Sales analytics is a key function to inform decisions that have an economic impact on the business. Organizations that do it well have a competitive advantage. But as data sets grow and demands for faster insight increase, analysts are caught between a rock and a hard place.
On one hand, they want to use ever-increasing amounts of data, from different data sources, in their analyses because more data offers more opportunities for deeper insight. Whether data is contained in on-premise data warehouses, data lakes, or in the cloud, analysts want to use any source of information they can to provide the best possible insights.
On the other hand, using larger data sets can mean bumping into slowdowns. Data movement produces delays. Access to data warehouses and data lakes becomes a manual process that can take days. And deploying new infrastructure needed to do larger and larger analyses takes time. In short, data and analytics velocity diminishes, and leaders can’t get the insights they need, in the time they need, to successfully drive the business.
In this guide, we’ll explore three areas of opportunity for improving sales analytics velocity to grow revenue by improving decision making. By addressing application data exchange, self service tools, and scalable infrastructure, your organization can improve data and analytics velocity, accelerating time to insight, and improving competitive positioning.
Simply put, in any business, data needs to flow from production applications into downstream workloads, especially analytics. But different data types and formats, different data locations, and increasing data volumes add complexity to what ought to be a simple exercise. Moving large-scale data sets from the cloud to on-premise analytics infrastructure is labor intensive, takes time, and often results in unwanted transfer costs. Combining data from data lakes and data warehouses often requires time-consuming manual intervention done by specialists who are in short supply.
What organizations need is a universal “single source of the truth” that sits between sources of data and downstream processes like analytics. This allows analytics tools like Excel and Tableau direct access to data sources, providing a semantic model of those data sources without data movement. This semantic layer has to be universal (covering all data sources), fast (accelerating access to data), and massively scalable.
AtScale provides the premier platform for data architecture modernization. AtScale connects you to live data using one set of semantics without having to move any data. Leveraging AtScale’s Autonomous Data Engineering™, query performance is improved by order of magnitude. AtScale inherits native security and provides additional governance and security controls to enable self-service analytics with consistency, safety and control. AtScale’s Intelligent Data Virtualization™ and intuitive data modeling enables access to new data sources and platforms without ETL and or needing to call in data engineering.
One of the biggest impediments to better sales analytics is, sadly, a common problem: inconsistent access to data. Most organizations with multiple data sources have multiple paths to access — in other words, no single sign-in for all data sources. An analyst who wants access to Google BigQuery or another cloud platform, or any other source, submits a trouble ticket…and then waits. If the analyst wants access to everything, they’re going through several processes, which takes time.
Self service tooling is the obvious solution to these challenges. Overall permissions must be set to allow analysts access to the data sets they need, but once the permissions are set, access can’t be blocked by a need for manual IT intervention, aka “trouble ticket hell.” Self service tooling empowers analysts to move forward faster, do more quickly, and accelerate analysis for improved time-to-insight and better business decision-making.
Ever expanded a Hadoop cluster? Any organization who has tried this knows it takes time. Hardware has to be purchased, deployed, tested, and added to the cluster. Continuous scaling to accommodate data growth becomes a constant battle that’s both time consuming and costly. Cloud infrastructure seems to be the obvious answer, as it’s both scalable and easy to provision, but using it as a way to expand on-premise data source infrastructure and/or data analytics infrastructure just isn’t intuitive or simple. Combining the two adds complexity, which results in delays and added costs.
What organizations like yours need is a way to span both on-premise infrastructure and cloud infrastructure. Ideally it creates simplicity across both, adds performance and scale across both, and offers all these capabilities in a cost-effective way.
AtScale helps organizations expand their revenue by improving the analysis used to make business decisions. Customers are able to deliver better sales analytics velocity and granularity by accelerating application data exchange, providing self service tooling, and facilitating a scalable infrastructure It’s a powerful Cloud OLAP tool for virtualizing data sources without data movement, and it’s used the Global 2000 to make million dollar decisions.
|AtScale provides Intelligent Data Virtualization™, giving organizations a way to virtualize any data source (data lake or data warehouse, on-premise or in the cloud) and create a virtual cube, without data movement, that serves the organization as a multi-source spanning Universal Semantic Layer™ for consistent analysis over time.|
|AtScale powers lightning-fast analytics at scale. Organizations that choose AtScale have a tool that powers live connections to any data source with accelerated performance. Analysts now have a way to access more data, from more sources, use more rows, more quickly, and deliver sales analysis that impact business performance right away. Query times are improved anywhere by 5x to 100x.|
|AtScale enables secure and governed self-service data access. Rather than submitting trouble tickets to access a data warehouse or waiting on a data scientist to send an extract, analysts have seamless live data access to any data set that’s connected to AtScale. Instead of dozens of sources, AtScale becomes “the single source of data” for any requirement. Organizations can minimize manual engineering like cube building and ETL. They can also easily add new datasets and quickly add new dimensions.|
|AtScale works with your existing preferred BI tools. Whether someone is using a BI tool, an analytics tool, or others, AtScale powers the virtual cube and business definitions. Direct data access from Excel, Tableau, Power BI and other tools lets organizations choose the right tool for the job.|
|AtScale improves cloud utilization. AtScale works with on-premise data warehouses and data lakes as well as cloud data sources. Organizations that experience delays due to data center provisioning can easily leverage the cloud without disrupting their AtScale virtual cubes.|
|AtScale is also a powerful alternative to SSAS. With SSAS, there’s no agility and often no performance, it takes IT ages to add a dimension or data set and takes minutes to access a data set.. AtScale allows you to do multi-dimensional modeling much faster and offers accelerated access.|
To summarize, we’re well positioned to add velocity to sales data and analytics initiatives.
AtScale is ready to help any organization increase their revenue through better sales analytics.
AtScale powers the analysis used by the Global 2000 to make million dollar business decisions. The company’s Intelligent Data Virtualization™ platform provides Cloud OLAP, Autonomous Data Engineering™ and a Universal Semantic Layer™ for fast, accurate data-driven business intelligence and machine learning analysis at scale. For more information, visit www.atscale.com.