Enterprise Analytics

What is Enterprise Analytics?

Enterprise analytics is the practice of harnessing available internal and relevant external data to inform business decisions and transform data into insight. It is a critical component of any enterprise’s digital transformation efforts.

Realize the value of enterprise data

Enterprises today have a wealth of data available to them, and most realize that this data has significant value for their business. But data alone is not enough; it must be processed through various analytics techniques to enable better informed operational and strategic decisions. Data-driven decision-making can help enterprises enhance their competitiveness, streamline operations, improve productivity, and increase profitability.

The right information to the right people at the right time

Having enough data within an enterprise is no longer a challenge; business processes, customer service interactions, sensors, and more are all gathering data daily. Knowing what data is relevant for an analysis is a primary consideration today. In order to be effective, an enterprise analytics program has to be able to provide the right data—and just the right data—to those who need it for their decision-making. That data also needs to be provided in a timely fashion; conditions change quickly in today’s business environment, so outdated information can do more harm than good.

Why do enterprise analytics matter?

Organizations that employ enterprise analytics practices can benefit from improved operational efficiency, higher profitability, faster decision-making, and greater ability to identify new business opportunities. The visibility into business processes, capital deployment, and workforce output that can be gained through analytics helps enterprises gain real-time insights on their current operations to be more resilient regardless of possible future disruption.

Additionally, analytics can help organizations improve the customer experience. Data from customer interactions, online feedback, published competitor information, and more can help enterprises identify patterns that enable them to anticipate and react to new market trends, optimize the customer experience, understand customers’ needs and preferences, increase retention, drive sales, and improve profitability.

Enterprise analytics can also provide valuable insights into an organization’s workforce. From mining human resources (HR) data to help attract and retain employees to analyzing processes and systems to identify opportunities to increase worker efficiency, enterprise analytics can save organizations significant time and costs related to their labor force.

Most enterprise analytics platforms offer real-time reporting, so business leaders have access to up-to-the-minute insights into the current state of operations across the organization. This helps increase agility as well as enhancing visibility across the various departments and lines of business (LOBs) throughout the organization.

What are the challenges in enterprise analytics?

As new technologies have become available to collect information from more sources, some enterprises focus more on gathering and storing as much data as they possibly can without formulating a strategy for how they will actually use that data. The volume and velocity of data being generated within organizations makes it difficult to analyze, particularly with the growth in unstructured and semi-structured data.

Without a solid data management strategy in place, data scientists and analytics platforms may not be able to connect to all the information they need. In some cases, data analysts may have to spend more of their time managing data than they do analyzing it. And as more data is generated at edges and stored in clouds, data silos can develop and impede access to critical information for both systems and analysts.

Legacy business systems that contain critical information are also a challenge that must be dealt with in enterprise analytics. Data from these systems may be stored in proprietary formats and structures, especially when an organization developed customized solutions meant to interact with monolithic applications. As enterprises move to new, cloud-native systems and architectures, incompatibilities with legacy systems may make it difficult for organizations to leverage their historical data now, possibly leading to blind spots.

What are enterprise analytics platforms?

Enterprise analytics platforms offer tools and solutions to assist with three types of analytics: descriptive, predictive, and prescriptive. In descriptive analytics, key performance indicators (KPIs) are tracked to understand the current state of the business. Predictive analytics looks at trends in the data to anticipate potential future outcomes. And prescriptive analytics looks at past performance to make recommendations for actions if similar situations arise in the future.

Like business intelligence (BI) tools, enterprise analytics platforms compile and analyze data. However, enterprise analytics platforms offer more in-depth and wide-reaching insights, helping organizations automate and optimize business processes. They combine data from multiple sources into integrated dashboard views showing KPIs across the organization. These dashboards should be intuitive for a wide range of users to access and offer the ability to drill down for more information on specific metrics. They should also be easily scalable to manage both increasing data volumes and number of users.

Enterprise analytics platforms may be open source or proprietary. One of the earliest analytics platforms is SAS, which started as a proprietary program but now offers versions that can run on cloud-native deployments. Other examples of enterprise analytics platforms include Splunk, Hadoop, SAP HANA®, Cloudera, Domo, Apache Spark, TensorFlow, and many others.

HPE solutions for enterprise analytics

With analytics and AI, your data pipeline can help you decisively solve some of your biggest challenges. HPE offers services, advanced technology solutions, and pay-as-you-go models to help you get the most value from your data.

The HPE Apollo 4200 Gen10 Plus System is specifically designed to unlock the business value of data stemming from digital transformation and data infrastructure modernization. This 2U ultra-dense system is designed for deeper data lakes and archives, throughput-demanding analytics, data-heavy HCI, and cache-intensive workloads.

HPE high performance computing (HPC) storage solutions provide high-speed data access using scalable, easily deployed technology. Cloud technologies, operating methods, business models, high-performance data analytics, artificial intelligence, and deep learning bring outstanding agility, simplicity, and economics to HPC. These solutions scale up or scale out, on-premises or in the cloud, with purpose-built storage and the software you need to power innovation.

HPE Ezmeral ML Ops provides pre-packaged tools to operationalize machine learning workflows at every stage of the ML lifecycle, giving you DevOps-like speed and agility. It is an end-to-end data science solution with an open-source platform that delivers a cloud-like experience combined with a curated collection of tools. It has the flexibility to run on-premises, in multiple public clouds, or in a hybrid model and respond to dynamic business requirements.

For enterprises using an SAP HANA® environment for data analytics, the HPE GreenLake edge-to-cloud platform makes it possible to leverage cloud capabilities while keeping your SAP landscape on-premises. You can gain superior performance and agility by running your mission-critical workloads on some of the biggest and fastest bare metal appliances available today, all in a pay-per-use model.