What Are the Three Main Component Pillars of SAP Analytics Cloud?

Rapid digitization is transforming the business world at an exceptional pace. This has made data and information more easily accessible for both customers and businesses. Since data is a significant asset for businesses, everyone finds new and innovative ways to gather data and generate valuable insights to drive informed and insightful business decisions.

One such prominent innovation in this data-driven world is the SAP Analytics Cloud solution. This cloud-based solution is equipped with nuanced features and capabilities to help businesses make the most of their data and grow their business. Therefore, let’s understand what really makes the SAP Analytics Cloud solution.

What is SAP Analytics Cloud?

Every business operating in the 21st century needs to leverage the potential of data to stay ahead of competitors and grow their business. SAP Analytics Cloud is ideally designed to help your business make the most of all data generated. The software-as-a-service (SaaS) platform comes equipped with advanced functionalities such as data analysis, data visualization, and business planning. This allows businesses to carry out informed and well-thought-out business planning and address the gaps within their stratifications to boost the growth of the business.

Primary Components of SAP Analytics Cloud

SAP Analytics Cloud solution is primarily centered around three different technological pillars that more or less give the solution its identity. All three of these components work in sync with each other to help businesses generate valuable insights and facilitate better decision-making. These components include:

1. Data
2. Models
3. Stories

1. Data

Before you can create anything of value, you will first need to gather the raw material for the final product. This is precisely what data is to SAP Analytics Cloud. The solution helps businesses gather data from different sources to generate valuable information for the business. Thus, before creating appealing models and engaging stories, one first needs to gather data.

As many might already know, the SAP Analytics Cloud solution is built on the SAP HANA Cloud Platform. The intelligence capabilities of the platform help businesses leverage business intelligence and planning functionalities. This further allows one to gather live data and information from different data sources. These sources generally include web sources, on-premise databases, as well as the cloud database.

Generating data becomes very simple when using the SAP Analytics Cloud solution. Furthermore, one can use the solution to facilitate effective live connections with different platforms such as the SAP S/4HANA, SAP BW, SAP HANA, SAP BW/4 HANA, and even the SAP Business Object Universe.

2. Model

With more straightforward methods to generate data from different sources, businesses are generating humongous volumes of data in a brief span of time. However, this data will yield no value unless one can sort it out and generate some valuable information.

This is where data modeling comes in super handy. This allows businesses to analyze the data and generate valuable and deep insights from the data. In addition, users can use SAP Analytics Cloud to create different planning and analytical models based on the requirements of the business.

• Planning Model: The planning model in SAP Analytics Cloud planning is used to carry out varied modeling functionalities such as version management, advanced data entry capabilities, and even data actions. These features help businesses to gain more control over their data.
• Analytics Model: The analytics model leverages the machine learning algorithms to clean all data automatically and determine any errors within the data while creating a model to feature in stories and visualizations.

Best Practices Used for Data Modeling

Data plays a crucial role in modeling as they are used in different tools and modeling features available in the SAP Analytics Cloud solution. Thus, here are a couple of practices one can leverage to enhance the effectiveness of analytics processes.

1. Hierarchy Creation: This practice to sort and better manage data across varied locations. One can create such hierarchies based on location, such as states, countries, and even continents. Hierarchy creation helps users’ layer and analyze their data much more effectively.
2. Setting Units or Currencies: Dealing with different currencies with variable units and different values makes managing data featuring different currencies easier. Other than that, one can even use preferred formulae on the relevant data to convert various currencies or units into a common one.
3. Data Wrangling: This practice helps users re-check data across different columns and rows on the model. Thus, determining typo errors and inconsistencies in data to prevent any adverse impact on reports and stories. This also allows one to merge different cells, replace varied synonyms with a common word and even carry out different operations for the same.

3. Stories

One can create stories over models to enhance the presentation of business data. This way, one can visualize and explore varied forms of graphs, charts, tables, geo maps, and different visual types. There are also other features and functionalities offered by SAP Analytics Cloud to be used in stories. These features include:

1. Search to Insight: This feature enables one to determine insights from data in interactive forms of graphs or charts by seeking answers in natural language.
2. Smart Discovery: Users can use this feature to determine hidden structures and relationships from the data. The smart discovery advanced technologies such as Machine Learning algorithms to determine critical influencers of the KPIs and generate valuable insights for the business.
3. Data Visualization: SAP Analytics Cloud is one of those few solutions that help users seamlessly integrate the R environment into the system. R is the open-source programming language that leverages varied graphical and statistical projects in this context. This environment is ideally designed for nonlinear and linear modeling due to its extensibility. Besides that, the R environment is also suited for clustering, classification complexities, analysis time-series, and other similar visualizations. Thus, users can create various visualizations to suit users’ requirements in the R script.

Final Thoughts

These are the three primary pillars of the SAP Analytics Cloud solution. Thus, keep this information in mind when you migrate to SAP Analytics Cloud and take your business heights. Also, feel free to reach the experts at SAP consulting company to clear any further questions.