Cloud Data Warehouse

Data Productivity Cloud

An immersive Data Cloud

Why is the Data Productivity Cloud Important?

Data is the lifeblood of a business. Every customer transaction is captured in digital form. Social media captures market sentiment and customer feedback about a product or service. To remain competitive, an organization must be attentive to its internal operations and the external influences on its performance. Teams need to make data-based, informed operational decisions to be productive.

Using guesswork is not productive. Accurate insight goes a long way. If an operational decision fails to produce the desired results, a business needs the data to inform them so they can make the appropriate course corrections. Cloud platforms allow businesses to move fast as they can provision the needed resources on demand without procuring IT assets in advance. When the market moves in your favor, you need the ability to capitalize on the opportunity before it is gone.

Uses for a Data Productivity Cloud

Now that we have outlined the components needed to visualize cloud-based data productively, we can consider some use cases where teams can use insights from operational data.


In the early stages of the sales cycle, every minute of responsiveness matters if you want to set the agenda for the prospect before the competition does. Inside sales normally handle the initial contact with a potential customer, so they need to track their every move that can inform a conversation. A sales dashboard that tracks lead scores from the sales and marketing systems enables the sales team to shift focus on the most active leads with a tailored response to any activity, such as a white paper download or webinar form fill.


Gaming companies don’t rely on dashboards; they must programmatically respond to player activities. For instance, if the activity log of a sports bettor indicates an interest in a particular league, team or player, then the ads and in-game recommendations can be prioritized accordingly.


Outbound marketing activities such as nurture emails must be as targeted as possible to their audience. Rather than send everyone on the corporate list the same email, marketing automation systems such as Marketo and Hubspot can segment the audience using smart contact lists. These dynamic lists can be filtered up to the last minute based on the latest activity by subscribers. For example, DiscoverOrg may report that some subscribers have Informix installed based on recent job listings. The marketing team can create a segment that receives news about that specific technology along with regular content.


A perfect use of geolocation technology is the use of geo-fences that tell a manufacturer that raw materials and parts are within 10 miles of the factory. The data productivity cloud can subscribe to the SMS alert of the approaching truckload and direct the gate to get the trailer parked at the factory door closest to the parts bin needing to be replenished, minimizing the chance of lost productivity due to a parts delay.

What are the Components of a Data Productivity Cloud?

A data productivity cloud must offer multiple data-centric functions to make data analysis more productive. These include:

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Data Productivity Cloud & Connectivity

Analytic dashboards require data from multiple source systems. Pre-built connectors to existing sources, including databases, flat files, logs, social media streams and operational applications such as SAP, Salesforce, and ServiceNow, are essential. Data integration technology such as Actian DataConnect includes such connectors.

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Getting raw source data to systems that can analyze it requires the orchestration of multiple data pipelines that must be able to transport, schedule, clean and transform it for analysis.

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Storage for Data Productivity Cloud

Cloud platforms such as Azure, AWS and Google Cloud offer endless capacity and scalability to store vast quantities of data. The platform must support multiple storage formats and access methods, including streaming and subscription mechanisms. Block storage enables file storage to be allocated using standard application programming interfaces (APIs) without worrying about filling up disk volumes that would otherwise have to be monitored and managed.

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To extract meaningful insights from data, it must be analyzed. The analytics processing can take the form of SQL functions such as aggregations, user-defined functions to execute programs that perform calculations or machine learning algorithms that mine for hidden correlations.

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Visualizations are the key to communicating findings in the data easily. A data productivity cloud must work with Business Intelligence tools like Tableau, Qlik and Power BI to create real-time dashboards.

Actian Data Platform

The Actian Data Platform can be deployed as the backbone of any cloud data productivity solution. Its capabilities include built-in connections to hundreds of data sources, orchestration and scheduling of data pipelines, efficient cloud storage and connectivity to all BI solutions.

Get started with a free trial of the Actian Data Platform.