Data integration is a critical capability for any organization looking to connect their data—in an era when there’s more data from more sources than ever before. In fact, data integration is the key to unlocking and sustaining business growth. A modern approach to data integration elevates analytics and enables richer, more contextual insights by bringing together large data sets from new and existing sources. That’s why you need a data platform that makes integration easy. And the Actian Data Platform does exactly that. It’s why the platform was recently honored with the prestigious “Data Int ..read more

With organizations using an average of 130 apps, the problem of data fragmentation has become increasingly prevalent. As data production remains high, data engineers need a robust data integration strategy. A crucial part of this strategy is selecting the right data integration tool to unify siloed data. Assessing Your Data Integration Needs Before selecting a data integration tool, it’s crucial to understand your organization’s specific needs and data-driven initiatives, whether they involve improving customer experiences, optimizing operations, or generating insights for strategic decisions ..read more

Achieving Success Through Data Integration & Analytics Data integration connects disparate data sources to support business decision making. Data integration solutions can include the following functions: Extract, Transform and Load (ETL) functions to connect, gather, clean and transfer data to a data mart or data warehouse for analysis. Extract, Load and Transform (ELT) technology to filter, transform and aggregate data sets inside a data warehouse. Replicating change from an operational system to a data warehouse. Data pipeline orchestration. Data transfer scheduling. Data deduplication ..read more

For data to be used effectively by analytics and machine learning applications, it must be preprocessed. Preprocessing data makes it easier to use by applying operations such as removing outliers, filtering, transforming and normalizing data from its source form. Why is data preprocessing important? Unrefined source data must be optimized for its intended use before contributing to dependable insights. Basing decisions on data that is not preprocessed will result in poorly informed decisions that are more likely to lead to unintended outcomes. Using unrepresentative samples will skew analytica ..read more

Cloud-based data integration allows organizations to connect on-premises and cloud-based applications or data sources using pre-built connectors with the management infrastructure hosted by a solution provider. The solution provider manages the software, including patching and software upgrades, which reduces the IT effort for the client organization. Cloud-based data integration can be extended to support new collaboration models such as integration-as-a-service (IaaS) and integration-platform-as-a-service (iPaaS), which make a single multi-tenant data integration system available to multiple ..read more

As businesses continue to tap into ever-expanding data sources and integrate growing volumes of data, they need a solid data management strategy that keeps pace with their needs. Similarly, they need database management tools that meet their current and emerging data requirements. The various tools can serve different user groups, including database administrators (DBAs), business users, data analysts, and data scientists. They can serve a range of uses too, such as allowing organizations to integrate, store, and use their data while following governance policies and best practices. The tools ..read more

Data-driven organizations are accustomed to using structured data. This type of data is well-defined, organized, stored in a tabular form, and typically managed in a relational database management system (RDBMS). The data is predefined and formatted to fit a set structure. A vast range of tools have been developed to optimize this type of data, which includes customer names, sales data, and transaction dates. The data is easily searchable by programming languages and data analytics tools.  Unstructured data is different.  It does not have a predefined data model or structure, making ..read more

What is Data Ingestion? Data must be ingested by an application program or database management system before it can be processed or analyzed. When an application or database system reads external data, it is ingesting data. Where Data Ingestion Fits in the Data Preparation Process Data preparation is a process that encompasses extract, transform & load (ETL) and includes a data ingestion step. Once the source data is identified, it must be brought into the analysis tools to gain business insights. The data will likely be a combination of structured and semi-structured data in different fi ..read more

Defining Exceptional Service  Organizations always aim to give their customers the best experiences. There are several interpretations of what constitutes good and excellent service, and sometimes these terms are used interchangeably. However, many companies fail to achieve exceptional service because they do not understand their customers.  Delivering any level of service requires that we anticipate what the customer is expecting. Sometimes they do not know what they want – so businesses use insights to predict what might be most useful to them. There have been many good companies w ..read more

As businesses continue to tap into ever-expanding data sources and integrate growing volumes of data, they need a solid data management strategy that keeps pace with their needs. Similarly, they need database management tools that meet their current and emerging data requirements. The various tools can serve different user groups, including database administrators (DBAs), business users, data analysts, and data scientists. They can serve a range of uses too, such as allowing organizations to integrate, store, and use their data, while following governance policies and best practices. The tools ..read more