Data Intelligence

Data Storytelling: How to Have Effective Data Communication

Actian Corporation

October 5, 2021

It’s been said time and time again that data can bring so much value to a company. However, for your data to give strategic insights into your customers, processes, and objectives, it must be understood by everyone! A good way to start is through Data Storytelling.

Let’s take a look at how to effectively communicate and represent complex concepts.

Data is everywhere. In marketing departments, in customer service, on production sites, on your website… Once you’ve managed to collect, centralize, deduplicate, and prioritize all of these information flows, you will need to draw operational lessons from them.

But data is, by nature, dry, complex, and often austere. Because your employees are not all data scientists, it is essential to make data “talk”.

How? By resorting to Data Storytelling. The principle is simple: to make data accessible to everyone, whatever their profile, training, or function, it’s better to tell a nice story than to hammer complex principles. Data Storytelling was born from the convergence of Storytelling and Data Visualization, also called Dataviz.

From Storytelling to Data Storytelling

From the time of Socrates to the present day, there is one constant: human beings love stories. Stories appeal to the imagination, allow the appropriation of the most elaborate concepts, and call upon mechanics that transport and transcend the mind. Politicians regularly use storytelling to reinforce the impact of their speeches. The media, too, regularly use this lever to popularize the most complex events or ideas.

The fact that data appears in many contexts and in many forms can be confusing for non-specialists.

However, data, when properly staged, lends itself perfectly to the creation of a narrative that will then be more easily accessible.

Although Dataviz has long been used for the purpose of popularizing the lessons and trends derived from the data, these graphic representations could still remain obscure for many employees. Going from numbers to images was no longer enough! From then on, the use of Data Storytelling became obvious in the business world.

Data Storytelling: Benefits all Around

The first benefit of well-designed Data Storytelling is that the message you want to convey to your teams, your prospects or your customers will be easily understandable.

Forget the technical jargon, the IT experts’ vocabulary, when faced with a story, we are all equal. But this is not the only advantage of Data Storytelling.

Beyond its accessibility, it is also a way to synthesize information. The more condensed the speech is, the more impactful and effective it is. But there is a third benefit. A good story is easily memorized and stays in the mind longer. Why is that? Simply because Data Storytelling, like any good story, places the message in an emotional dimension. The story makes the listener react, it appeals to him and strikes the mind, either because it is funny, or because it calls upon personal memories, or because it allows one to project oneself into the future.

This is the challenge of Data Storytelling: to move from raw data to a story that arouses emotion, reaction, empathy and, ultimately, involvement.

The Keys to Effective Data Storytelling

To create impactful data storytelling, it must be adapted to the target audience. We don’t talk the same way to a marketing team, a sales force or the customer service department. The Data Storytelling mechanism is based on three pillars. The first is your data, of course. Depending on the type of information you have, you will need to define the other two pillars.

First, look at the form of the narrative. The choice of lexical field, the nature of the story, the tone chosen (humorous, dramatic, satirical), all elements that must be defined according to the audience you are targeting. Finally, the third key element of your project is the visual representation.

Move away from mere aesthetic considerations, your challenge is to effectively convey the message and information. On the basis of these three essential elements, you can build your Data Storytelling and thus enhance your data capital even more.

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 Architecture

An Easier Modernization Journey for Ingres and Database Applications

Teresa Wingfield

October 1, 2021

Actian’s Ingres NeXt Initiative Paves the Way for Transformation

Are you considering modernizing either your Ingres database or your “green screen” ABF or thick-client OpenROAD applications? If so, you may have read about modernization options like those from Gartner listed below. Gartner has ranked these from easiest (Encapsulate) to most difficult (Replace). The easier the approach, the lower the risk, but also the lower the business benefit both technically and operationally. The more difficult the approach, the greater the risk but the greater the reward.

Source: Gartner, 7 Options to Modernize Legacy Systems

Organizations need to modernize infrastructure to respond more quickly to market changes, keep up with the competition, and support digital transformation. If you’ve been wishing that you could move Ingres to the cloud and/or extend your database applications with a modern user interface and/or web and mobile access, that’s normal. At the same time, it’s also understandable if the effort and risks involved in modernization seem terrifying. According to a study by Hitachi Vantara, more than 50% of modernization projects fail.

But what if I told you that Actian’s Ingres NeXt Initiative can provide a safe and easy way to execute a high-value modernization project involving Ingres and your database applications? Let’s have a closer look at how we do this.

Modernizing Your Ingres Database

The Ingres NeXt Initiative provides four flexible options to meet your needs.

On Premises (Linux, Windows, UNIX, VMS, virtualized or bare metal).
Bring Your Own License (Your Infrastructure, Your Software, Self Managed).
Platform as a Service (Actian Infrastructure, Your Software, Actian Managed).
Database as a Service (Actian Infrastructure, Actian Software, Actian Managed).

On-Premises

Striking a balance between preservation and innovation with Ingres has led to continuation of our heritage UNIX and VMS platforms as well support for Linux and Windows. These are available on-premises as virtualized or bare-metal options.

Bring Your Own License

Bring-Your-Own License (BYOL) allows you to simply host and manage your Ingres database in the cloud. Container-based deployment delivers a more portable and resource-efficient way to virtualize the compute infrastructure. Because containers virtualize the operating system rather than the underlying hardware, applications require fewer virtual machines and operating systems to run them. Plus, containers are more lightweight than traditional virtualization schemes and make deployments more predictable, dependable, and repeatable.

Platform as a Service

Customers can use their existing licenses in the Platform as a Service (PaaS) option that completely rearchitects Ingres as a cloud-native solution hosted and managed by Actian. This is a big deal since a cloud-native approach delivers the full advantages of the cloud computing delivery model such as cloud scalability, cloud economics, portability, resource efficiency, improved resiliency and availability, and stronger security in the cloud.

Database as a Service

Database as a Service (DBaaS) provides the very same benefits as PaaS for new licensed purchased through Actian.

Safe and Easy Database Modernization

The best thing about BYOL, PaaS and DBaaS is that you a choice of Google Cloud, Microsoft Azure, or Amazon AWS without the risk and effort that’s typically associated with moving to the cloud. Actian has already done the heavy lifting. We ensure uniform operation at the SQL and business logic level across on-premises deployments on UNIX, VMS, Linux and Windows platforms and the cloud. We’ve fully tested the Ingres BYOL option and provide cloud license portability, ensuring a smooth cloud deployment. Visit the Actian Academy to learn more.

Modernizing Database Applications

As for modernizing your database applications? That’s where OpenROAD comes in. OpenROAD is a database-centric, object-oriented, 4GL rapid application development (RAD) tool that lets you develop and deploy mission-critical, n-tier business applications on Windows, Linux and Unix connecting to databases such as Ingres, Microsoft SQL Server, Oracle, Actian Zen, and more via ODBC.

The Ingres NeXt Initiative provides four options to modernize your application infrastructure using OpenROAD:

ABF and Forms-Based Apps

OpenROAD migration tools allow you to modernize “green screen” Ingres Applications-By-Forms (ABF) and form-based applications by converting them into OpenROAD frames as shown in Figure 3. Modernized applications support cloud and on-premises databases.

OpenROAD Fat Client

OpenROAD thick-client applications can be transformed to browser-based equivalents without the cost, resource, effort, or risk associated with rewriting or replacing code. Developers can then extend these applications for web and mobile deployment, using HTML5 and JavaScript. Further, OpenROAD supports incremental application migration where modernized applications can run alongside unconverted applications.

OpenROAD Server

OpenROAD supports encapsulating and deploying business logic within the OpenROAD Server. Reuse of existing business logic avoids rewriting decades of business logic from scratch. A client, such as an HTML page with JavaScript, can connect to the OpenROAD Server via JSON-RPC with no additional libraries or plugins/add-ons.

OpenROAD as a Service

OpenROAD as a Service delivers an Actian hosted and managed OpenROAD Sever. Business logic is exposed as a web service that is available to web-deployed applications.

Take the Next Step

Actian’s Ingres NeXt Initiative is designed to help you modernize and make the most of your existing and future investments in Ingres and OpenROAD. You can choose to modernize Ingres or your database applications. Or you can choose both with efforts that can occur concurrently or sequentially, depending on your needs. Register here to learn more about our Early Access Program.

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 Management

DataOps: How to Turn Data into Actionable Insights

Traci Curran

September 30, 2021

Enterprises‌ ‌have‌ ‌struggled‌ ‌to‌ ‌collaborate‌ ‌well ‌around‌ ‌their‌ ‌data, which can impact everything from digital transformation to advanced concepts like AI and ML.‌ ‌DataOps is not without challenges; building, managing, and scaling data pipelines requires careful thought around reusability, portability across infrastructure and applications, and long-term maintenance and governance. And these are just a few of the issues facing enterprises. For this reason, DataOps technology stacks need to focus on providing key capabilities including data extraction, integration, transformation, and analysis.

DataOps – Why is it Important?

The enterprise is undergoing a seismic shift from a siloed application development and data repositories, to a more composable and reusable architecture. Additionally, there is a growing demand for speed and agility, as well as the influx of disruptive technologies, such as the cloud, IoT, and AI. Organizations that desire to win big must act and adapt quickly to deploy and scale new software and solutions that provide customers with a superior experience and satisfy their rapidly evolving needs. To do that, they also must be able to rapidly aggregate, integrate and analyze data sources.

Even with this need for speed, the tools and processes for the creation and processing of data are not standardized‌ in‌ a way that promotes rapid innovation‌ and ultimately help organizations transform. DataOps is critical to address the challenges involved in acquiring, storing and governing data. In addition, companies are dealing with increasing complexity in their IT environments. In a recent survey, more than 80% of enterprises have a hybrid cloud or multi-cloud strategy. In order to have cost-effective and secure management of increasingly large amounts of data, enterprises must adopt DataOps.

DataOps for Managing Data Growth

DataOps has been around for about a decade but has recently gained momentum because of the overwhelming challenge companies face today in dealing with a large, complex set of data that is being generated at increasingly faster rates. With new technologies like Internet of Things (IoT), cloud computing and the power of Big Data now integrated into everyday use, companies are generating at least 50 times more data than they were just five years ago. And with more data comes a need for greater efficiency, and data experts are in high demand.

When we first heard about DataOps and thought about its potential impact on businesses, we were excited but thought of it as a radical new methodology. What we didn’t realize then was that many companies had been using similar practices to deal with some aspects of data management, particularly around data warehousing and analytics. And just like DevOps, DataOps isn’t a product, but rather a cultural shift supported by many products – many existing products. So it’s important to for businesses looking to adopt DataOps to consider what they already have, such as enterprise data warehouses and ETL tools, and what they may need to acquire, replace or modernize. In the end, companies will end up with several systems to support the data pipeline.

DataOps and Big Data Challenges

Most enterprises have invested in significant data infrastructure in order to extract, load, and store their data to take advantage of Big Data analytics and technologies. However, often these infrastructures are layered, hard to manage, and full of legacy tools which hinder the transfer and integration of data. Additionally, organizations have invested significant resources in tools such as data warehousing, data warehouses, data marts, and data marts. These data warehouses have been utilized to model data but have been typically implemented with a predefined data model. A Business Intelligence (BI) system, an enterprise data warehouse, or any other set of tools might assist in the execution of a data transformation job.

Conclusion

The technological revolution is creating unprecedented opportunities to make things happen with data. By analyzing the data that powers modern business, we are able to provide value in every dimension of our organizations. As is the case with every transformation, the benefits of building an integrated, self-service analytics environment will be realized by “users” rather than IT. Leveraging the power of data science, business leaders can achieve operational excellence and compete effectively against the growing waves of competitors. For the data science and analytics staff to be successful, they must be exposed to all of the necessary technology and processes to support effective use of data.

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 Architecture

Cloud OLAP in Data Warehouses

Teresa Wingfield

September 27, 2021

Decisions within an organization are made in three possible ways. One way is based on the experience of the individual making the decision. The second way is based on analytics. The third way is based on a combination of one and two, knowledgeable experience and analytics. Based on experience or expertise in a subject, we can often make very well-informed decisions and obtain desired outcomes. To enhance our expertise, we can use metrics based on factual data. The data may reveal information that was absent from our expert opinion. Of course, using both expert opinion and analytics is the best approach for solving problems or thinking strategically for the business. The use of data warehouses is one way to gather analytics to improve decision-making within an organization.

Use of Cloud OLAP in Data Warehouses

Online Analytical Processing in Data Warehouses allows rapid calculation of analytical business information using metrics for modeling, planning, or forecasting. OLAP is the foundation of analytics that supports many business applications for reporting, simulation models, information-to-knowledge transitions, and trend and performance management.

Data contained in a data warehouse is often used for OLAP. OLAP solutions enhance a data warehouse with aggregate data and business calculations.

OLAP vs. OLTP

Online transaction processing (OLTP) is designed to handle transactions by getting data organized and written to a database as quickly as possible. OLAP, on the other hand, focuses on reading data as quickly as possible to service business analytics. OLTP data is sent to the OLAP data warehouse for computations so as not to affect the real-time online users of the OLTP database that often number in the thousands.

OLAP works with large amounts of data stored in a data warehouse. This data is not real-time but is synced to be as relevant as possible to the decision it will support. Techniques such as data mining and big data analytics are used to gather intelligence from all the data stored in the data warehouse. Processing as such for OLAP data is very performance intensive. An online user would experience a degradation in the application’s response time if accessing real-time data. When to use OLAP – when you need help with decisions to analyze the business. Data warehouses are typically used by 100s of people at the same time.

What is OLAP Cube in Data Warehouses

An OLAP cube is a data structure in the data warehouse that is optimized for improving the performance of data analysis. An OLAP cube is sometimes referred to as a hypercube. OLAP cubes contain multidimensional data and information from different unrelated sources for logical and orderly analysis. The cube could incorporate different data types from multiple data sources that have been transformed. Subsequent analytical operations are performed on the data to create relationships with the other acquired data, including “slicing and dicing” the data to fit specific criteria to enable additional perspectives for decision support.

One of the challenges with OLAP is that it requires the use of complex schemas to implement and administer the technology. Managing and administering the cube is very time consuming, but it provides excellent value to the organization when done.

Use Cases of OLAP in a Data Warehouse

How to use OLAP becomes a capability based on the creativity and expertise of the user. With all the data and information available in the data warehouse, including manipulating and viewing the data from many different perspectives, OLAP can become a critical capability needed by the business. OLAP in a data warehouse can help with:

Planning.
Budgeting.
Reporting.
Various analysis.
Asking “what if” questions.
Business modeling.
Creating data relationships that did not exist.

OLAP is used to support the use of data in any way experts see fit for the decision that needs to be made for the organization. Many business applications can take advantage of OLAP capabilities, including different roles in the organization, viewing data and information from unique perspectives to enable dynamic decision-making.

Actian can help OLAP users looking to simplify the BI life cycle. The Actian Vector analytics database provides a viable alternative to OLAP Cubes with its ground-breaking technology, superior performance and in-database analytic capabilities.

About Teresa Wingfield

Data Analytics

Customer Analytics: How to Measure a Customer’s Journey

Traci Curran

September 25, 2021

shopping cart showing how to increase revenue

A true 360-degree view of a customer can’t be sourced exclusively from internal data let alone from a single system or department. Organizations trying to correlate customer interactions, transaction history, and behaviors to improve customer acquisition ROI and retention struggle to aggregate and analyze data and signals across operational, social engagement, customer experience, and outreach systems to provide real-time customer insights.

Why Customer Analytics?

Customer analytics helps enterprise-wide stakeholders bridge the gap between customers and sales and marketing, whether the goals are to increase revenue, build customer loyalty, and create a memorable brand experience. While it’s possible to define the customer journey from a single source, many organizations have a fragmented view of the customer journey that spans disparate systems and organizational silos. Without a unified view, you won’t be able to determine how customers are interacting with your brand to improve customer retention and better market to them. Data can’t speak if you aren’t listening. To understand a customer’s journey, you need to assess the different touch points customers experience while engaging with a brand.

Customer Analytics Architecture

Customer analytics are an important component of customer experience management that measures both transactional and emotional customer journeys. Think of a 360-degree view of a customer in this context and you get the idea: any way you interact with a customer can be tracked and measured. The analytics architecture is the underpinning for how to help siloed systems communicate and reconcile their various information and knowledge sets so the business can act on the information.

The tools and process involved with creating customer analytics must have the ability to integrate and orchestrate disparate data systems and applications. This needs to include legacy and third-party applications to get the best view of customer behaviors and actions and, ultimately, drive change throughout the entire company.

By going beyond manual reports and spreadsheets, organizations can dive deeper into customer intelligence and journey mapping, companies can identify the right products, services, and content, build trusted relationships, and optimize customer engagement to generate greater revenue and customer satisfaction.

How to Measure a Customer’s Journey From One Point to Another

The journey from lead to customer can vary drastically depending on the channel, the stage of the sales process, and other factors. You need to know the core metrics and activities that you need to track in order to strengthen revenue streams. Unfortunately, these are often locked in multiple systems and lack a holistic view of business heath. This is where a cloud data warehouse can aggregate multiple sources providing a holistic, unified view of business health. A cloud data warehouse can also provide the ability to integrate, cleanse and ensure data is accurate across systems. Once you have access to all the data, you can begin mapping your customer journey. This will help to better measure the alignment of the customer experience with their overall business objectives and identify customer problems and challenges along the path to purchase.

Conclusion

While solutions like CRM, marketing automation and even customer data platforms are useful in collecting and managing customer data, they still often fall short when bring together internal or historical system data. Creating a holistic view of a customer’s journey, which then provides the customer with a truly 360-degree view of the brand, requires an end-to-end view of the customer’s data. Customer analytics is the answer to the hunger for a holistic view of the customer. By integrating customer data from across systems, tools, departments and regions, enterprises can achieve real-time data and deeper customer insights to increase the value of each customer, lead, and customer experience.

About Traci Curran

Data Management

What’s New in Zen V15: Easily Tracking Data Changes

Actian Corporation

September 22, 2021

clocks moving forward for decision-making

Data maintenance is an ongoing requirement in every database environment. Archiving historic data, synchronizing data after offline access, or auditing changed data are all issues that typically require customized programming. Most of these solutions often need database design changes or time-consuming processes to complete these tasks. With the release of Zen v15, there is now an easy way to do this for any existing Zen data file without impacting existing applications and data layouts – it’s called “System Data v2”.

System data has been around for a long time; it provides a hidden unique identifier on every record in a data file. It is used in conjunction with transaction logging to provide data integrity and recovery in case of system failure. It is also used by DataExchange (used for data replication between various instances of Zen Windows servers in distributed data environments) to uniquely identify records in files being replicated between systems. The hidden values can be retrieved via standard Btrieve Get operations by reading along key number 125; however, beyond being unique, the system data does not provide any additional information.

Zen v15 introduces System Data v2, which provides two hidden unique values on every record. These values are actual time stamps which represent when the record was inserted into the file, and when it was last updated. These time stamps are automatically handled by the engine for every insert and update received, regardless of the interface used. So, applications written using Btrieve, Btrieve 2, ODBC, ADO.NET, PDAC, Java, etc. will all cause the system data v2 time stamps to be maintained if the data file has this option enabled. The 13.0 file format is required for system data v2, and the rebuild utility can be used to enable this option on the files you select.

Like the original system data, the new hidden values can be retrieved via standard Btrieve methods by reading along key numbers 125 (insert time) and 124 (update time). In addition, system data v2 values can be accessed via any SQL interface using the virtual column names sys$create and sys$update. The data in these columns is stored as a Timestamp(7), which is a standard time stamp with septasecond granularity.

Let’s look at an example executed in the Zen Control Center (The Zen Database Management Console):

To create a table including system data v2, add the “SYSDATA_KEY_2” keyword to the CREATE TABLE statement:

create table sensorData SYSDATA_KEY_2
(location varchar(20), temp real);

This keyword can also be used in an ALTER TABLE statement to rebuild an existing file to include the new syskey values. Both cases will result in a 13.0 version file.

Now, let’s insert a few rows and see what the virtual columns look like:

insert into sensorData values(‘Machine1’, 77.3);
insert into sensorData values(‘Machine2’, 79.8);
insert into sensorData values(‘Machine3’, 65.4);
insert into sensorData values(‘Machine4’, 90.0);

select “sys$create”, “sys$update”, sensorData.* from sensorData;

sys$create sys$update location temp
=========================== =========================== ======== =====
2021-09-13 12:49:45.0000000 2021-09-13 12:49:45.0000000 Machine1 77.3
2021-09-13 12:49:45.0000001 2021-09-13 12:49:45.0000001 Machine2 79.8
2021-09-13 12:49:45.0000002 2021-09-13 12:39:45.0000002 Machine3 65.4
2021-09-13 12:49:45.0000003 2021-09-13 12:49:45.0000003 Machine4 90.0

Initially, the create time and the update time are recorded as the same value. You’ll notice that the syskey data values show the fractional seconds as seven digits. This portion of the time stamp is used to guarantee uniqueness in the value, as opposed to representing the actual septaseconds of the insert.

After updating a row, you’ll see that only the sys$update value has changed:

–update a row:

update sensorData set temp = 90.1 where location = ‘Machine1’;

–find rows that have been updated:

select “sys$create”, “sys$update”, sensorData.* from sensorData
where sys$update > sys$create;

sys$create sys$update location temp
=========================== =========================== ======== =====
2021-09-13 12:49:45.0000000 2021-09-14 11:57:46.0000000 Machine1 90.1

Other examples of queries:

–find rows inserted or updated in the last 20 minutes:
select “sys$create”, “sys$update”, sensorData.* from sensorData
where “sys$update” > Timestampadd(SQL_TSI_MINUTE, -20, now());

–return all CHANGED rows, including how many minutes since the last update
select sensorData.*, Timestampdiff(SQL_TSI_MINUTE,”sys$update”,now()) NumMins
from sensorData where “sys$update” > “sys$create”;

–return the number of rows, inserted in the last 24 hours:
select count(*) as Last24Count from sensorData
where Timestampdiff(SQL_TSI_hour, “sys$create”, now()) < 24;

The system data v2 indexes are fully optimizable by the SQL engine. So, a query with restrictions or sorting on the virtual columns will use the index when appropriate.

Tracking create time and last update time can now easily be accomplished with Zen v15 and the System Data v2 feature. Download the trial version here and try it out!

About Actian Corporation

Data Architecture

Hybrid Cloud Benefits and Risks

Traci Curran

September 21, 2021

Hybrid cloud adoption is on the rise. As companies look to accelerate their cloud journey, it’s becoming clear that there are numerous values – and constraints – when blending the use of private on-premises clouds with those of public off-premise clouds. Creating a strategic balance between the two can often yield more favorable results for the business, but it’s important to carefully consider the hybrid cloud benefits and risks associated with data that moves between public and private infrastructures.

What is a Hybrid Cloud?

A hybrid cloud environment or architecture combines a public cloud and a private cloud to deliver or support a service. One application can be architected to take advantage of both types of clouds, forming the basic concept of a hybrid cloud. Using this hybrid model does create complexity that has to be managed, but at the same time, it often increases the application’s agility and performance. Basically, a cloud hybrid model is a combination of both public and private clouds.

All clouds have the five essential characteristics of resource pooling, on-demand self-service, broad network access, rapid elasticity, and measurable services. The characteristics of a hybrid cloud include the five essential characteristics but also allow better management of workloads, improved big data processing, improved data ownership, and improved overall performance.

Hybrid Cloud Benefits

Hybrid cloud benefits can be tied highly to the management of data. Many organizations are concerned about their data. Their data is the organization’s lifeblood; if it becomes compromised or corrupt, the organization is in danger of failure. With a hybrid cloud architecture, organizations can architect solutions that allow sensitive data to be held in a private cloud and non-sensitive data to be stored in a public cloud. The advantages of hybrid cloud storage can improve the overall analytical ability of the organization to run the day-to-day business as well as analyze the business trends.

Benefits of hybrid clouds include:

Better security options for data and applications, data that needs to have increased security can be kept in a private cloud and managed by the organization.
Better control of data allows for better management of data agility for services.
Better control of demand with separation of transactional workloads between public and private clouds.
Improved service innovation relative to taking advantage of capabilities of both private and public cloud abilities.
Improved network management of data transactions by locating data closer to the user of the data.
Improved scalability of a private cloud solution to allow the organization to make better usage of cloud scalability.

Business Benefits

When businesses have better control over their data, they also have better control of how decisions are made. Data is everywhere and massive. Timely, real-time management of data is essential for organizational decision support. Besides the management of data listed below, there are other business benefits of a hybrid cloud environment for the organization.

Business benefits of hybrid cloud include:

Reduce cost, private data hosting costs the organization, and there is a trade-off between managing data and securing data. Organizations can move non-sensitive data to the public cloud and keep sensitive data in a highly secured cloud, such as one that has FedRamp certification. They also can choose to keep certain workloads in on-prem data centers.
Better management and control of access to data anywhere at any time.
Improve service continuity by improving the management of business service continuity, availability, and capacity.
Improved governance, risk, and compliance (GRC) for the organization’s services’ strategy, tactics, and operations when using a hybrid cloud solution.

Disadvantages

The disadvantages of hybrid cloud pros and cons will always depend on the organization. Each organization should a have risk management and backup policy. This includes creating risk registers and deciding appropriate actions based on risks that the organization might experience. Listed are a few of the general cons and disadvantages associated with hybrid clouds, though mileage may vary depending on your organization.

Cons for the usage of a hybrid cloud solution are:

Lack of knowledge in organizations for the usage of cloud computing, including public and private cloud knowledge. Organizations must have knowledgeable people or suppliers who understand their business needs and how cloud environments can help with their strategy and overall operations.
Hybrid cloud complexity can be cumbersome. Current IT solutions need to be positioned adequately for now and the future based on business needs.
Not enough budgets to invest in transformation efforts to the cloud because of other urgent business needs.
May need enhanced network security between public and private cloud for hybrid cloud service usage.

Disadvantages of hybrid clouds:

Increased complexity increases cost and the need for organizational expertise to manage public and private cloud environments, including vendors, platforms, and internal IT resources.
Capital expenses associated with on-prem environments.
With increased complexity also increases the risk of security attacks.

Disadvantages and cons are in the eye of the organization. Organizations should partner with external vendors to help with organizational assessments to determine challenges and then create actionable business plans and high-level roadmaps for success.

Is Hybrid Cloud Right for Your Business?

There are pros and cons to the usage of a hybrid cloud solution. Many of the reasons, either way, depend on the organization’s desired outcomes. Companies should assess their risk tolerance and carefully consider how risk is managed. Pros for the usage of a hybrid cloud solution are:

Better management of workloads. Balancing workloads and access to data with a hybrid cloud solution will help improve the availability and performance of services.
Improved utilization of IT resources. When tracking IT asset utilization, many organizations may find that utilization is very low, and it may be more cost-effective to move those workloads to a public cloud as needed instead of having IT assets that are not used for long periods consuming financial resources.
Management of big data and improving big data analytics for organizational decision support. Big data management is enhanced with hybrid cloud solutions and data warehouses that can be public or private cloud solutions.
Improve time to market for services. With public cloud IT assets readily available, organizations do not have to go through the timely provisioning of on-premises IT assets and make services available to customers anywhere around the globe.

Vendors of cloud solutions make the maintenance of IT components and capabilities easier. Infrastructure is becoming more of a commodity and many organizations can see cost savings using the cloud. Shifting the focus to business strategy and viewing hybrid clouds as an enabler of the business increases agility, competitiveness, and customer focus of the outcomes supported by hybrid cloud solutions.

The usage of Hybrid clouds will continue to grow as the organizational trust of these solutions grows. This will allow businesses to focus on core competencies and innovation so they can maintain an edge over the competition.

Actian is a fully managed hybrid cloud data warehouse service designed to deliver high performance and scale across all dimensions – data volume, concurrent user, and query complexity. It is a true hybrid platform that can be deployed on-premises as well as on multiple clouds, including AWS, Azure, and Google Cloud, enabling you to migrate or offload applications and data to the cloud at your own pace.

About Traci Curran

Data Intelligence

The Challenges and Benefits of Data in the Healthcare Industry

Actian Corporation

September 20, 2021

According to a study conducted by IDC, the data production growth rate in healthcare is expected to exceed that of any other industry sector through 2025. To keep up with the volume of information generated by imaging, telemedicine, electronic medical records and other data sources, healthcare will inevitably face data challenges.

The digitization of healthcare is no longer a debate. According to IDC’s FutureScape: Worldwide Health Industry 2021 Predictions, by 2023, 65% of patients will have access to care via a digital platform. Driven by technological acculturation, this phenomenon is encouraged by healthcare providers who are looking for better ways to improve access, engagement, and experiences across all healthcare services.

This transformation calls for another prediction pointed out by IDC:

By 2024, the proliferation of data will result in 60% of healthcare organizations’ IT Infrastructure being built on a data platform that will use AI to improve process automation and decision-making.

From this point on, one observation is clear: data is at the heart of everything in the healthcare industry. From research to production, from development strategies to performance optimization, this sector is faced with the need to adopt, more than ever, a true data culture.

The Benefits of Data in the Healthcare Sector

According to a survey of specialist doctors conducted in 2019 by Elsan (France’s 2nd largest private healthcare operator), more than one out of two healthcare professionals is looking for digital solutions that will enable them to facilitate data collection and share information between professionals.

Among the uses favored by more than 60% of respondents is the collection of patient information before a consultation, to avoid re-entering prescriptions. This would save time in medical practices, and would involve a better flow of information between healthcare professionals for a more precise, rapid, and rigorous follow-up of the patient.

Everyday medicine is one of the first to benefit from the use of data. Researchers are also making massive use of data. Throughout the last 18 months, as the health crisis affected the entire globe, Big Data has helped to win the race against COVID-19 by allowing laboratories to share knowledge and thus, adapt measures to slow the progression of the epidemic.

The contribution of data in the healthcare sector and the pharmaceutical industry is indisputable: accelerated research, animation of the scientific community, efficiency of medical follow-up, etc. But beyond the benefits, the challenges are numerous.

A Sector Where Data is Highly Regulated

To exploit the full potential of data, healthcare professionals and pharmaceutical industry players must be up to the challenge.

Indeed, data remains a very sensitive asset, and citizens and legislators alike are making sure that certain limits (in terms of privacy and confidentiality) are never crossed.

Behind the notion of data governance also lies that of sovereignty. In France, for example, the question of storing data related to the current pandemic has been raised. A public database called Health Data Hub was created and developed throughout the COVID-19 crisis.

This database by Microsoft Azure was certainly controversial. Having ethical and moral values related to data confidentiality is a central issue, not to mention the risks of data security breaches. Indeed, the IBM Cost of Data Breach report reveals that the average cost of a data breach has increased by 10% in one year, reaching $4.24 million.

In France, the average cost of a data breach is €3.84 million. As for the average cost per lost or stolen record, it reaches 213 € in the pharmaceutical sector. A reality that leads to creating excellent conditions in terms of data security.

The Challenge of Data Quality in Healthcare

Although highly regulated, access to health-related data can save lives or simply enable patients to be managed in an appropriate manner by having access to their health history.

It is therefore crucial to rely on data infrastructures capable of protecting and maintaining the quality of this sensitive information, which once altered or erroneous, can have serious consequences.

To meet the data challenges of today and tomorrow, new and highly innovative companies have appeared in the sector, known as “Healthtech”. They have come to shake up an industry in need of tools to digitize and automate access to large volumes of data on a daily basis.

The healthcare sector, although highly regulated for obvious reasons of data security and confidentiality, must be able to take advantage of the many benefits offered by the proper circulation of quality data.

The challenge is to find the right balance between a strong defensive approach to data access permissions, while facilitating innovation to create the services of tomorrow.

About Actian Corporation

Data Integration

Understanding Big Data Integration

Traci Curran

September 14, 2021

The topic of data integration has been around forever. Before we used technology to manage data, we integrated data in manual ways. At the time, we needed to integrate simple data structures such as customer data with purchase data. As the industry progressed, we have gone from managing flat data files and integrations to using applications to creating databases and data warehouses that automate the integration of data. The early data sources were few compared to today, with information technology supporting almost everything that we do. Data is everywhere and captured in many formats. Managing data today is not a small task but a much bigger job and grows exponentially every year.

What is Big Data Integration?

Data integration is now a practice in all organizations. Data needs to be protected, governed, transformed, usable, and agile. Data supports everything that we do personally and supports organizations’ ability to deliver products and services to us.

Big data integration is the practice of using people, processes, suppliers, and technologies collaboratively to retrieve, reconcile, and make better use of data from disparate sources for decision support. Big data has the following characteristics: volume, velocity, veracity, variability, value, and visualization.

Volume – Differentiates big data from traditional structured data managed by relational database systems. The number of data sources is much higher than the conventional approach to managing data inputs.
Velocity – Data source increases the rate of data generation. Data generation comes from so many sources in various formats and unformatted structures.
Veracity – Reliability of data, not all data has value, data quality challenges.
Variability – Data is inconsistent and has to be managed from various sources.
Value – Data has to have value for processing; all data does not have value.
Visualization – Data has to be meaningful and understood by a consumer.

Integration of big data needs to support any service in your organization. Your organization should run as a high-performing team sharing data, information, and knowledge to support your customers’ service and product decisions.

Big Data Integration Process

Big data integration and processing are crucial for all the data that is collected. Data has to have value to support the end result for the usage of the data. With so much data being collected from so many sources, many companies rely on big data scientists, analysts, and engineers to use algorithms and other methods to help derive value from the data received and processed.

The processing of big data has to be compliant relative to organizational governance standards. Ensure the reduction of risk related to decisions with the data. Help enable organizational growth and enablement. Reduce or contain cost. Improve operational efficiency and decision support.

The basic process is;

Extract data from various sources.
Store data in an appropriate fashion.
Transform and integrate data with analytics.
Orchestrate and Use/Load data.

Orchestrating and loading data into applications in an automated manner is critical for success. Technology that does not allow ease of use will be cumbersome and hamper the organization’s ability to be effective using big data.

Challenges of Big Data Integration

Data is constantly changing. Trends have to be managed and assessed for integrity to make sure the data being received is timely and valuable for decision-making within the organization. This is not easy. In fact, integrating big data can often be the biggest challenge. Other Big data integration challenges are:

Using appropriate data sources to create a single source.
Consistent use and improvement of analytics to deliver valuable data. Data sources increase, change.
Creating and maintaining valuable data warehouses and data lakes from the collected data. Improving business intelligence.

One of the biggest challenges besides those listed is the enablement of people to use technology. Organizations should look for technology that provides ease of use for users across the organization, but they also need to make sure they choose data management platforms that are robust enough to meet complex use cases. Products and technologies that are not easy to use will not be used effectively and efficiently to support business outcomes.

Strategies of Big Data Integration

Big data integration strategy has to include the following;

Data governance – data has to be controlled and needs to follow enterprise standards.
Management of data and risk reduction when storing data.
Ensuring appropriate controls for data compliance.
Management of data quality.
Management of data security.
Understanding of integration needs between tools, consumers and data sources.
Understanding of how, why, where, when and what decisions need to be made and how they are made with data.

Big data architecture and platform capabilities must support the broader data strategy. With a good data strategy, you can then consider the tactics and technologies that will be utilized and determine the capabilities needed to support and improve the data driven decision making.

Actian and Big Data Integration

Actian Dataconnect allows organizations to integrate without limits. Organizations can integrate anything, anywhere, anytime, and create a dynamic data cloud for easy access. Automated workflows can be built quickly to support changing business needs and reduce the time and risk associated with manual processes. Integration is seamless, enabling dynamic data usage to support all organizational needs. Business users, integration specialists, SaaS administrators, and others can be empowered to take full advantage of Actian’s big data management and integration capabilities.

About Traci Curran

Data Intelligence

What is Data Lineage?

Actian Corporation

September 13, 2021

In order to access and exploit your data assets on a regular basis, your organization will need to know everything about your data! This includes its origins, transformations over time, and overall life cycle. All of this knowledge can be gathered from Data Lineage.

In this article, we will define Data Lineage, give an analogy, and explain its main benefits for data-driven organizations.

After human resources, data has become the most valuable asset for businesses today.

It is the foundation that links companies, clients, and partners together. Knowing this, data must be preserved and leveraged as it contains all of an organization’s intelligence.

However, with great information comes great responsibility for those who manage or use this data. On one hand, they must identify the data that reveals strategic insights for the company, and on the other, they must implement the right security measures to prevent devastating financial and reputational consequences.

With the arrival of data compliance laws such as the BCBS-239 or the GDPR, the person in charge (usually the DPO) of data compliance must put in place transparent conditions to ensure that no data will be exploited to the detriment of a customer.

This is where Data Lineage intervenes. Behind the word lineage lies an essential concept: data traceability. This traceability covers the entire life cycle of the data, from its collection to its use, storage, and preservation over time.

How Data Lineage Works

As mentioned above, the purpose of Data Lineage is to ensure the absolute traceability of your data assets. This traceability is not limited to knowing the source of information. It goes much further than that.

To understand the nature of lineage information, let’s use a little analogy:

Imagine that you are dining in a gourmet restaurant. The menu includes dishes with poetic names, composed of many more or less exotic ingredients, some of which are foreign to you. When the waiter brings you your plate, you taste, appreciate, and wonder about the origin of what you are eating.

Depending on your point of view, you will not expect the same answer.

As a fine cuisine enthusiast, you will want to know how the different ingredients were transformed and assembled to obtain the finished product. You will want to know the different steps of preparation, the cooking technique, the duration, the condiments used, the seasoning, etc. In short, you are interested in the most technical aspects of the final preparation: the recipe.

As a controller, you will focus more on the complete supply and processing chain: who the suppliers are, places and conditions of breeding or cultivation of raw products, transport, packaging, cutting and preparation, etc. You will also want to make sure that this supply chain complies with the various labels or appellations that the restaurant owner highlights (origin of ingredients, organic, “home-made”, AOC, AOP, etc.).

Others may focus on the historical and cultural dimensions – from what region or tradition is the dish derived or inspired from? When and by whom was it originally created? Others (admittedly rarer) will wonder about the phylogenetic origin of the breed of veal prepared by the chef…

In short, when it comes to gastronomy, the question of origin does not wait for a unique and homogeneous answer. And the same is true for data.

Indeed, With Data Lineage, You Will Have Access to a Real-Time Data Monitoring Tool

Once collected, the data is constantly monitored in order to:

Detect and monitor any errors in your data processing.
Manage and continuously monitor all process changes while minimizing the risks of data degradation.
Manage data migrations.
Have a 360° view on metadata.

Data Lineage ensures that your data comes from a reliable and controlled source, that the transformations it has undergone are known, monitored, and legitimate, and that it is available in the right place, at the right time and for the right user.

Acting as a control tool, the main mission of Data Lineage is to validate the accuracy and consistency of your data.

How do you do this? By allowing your employees to conduct research on the entire life cycle of the data, both upstream and downstream, from the source of the data to its final destination, in order to detect and isolate any anomalies and correct them.

The Main Advantages of Data Lineage

The first benefit of Data Lineage has to do with compliance. It helps identify and map all of the data production and exploitation processes and limits your exposure to the risk of non-compliance of personal data.

Data Lineage also facilitates data governance because it provides your company and its employees with a complete repository describing your data flows and metadata. This knowledge is essential to design a 100% operational data architecture.

Data Lineage makes it easier to automate the documentation of your data production flows. So, if you are planning to increase the importance of data in your development strategy, Data Lineage will allow you to save a considerable amount of time in the deployment of projects where data is key.

Finally, the last major benefit of Data Lineage concerns your employees themselves. With data whose origin, quality and reliability are guaranteed by Data Lineage, they can fully rely on your data flows and base their daily actions on this indispensable asset.

Save time, guarantee the compliance of your data, make the action of your teams more fluid while inscribing your company in a new dimension, based on an uncompromising data strategy. Don’t wait any longer, get started now.

About Actian Corporation

Data Integration

Data Integration Architecture – What is it and Why Does it Matter?

Traci Curran

September 8, 2021

Data is everywhere. In any organization, you will find data in multiple applications, databases, data warehouses, and in many cases, public and private clouds. Usually, data does not belong to a specific group within an organization and is often shared across teams and applications.

Similar to a professional sports team, each function in the organization should have a specific role, sharing data and information in real-time to support better outcomes.

In order to efficiently share data, businesses need to focus on integrating their data in an automated and timely manner. This can be a challenge when functional units use multiple applications and store data in multiple locations. Organizations need a data integration architecture to connect secondary to primary data sources, normalize the information, and automate the flow of information without manual interventions.

What is Data Integration Architecture?

Data integration architecture defines the flow of data between IT assets and organizational processes to enable interoperability between systems. Today, data is everywhere, often stored in multiple formats in a complex non-integrated fashion. This means users spend far more time searching for data and information instead of using it to make better business decisions. When data is shared manually, obtaining knowledge for decision support becomes cumbersome and impacts customers and business performance.

Creating a data integration architecture allows integration of disparate data and provides normalization to enable faster decision support. The underpinning data and information used by functional units must be systemized and architected to enable collaborative decisions and faster innovation.

Creating a data integration architecture does not mean combining all data sources into one data source, such as a giant database or a data warehouse. It does mean understanding data relationships enabling interoperability between the systems and tools used across the organization. Data integration architecture helps define the flow of data between internal and external people and technologies. This helps remove data silos and enable accurate data usage across the organization.

Data integration design consists of mapping primary systems and secondary systems. Secondary systems feed data and information to the primary systems. The primary system can vary across functional units, but the data needs to remain consistent across the organization. Each functional unit in an organization can have a specific primary perspective based on their job function and the decisions they have to make. Some secondary systems will always be secondary systems. The overall architecture has to consider the users of the systems and the data sources that need to be accessed. In other words, enterprises need a single source of truth.

The Need for Data Integration Architecture

Data integration needs architecture to map, reconcile, and deliver data across multiple sources, often with different expressions. The architecture should understand the source of the data and help reconcile and normalize the data for use. This helps enable better overall communication between functional units in the organization and improves service performance.

Integration architecture management can be done from multiple perspectives.

Service-oriented data integration architectures (SOA).
Operational data integrations looking at key performance indicators (KPI) from multiple related operational processes.

All types of data can be segmented into a specific area with its architecture, data model, scope, and details. Organizations should understand the value of data integrations for decision support and knowledge management.

Examples of Data Integration Architecture

There are many starting points for the creation of a data integration architecture. Organizations can begin with a single functional unit or set of applications. Investigating what data sources are used to make decisions helps map data sources into primary and secondary use cases.

Examples of data integration architectures are:

Configuration Management Database (CMDB) feeding Configuration Management System (CMS) feeding Service Knowledge Management System (SKMS).
Marketing systems feeding into a Customer Relationship Management System (CRM) or Enterprise Resource Planning (ERP) application.
Moving SharePoint data into a Knowledge Management System (KMS).
Multiple data sources feeding an application.

Using a data integration architecture can also help with technology consolidation, saving money, time and improving the performance of functional units within the organization. Many times, duplicate sources of information may be discovered that cause inconsistent decisions and degrade the performance of the business. The organization should apply Lean principles when performing data integration architecture projects.

Actian and Data Integration Architecture

Actian is a leader in data management, including data integration architecture. Our data solutions enable organization performance and reduce the risk of manual processes. Actian helps ensure that business-critical enterprise information is effectively harnessed for real-time service delivery success no matter where it resides.

Specific enterprise data integration solutions are:

DataConnect – Highly scalable hybrid integration solution that enables you to quickly and easily design, deploy and manage integrations on-premises and in the cloud.
DataFlow – Provides a parallel execution platform for real-time processing of data-in-motion. Accelerate the analysis, extraction, transformation, and loading of data across the business.
Business Xchange – Fully managed B2B integration service that enables electronic data interchange (EDI) to exchange procurement and supply documents.

About Traci Curran

Data Intelligence

The Data Catalog: An Essential Solution for Metadata Management

Actian Corporation

September 6, 2021

Your company produces or uses more and more data? To better classify, manage, and give meaning to your data, there must be order. By putting in place rigorous metadata management, with the help of a data catalog, you can gain relevance and efficiency.

Companies are producing more and more data. To the point where processing and exploitation capacities can be undermined, not because of a lack of knowledge, but rather because of a lack of organization. When data volumes explode, data management becomes more complex.

To put it all in order, metadata management becomes a central issue.

What is Metadata and How Do You Manage It?

Metadata is used to describe the information contained in data: source, type, time, date, size, … The range of metadata that can be attached to data is vast.

Without metadata, your data is decontextualized; it loses its knowledge and becomes difficult to classify, order, and value. But because they are so numerous and disparate, you must be able to master this mountain of information.

Metadata management is becoming an essential practice to ensure that it is up-to-date, accurate, and accessible. To meet the challenge of optimal metadata management, it is essential to rely on a Data Catalog.

Data Catalog: What is it For?

A data catalog is a bit like the index of a gigantic encyclopedia. Because the data you collect and manage daily is diverse by nature, it must be classified and identified. Otherwise, your data portfolio would become an unfathomable mess from which you would not derive any added value.

We define a data catalog as:

A detailed inventory of all of an organization’s data assets and their metadata, designed to help data professionals quickly find the most appropriate information for any business and analytical purpose.

A Data Catalog is a Pillar of Metadata Management Through the Following Features

Data Dictionary

Each piece of data collected or used is described in such a way that it can be put into perspective with others. This metadata thesaurus is a pillar of efficient and pragmatic exploitation of your data catalog. By referencing all of your company’s data in a Data Dictionary, the Data Catalog helps optimize accessibility to information even if the user does not have access to the software concerned.

Metadata Registry

A dynamic metadata repository intervenes at all levels: from the dataset to the data itself. For each element, this metadata registry can include a business and technical description, give you information on its owners, have quality indicators or even help create a taxonomy (properties, tags, etc.) for your items.

The Data Search Engine

Your data catalog will allow you to access your data through its integrated search features. All the metadata entered in the registry can be searched from the data catalog search engine. Searches can be sorted and filtered at all levels.

Data Catalog and Metadata: The Two Pillars of Data Excellence

There’s no need to try to oppose having a data catalog and the concept of metadata management because they simply go hand in hand.

A Data Catalog is a kind of repository that cannot be ignored to standardize all the metadata that are likely to be shared in your company. This repository contributes to a detailed understanding and documentation of all your data assets.

But beware! The integration of a data catalog is a project that requires rigor and method. To begin this project and unleash your data potential, start by conducting a complete audit of your data and proceed in an iterative manner.

Data Storytelling: How to Have Effective Data Communication

From Storytelling to Data Storytelling

Data Storytelling: Benefits all Around

The Keys to Effective Data Storytelling

About Actian Corporation

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

An Easier Modernization Journey for Ingres and Database Applications

Actian’s Ingres NeXt Initiative Paves the Way for Transformation

Modernizing Your Ingres Database

On-Premises

Bring Your Own License

Platform as a Service

Database as a Service

Safe and Easy Database Modernization

Modernizing Database Applications

ABF and Forms-Based Apps

OpenROAD Fat Client

OpenROAD Server

OpenROAD as a Service

Take the Next Step

About Teresa Wingfield

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

DataOps: How to Turn Data into Actionable Insights

DataOps – Why is it Important?

DataOps for Managing Data Growth

DataOps and Big Data Challenges

Conclusion

About Traci Curran

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

Cloud OLAP in Data Warehouses

Use of Cloud OLAP in Data Warehouses

OLAP vs. OLTP

What is OLAP Cube in Data Warehouses

Use Cases of OLAP in a Data Warehouse

About Teresa Wingfield

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

Customer Analytics: How to Measure a Customer’s Journey

Why Customer Analytics?

Customer Analytics Architecture

How to Measure a Customer’s Journey From One Point to Another

Conclusion

About Traci Curran

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

What’s New in Zen V15: Easily Tracking Data Changes

About Actian Corporation

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

Hybrid Cloud Benefits and Risks

What is a Hybrid Cloud?

Hybrid Cloud Benefits

Business Benefits

Disadvantages

Is Hybrid Cloud Right for Your Business?

About Traci Curran

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

The Challenges and Benefits of Data in the Healthcare Industry

The Benefits of Data in the Healthcare Sector

A Sector Where Data is Highly Regulated

The Challenge of Data Quality in Healthcare

About Actian Corporation