Data Analytics

Are Real-Time Analytics in Supply Chain Management Important?

Actian Corporation

February 22, 2021

Processing Data in Real-Time

Every organization processes data in many ways. For example, batch data process still exists for heavy data-process workloads, such as payroll. Many of these data processing requirements are executed by batch scheduling software; however, there is a growing need for data processing to be more automated and processed in real-time. Processing data in real-time is becoming more and more crucial to many organizations, especially for supporting customers in so many ways not thought of before.

Today’s consumer is empowered, mobile, and can ask for services or products anywhere and anytime. Organizations have to be responsive and knowledgeable about their customers. Individual Customers and their organizations today are impatient and know they can find most products and services elsewhere.

The faster data can be acquired and processed, the better the organization can deliver to stakeholder expectations both internally and externally. Real-time analytics of data and information is necessary for a supply chain and necessary for the timely processing of most data.

Business Analytics

Gathering and analyzing large amounts of data from many sources is a challenge. More cloud-based businesses are starting every day, capturing data and enabling data exchanges with other businesses and customers. Business analytics helps solve many problems and issues, including challenges with running and growing the business revenue.

Analytics are also important in understanding the competition and making decisions for innovation. Organizations are collecting a tremendous amount of data every day; they cannot just rely on their experts’ opinions in the company. Business analytics and expert opinion have to be used together for a competitive advantage.

Supply Chains

A high cost within supply chains is the cost of manufacturing the products and goods delivered. Data and information for decision support in the manufacturing process are crucial for success with organizational profitability.

Raw goods, parts, components, and finished products must be tracked in real-time at each step in the chain until it is delivered to its final destination; the supply chain has to be continually improved, strengthened and made economical. Real-time analytics are of utmost importance for the efficiency and effectiveness of the supply chain.

The Importance of Real-Time Analytics

Real-time analytics help with decision support for the organization’s experts and all employees that need data to do their jobs effectively. Better decisions are made faster and more efficiently using technology that supports real-time analytics and an overall Enterprise data management strategy.

Security risks can also be identified faster using real-time analytics. Analytics can help detect abnormal system and data behaviors that could be the result of a cyber attack. Security is a major concern within a supply chain. Any security incident that could have been detected with real-time analytics can save much heartache and reduce future potential problems within a supply chain.

Supply chain end-to-end product management can be improved to help remove constraints and backlogs in product delivery. The supply chain has to be intelligent and needs to provide real-time data visibility for actionable responsiveness.

Discovery, interpretation of data into information, and information into actionable insights for decisions is essential and needs to be done well. The analysis of data and information for specific purposes has to be well thought out. What decision needs to be made for supply chain efficiency? Each asset in the supply chain and every activity of the asset used to deliver the products on time and in perfect condition needs to be analyzed.

Are real-time analytics in supply chain management important? Without this capability, there are so many challenges that organizations will be unable to address that can affect their ability to do business. Organizations have to have the ability to respond to real-time issues. They have to be predictive and deliver products to customers meeting or exceeded their service level agreements. They have to deliver a good experience; good experiences disappear with bad service.

Real-time analytics is a mandatory capability for supply chain management. This capability cannot be optional if the supply chain is to be intelligent and responsive. An enterprise data strategy is a must for any organization that has to manage a critical supply chain.

Actian enables the enterprise data strategy with a single data management platform to support the most demanding supply chain real-time analytics use cases and mission-critical applications. Actian is a fully managed cloud data warehouse service that delivers high performance at scale.

Enterprise data integration improves decision support and consistency for success. Leveraging a solution like Actian DataConnect supports a data-enabled enterprise and speeds up the decision-making process, no matter where the data resides, by leveraging the analytical performance, data collection for continuous improvement of supply chain management.

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 Intelligence

COVID19: The Rise of Digital Transformations for Enterprises

Actian Corporation

February 18, 2021

2020 has marked a turning point in companies’ digital transformation. The coronavirus and its procession of health measures, restrictions and precautions have strongly impacted human societies as well as the business world. Let’s take a look back at the data management challenges related to COVID-19.

With the closure of physical points of contact, the widespread use of remote work, logistical tensions and uncertainties all around the world, throughout 2020, companies have had to reinvent codes, implement new methods and develop new strategies. The challenge? To maintain the link between employees and customers, who are kept at a distance by more or less strict measures.

According to a study by Gartner, 69% of companies believe that the health crisis has accelerated their digital business initiatives. For 60% of them, digital transformation is a perspective to improve their operational efficiency. Another study conducted by Pega Systems reveals that 56% of companies have increased their budgets related to their digital transformation. Sixty-nine percent consider that the health crisis is leading them to become more empathetic to their customers. At the heart of these challenges: data management.

Accelerating Digital Transformations…With Data

According to estimates from a study carried out at the end of 2020, the COVID-19 crisis has accelerated enterprises’ digital strategy by an average of 6 years. 97% of business decision-makers believe that the pandemic has accelerated their digital transformation.

The same study reveals that in the face of the crisis, 95% of companies are looking for new ways to engage customers, and 92% say that the transformation of digital communications is critical to meeting today’s business challenges.

Behind these findings, there is a compelling need: to make the most of data. Indeed, beyond identifying new levers to engage customers and keep in touch with your audiences, it’s first and foremost about understanding their needs. To show empathy, you need to know who you are talking to and which channel you can use to interact effectively with them.

Data management is the foundation of this whole process of accelerating your digital transformation.

Data allows you to define your marketing strategies as well as define the axes of your campaigns (which is essential to preserve the continuity of your business activity in times of crisis). It also conditions your priorities in terms of R&D. In fact, the customer knowledge provided by data management allows you to include innovation in a data-driven dimension.

The objective: to design and develop products and services corresponding to the expectations and needs of your target audience. A study by Solocal, published at the end of 2020, highlighted that for 81% of companies seeking to accelerate their digital transformation, their objective is to solicit and respond to customer feedback.

Certain Sectors at the Forefront of Digital Transformation

Integrating real-time data into business strategies, analyzing customer journey, deploying predictive analytics solutions to accelerate commitments or detecting weak signals in order to anticipate them…the scope of data management is widening every day.

During the COVID-19 crisis, some business sectors had to completely reinvent themselves and, thanks to their data assets, managed to find levers to maintain business continuity.

For example, a recent study by the consulting firm QuantMetry, published last October, showed that 68% of companies have maintained or even increased their data-related budgets in 2020. During the health crisis, Uber for Business noted an explosion in demand for meal delivery. With the careful use of their data assets, the company managed, in just a few months, to design a new offer for marketing departments. The concept? To offer meal delivery vouchers not only to employees working from home, but also to corporate customers as a loyalty lever.

The offer, with a B2B2C strategy, is also positioned as an alternative to all those moments of sociability linked to professional events. These vouchers are also first-rate marketing tools because many indicators are related to them (type of use, type of meals ordered, geographical area…). These are all useful KPIs for Uber for Business, but also for the companies that use them to refine their customer retention strategies… or talent retention strategies.

If you are in the middle of digitally transforming your business, check out our tips on how to succeed your transformation.

About Actian Corporation

Data Architecture

Data Lakes, Data Warehouses, Data Hubs – Do We Need These?

Actian Corporation

February 17, 2021

Data Hubs Lakes and Warehouses depiction

There’s a long-standing debate, dating back to the early days of Hadoop, about what kind of data repository is best for a given data analytics use case. A data lake? A data hub? A data warehouse? Despite Hadoop’s fall from grace, the debate not only persists but grows more complicated. Today’s cloud-based repositories, including AWS S3, Microsoft Azure ADLS, and Google Cloud Store, look very much like data lakes in the cloud. Similarly, cloud-based offerings like Snowflake look very much like enterprise data warehouses but in the cloud. Granted, for an apples-to-apples comparison for data lakes you’d need to pare down Hadoop to be just HDFS or add in the tools for data repository management, query, and so forth associated with the three public cloud providers portfolios.

At the same time, it should be noted that none of the vendors promoting these offerings are using those terms. . . Microsoft, Amazon, and Google identify their cloud repositories as “enterprise data hubs.” Snowflake positions itself as a cloud data warehouse but is pivoting to call itself a cloud data platform via its expansive ecosystem but, standalone it is really an “analytics engine.”

Changing the descriptor doesn’t change the question driving the debate, though, and the simple truth is that no data lake, data hub, nor a data warehouse — on-premises or in the cloud — has ever been able to effectively support all the multi-disciplinary teams of business analysts, data engineers, data scientists and power users within different lines of business. That was evident before there was a cloud, and it’s only become more evident as teams try to incorporate new data sets (think web services and IoT) and try to merge semi-structured data into structured repositories. Don’t even get me started on the stream of Excel data sheets that were supposed to go away (but never did) when we got more sophisticated about analysis and data management.

But here’s the thing: There are real differences between these platforms and it’s important to understand those differences. In the end, though — watch for it — the operational differences between these platforms aren’t the root cause of why they’re not providing the support that all the different stakeholders expect.

Level Setting

Let’s start by talking about what we’re actually talking about:

Data Hub

Here, we’ll define a data hub as a gateway through which virtual or physical data can be merged, transformed, and queued up for passage to another destination. That destination might be an application or a database or some other kind of repository (such as a data lake or data warehouse). In any event, data in a data hub is transient; it is not locally stored and has no persistence.

An example of a data hub would be something like Informatica, which can accommodate every imaginable data type and link both upstream and downstream data sources and destinations. Historically, data hubs have been managed and used by IT personnel that work with separate siloed groups from across the enterprise to create integrations where none naturally existed.

Data Lake

Unlike a data hub, a data lake acts as a repository for persistent data. It is not simply a pass-through. Data lakes can typically ingest and manage almost any type of data and, as exemplified by Hadoop (historically the most popular type of data lake), they provide tools for enriching, querying, and analyzing the data they hold. Problem is that Data Lakes are generally sandboxes for dumping large sets of data used in experimental projects by highly-skilled technical resources, largely IT and developers.

Data Warehouse

A data warehouse differs from a data lake in that it acts as a repository for persistent and primarily structured data, incrementally built over time from multiple downstream data source silos. A data warehouse also differs from a data lake in that it requires some sort of data hub technology to prepare the data for ingestion. On-premise data warehouses such as those from the big legacy players like Oracle, IBM, and Teradata are very IT centric, managed by one or more database administrators (DBA). While the bulk of data used by business users may ultimately reside in a data warehouse, most of these users have no direct interaction with the data warehouse and may not even know they have one or what it is.

Virtual Rubber Meets Virtual Road

Historically, data hubs, data lakes, data warehouses all have several things in common: They each require personnel with specialized skills to set them up, maintain them, manage them, . and experts who can convert the requests of non-technical business users and analysts into queries and reports that can be run against these data repositories.

As an aside, the complexity of these platforms is one reason for the demise of Hadoop. Hadoop data lakes tended to become dumping grounds for data, and they were only manageable by developers and very skilled (and costly) IT personnel, which limited the business value a Hadoop data lake could generate. It’s not entirely surprising that, as a result, of the big three vendors formerly supporting Hadoop, only Cloudera remains the last “man” standing.

This need for specialized resources had affected the use of data hubs, data lakes, and data warehouses in other ways as well, and this in turn has further complicated the original question about which platform is best for different use cases. With the move from on-premises to cloud-based infrastructures, there’s been a reduction in demand for all these specialized resources. More and more operational support has been provided by the cloud vendors, which has helped to reduce operating costs. Moreover, the architectural changes in the most recent generations of cloud offerings (separate of compute and storage offerings, pay for what you use, etc.) have created further incentives to move to the cloud to reduce costs.

Increasing Complexity Still Further

While all these structural changes have been taking place, though, the fundamental demand for data-based insights has not changed. The answer to the question about how best to gain these insights has only become more difficult to answer. The data that used to be going into on-prem data lakes or data warehouses (via data hubs) is going to the cloud, but the offerings in the clouds are not quite the same as they were on-prem. Their object storage models differ. Microsoft, Amazon, and Google offer persistent data stores and, in that way, may resemble a data lake, but they rely on other tools to perform the data hub functions and cannot therefore be defined as anything more than data stores. They still require data integration or data hub functionality, and their business value is limited in the same way it always has been. The people who directly generate business value — the business analysts, data scientist and (for lack of a specific title), the other line of business power users — still cannot easily access and unlock the insights bound up in the data.

These days, most business analysts and power users are using either the built-in analytics and visualization capabilities of siloed applications like Salesforce, Marketo, or whatever ERP platform they need to understand in terms of business operations or historical outcomes. At the same time, they strive to do more. Business users may try to incorporate data from flat files such as Excel or semi-structured JSON data exposed through web services APIs. Oftentimes, they will get help from IT to export data out of one or more systems and combine it with excel spreadsheets and send it to a cube on a periodic basis. The result is painfully familiar: siloed data pipelines tied to siloed analytics and visualization results. Unbeknownst to these business users, when they employ help from IT, they may actually be leveraging a data hub, because there’s no data persistence in the hub they’ve simply used the hub as a switch to tie a set of data silos and an analytics silo together to create an ad hoc organizational or project silo.

Data scientists and data engineers may be using many of the same data silos but they may also be using data from semi-structured data sets such as clickstreams, IoT, and web services and their destinations may include the same visualization tools but of course also include advanced analytics tools to support AI/ML. They may employ IT to support getting the data for them and, in turn, create the same point-to-point spaghetti network.

Put another way and put simply, the single shared repository of data promised by data lakes, data warehouses, and data hubs still remains a dream unrealized. A true analytics hub has yet to be realized — not on-prem and not in the cloud.

Yet.

Shifting the Focus

Cloud vendors are starting to realize the problem, and some are rapidly shifting to address it. However, the way that’s being done by most of them is by making sure that a cloud data warehouse can act as an upstream data repository to any downstream analytics, reporting, and visualization tool. Often this is being tried through a partner ecosystem, as in Snowflake. This is necessary but insufficient for the analytics hub we all really need.

But wait. An Analytics Hub? Where was that in the definitions above?

Fact of the matter is that the cloud data warehouse is currently an analytics engine but without a data hub built-in on the back end and a focus on separate point-to-point connections to various BI and analytics tools on the front-end. Vendors like Snowflake do not mention analytics hubs let alone claim to be one. Further without the ability to easily get data from data sources and tie composite elements of data from those various sources for presentation out to the analytics tools, you don’t really have a analytics hub, chiefly because you don’t have a data hub.

Instead of just a data hub or analytics hub both usable only by IT, what’s really needed is a data analytics hub that is used by a broad array of IT and business users. More on what this is and why it matters in the next blog.

About Actian Corporation

Data Management

Building a Data Management Strategy for Supply Chains

Actian Corporation

February 11, 2021

Business Strategy

A business strategy consists of setting goals, objectives, and priorities for the business vision and mission. The strategy owners use their portfolios of assets, services, and products to achieve the business’s goals. A business will often have multiple strategies, such as having a digital strategy that supports their service or product strategy.

One of these strategies might be a data management strategy for supply chain visibility. Within this strategy, there will be multiple goals supported by multiple objectives for achieving the strategy’s results.

Within most strategies is the objective to maintain, grow and innovate the business with key metrics to measure and to improve return on investment (ROI), the total cost of ownership (TCO), and the value of investment (VOI). Strategy drives tactical and operational plans and actions of the organization.

Data Management

A data management strategy aims to support and drive the overall strategic intent and benefit of the organization. Data management is supported by technology; technology supports business processes and enables people to make decisions. It also supports the coordination and collaboration of automated exchanges of data and exchanges with people.

Supply Chain Visibility

The goal of supply chain visibility is to have detailed data and knowledge of how the supply chain works and its overall health and effectiveness. This data is enabled by technology that collects and presents the data to people that consume and interpret the supply chain’s data and information.

Organizations have to know what is going on within their supply chain at every moment of the day. The entire supply chain has to be visible relative to the assets used in the supply chain. When an organization uses multiple suppliers, they do not need all the details of how each supplier does its part in the supply chain but still needs to include data and information relative to the multiple suppliers achieving their agreements with the organization. Having this data automated can provide timely visibility into meeting or exceeding expectations with the supply chain.

The Supply Chain

A supply chain consists of many assets. These can be categorized into capabilities and resources of all the organizations involved in the supply chain, not just the organization that produces the final product. Supply chains have many moving and stationary parts, each with different characteristics, capabilities, and values to the organizations.

Building a Cohesive Data Management Strategy

The data management strategy is the foundation for organizations to achieve their goals and objectives. Collection, analytics, and collaboration between functional units have to be in unison; one organization, one team, and each team member plays a part in its strategic outcome. The entire supply chain is a team and should function like one and not in an independent fashion related to the product’s production. Supply chain data and information need to be integrated and managed appropriately, not in functional silos.

Communication effectiveness and efficiency are essential aspects of any enterprise data strategy and are reliant on enterprise architecture and data management technology. Communication is enabled with data and information, including understanding how data is processed for decision support. Organizations need to pay attention to data exchanges and translations of data between functional units and outside organizations. If not done properly, this can result in additional work to interpret the communication, causing supply chain delays because a timely decision could not be made.

It is important to have a bi-directional data and information flow in a data management strategy. Data related to the data management strategy’s goals and objectives must flow upstream and downstream. Strategic data flows to tactical data, which flows to operational data. This data flow, which drives initiatives and projects for running, growth, and innovation, also has to be managed in reverse order to know if the strategy is working. Take the time to identify activities or projects in the supply chain data flow that create noise or inhibit the supply chain’s efficiency and effectiveness.

A cohesive data management strategy for supply chain visibility has to consider people’s needs, how the supply chain process works, and how technology enables the strategy.

An Enterprise data integration strategy improves decision support for supply chain management. Actian empowers the data-driven enterprise with a single data management platform to provide for the needs of the most demanding supply chain operations. Actian is a fully managed, hybrid cloud data warehouse service that delivers high performance and scale across all dimensions – data volume, user concurrency, and query complexity. Actian DataConnect manages the movement and transformation of data across the supply chain, no matter where the data resides.

About Actian Corporation

Data Intelligence

Best Practices for Succeeding Your Digital Transformation

Actian Corporation

February 2, 2021

Today, data impacts all sectors; all companies are confronted with data management challenges in one way or another. Despite these observations, most of them are still struggling to really transform their enterprises into data-driven organizations. One of the reasons why they do not succeed is that they are often faced with complex and time-consuming information systems, which represent a real obstacle for their digital transformation.

Indeed, few organizations can truly find their enterprise data, especially if their data ecosystem includes different formats, sizes, and varieties of data. Moreover, it is difficult to interpret them or even to know if they are of quality when they are poorly, or simply not, documented.

In this article, we share some best practices so that companies can find the keys to start their digital transformation through data discovery.

Rethink Your Corporate IS

For many years, data and data management challenges were reserved for “Tech Giants”, such as GAFAM (Google, Apple, Facebook, Amazon, Microsoft). Being actors of the web and working mainly with digital resources, their IS and infrastructures were already developed around data. It was, therefore, more difficult for other market players to implement new models and strategies: they were at a disadvantage.

These other organizations were confronted with technologies built on an accumulation over time. It is obvious that it is more complex to undertake digital transformation in this case.

A striking example can be found in the banking sector. While banks have been able to standardize data due to a succession of international directives, they have had to deal with new digital banks offering much more agile and efficient services. To remain competitive, they realized that it is necessary to change their strategic model to stay in the digital race.

Another example can be found in the automotive sector. Buyer behaviors tend to change gradually over time, and these changes have not gone unnoticed. The increased demand for “eco-friendly” travel through the use of bicycles, access to VTC services, or shared mobility with the increase in carpooling. These facts mainly show that the mobility market is changing. Demand is no longer based on the acquisition of new goods but rather on the mobility services made available to them. Thus, in order to remain competitive and meet the expectations of their users’ new behaviors, automotive market players must diversify.

Getting the Right Data Solutions

In order to meet the needs of data users, it is essential to equip your data teams with the right solutions. This means answering the question: which cross-functional technologies across all the company’s silos need to be deployed in order to have an agile configuration? To do this, IT resources need to be organized to cover three main stages:

Data discovery.
The preparation and transformation of these data.
The consumption of these by the different departments of the company.

These three steps are essential to start its transformation to a data-driven enterprise.

On the one hand, they help implement appropriate security measures to prevent the loss of sensitive data and avoid devastating financial and reputational consequences for the company. On the other hand, it enables data teams to drill down into the data to identify the specific elements that reveal the answers and find ways to show the answers.

All This With a Data Catalog

We define a data catalog as being:

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

With the help of a data catalog, both data teams and managers will be able to start their digital transformation based on their company’s data.

Choosing the Actian Data Intelligence Platform is choosing:

An overview of all of an enterprise’s data assets through our connectors,
A Google-esque search engine that enables employees to intuitively search for a dataset, business term, or even a field from just a single keyword. Narrow your search with various personalized filters (reliability score, popularity, type of document, etc.).
A collaborative application that allows enterprises to become acculturated to data thanks to collective knowledge, discussions, feeds, etc,
Machine learning technology that notifies you and gives suggestions as to your catalogued data’s documentation,
A dedicated user experience that allows data leaders to empower their data explorers to become autonomous in their data journeys.

Learn more about our data catalog.

Start Accelerating Your Data Initiatives Now

If you would like more information, a free and personalized demo, or if you just want to say hello, do not hesitate to contact us and our sales team will answer you as soon as we receive your request.

About Actian Corporation

Data Intelligence

From Databases to Knowledge Management Systems for Data Power

Actian Corporation

January 29, 2021

Databases

Databases are everywhere. They store data and information organized by fields and tables. There are different types of databases that organize data into relational tables, hierarchical structures, networks, and objects. Databases can be personal, corporate, and shared resources. They can be used for recording business transactions to support decision-making. The value of databases today is tremendous for every industry and every consumer.

With the great value that databases contribute, there are many challenges related to overall management, data integrity, data security, performance, availability, and many others. Yet, the challenges currently do not outweigh the value.

Data collections in databases are growing exponentially, making the business of extracting value from it all increasingly challenging. Much of the data will migrate to the cloud because many companies and personal users do not want to host and manage the data themselves. They want to focus on outcomes, not the technical complexity of hosting and managing data.

Tools and Technology to Get Value From Your Data

Having a great database is nothing without the capability to access, collect, and store the data with tools that simplify the process. Databases are everywhere, and each is unique across the industry. Today, there is no single data source that organizations can use to understand their customers effectively.

Digital tools and technologies leverage big data collections to unlock the stored data’s insights. Organizations learn from how consumers interact with their data, such as what search keywords were used to locate a particular product. This, in turn, enables them to make shopping easier for others.

Public, private, hybrid, and multi-cloud data sources will increase. Artificial Intelligence (AI) and Machine Learning (ML) tools and technologies continue to evolve to help manage and leverage cloud data collections and improve the quality of the decisions made in a business.

Collaboration

All data has to be collected and collaborated across multiple platforms, database types, and sources today. Tools and technologies are needed to understand various architectures, including the databases and the platform and the various infrastructures.

Data in the cloud increases the organization’s capability to collaborate and share data for the common good of both the organization and its customers. Data in the cloud can readily be used for collaborative technologies, including smart robots, virtual assistants, smart devices, IoT interfaces, and many others. This data has enabled innovation across many industries.

Knowledge Management Systems

A Knowledge Management System (KMS) is a collaborative interrelated, sometimes normalized system for storing data and information with the ability to retrieve any related data across the system for decisions. The data can be accessed using specific tools that enable the centralization of the data and information. Data can be extracted from various data stores and applications, including multiple cloud platforms. Each platform and tool can be viable independently. Still, the system brings together the various data sources to transform data to information, information to knowledge enabling centralized access to knowledge.

The more data, the more the capability for a smart decision can be made with the caveat the data is being used efficiently and effectively. Having lots of data does not mean success, but what specific data and how it is utilized can significantly differ.

Knowledge management systems are not just for consumption by people but also for smart technology. Natural Language Processing (NLP) technology, for example, relies on a very efficient knowledge management system.

Moving the “Normal” – From Data-Driven to Data-Powered

Using data to support consumer and business outcomes is normal for many businesses today but this evolving and changing. Data collection and reactive responses will evolve to be more proactive and service-oriented. The tools and technologies that you purchase today will need to evolve with you to the new normal.

Actionable data will evolve driven by data normalization and unique data models that enable fast data consumption. The goal is to move from data-driven to data-powered, transforming the data collected for specific decisions by understanding and normalizing what it takes to make a decision.

Real-time decision-making is dependent upon the ability to empower a data-driven enterprise. Analytics, data integration, superior architecture, and an intelligent platform can change the current normal to the new normal enabled by fast timely data consumption to drive intelligent high performing results.

Actian empowers the data-driven enterprise with a single data management platform to provide for the needs of the most demanding analytics use cases and mission-critical applications. Actian Data Platform is a fully managed, hybrid cloud data warehouse service designed from the ground up to deliver high performance and scale across all dimensions – data volume, concurrent users, and query complexity, providing high-speed analytics at a much lower cost than alternatives.

About Actian Corporation

Data Platform

The Best Cloud Data Strategy for Value

Actian Corporation

January 29, 2021

Which Cloud Makes Sense?

Nothing is perfect, but we want to approach perfection with continuous improvement and the management of risk in our business environments. IT capabilities and resources underpin our business environments. On-premise capabilities and cloud capabilities have to deliver a return on investment (ROI) for our efforts and, at the same time, help the organization reduce IT cost. Every cloud is architected differently with a range of functionality, integrations, models, and specific characteristics. Pricing differs based on your cloud data capability choices. To deliver effective and efficient business value in a complex, growing cloud environment requires advanced analytics. Guessing is not an option.

A multi-cloud environment using multiple public cloud services, usually from different service providers, allows the distribution of risk across multiple cloud environments to improve and assure business continuity. Individual cloud environments can have more risks due to the unique service that the provider delivers. Hybrid, which includes public and private, can have value for some organizations but may seem more costly to manage.

In either case, organizations have to be consumer-driven and drive superior experiences for their customers. If a customer cannot access a supplier’s services because of a cloud failure, that supplier is in danger of becoming optional to that customer. Architecting a multi-cloud environment may be the best choice for business continuity, overall cost, and lowering the risk of failure due to your ability to deliver services to the customer.

Cloud Growth for the Customer

Cloud environments have to perform well. The performance of your cloud environment is partially based on how easily you can obtain data and make decisions. Decision insight capabilities have to be real-time and predictive. The data for the entire enterprise has to be integrated to support the mission of the organization. This includes current cloud data and the ability to easily integrate future data platforms.

As your organization grows, so does the data. Dynamic analytics is a must across all environments, especially if you chose a multi-cloud environment. Since each cloud is different, you have to have a common data warehouse solution such as Actian that can address your current cloud architectures and your future cloud initiatives.

Speed, real-time data, performance, and cost are key factors for the empowerment of a data-driven enterprise. Data that each decision-maker can use quickly, in a collaborative real-time fashion that improves the performance of the organization for its customers is mandatory for managing costs, customer experiences, and dynamic decisions.

Services should continuously improve over time, as competition and innovation are never-ending. The use of the cloud will continue to grow, and organizations have to take advantage of the technology growth in the industry. As this happens, customers want more from every company from whom they consume services or products. Cloud growth then becomes synonymous with customer growth. The data will continue to grow, along with the maturity of data management capabilities across all the platforms. Build a strong enterprise data management foundation and select technology to support your current build and future projects.

Improving Performance, Costs, and Value

On-premise capabilities can also play a significant role in managing multi-platform environments. Remember the old saying, “He who owns the gold makes the rules.” This is also true for your data. An on-premises solution leveraged with a cloud solution can help you manage risk to your organization’s data. Keeping sensitive data on-premises and exploit the elasticity of the cloud as a strategic data decision.

Improving performance and reducing cost always has value to an organization. Including risk avoidance brings with it business opportunity values that improve return on investment (ROI) and the overall value of the investment (VOI) for the organization. Empowering a data-driven enterprise with cost-effective solutions for data analytics anytime and anywhere is a good investment for managing risks.

Building Knowledge for Decisions

The decision support foundation strategy that is set for business intelligence and analytics is important. The key is building on a great platform for supporting cloud intelligence and analytics that services your current needs and is positioned for the future. The technology that you choose has to be built with the following capabilities.

Real-time decision-making.
The ability to manage all clouds.
The ability to support existing on-premises data.
The ability to easily integrate enterprise data.
High performance and low cost.

Data empowers decisions and has to be enabled and empowered with leading technology.

Actian is a fully managed hybrid cloud data warehouse service designed from the ground up to deliver high performance and scale across all dimensions – data volume, concurrent user, and query complexity – at a fraction of the cost of alternative solutions.

Actian 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 Actian Corporation

Data Management

From Data Management to Decision-Making for Empowerment

Actian Corporation

January 28, 2021

person pointing to represent decision-making

Everyone in an organization makes decisions every day. They go through the process of identifying that a decision needs to be made, then gather data and relevant information from various sources to identify the choices that they can make. By Analyzing the data and other considerations based on their experience, they can make a choice and finally take action. The decisions are reviewed for continuous improvement in their decision-making process.

The Four Stages of Decision-Making

Decision-making is one of the most important responsibilities people have in any organization they work in. For every decision, people go through the four stages of learning.

The four stages are:

Unconscious incompetence.
Conscious incompetence.
Conscious competence.
Unconscious competence.

The stages of conscious incompetence and conscious competence are stressful and require help with data that has to be gathered or presented to them for the decision.

Delivering Services and Products

Every company delivers a service or product to its consumers. The company’s assets enable these services or products. An important asset is the people in the organization. Every person is unique and at a different stage of maturity and expertise in their functional role. Each person does their job a little differently and at a different pace or performance level.

Delivery of the organization’s services and products has to be predictable and consistent. To enable this, an efficient and effective value chain of activities needs to occur between functional units in the organization enabled with technologies, data, and tools that need to be collaborative.

Identifying and managing constraints helps the organization to improve performance, but decision-making can be a constraint for productivity. The process can be slowed down when technology does not support the collaboration and the data exchanges need to make informed decisions. Enterprises are people and data-driven. The data needs to be accurate and timely, and the people need to be empowered to use it for decision-making.

Role of Data and Data Relationships

A decision can be automated and should enable improved performance of the organization and its people. One of Enterprise Resource Planning (ERP) systems’ biggest values was the enablement of consistent, collaborative data exchanges across the organization for decision support.

Today as with ERP systems, organizations need to improve the empowerment of the data-driven enterprise. The data managed by an ERP is important, but all the organization’s data is also important, including external data sources that support the organization’s goals. The organization is one team with different roles and should perform in an efficient collaborative way to enable high performance.

Data Capture to Understanding Experience

Enterprise data systems should capture data from anywhere and everywhere, both internally and from cloud sources. Capturing data from all organizational functions, including other enterprise data integrations, will improve its ability to make decisions based on data and improve people’s overall performance and abilities.

Understanding experiences when making a decision can help enrich data capture and presentation usage for faster decisions. Understanding your employees’ experiences and understanding your customer and other stakeholders is driven by cloud data collection, enterprise data collection, and the integration between them. Using a solution like Actian DataConnect enables quick and easy deployment and management across on-premises, cloud, and hybrid environments. It allows you to connect to virtually any data source, format, location, using any protocol, and use this data to understand the experience of customers or suppliers.

Consistency for Success

Enterprise data integration improves decision support and consistency for success. Leverage a solution like DataConnect that supports current needs to drive a data-enabled enterprise. Remove the stress as quickly as possible for decision-making by leveraging the analytical performance, data collection, knowledge intelligence, no matter where the data resides, for continuous improvement of service and product delivery.

Improving consistency using an enterprise-class data-driven solution is just a good service to yourself. Taking care of your needs for less stressful decisions empowered by enterprise data is also good for your business objectives and your customers. Learn more here.

About Actian Corporation

Data Analytics

Data Analytics is a Core Element Creating Business Value

Actian Corporation

January 22, 2021

Valued Assets

Organizational assets are defined as capabilities and resources. To be valued strategic assets, they have to contribute to the success of the organization. There is no business value contributed to an asset that does not have an understood return on investment (ROI) for the organization.

Capabilities can be defined as the ability, power, potential, or competence to do something or carry out an activity for producing a service or product. Examples of categories of capabilities are people, organization, process, knowledge, and management. Capabilities can be generalized or specialized. When specialized, they can create a unique advantage for an organization. For example, an organization can follow a standard industry process and achieve the same results as another organization or the organization can improve on the standard industry process and achieve much better results by making the process specialized and more efficient.

Resources can be defined as a supply, materials that enable functionality, or the building blocks within an organization to produce services or products. Examples of resources are financial capital, infrastructure, applications, information, and people. Resources are drawn on to extract value for transformation into the needed product or service. Extraction of value has a direct relationship to the capability of the resource.

Valued strategic assets contribute to the organization’s success by supporting the effective, efficient, and economical delivery of products and services. Assets that do not contribute to the organization’s top and bottom line are liabilities and can contribute to organizational failure.

People-Driven

The most valuable assets in an organization are its people. People need to be enabled to perform at their highest level. This can mean investing in people’s capabilities, especially their abilities to make decisions rapidly and effectively. People deliver specialized capabilities for the organization in various functions. Each of the functions that people serve contributes to a business value chain for producing a product or service for consumption. People are enabled by a process, technology, and other organizational capabilities, such as management systems.

Although, people are directed with the process, procedure, and work instructions. Then further enabled with tools, technology, and automation. There remains lots of manual work. Manual repetitious activities performed by people need to be automated to improve organizational performance. Data is continuously processed. Information is extracted from data. With information, knowledge is obtained for decision support. Contributing to the manual activities exists in functional silos, not entirely integrated across the organization.

A Team Sport

Behind every organizational initiative is a person or team of people. We are still a long way from the age where machines and artificial intelligence can completely drive an organization. Also, if this was the case, there are still people entablements that have to occur for automated organizations.

Enablement of teams and people is improved with the proper management of data lakes. Data needs to be collected efficiently and economically for the end goal of enablement of decision support capabilities for the organization. Data automation increases people’s capabilities by replacing manual daily activities with time to think, learn, study, improve, and utilize the unique capability of a human that a machine cannot accomplish.

Experience and Data

Experience is what builds expertise in people. Experience is not just the participation in an activity but includes the combination of empathy and analysis of the activity for improvement and evolution. The same applies to decisions for business innovation, evolution, and transformation. These things don’t just happen but happen when people have time to do higher-level activities. High-level activities are enabled to successfully manage data, information, and knowledge for organizational decision-making.

Simplifying data collection and analysis helps an organization understand customer habits and helps with predictions. Collected experiences interpreted by experienced people help us understand the customer in a way that machines cannot comprehend yet. Combining both as a capability and, over time, creates a unique capability for an organization with data and human decisions can improve overall service, product, and business value.

Accelerate the use of data analytics by assuring and addressing people collaboration and decision-making across the organization. Improve real-time decision-making by understanding how to enable people with actionable data and information.

Using the Actian Data Platform Hybrid Data Warehouse solution includes data integration and unique capabilities such as blazing fast analytics, real-time data ingestion, and an ability to deploy on-premises and across multiple cloud platforms, so you can combine your business experience and data to make decisions in the business moment.

About Actian Corporation

Data Intelligence

Data Governance, a Reinforced Priority for Companies in 2021?

Actian Corporation

January 19, 2021

With both the increasing need for digital transformation and the power of IT solutions, the place of data in corporate strategies is exploding. A reality that makes the notion of data governance an unavoidable priority. Here’s a look back at a challenge that will remain crucial in 2021.

With the growing importance of new technologies, companies are at a crossroads. On the one hand, they are collecting and producing huge volumes of data. On the other hand, they must be able to harness the full richness of this data to adapt to their markets in real time.

The challenge? Implement robust data governance strategies to ensure not only the accuracy and relevance of data but also its reliability and security.

But the challenge does not stop there. They must also provide their teams, internally, with the information they need to fulfill their missions. According to estimates published in Statista’s Digital Economy Compass 2019, the annual volume of data created worldwide has increased more than twentyfold between 2010 and 2020 and reached 50 zettabytes this year. 50 zettabytes, that’s 500 million 100TB hard disks. A dizzying figure, which only goes to illustrate the importance of defining a real data governance policy.

The question is not limited to a simple concern for storage or security, but also, and above all, for the exploitation of the data. An exploitation that allows the company to develop a precious asset to facilitate the daily life of its teams and the satisfaction of its customers.

Gartner stated, “The uncertainty ushered in by 2020 will stay with us for multiple years to come. But with disruption comes an enormous opportunity to not just restart what we used to do but forge new paths. Data and analytics leaders who thrive will design and execute on a strategy that accelerates change, builds resilience, and optimizes business impact.”

Starting Data Governance

No one doubts the importance of a data governance policy anymore. The COVID-19 crisis is a clear illustration of this. Health data are critical to controlling the epidemic and when governance is not properly in place, the consequences can be disastrous.

Strictly speaking, data governance is the overall management of the availability, usability, integrity and security of the data used in an organization. But behind this principle, there are the facts… and organizational or technical difficulties. Within a company, the definition of an appropriate data governance policy must rely on the right people. The team in charge of the data governance policy guarantees the determination of standards, the use and integration of data between projects, domains and business sectors…a demanding mission that requires taking up complex challenges.

Meeting Today’s Data Governance Challenges

Since the place of data is central to the life of a company, it is, more than ever, essential to abolish the silos that too often hinder the optimal use of data. This is the very heart of a data governance project: ensuring that data becomes valuable information. A challenge that involves democratizing data access to non-IT profiles.

All business departments must be able to manipulate, exploit and interrogate data.

To achieve this, the solutions deployed in organizations must offer an intuitive and ergonomic experience. But behind the sharing of information, which brings with it the notion of data quality, there is the constant challenge of securing data… especially when your employees are not physically present in the company and access this strategic asset from home, for example. Identity management and compliance with “best practices” in terms of IT security must be the subject of constant support. This support must be the immediate counterpart to the development of an internal culture of data governance.

Developing policies, procedures and practices that enable effective control and protection of data, while at the same time strengthening the way it is handled and used, is the DNA of a Data Governance policy.

About Actian Corporation

Data Intelligence

IoT in Manufacturing: Why Your Enterprise Needs a Data Catalog

Actian Corporation

January 12, 2021

Digital transformation has become a priority in organizations’ business strategies, and manufacturing industries are no exception to the rule! With stronger customer expectations, increased customization demands, and the complexity of the global supply chain, manufacturers are in need of finding new, more innovative products and services. In response to these challenges, manufacturing companies are increasingly investing in IoT (Internet of Things).

In fact, the IoT market has grown exponentially over the past few years. IDC reports the IoT footprint is expected to grow up to $1.2 trillion in 2022, and Statista, by way of contrast, is confident its economic impact may be between $3.9 and $11.1 trillion by 2025.

In this article, we define what IoT is and some manufacturing-specific use cases, as well as explain why the Actian Data Intelligence Platform Data Catalog is an essential tool for manufacturers to advance in their IoT implementations.

What is IoT?

According to Tech Target, the Internet of Things (IoT), “a system of interrelated computing devices, mechanical and digital machines, objects, or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.”

A “thing” in the IoT can therefore be a person with a heart monitor implant, an automobile that has built-in sensors to alert the driver when tire pressure is low or any other object that can be assigned an ID and is able to transfer data over a network.

From a manufacturing point of view, IoT is a way to digitize industry processes. Industrial IoT employs a network of sensors to collect critical production data and uses various software to turn this data into valuable insights about the efficiency of manufacturing operations.

IoT Use Cases in Manufacturing Industries

Currently, many IoT projects deal with facility and asset management, security and operations, logistics, customer servicing, etc. Here is a list of examples of IoT use cases in manufacturing:

Predictive Maintenance

For industries, unexpected downtime and breakdowns are the biggest issues. Hence manufacturing companies realize the importance of identifying potential failures, their occurrences and consequences. To overcome these potential issues, organizations now use machine learning for faster and smarter data-driven decisions.

With machine learning, it becomes easy to identify patterns in available data and predict machine outcomes. This works by identifying the correct data set, combining it with a machine to feed real-time data.This kind of information allows manufacturers to estimate the current condition of machinery, determine warning signs, transmit alerts and activate corresponding repair processes.

With predictive maintenance through the use of IoT, manufacturers can lower the maintenance costs, lessen the downtime and extend equipment life, thereby enhancing quality of production by attending to problems before equipment fails.

For instance, Medivators, one of the leading medical equipment manufacturers, successfully integrated IoT solutions across their service and experienced an impressive 78% boost of the service events that could be easily diagnosed and resolved without any additional human resources.

Asset Tracking

IoT asset tracking is one of the fastest growing phenomena across manufacturing industries. It is expected that by 2027, there will be 267 million active asset trackers in use worldwide for agriculture, supply chain, construction, mining, and other markets.

While in the past manufacturers would spend a lot of time manually tracking and checking their products, IoT uses sensors and asset management software to track things automatically. These sensors continuously or periodically broadcast their location information over the internet and the software then displays that information for you to see. This therefore allows manufacturing companies to reduce the amount of time they spend locating materials, tools, and equipment.

A striking example of this can be found in the automotive industry, where IoT has helped significantly in the tracking of data for individual vehicles. For example, Volvo Trucks introduced connected-fleet services that include smart navigation with real-time road conditions based on information from other local Volvo trucks. In the future, more real-time data from vehicles will help weather analytics work faster and more accurately; for example, windshield wiper and headlight use during the day indicate weather conditions. These updates can help maximize asset usage by rerouting vehicles in response to weather conditions.

Another tracking example is seen at Amazon. They are using WiFi robots to scan QR codes on its products to track and triage its orders. Imagine being able to track your inventory—including the supplies you have in stock for future manufacturing—at the click of a button. You’d never miss a deadline again! And again, all that data can be used to find trends to make manufacturing schedules even more efficient.

Driving Innovation

By collecting and audit-trailing manufacturing data, companies can better track production processes and collect exponential amounts of data. That knowledge helps develop innovative products, services, and new business models. For example, JCDecaux Asia has developed their displaying strategy thanks to data and IoT. Their objective was to have a precise idea of the interest of the people for the campaigns they carried out, and to attract their attention more and more via animations. “On some screens, we have installed small cameras, which allow us to measure whether people slow down in front of the advertisement or not.”, explains Emmanuel Bastide, Managing Director for Asia at JCDecaux.

In the future, will displaying advertising be tailored to individual profiles? JCDecaux says that in airports, for example, it is possible to better target advertising according to the time of day or the landing of a plane coming from a particular country! By being connected to the airport’s arrival systems, the generated data can send the information to the displaying terminals, which can then display a specific advertisement for the arriving passengers.

Data Catalog: One Way to Rule Data for any Manufacturer

To enable advanced analytics, collect data from sensors, guarantee digital security and use machine learning and artificial intelligence, manufacturing industries need to “unlock data,” which means centralizing in a smart and easy-to-use corporate “Yellow Pages” of the data landscape. For industrial companies, extracting meaningful insights from data is made simpler and more accessible with a data catalog.

A data catalog is a central repository of metadata enabling anyone in the company to have access, understand and trust any necessary data to achieve a particular goal.

Actian Data Intelligence Platform Data Catalog x IoT: The Perfect Match

Actian Data Intelligence Platform helps industries build an end-to-end information value chain. Our data catalog allows you to manage a 360° knowledge base using the full potential of the metadata of your business assets.

Success Story in the Manufacturing Industry

In 2017, Renault Digital was born with the aim of transforming the Renault Group into a data-driven company. Today, this entity is made up of a community of experts in terms of digital practices, capable of innovating while delivering agile delivery and maximum value to the company’s business IT projects. Jean-Pierre Huchet, Head of Renault’s Data Lake, states that their main data challenges were:

Data was too siloed.
Complicated data access.
No clear and shared definitions of data terms.
Lack of visibility on personal/sensitive data.
Weak data literacy.

By choosing the Actian Data Intelligence Platform Data Catalog as their data catalog software, they were able to overcome these challenges and more. Actian Data Intelligence Platform today has become an essential brick in Renault Digital’s data projects. Its success can be translated into:

Its integration into Renault Digital’s onboarding: mastering the data catalog is part of their training program.
Resilient documentation processes & rules implemented via the Actian Data Intelligence Platform.
Hundreds of active users.

Now, the Actian Data Intelligence Platform is their main data catalog, with Renault Digital’s objectives of having a clear vision of the data upstream and downstream of the hybrid data lake, a 360 degree view on the use of their data, as well as the creation of several thousands of Data Explorers.

Actian Data Intelligence Platform’s Unique Features for Manufacturing Companies

Our data catalog has the following features to solve your problematics:

Universal connectivity to all technologies used by leading manufacturers.
Flexible metamodel templates adapted to manufacturers’ use-cases.
Compliance to specific manufacturing standards through automatic data lineage.
A smooth transition in becoming data literate through compelling user experiences.
An affordable platform with a fast return on investment (ROI).

Are You Interested in Unlocking Data Access for Your Company?

Are you in the manufacturing industry? Get the keys to unlocking data access for your company by downloading our new Whitepaper “Unlock Data for the Manufacturing Industry”. Download our Whitepaper.

About Actian Corporation

Data Management

Leveraging IoT and Industry Data for Managing Experiences and Decisions

Actian Corporation

January 12, 2021

Leveraging Data

Data is everywhere. Data comes from social interactions, personal devices, cloud service interactions, the environment, and many other places and things. There is a wide variety of data and a significant volume of data being collected every second. The velocity and quality of data are increasing every minute.

Big data can be considered a moment in society that will continue to grow by double figures each year. Any research on big data will give you insights into the magnitude that this moment is growing across the globe. People, organizations, and machines crave data to support their existence.

Every person is a participant in data collection. Every industry wants to leverage this data for truthful decisions that can affect their organizations. Many personal devices and organizational devices are connected to the Internet, which is usually referenced as the Internet of Things (IoT).

Leveraging all this data takes careful planning to support organizational strategic intent and overall decision-making capabilities. People and organizations use data to help remove slow manual interactions to improve the ability to react and be proactive for decisions and the overall well-being of the person or organization.

Data Visualization, Interoperability, and Connectivity

Quick decisions require the ability to visualize the data message from the noise easily. No one system functions without connectivity to another system for data exchanges by turning inputs into outputs for processing and analytics. Interfaces and data relationships have to be completely understood and interoperable across many distinct platforms. Enterprise data solutions are needed to leverage interoperability and connectivity across multiple data sources.

Data Decisions for Continuity

People and organizations want continuity of services. Continuity consists of:

Ensuring the service is available when needed.
Having enough capacity to support requests and analytic computations.
Secure enough to protect the confidentiality, integrity, and information privacy.

Organizations have to continuously monitor and collect data, perform analysis and generate analytics, quickly implement solutions, perform constant tuning, and repeat these activities to listen to their systems and customers, including monitoring for threats, such as those related to cybersecurity.

If a threat to business continuity becomes apparent through data analysis, the organization needs to respond to the threat as soon as possible. If the threat creates an incident, the incident must be first detected, then addressed as soon as possible. If the incident creates damage, the damage must be repaired as soon as possible. The recovery actions need to be evaluated to stop the loss of business continuity as early in the cycle as possible. The ability to do proactive data detection and trend analysis across the organization’s products and services and across connected devices of their customers becomes important, especially for critical services impacting customers.

To accomplish this, business continuity plans need to be created, and IT continuity plans have to support business plans. Services have to be classified as critical or non-critical especially if they impact customers or revenue. Organizations may formally perform impact analysis and risk assessments. People may do the same informally to determine the criticality of products and services. There needs to be an exchange of data from the customer’s perspective to the organization for the creation of a proper response for the benefit of both the customer and the organization.

Organizations need to formalize the creation of proactive plans, testing, training, audits, and invocation of plans to collect data and help assure that the organization continues to function following a disruption. Without these formal activities, people will behave in a reactive manner to try to restore the capability of a service, which can compound an incident.

Managing Experience and Decision

Organizational needs and people needs have to be aligned. Understanding how people experience the services and products, including their dependency, continuity, and security needs, becomes very important for organizational survival. People who use critical products or services from an organization and then face a challenge with that deliverable will usually try to make a rapid decision to support their outcomes with the new provider. Employees who engage a new provider may never return to the provider that they had the issues with. An organization that loses a customer usually spends over three times more time and effort trying to regain loyalty.

An enterprise data strategy built on high-performing technology can help ensure high customer retention, low cost, and mitigate reputational damage to the organization. It is important to obtain and manage the right data about your customers across multiple data sources. Actian DataConnect makes it easy to connect operational data from any data source and transform it to facilitate effective data analysis. DataConnect makes it easy for data-driven enterprises to design, deploy, and manage data flows across on-premises, cloud, and hybrid environments.

Are Real-Time Analytics in Supply Chain Management Important?

Processing Data in Real-Time

Business Analytics

Supply Chains

The Importance of Real-Time Analytics

About Actian Corporation

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

COVID19: The Rise of Digital Transformations for Enterprises

Accelerating Digital Transformations…With Data

Certain Sectors at the Forefront of Digital Transformation

About Actian Corporation

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Data Lakes, Data Warehouses, Data Hubs – Do We Need These?

Level Setting

Data Hub

Data Lake

Data Warehouse

Virtual Rubber Meets Virtual Road

Increasing Complexity Still Further

Shifting the Focus

About Actian Corporation

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

Building a Data Management Strategy for Supply Chains

Business Strategy

Data Management

Supply Chain Visibility

The Supply Chain

Building a Cohesive Data Management Strategy

About Actian Corporation

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

Best Practices for Succeeding Your Digital Transformation

Rethink Your Corporate IS

Getting the Right Data Solutions

All This With a Data Catalog

Start Accelerating Your Data Initiatives Now

About Actian Corporation

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

From Databases to Knowledge Management Systems for Data Power

Databases

Tools and Technology to Get Value From Your Data

Collaboration

Knowledge Management Systems

Moving the “Normal” – From Data-Driven to Data-Powered

About Actian Corporation

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

The Best Cloud Data Strategy for Value

Which Cloud Makes Sense?

Cloud Growth for the Customer

Improving Performance, Costs, and Value

Building Knowledge for Decisions

About Actian Corporation

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

From Data Management to Decision-Making for Empowerment

The Four Stages of Decision-Making

Delivering Services and Products

Role of Data and Data Relationships