes across business functions.
IT systems spread across operating environments (on-premises, multi-cloud, mobile, etc.).
Growth in operational analytics and business-led data modeling.

Discover More About Data Fabrics

If you’re working in a complex analytics environment and decision makers want real-time data, you should consider working with a data fabric. Actian Data Platform offers users capabilities to implement a modern data fabric and access the data in real-time for informed business decision-making. The platform’s built-in integration can also help you manage the flow of data across your organization. Learn more about how Actian can support your company’s data fabric journey by viewing the variety of data services we provide: https://www.actian.com/product-overview/.

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.

Data Intelligence

The Most Common Data Quality Issues and How to Solve Them

Actian Corporation

December 18, 2022

Quality Management With Qa (assurance), Qc (control) And Improvement. Standardisation And Certification Concept. Compliance To Regulations And Standards. Manager Or Auditor Working On Computer.

To stand out from your competitors, innovate, and offer personalized products and services, collecting data is essential. However, managing data isn’t a walk in the park: small problems can affect their quality every day. Incomplete or inaccurate data, security problems, hidden data, duplicates, inconsistencies, inaccuracies, and so on.

Here is an overview of the most common data quality-related issues and some best practices to use to curb them for good.

The Risks Associated With Poor Data Quality

As it’s been said over and over again, when it comes to data, the real issue is not the quantity of data but its quality. Data Quality Management (DQM) is a demanding discipline that relies on the endless questioning of data processes and constant surveillance of the very nature of the information that constitutes your data assets. Poor data quality can directly translate into lower revenues and higher operational costs, potentially resulting in financial losses for your company.

When data quality is degraded, analyses, projections, forecasts, and even decisions can be distorted. The greater the volume of degraded data, the greater the gap between reality and one’s understanding of reality. Ensuring data quality starts with a good understanding of the errors that can affect it.

The Most Common Data Quality Issues

Ensuring data quality is a key topic for any company that bases its development strategy on data. To carry out targeted actions, you need to prioritize tasks and not spread yourself too thin. Data Quality Management consists of identifying all the erroneous information that could distort your decision-making. This erroneous data can be classified into four categories.

Duplicate Data

When data is duplicated, it means that the same information is present multiple times in the same database or file. Data duplication is hence one of the most harmful issues because it is often difficult to detect. Beyond 5% of duplicated data, it is considered that the quality of the data starts to be degraded. For example, CRM tools often generate duplicate data, because their users sometimes add contacts without checking their presence in the database.

Hidden Data

On a daily basis, your business generates an increasing amount of data. Very often, you only leverage a limited portion of the available information. The rest of the data produced by your business gets scattered and diluted in data silos. It then remains permanently untapped. For example, a customer’s purchase history is not always available to customer service teams. Yet, this information would allow them to better identify the customer’s profile and therefore, provide more relevant answers to their specific requests, or even upsell or cross-sell by making adapted suggestions.

Inconsistent Data

Are John Smith and Jon Smith really two different customers? Inconsistent data significantly affects data quality. It can also be created by another well-known phenomenon: redundancy. This phenomenon occurs when you work with multiple sources (including third-party data) in addition to your own data. Discrepancies in data formats, units, or even spelling must be tracked in a data quality approach.

Inaccurate Data

It may seem obvious, but inaccurate data is probably one of the worst issues that can undermine data quality. When customer data is inaccurate, any personalized experience will not be relevant. For example, if your data inventory is inaccurate, supply difficulties or storage costs can skyrocket. Whether it’s incorrect contact information or missing or empty fields, you need to do everything you can to eradicate inaccurate data.

How to Solve Data Quality Problems

While common sense often presides over good data quality management, they are not enough to ensure it.

To meet these challenges and solve your data quality issues, you’ll need a Data Quality Management tool. But in order to choose the right solution, you will need to start by mapping your data assets in order to identify and evaluate their actual quality. Deploying a Data Quality Management solution, data governance, training, and raising awareness of your teams to good data management…are all essential pillars to limit data quality-related issues.

About Actian Corporation

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

Data Integration

Make Your Marketing Campaigns More Successful This New Year

Teresa Wingfield

December 16, 2022

Man in a suit drawing a red upward-curving arrow as an example of success

The key to a successful marketing campaign is to reach customers at the right place, at the right time, with the right message. Traditional market segmentation used in campaigns aggregates prospective buyers into groups with common needs. Marketers hope that customers in the same segment will respond similarly to a marketing promotion. But will they?

Not according to McKinsey & Company’s research that found that 71% of consumers expect companies to deliver personalized interactions. In addition to this, 76% answered that they get frustrated when this doesn’t happen. McKinsey & Company also reports that companies that excel at personalization generate 40% more revenue from those activities than average players.

Segmentation introduces more challenges than just insufficient personalization. Segmentation, even micro-segmentation, is an increasingly flawed campaign tactic since a customer with constantly changing shopping behavior quickly falls into a different segment than the ones initially identified for a promotion. This situation is made more complex because marketers can’t adjust campaigns quickly enough to keep up with customer and market changes throughout a campaign’s duration.

A more effective campaign optimization approach is to use data integration to build a 360-degree customer profile that enables the creation of individual-level messaging and relevant offers delivered in the best channel to reach the customer at exactly the right time. This data integration combined with real-time data analytics will help you sense and respond to campaign performance changes in real-time.

360-Degree Customer View

If you don’t understand who your customers are, your campaign promotions will not be able to target them effectively and you’ll lose opportunities to your competitors. The impact on your business can be tremendous; 66% of consumers say encountering content that isn’t personalized would stop them from making a purchase. Yet, many marketing departments lack access to the comprehensive data they need to create a 360-degree customer view, relying on limited historical data that has been extracted from their sales and CRM systems.

A 360-degree view requires access to real-time customer engagement data across all touchpoints, including your call center, your website, emails, social media, and more. In addition to this first-party data that you’re collecting directly from your customers, zero-party data collection is becoming more important. Forrester coined the term zero-party data and defines it as data that a customer intentionally and proactively shares with a brand. This includes data such as personal information, contact preferences, and purchase intentions.

Customer data should also come from external data sources (second-party data from partners and third-party data from aggregators). Examples include credit history, demographics and market data to create a broader picture of the customer that helps produce better-performing campaigns.

Real-Time Campaign Performance Analytics

Once you execute your campaign, you will benefit from measuring its performance in real-time. Operational analytics of sales data tied to marketing promotions can identify when you should adjust your campaign. You’ll have to build, test, and deploy campaigns in rapid succession to quickly adapt to constant changes in the market and customer behavior. Quick action will help you drive more revenue and optimize your marketing spend with greater accuracy.

How to Improve Your Campaign Performance

To fully optimize campaigns, you’ll need a cloud data platform with two important characteristics. The platform should include data integration to make it easier to create an accurate, complete, and timely 360-degree customer profile. Since data sources for creating a 360-degree customer view are so diverse, the integration should handle structured, semi-structured and unstructured formats. Also, the platform must provide real-time data analytics so that you can make fast campaign optimization decisions based on how your campaign is performing.

Actian Data Platform provides all these capabilities, making it easy to connect, manage and analyze customer data and campaign performance.

About Teresa Wingfield

Data Management

How to Overcome Data Management Challenges

Teresa Wingfield

December 14, 2022

Group of people working on data analysis to overcome data management challenges

An impactful customer experience (CX) requires accurate and relevant data that’s easy to access, manage, and use. For businesses today, this data is the lifeblood for knowing more about customers to optimize CX. However, many organizations are saddled with data management challenges that reveal roadblocks to this data, resulting in an inaccurate and incomplete picture of the wider data set. This has a cascading impact for business teams to interpret data, which can lead to uninformed and less optimal business decisions.

The data management issue isn’t due to the lack of data. Rather, data silos, shadow IT, and aging legacy infrastructure (among other hurdles) all hold a business back from using data effectively to grow and scale. To make matters worse, traditional data management solutions require specialized IT resources and labor-intensive data preparation, which slows down processing. These data challenges also make data inaccessible to non-technical business users, such as sales, marketing, and customer success teams, who need to be more empowered through democratized, easy access to data.

Delivering exceptional CX requires businesses to remember who’s up front and center for them: their customers. This keeps the focus on finding ways to clear their data hurdles and identify useful and repeatable efforts that drive down as many of the hurdles as possible. We’ll further examine challenges, explore ways to clear them, and share best practices to help ensure organizations have the right data available to paint a 360-degree view of their customers.

Data Management Risks to CX

The amount of data businesses can access is no longer a challenge – the gap lies in the quality of that data, the level of access to it, and gaining a clear understanding of how it fits into broader business goals. A recent study found that nearly eight in 10 data management decision-makers believe cataloging issues (such as knowing where data lives and who the owners are) are among the top challenges in the data ecosystem.

This lack of knowledge and poor management of who owns the data and how to access it leads to two major issues: data silos and shadow IT. Silos around data collection limit data sharing. For example, a sales department may have collected data for a customer’s previous sales history. This data would be valuable to a CX team to resolve a customer issue, but the sales department doesn’t share its data with other departments.

Teams unable to access silos can turn to other systems or applications to help plug in missing information. In the example above, the CX team may use unapproved third-party applications or services to source information to resolve the customer’s issue. Without the knowledge of IT, this scenario can turn into a sprawl of unapproved resources for collecting and using data, further deepening the silo conundrum.

Who is at the receiving end of poorly managed data accessibility? Customers. Consumers are demanding a more personalized and unified CX, with a recent study finding that over 75% of consumers are frustrated when their experiences aren’t personalized. If silos or other issues stall customer teams from accessing critical data, it’s hard to create an experience that’s relevant to a customer’s buying journey.

Tackling this issue head-on means assessing if your systems are set up to simplify and automate data integration and provide access to that data across the enterprise. Many traditional data management solutions are laborious and ineffective from a time and money standpoint. To be truly effective, solutions should unify core technologies and function as a one-stop center for all things data.

Modernizing CX Through Unified Systems

It’s evident that customer demands reflect the backdrop of today’s fast-paced society. Any delay can impact the business, making it less proactive in understanding and addressing customer needs in the most meaningful way. To be successful, companies need access to solutions built with data integration and ease-of-use at their core. The Actian Data Platform makes data easy, enabling businesses to simplify how people connect, manage, and analyze their data.

The Actian Data Platform is purpose-built for the future of data-driven businesses. It reduces complexity and risks associated with digital transformation. The Actian platform enables businesses to streamline data processing, getting data into the hands of those who need it in the most flexible and easy way.

With the Actian platform, data is accessible from on-premises, cloud, or hybrid environments. This gives CX teams unparalleled access to business-critical customer data, allowing them to make decisions quicker and create a more engaging experience for customers. Additionally, the Actian platform makes data-driven projects even easier with the ability to have any – or all – of the platform’s capabilities managed or co-managed.

Transforming your data management strategies to improve CX can be tricky, but it’s imperative for organizations to be fully equipped to deliver the best for their customers. See how the Actian Data Platform can modernize your data management strategy.

About Teresa Wingfield

Data Analytics

The Bridge From Cloud to Edge for Superior Operational Analytics

Teresa Wingfield

December 12, 2022

people in front of a touch panel displaying a visual representation of data management

Data and analytics are the heartbeat of any business looking to drive revenue and innovate – especially during volatile and uncertain times. Businesses have gradually transitioned away from traditional data centers in favor of cloud data storage and analytics solutions to access data and unlock business insights more easily. We expect more enterprises will transition to the cloud in 2023, as Gartner is projecting that enterprise cloud spending will be nearly $600 billion by next year. That number will continue to climb as data generation continues to explode.

However, for many businesses, relying on the cloud for their data and operational analytics needs is challenging. Distributed workforces located in hard-to-reach areas often suffer from latency due to slow Internet connections. Organizations that rely on real-time insights can’t afford to have a lag between the data that’s being generated and the subsequent analysis.

For maximum output, the flexibility and reliability of cloud services need to be met with the power of edge computing. Here we’ll look at the benefits of bridging cloud and edge for analytics, real-life examples of distributed use cases, and best practices for implementing edge technology.

Operational Analytics is Everywhere

Operational analytics refers to the real-time analysis of information on the internal functions and processes of a business. Examples include: how many orders, calls or service logs are being processed, how much revenue is generated at any given moment, or the current status of shipments or inventories. Industries of all kinds deploy operational analytics, ranging from retailers who use insights to target customers while they’re inside a store, and manufacturers who analyze IoT sensor data to identify and resolve potential problems with production-line equipment before they happen.

Data is being produced at an explosive rate and is growing increasingly complicated to process for analysis. This is particularly true for industries within which data collection is in areas with poor Internet connectivity. Offshore oil platforms are an example. These operations require intense analysis of things such as equipment status, GPS location data, current oil prices and more. Any delay in processing and transmitting data from the oil platform to its headquarters could result in poor maintenance of equipment, support teams troubleshooting issues too slowly, or a miscommunication about the cost of oil.

Edge Analytics Use Cases

To process data in a variety of environments, many businesses have shifted operations to the edge. Edge computing allows businesses to generate, collect, store, and process data locally without relying on Internet connectivity. Rather than worrying about being able to connect to a cloud service provider, users of edge computing can enjoy around-the-clock availability of data and systems, allowing for real-time analysis of operations.

Edge computing is becoming increasingly popular, with Gartner estimating that by 2025, more than 50% of enterprise-critical data will be created and processed outside the data center or cloud. This represents a huge opportunity for businesses to capitalize on edge and merge it with their current cloud architecture to ensure continuity with their analytical efforts.

Bridging the cloud to the edge for analytics also offers unprecedented speed in data processing. Without needing a connection, intelligent decisions can be made on the fly based on data that’s instantly generated and available. Additionally, edge analytics can operate on time series, spatial, and other IoT specific data more securely, without worrying about being targeted by external threat actors.

Another use case of analytics on the edge that’s not often discussed is emergency response management in buildings with smart sensors. Devices like fire and smoke sensors can communicate with a dashboard on the edge in the event of an emergency and can automatically trigger a call to a local emergency response team.

The use cases for cloud integration into the edge are numerous, requiring businesses to take careful steps through implementation to reap the benefits.

Bringing in the Edge

Businesses looking to integrate their cloud solutions into the edge for operational analysis must consider many factors. These include having a crystal-clear picture of where data resides, which data points are relevant, when the data expires, and which data needs to be aggregated. Also, constantly moving data to the edge can be costly and there is the potential for network bandwidth, storage and latency issues.

Building the bridge from cloud to edge for operational analytics can take a lot of work, but will up-level analysis of operations, increase security and provide better real-time insights. Take a deeper look into how Actian is modernizing edge application data processing to make data analytics a breeze here.

About Teresa Wingfield

Data Analytics

Vector 6.3 Delivers Easier Administration for Data Analytics

Teresa Wingfield

December 9, 2022

Dark blue illustration with orange bars symbolizing data analysis, one of the benefits of Vector 6.3

Did you know that Vector is one of the world’s fastest analytics databases? We’re excited to announce that a recent Enterprise Strategy Group evaluation found that Vector can outperform its competitors by up to 7.9x.

Adding to this momentum is the release of Vector 6.3 in early December. Key highlights of the release include making administration easier, enhancing engine automation, and improving programmer productivity. We’re sharing six new data analytics features and benefits that will improve your data analytics journey.

Three main groups of benefits of Vector 6.3

Automatic Diagnostic Log Rotation Ensures Reliable Archives

Since a log file for the Vector X100 analytics engine tends to grow very large over time, log rotation is useful to archive a current log file and open a new one. Previously a manual process, Vector 6.3 introduces automatic diagnostic log rotation based on either the log file’s maximum size or a custom time interval (e.g., every 30 days).

Query Result Caching – Spill to Disk Eliminates Waiting for Available Memory

Vector 6.3 further extends the query result cache with an option to spill cached results to disk when cache memory runs low. A job/workload does not have to wait for memory to free up before completion.

Vector disables spill to disk by default so that its overhead and workspace utilization doesn’t impact your setup after an upgrade. Once enabled, the management database can monitor spill to disk activity.

Smart Min-Max Indexing Improves Memory Management

A Vector database table can have up to a thousand columns. By default, Vector creates Min-Max indexes on all columns that require large amounts of memory when many tables or tables with lots of columns are created. Min-max reduces the number of columns inspected through new auto-tune functionality that determines scores for index and non-index columns. Based on these scores, Vector decides which columns it should add to the min-max index and which ones it should drop. As a result, this reduces memory consumption without negatively impacting query performance.

Shareable DBA User Defined Functions (UDFs) Increase Developer Productivity

Vector supports the creation of UDFs to use in queries to extend database functionality. With release 6.3, users can now share and reuse UDFs created by DBAs through user groups and authentication, enhancing collaboration and self-service.

Exception Handling for Database Procedures Provides Greater Control

Improved exception handling for database procedures is now available for managing unwanted and unexpected events due to run-time errors caused by faulty design, hardware failure and code issues.

Pattern Matching Makes String Manipulation Easier

Users can now run pattern matching queries with SIMILAR TO on Vector. Vector can determine character string similarity based on character repetition, limiting character sets, character properties such as letter, hex, case, punctuation mark, and grouped characters.

Navigate Your Analytics Journey Better With Vector

Visit our website to learn more about Vector’s extensive performance optimization, features, and use cases for data analytics.

About Teresa Wingfield

Data Intelligence

The Benefits of Data in the Insurance Industry

Actian Corporation

December 7, 2022

Insurers today have access to massive amounts of data, including past and real-time data, enabling them to make data-driven decisions. By deepening the analysis of this data, insurers get the information they need to adapt their products and services, improve processes, make quick and more strategic business choices, and better fight fraud. Here is a look at the main benefits of data for insurers.

The insurance industry is based on the identification, measurement, evaluation, anticipation, and prediction of risk. The objective? To control this risk. To accomplish these complex missions, insurance carriers have always used data. However, through the digital transformation of this business sector, the volume of insurance data has exploded. For example, according to a study published in early 2022, it appears that nearly 80% of insurers now use predictive models and algorithms to identify fraud. That figure was as low as 56% in 2018. Another report, this time by Friss on insurance fraud, points out that when it comes to detecting fraud, 100% of insurers surveyed have mechanisms in place to identify potentially fraudulent claims.

From customer knowledge to the adaptation of insurance products, and the fight against fraud, the use of insurance data has many advantages:

Benefit 1: Better Customer Retention

The insurance industry is particularly competitive, as it is becoming easier and easier for customers to cancel their insurance contracts. Customers are therefore more volatile and require a much more ambitious customer retention strategy. With insurance data, insurance companies can better adapt their products & offerings to the profile of the insured.

Benefit 2: More Accurate Risk Assessment

To cope with the hyper-competition that now characterizes the insurance industry, one must be able to offer contracts and pricing packages that are calculated as accurately as possible. The massive use of data and algorithms allows insurers to assess risks more accurately in order to offer coverage that is personalized to the reality of policyholders’ risk exposure. As a result, pay-as-you-go and on-demand insurance programs are booming.

Benefit 3: Fraud Reduction

The bane of an insurance carrier’s profitability is fraud. Not only do false claims represent a colossal loss of revenue for insurance companies, but they also burden internal processes, inducing the use of adjusters, investigators, and even particularly costly litigation procedures. By using the right data, in the right place, and in real-time, insurers can detect potential fraud as quickly as possible throughout the life cycle of the contract, from the signature to the claim management.

Benefit 4: More Relevant Product/Service Innovation

Understanding and anticipating policyholders’ needs in order to offer tailored policies is a major challenge for insurance companies. To do so, they rely essentially on how the claims are identified through data. Defining insurance products, estimating risk, and controlling costs are all essential lessons learned directly from insurance data.

Benefit 5: Process Automation

One of the levers of competitiveness for insurance companies, and Insurtechs in particular, is process automation. From the underwriting of contracts to their day-to-day management, data makes it possible to automate a large number of operations. This automation helps reduce the time it takes to process customer requests (in order to maximize customer satisfaction), to control operational costs, to refocus teams on higher value-added tasks. The latter being essential to combat the difficulties of retaining talent in a tight job market.

Benefit 6: Personalized Customer Paths

No one ever solicits their insurance company for fun! Broken windshield, road accidents, water damage. Every interaction with an insurer is a moment of stress and worry for the consumer. With data, the insurer is able to support each of these interactions with a 100% personalized approach and to ensure an optimized follow-up of the claim.

About Actian Corporation

Data Management

Uplevel Your Cloud Migration Strategy – No Matter Your Organization Size

Teresa Wingfield

December 7, 2022

overlaid graphs of statistics and data, representing a cloud migration strategy

Cloud migration and digital transformation are among the top ways for businesses to modernize and keep pace with competitors. Moving these endeavors forward is more about the underlying parts involved than the infrastructure. Enabling data-driven operations and intelligence can help ensure success.

When businesses begin their transformation and modernization journey, it may be inevitable to run into roadblocks, such as having limited budget and IT resources or a business culture that’s slow or resistant to technological change. Unfortunately, the pace of digital transformation has grown significantly greater. As a result, businesses need to find ways to clear these hurdles or risk losing customers.

No matter the size of an organization’s IT architecture, cloud data migration can provide many advantages. Moving data to the cloud is becoming more useful as increasingly complex datasets require intense analysis to derive business value. Let’s take a look at the benefits of up-leveling your cloud migration strategy.

Cloud Migration Hurdles

Cloud migration involves complicated processes with lots of moving parts. Businesses must consider the type of cloud vendor they select, storage capabilities, how much (and which type of) data they want to move, and how to access it for analysis. Low budgets and limited IT resources often limit brands in accessing the full benefits of the cloud.

To combat these issues, cloud migration is imperative for businesses that need mission-critical technology to meet their goals and key performance indicators (KPIs). To help gain buy-in for a cloud migration journey, IT leaders must demonstrate the value of the cloud and its ability to easily provide data that businesses can leverage for insights that drive growth.

When searching for a solution, organizations need to look for a vendor that allows them to transform their business processes with a flexible and fully managed approach. The benefits of an effective cloud migration strategy are numerous, and when done properly, can enable any business to start small with its digital transformation, scale quickly, and make a meaningful impact on the organization.

The Benefits of Modernizing Cloud Migration

Businesses need fast and simple access to their data, which requires integration with cloud services that provide flexibility and ease of use. Solutions such as Actian’s fully managed Actian Data Platform can run on all major cloud providers, including Google Cloud, Amazon Web Services (AWS), and Microsoft Azure.

Cloud agility enables businesses to quickly access data needed for analysis, whether they are examining customer purchasing behavior or looking to improve internal processes. Effective cloud data platforms allow for easy creation of data pipelines to move and transform data from source systems to a centralized repository and include a wide range of services for data management and analytics.

Data unification gives teams a clear, holistic view of data so they can understand how the business is doing in real-time. The Actian Data Platform enables users to pull data from anywhere and create visualizations and dashboards. These tools offer a single pane view into complicated trends and insights on how to better meet the changing needs of customers and streamline complex processes. Taking the next step in a cloud migration or digital transformation journey can be daunting. Without easy access to data in the cloud, businesses may find it difficult to scale, innovate, compete to meet the dynamic needs of customers and increase revenue. When business leaders keep growth and innovation on top-of-mind, they’re more likely to be guided by a clear roadmap and supported by a strong cloud migration strategy.

To see how Actian can help transform your brand’s cloud migration journey, no matter the size of your organization, read more about the Actian Data Platform.

About Teresa Wingfield

Data Intelligence

BARC Data Culture Survey 23 – Data-Driven Trends

Actian Corporation

December 7, 2022

data-driven-decision-BARC-data-culture-survey-23

In last year’s BARC Data Culture Survey 22, “data access” was selected as the most relevant aspect of BARC’s ‘Data Culture Framework’. Therefore, this year, BARC examined the current status, experiences, and plans of companies concerning their efforts to create a positive data culture with a special emphasis on ‘data access’.

The study was based on the findings of a worldwide online survey conducted in July and August 2022. The survey was promoted within the BARC panel, as well as via websites and newsletter distribution lists. A total of 384 people took part, representing a variety of different roles, industries, and company sizes.

In this article, discover the current status of data-driven decision-making of BARC’s Data Culture Survey 23.

Data-Driven Decision-Making vs. Gut Feeling

74% of “best-in-class”* companies rely on data-driven decision-making.

Over the years, companies have relied on data & analytics for decision-making rather than purely on experience or gut feeling. However, while the share of companies making decisions solely based on experience is decreasing, it isn’t completely off the radar. In fact, according to the BARC Data Culture Survey 23, half of the companies surveyed said their decision-making process was based on a mixture of data and gut feeling. In particular, there was a massive shift towards data-driven decision-making in 2021, probably driven by external factors such as the COVID-19 crisis.

The value of data for decision-making thus remains clear to most organizations, especially in the current economic and political environment. The challenge was more related to being able to bring value to data at a reasonable cost.

It is noteworthy that 74% of “best-in-class”* companies completely rely on data to make decisions. If we look closely at the numbers below, this reveals a significant difference compared to the average of all participating companies, of which only 32% make decisions purely based on data.

The Most Data-Driven Departments of a Company

When asked about the departments they considered to be the most data-driven, 59% of companies responded that it was their finance/accounting department, followed by their sales & distribution department at 44%. These answers were expected, as these areas have the highest BI and analytics tools usage. BARC also observed that the Logistics/Supply Chain department as well as the Production department were a lot higher than expected. This increase is the result of the rise of IoT technologies in recent years.

Data-Driven Decision Support Should be at All Levels of the Company

Data knowledge is key to the successful use of data & analytics – 83% of companies confirm that they see data/information as an asset, but only half of the companies surveyed use data as a major source of revenue. Indeed, 74% of users identify data knowledge as the collecting, linking, and analyzing of metadata.

Metadata provides contextual information necessary to help data users find, understand, and trust their data. However, the study shows that few companies currently invest in technologies that help leverage metadata – whereas 95% of the “best-in-class”* companies acknowledge the importance of investing in such technologies.

The use of data at various levels of decision-making is noteworthy: At both operational and tactical levels in business units, 39% of survey respondents claim that decisions are not made on the basis of data. This is quite a high figure – data-driven decision support should be in place throughout the company at all levels.

* The sample was divided into ‘best-in-class’ and ‘laggards’ in order to identify differences in terms of the current data culture within organizations. This division was made based on the question “How would you rate your company’s data culture compared to your main competitors?”. Companies that have a much better data culture than their competitors are referred to as ‘best-in-class’, while those who have a slightly or much worse data culture than their competitors are classed as ‘laggards’.

About Actian Corporation

Data Platform

Moving Your Data Warehouse to a Modern Cloud Data Platform

Teresa Wingfield

December 5, 2022

Corridor with lockers to the left and right representing a data warehouse

Do you still have an on-premises data warehouse? If so, are you evaluating whether you should move it to a cloud data platform? There are two main things to consider when making this decision: the advantages of the cloud versus on-premises deployment and the advantages of a modern cloud data platform versus an on-premises data warehouse.

Cloud Modernization

Regarding cloud deployments, I enthusiastically agree with David Linthicum’s statement that the industry has oversold and overstated cloud cost savings. David is a noted cloud expert and is the Chief Cloud Strategy Officer at Deloitte Consulting. I recommend that you read his InfoWorld Article, Was moving to cloud a mistake, to get some great advice on how to improve cloud return on investment (ROI).

I have also written a blog post called Will Cloud Data Warehouses Really Help You Cut Costs? In this post, I discuss why asking how much money you will save by moving your on-premises solution to the cloud may be the wrong question. Instead, you should be looking at cloud deployment benefits such as greater agility, accelerating innovation, ensuring availability, expanding reach, and more.

Data Platform Modernization

The Actian Data Platform makes it easy for businesses to connect, manage, and analyze their data in the cloud and on-premises if this is still needed for certain use cases. The table below highlights some of the main advantages that the Actian Data Platform offers versus what’s typically available in a traditional on-premises data warehouse.

	*Actian Data Platform*	*Typical On-Premises Data Warehouse*
Solution Breadth	One solution for data integration, data management and data analytics lowers risk, cost, and complexity while allowing easier sharing and reuse across projects.	Cobbled together solutions from different vendors are complex, time consuming, and costly and make sharing and reuse difficult.
*Scalability*	Cloud elasticity provides the ability to quickly shrink or grow CPU capacity, memory, and storage resources. Composable infrastructure and analytics make data readily shareable across use cases. Management, governance, and compliance controls are consistent.	Overprovisioning is required to accommodate peak demand. Rigid systems make it hard to share data. Controls across data are weak and inconsistent.
Timeliness	Real-Time data empowers “next best action” decisions in the moment.	Historical data with weeks to respond to requests for the right data.
Data Security + Data Privacy	Data security and data privacy using fine-grained techniques such as column-level de-identification and dynamic data masking. These prevent inappropriate access to personally identifiable information, sensitive personal information, and sensitive data.	Data security focus with limited support for data privacy.
*Data in Context*	Real-time analytics is embedded within the environment users operate in for instant accessibility.	Users need to leave their workflow to look at data analytics in a separate set of tools which slows decision-making.
*Data Democratization*	Technical and business users with no IT involvement. Broad use by front-line workers across finance, sales and marketing, supply chain, customer service, customer success, healthcare, spend management, fraud, and risk management.	Technically savvy data engineers, data scientists and analysts representing a small percentage of an organization’s employees.

Accelerate Modernization With the Actian Data Platform

The Actian Data Platform is trusted, flexible, and easy to use. These six features illustrate what ambitious businesses like yours can do to help modernize your on-premises data warehouse:

Quick Ingestion: A single user interface for self-service integration, analytics, and management. This enables anyone to be a data practitioner and helps build a data-driven culture.

Superior Price Performance: Built to maximize resource utilization delivering unmatched performance and an unbeatable total cost of ownership.

REAL Real-Time: Patented technology allows real-time data updates without impacting query performance and costs. This enables data consumers to analyze always up-to-date data.

Single Platform: One solution for integration, management and analytics lowers risk, cost, and complexity while making sharing and reuse across projects easy.

Flexible Anywhere Deployment: Any cloud, hybrid, and on-premises – plus it is API-driven to embed analytics within applications and systems so that relevant data is delivered in context.

Role-Based Security Policies: Reduce the time and effort to comply with data and privacy regulations without compromising the usefulness of data to intended consumers.

About Teresa Wingfield

Data Management

Data Silos Suck. Here’s How to Break Them Down.

Teresa Wingfield

December 1, 2022

Business person writing on a virtual whiteboard with data silos

Data management issues are common among businesses today. These issues have had a direct negative impact on marketing departments, with over 60% of CMOs agreeing that gaps in marketing data and analytics (D&A) and marketing technology are hindering strategic growth strategies. Data and systems stored in silos represent a huge problem for data managers, who are unable to access critical data that they need to deliver a great customer experience (CX). At a time when customers are more likely than ever to try a new brand after a bad buying experience, ensuring that data is freely accessible to those who need it is mission-critical for businesses to drive revenue.

Let’s take a look at the problems faced with siloed data, how to break these silos down, and explore the benefits of a fully connected data enterprise.

The Problems in the Silo

To understand the problems with data silo challenges, it helps to understand what a data silo is. A data silo is any independent repository of data within an organization. A key characteristic of a data silo is that only a small group of people can access it. This can occur when independent business units, with their own objectives and IT budgets, maintain different versions of data. For example, sales may keep its data on customer buying trends separate from the customer service team’s customer data which includes messages, feedback, and returns.

This fragmentation leads to duplicate data, data integration issues, and, more importantly, has negative impacts on the broader organization. Data silos slow down productivity, hinder business agility, and frequently paint incorrect pictures of customers, leading to a poor CX. Also, IT teams often hoard data and create silos to receive a greater share of the IT budget.

Businesses need a plan to assess where data silos exist and how to knock them down. This takes thorough analysis, cultural change, and an infrastructure for data management that can support enterprise needs.

Break Down Data Silos

The first step for any business to break down silos is to take a holistic look at its culture. Without interdepartmental collaboration and cooperation across the business, there won’t be the opportunity to share data for analysis. Only in a unified setting can a data silo truly be broken down.

Businesses must establish a company-wide data model that all relevant stakeholders in the organization can easily access and use. A standard model helps inform businesses on next steps and analyze customers, whereas a lack of standardization (including data-usage policies, processes, and controls) can lead to a patchwork of datasets used in disparate ways.

Once a company establishes standards and builds a collaborative culture, businesses should implement a data platform solution that can scale and grow with the organization.

Benefits of Effective Data Platform Solutions

When data is trapped in spreadsheets, databases, data warehouses and applications, it can be nearly impossible for IT to prepare for future growth and innovation, making it even harder to spin up new projects. An effective data management platform must be able to break down silos and unlock the full potential of data for analytics, reporting, automation, and developing mission-critical insights.

The Actian Data Platform allows businesses to unify disparate data for business decision-making to drive efficiency and growth. The Actian platform includes data integration to extract data from multiple data sources. From there, the Actian platform combines that data to give users a single pane view for insights and analysis.

Break free of siloed, stale, and incomplete data, routine reporting and harness real-time data and analytics to fuel faster innovation, scale your processes, and shift direction on a dime. Silos suck, but together with Actian, you can unify siloed data and get more value from your data.

Advantages of Implementing a Cloud Data Platform

The Crucial Role of the Cloud Data Platform and Data Warehouse

Still, it’s Important to Have a Data Warehouse. Here are a Few Reasons:

Consistency is Key

A Data Warehouse Maintains Data You Can Trust

A Data Warehouse Enables Self-Service

Data Warehouses Improve Data Quality

Common Use Cases for a Cloud Data Platform

Improving Marketing Performance

Embracing Legacy

Improving Operational Performance

Conclusion

About Traci Curran

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What is a Data Fabric and Why do You Need It?

What is a Data Fabric, Anyway?

What is a Data Fabric Used For?

Discover More About Data Fabrics

About Teresa Wingfield

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The Most Common Data Quality Issues and How to Solve Them

The Risks Associated With Poor Data Quality

The Most Common Data Quality Issues

Duplicate Data

Hidden Data

Inconsistent Data

Inaccurate Data

How to Solve Data Quality Problems

About Actian Corporation

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Make Your Marketing Campaigns More Successful This New Year

360-Degree Customer View

Real-Time Campaign Performance Analytics

How to Improve Your Campaign Performance

About Teresa Wingfield

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How to Overcome Data Management Challenges

Data Management Risks to CX

Modernizing CX Through Unified Systems

About Teresa Wingfield

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The Bridge From Cloud to Edge for Superior Operational Analytics

Operational Analytics is Everywhere

Edge Analytics Use Cases

Bringing in the Edge

About Teresa Wingfield

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Vector 6.3 Delivers Easier Administration for Data Analytics

Automatic Diagnostic Log Rotation Ensures Reliable Archives

Query Result Caching – Spill to Disk Eliminates Waiting for Available Memory

Smart Min-Max Indexing Improves Memory Management

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Navigate Your Analytics Journey Better With Vector

About Teresa Wingfield

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