In the modern business environment, data analytics is the backbone of many businesses.
Today, it is the enabler of the managerial decision-making process; every decision you make, ranging from what to produce to how distribution channels should be set up, is based on insights produced by data analytics solutions.
Your organisation, therefore, needs to invest in robust systems that collect, store, and analyse large amounts of data efficiently, helping you gain a significant competitive advantage over your competitors.
While most organisations still rely on an on-site data analytics implementation, future-focused businesses have migrated to cloud-based computing environments like Amazon Web Services (AWS) to implement cost-effective and scalable data analytics solutions.
AWS allows you to configure your cloud infrastructure to suit your specific analytics requirements. It helps you optimise the data analytics pipeline, which consists of data collection, storage, processing and visualisation.
In this post, we dive deeper into how AWS configuration can help you deploy better cloud-based analytics platforms by optimising the analytics pipeline.
Data collection is the most important step in your data analytics pipeline, as it delivers the resources the analytics platform needs to produce actionable insights.
To create the best data collections system for your business needs, you need to consider ingestion frequency, latency, cost and the durability of data collection processes.
The first thing you need to consider when configuring your AWS environment is how often data is sent through your data collection system. There are three distinct ingestion frequencies: hot, warm and cold.
The ingestion frequency determines what kind of AWS configuration you will need to meet your data ingestion requirements.
Transactional data, for example, does not require your AWS environment to be configured to facilitate constant data ingestion. This type of data is better ingested using the Amazon Data Migration service.
Real-time data, on the other hand, requires tools such as Kinesis Firehose and Kinesis Data Streams.
Your data storage requirements will be determined by your data collection systems. That said, every AWS data analytics deployment uses two different data storage methods: data warehouse and data lakes.
A data warehouse stores structured data; this means that the data stored in your data warehouse is cleaned, enriched and processed. Storing data like this is ideal for operational reporting and analysis; you can configure and build data warehouses in AWS using the Redshift tool.
Data lakes, meanwhile, store structured and unstructured data. This particular data storage method can store relational data from applications, and nonrelational data generated from other sources. Data lakes can be built using the S3 tool during the AWS configuration process.
Depending on your needs, you can configure your AWS environment to prioritise either data storage methods.
Raw, unstructured data is not useful in your decision-making process. To drive value in decisions, you need actionable and accurate insights from your cloud-analytics platforms.
To produce actionable insights, your data analytics platforms need to be fed relational data that is free of errors, duplicate entries and unrelated fields. The process of making sure unstructured data is free of errors and suitable for the analysis process is called data preparation.
Data preparation, however, is not a straightforward task. You need to extract data from various sources, clean and organise it into the required format, and then load it into data warehouses. In traditional analytics workflows, data scientists spend more than 75% of their time preparing data for analytics.
AWS offers you several automated tools for data preparation such as AMDA, Amazon EMR and AWS Glue. You need to configure your cloud environment with the tools that best suit your data preparation needs. These tools reduce the time spent on data preparation significantly.
The final step in the analytics pipeline is data visualisation. At this stage, data is pulled from data warehouses, curated and analysed, and presented as useful information to you.
To facilitate visualisation, you need to configure your AWS environment using the Amazon QuickSight tool during the configuration process. It is a robust, cloud-powered data visualisation tool that augments your data analytics capabilities.
As data analytics requirements continue to evolve, many organisations are beginning to migrate their analytics infrastructure to the cloud.
With AWS being the premier cloud service for analytics needs, understanding how to configure it can help you optimise your cloud-data analytics infrastructure.
One of the biggest concerns most businesses have when it comes to adopting analytics platforms is the cost and scale often needed to make full use of the platforms. While these concerns are understandable, there are no longer the stifling limitations they used to be because of cloud for analytics. Cloud-based analytics platforms have become a versatile asset for organisations looking to take on data analytics.
Let us take a deep dive into the benefits of cloud for analytics platforms.
There are several reasons why you should use cloud for analytics.
From universities to large corporations, most organisations store their data in disparate sources, which makes it difficult to pull off even the most basic functions related to data collection and analysis. However, by implementing cloud for analytics, it becomes much easier to conduct a thorough, holistic data analysis. Since cloud-based analytics platforms are not restricted to a single server, it becomes much easier to breakdown data silos and unifies data sources to perform a comprehensive analysis. This allows for several benefits like a greater collaboration between professionals from different backgrounds.
Several tools, like self-service analytics tools, make data collection and analysis much easier to analyse in comparison to conventional tools. With self-service tools, data analysis can be conducted by people who don’t necessarily have the technical skills to break down data using normal analytics tools.
As we breakdown data silos, it allows organisations to discover insights at a much faster rate. This happens due to several reasons. Cloud for analytics allows organisations to operate more efficiently because of the greater processing power that is avaliable on the cloud. However, beyond technical power, there is also the issue of expertise. Thanks to cloud-based, self-service analytics platforms, data collection, and analysis are now more accessible to people who don’t have a technical background.
When organisations grow and evolve, so do their demands. In such cases, conventional analytics systems can quickly become a hindrance rather than an asset due to server limitations. However, thanks to cloud for analytics, there are no such limitations to be concerned with. This is because cloud-based analytics platforms are far more flexible and can scale up or down, in accordance with their operations. Cloud analytics platforms are also far more agile, making them much easier to work with, which translates to better operational efficiency for the organisation in question.
When data is stored across different sources, it often comes in different formats. With conventional analytics systems, this makes data collection and analysis a very difficult process. However, thanks to cloud for analytics, it becomes much easier to unify data from disparate formats, making data analysis easier. The ability to unify data from different sources makes it so much easier to test, explore, and evaluate data to find strategic insights that can help your organisation.
Cloud for analytics can help improve access controls, improving data governance. For example, it becomes much easier to regulate and control access on cloud for analytics solutions compared to other platforms. When it is easier to regulate access and control, it becomes much easier to protect data and regulate access to data. Considering that data is a key asset for most organisations, the ability to tightly regulate its access and use would be a huge boost in confidence for the organisation.
Given the growing volume of big data and the need to conduct data collection and analysis more efficiently than before, it is important to invest in the technology that can remove the silos from data collection and analysis. This is where cloud for analytics becomes an invaluable asset. The analytics platform allows organisations to conduct their analysis more efficiently and discover insights at a much faster rate. Furthermore, cloud-based analytics platforms make data collection and analysis a much more collaborative process. A desirable development in any organisation because it enriches the data analysis process, allowing for richer, better insights that will improve decision-making.
An analytics cloud platform is a vital asset for any business. With the world forced indoors due to the COVID-19 pandemic, organisations need to find ways to meet their data analytics needs, and many analytics providers have stepped up to the plate. For example, SAS uses deployment patterns like containers and Kubernetes to orchestrate and streamline deployments across major cloud infrastructure providers like Microsoft and Google.
However, despite this immense progress, there are still several obstacles that prevent private and public organisations from adopting an analytics cloud platform. Anticipating these objections can help SAS analytics engineers proactively address them.
Security is one of the biggest concerns for organisations looking at an analytics cloud platform. Analytics engineers have to maintain a balance between tight security protocols and sufficient flexibility to make working with data easier.
After all, most companies don’t want their information stolen, but neither do they want a rigid system that makes standard operations difficult to carry out. To maintain balance, analytics platforms must have built-in flexibilities that allow them to integrate popular security tools.
Furthermore, since security threats are always evolving, platform developers need to keep an eye on the latest security trends and safeguards to assure potential customers that their information is safe from evolving dangers.
An analytics cloud platform should seamlessly integrate into an organisation’s processes. Organisations want to use their current technologies and processes, alongside the new cloud platform, instead of having to revamp their entire infrastructure to accommodate it.
The challenge for most analytics engineers is to ensure that their cloud platform is not confined to a specific operating system or hardware requirement because it reduces accessibility, locking out organisations who might have been interested in the platform.
However, creating a system like this is not an easy task because it requires the system to be self-aware and make decisions based on available resources.
For example, should the system use in-stream processing or a single-threaded processor? If an analytics cloud platform is going to work, the system needs to maintain processes, while scaling resource use up and down when needed, without intervention from programmers.
One big concern for organisations is the work involved in getting data pipelines ready for use. As any cloud analytics engineer knows, operationalising a data pipeline complete with fault-tolerant recovery and full monitoring is not a task that can be completed overnight.
Establishing multi-cloud and hybrid workflows, setting parameters for dynamic control, debugging production runs, resource management and fault tolerance are just some of the operations that need to be completed before an analytics cloud platform is ready.
The effort needed to operationalise pipelines is an obstacle because most organisations are not clear about how all this technical work will affect their scheduling and production. They want to know how soon they can shift their data to the cloud, and use it to improve production processes.
As cloud analytics engineers, it’s your job to make the process as transparent as possible to build confidence with customers and encourage them to invest in the cloud platform.
A hybrid environment is often used as a solution for an organisation that needs 100% uptime performance. However, this arrangement comes with several challenges that hinder the appeal of the analytics cloud platform.
The main reason being that it takes a lot of work to operationalise the data pipeline across multiple cloud platforms and on-premise environments. Analytics engineers need to devise ways to optimise processes, to ensure they are as timely and efficient as possible.
Migration from legacy systems, such as data warehouses can be time consuming and expensive. Data can be lost or corrupted if the migration process is not done properly. When shifting to the cloud, it is important to set up proper procedures, including backing up data.
Analytics programmers and engineers need to consider all aspects of platform migration and plan out the process in detail, which includes backing up data in a secure fashion.
Who is responsible for monitoring cloud systems? How are servers going to be upgraded and maintained if applications need to be up 100% of the time? Establishing who is responsible for ownership can be challenging for many organisations, especially if they are not experienced in dealing with cloud platforms.
In my experience, the best way to resolve this uncertainty is to keep the terms as simple as possible and remain transparent. As analytics programmers and engineers, it can be very difficult not to discuss governance and maintainability without bringing in the technical terms.
But most clients are often put off by this method of communication, so it’s best to state the terms of maintenance and governance in a language that’s as simple as possible.
An analytics cloud platform is an important asset, but it is filled with challenges that often obstruct companies from undertaking the platform. However, if we can anticipate these problems and address them, we can remove the obstructions that discourage companies from adopting cloud platforms.
The ideal analytics cloud platform allows businesses to leverage powerful analytics platforms without the need for a programming team and effortlessly integrates itself into business operations.
Cloud analytics is going to win over on-premise analytics. Cloud computing, NLP and AI are transforming the way organisations are interacting with analytics platforms. As data analysts, one of our jobs is to keep an eye on the technologies transforming our industry, and analytics in the cloud is one of these trends. Analytics vendors who neglect this growing trend are taking a great risk because clients will ask for these services. Those who fail to provide them will lose business. So, in this blog post, I am going to discuss the importance of cloud-based analytics and why it wins over its on-premise counterpart.
Cloud analytics refers to analytics services provided over the cloud, as opposed to on-premise software. There are several advantages to using this system over others.
With data being more valuable than oil, its security is one of the biggest concerns facing any organisation. In an on-premise solution, security is in the hands of the organisation, which leads to problems. For example, it can take an internal team weeks to detect a data breach. However, in cloud analytics, data security is in the hands of the vendor, which means there is a dedicated team performing security assessments and monitoring the system around the clock. If there is a data breach, it will be found within minutes or hours. Furthermore, the infrastructure is constantly updated with new patches to keep security up to date. With data being so valuable, organisations are better off investing in a cloud-based platform for better security.
Cloud analytics delivers incredible processing power more efficiently compared to on-premise solutions. Organisations don’t have to invest in expansive data servers or expensive hardware to get an analytics platform up and running. Instead, data can be processed over the cloud, meaning, organisations can have their data assessed, but more efficiently than with on-premise solutions. It is especially advantageous for SAS analytics because these platforms specialise in handling large packets of data and require a huge investment, which only large corporations could afford. With the cloud, organisations benefit from the power of SAS analytics, while paying a lower price, lowering their costs and increasing ROI.
On-premise analytics is great, but one of its drawbacks is that smaller to medium-sized organisations cannot afford them because they are so expensive. However, with cloud analytics, the power and benefits of analytics are more accessible than ever before. Thanks, to the cloud, analytics is not as expensive, which means services are available at more affordable prices. The accessibility of cloud analytics also benefits providers because they are not restricted by geography. They can provide analytics services to any organisation in any country, no matter their location.
Cloud analytics is far more scalable than on-premise analytics. When organisations want to expand their operations, they will have a hard time doing so with on-premise platforms because there are several problems to contend with. For example, on-premise solutions use physical servers, which means organisations have to invest in more servers to scale operations. By contrast cloud analytics, makes scalability easier because organisations can increase the number of servers they are using within minutes. If organisations are looking to scale for the future, then the cloud is the option to take.
Cloud analytics makes it easier to clean, consolidate and analyse data. On-premise solutions often cause delays in delivery time and handling speed, especially if there are server issues. There is also the question of accessibility, most analytics platforms are built for the analytics team, but not necessarily for those who don’t have a background in analytics. Accessibility is not a problem with cloud analytics because findings are easier to read due to data sharing and visualisation, making it possible for cross-organisational analysis. Furthermore, cloud-based analytics with real-time capabilities make it easier to keep data organised and up to date. Cloud-based analytics reduces the chances of data mismatches and delays for better insights and decision making.
Cloud analytics brings a lot of exciting prospects for the future because it allows analytics teams to deliver better service to clients around the world. Organisations benefit because the ROI from investing in analytics will improve. Data security will be more secure and operations will be more flexible. It is only a matter of time before organisations ask for cloud analytics services and our team at Selerity, as service providers, are ready to meet the challenge.