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Cloud Data Platform for Shopfloor Management

How Cloud Data Platforms improve Shopfloor Management

February 5, 2023/0 Comments/in Big Data, Data Engineering, Data Migration, Data Mining, Data Science, Data Warehousing, Database, Deep Learning, ETL, Machine Learning, Main Category, Predictive Analytics /by Benjamin Aunkofer

In the era of Industry 4.0, linking data from MES (Manufacturing Execution System) with that from ERP, CRM and PLM systems plays an important role in creating integrated monitoring and control of business processes.

ERP (Enterprise Resource Planning) systems contain information about finance, supplier management, human resources and other operational processes, while CRM (Customer Relationship Management) systems provide data about customer relationships, marketing and sales activities. PLM (Product Lifecycle Management) systems contain information about products, development, design and engineering.

By linking this data with the data from MES, companies can obtain a more complete picture of their business operations and thus achieve better monitoring and control of their business processes. Of central importance here are the OEE (Overall Equipment Effectiveness) KPIs that are so important in production, as well as the key figures from financial controlling, such as contribution margins. The fusion of data in a central platform enables smooth analysis to optimize processes and increase business efficiency in the world of Industry 4.0 using methods from business intelligence, process mining and data science. Companies also significantly increase their enterprise value with the linking of this data, thanks to the data and information transparency gained.

Cloud Data Platform for shopfloor management and data sources such like MES, ERP, PLM and machine data. Copyright by DATANOMIQ.

If the data sources are additionally expanded to include the machines of production and logistics, much more in-depth analyses for error detection and prevention as well as for optimizing the factory in its dynamic environment become possible. The machine sensor data can be monitored directly in real time via respective data pipelines (real-time stream analytics) or brought into an overall picture of aggregated key figures (reporting). The readers of this data are not only people, but also individual machines or entire production plants that can react to this data.

As a central data architecture there are dozens of analytical applications which can be fed with data:

– OEE key figures for Shopfloor reporting
– Process Mining (e.g. material flow analysis) for manufacturing and supply chain.
– Detection of anomalies on the shopfloor or on individual machines.
– Predictive maintenance for individual machines or entire production lines.

This solution scales completely automatically in terms of both performance and cost. It looks beyond individual problems since it offers universal and flexible scope for action. In other words, it will result in a “god mode” for the management being able to drill-down from a specific client project to insights into single machines involved into each project.

Are you interested in scalable data architectures for your shopfloor management? Or would you like to discuss a specific problem with us? Or maybe you are interested in an individual data strategy? Then get in touch with me! 🙂

7 Reasons Why You Need Cloud Cost Management Tool

November 2, 2022/0 Comments/in Cloud /by Nadica Metuleva

Many businesses today use a CCM (Cloud Cost Management) tool to optimize their spending on the cloud. Read this article to learn why.

7 Reasons Why You Need Cloud Cost Management Tool (And Where to Find One)

The Cloud. Do you remember the time when this sounded like a silly, fiction-like buzzword? No one imagined that a few years later, we’d be amazed by the technology. Or, that the clouds will be used by millions of people and businesses.

As time passes, more and more businesses shift to the cloud. They use clouds for data warehousing and to share information with remote workers and clients. They use it to keep information easily accessible and safely stored in a digital format.

Clouds have become irreplaceable in the world. In 2020, the total worth of this market reached $371.4 billion. With a compound annual growth rate of 17.5%, the projections tell us that this market will reach $832 billion by 2025.

Today, zettabytes of data are placed in the top storage services such as Google Drive, Google Workspace, and Dropbox, as well as private IT clouds.

Source: https://www.cloudwards.net/cloud-computing-statistics/

In a few years from now, there will be over a hundred zettabytes of data placed in the cloud, which amounts to nearly half of the global data storage projected for 2025. At this point, it is safe to say that clouds are trending in the business world and we can only expect these numbers to grow.

This raises a very big question – how will businesses handle their cloud usage?

One of the biggest challenges that companies face when it comes to the cloud is controlling cloud costs. Fortunately, there’s such a thing as cloud cost optimization, used to help businesses minimize their expenses and achieve better cloud performance.

This article will tell you all about cloud cost optimization and the tools used to make this happen. Read on to learn about the top 7 reasons why you need to invest in a good CCM tool.

What is cloud cost optimization?

Before we jump at the benefits of using a CCM, let’s discuss cloud cost optimization a bit. CCO refers to the act of reducing resource waste on clouds by scaling and choosing the resources required for cloud functions.

Data analytics in the cloud can transform your business altogether. While this is impossible to analyze on your own, certain tools can help you find ways to optimize the cloud performance.

Depending on what you use for this purpose, the success of your CCO strategy can vary. Nevertheless, with a good tool, you can get insight into what you’re doing right and what you need to change to get the most out of your cloud investment.

Choosing one of the best cloud cost management tools

When it comes to choosing cloud cost management tools, Zluri is the answer to all your questions. The popular SaaS management platform has a full range of top-ranked tools to be used for cloud cost optimization.

According to Zluri, the top choice available to businesses today is Harness.

The number one CCM tool will proactively detect anomalies and tell you all you need to know about your cloud resources. It will also predict your cloud spending and help you make smart data-based decisions. Lastly, Harness allows users to automate idle resource management.

Source: https://www.zluri.com/blog/cloud-cost-management-tools/

Some of the things to look for when choosing a cloud cost management tool are:

Automated cost optimizatione. the option to schedule resources to turn on and off to reduce costs and manual efforts.
Visibility of resources. The tool you use should make various resources used in the cloud environment visible such as applications, containers, and servers.
State of resources. A good CCM will tell you about the state of resources, how they are used, and whether they are underutilized.
Multi-cloud support. Many businesses store their data across different cloud providers. If you are one of them, you need a tool that offers multi-cloud support.

Naturally, the pricing and ease of use are also important when you’re making your choice.

Reasons to invest in a cloud cost management tool

Now that you know where to find the best CCM, let’s talk about what you get if you use a cloud cost management tool.

It all starts with what the CCM can do for you. Here are just a few of the most popular functions:

Forecast and budget the cloud spend with a great deal of accuracy
Inform on the current cloud costs
Discover areas that can increase profitability and find the least profitable projects within the cloud
Give you tips on adjusting the pricing structure and re-thinking unnecessary features

https://www.jamcracker.com/blogs/fundamentals-of-cloud-cost-management

With the right tool in your arsenal, you can reap many benefits. We present you with the top seven.

https://www.techtarget.com/searchcloudcomputing/feature/5-ways-to-reduce-cloud-costs

1. Define the cloud costs

451Research reported that 73% of US cloud users look at their cloud expenses as a fixed cost. But, these expenses aren’t fixed – they are variables. That’s why businesses often come across cost inefficiencies. Cloud service charging changes every day and interpreting their plans is not as straightforward as it might seem.

However, with a good CCM tool, you can tie all the expenses to pricing and value and figure out a fixed quote for cloud spending. You can use this data to plan and manage your budget or figure out ways to make your cloud use more cost-conscious.

2. More transparency

A good model for cost optimization will positively impact your operational, as well as financial aspects. Thanks to the analytics feature in a CCM tool, you can monitor and better organize your cloud expenditures. You’ll know how your cloud is used, how much every action costs, and what you can do to optimize this. More transparency leads to better decision-making.

3. Reduce waste and correct defects

Let’s say that you invest in a cloud cost management tool. It will discover the resources you are spending and detect features that are underused. You can use this information to manage neglected tools and eliminate the unnecessary. You can correct the defects and reduce waste.

As a result of the transparency and analytics, users of a cloud can find a better cost-performance balance and optimize their cloud.

4. Smart predictions

A cloud cost management tool will provide you with more than waste information. The usage patterns you’ll get access to can be analyzed and used to make smart predictions. You can use the data to perform consolidated cost analysis and predict future expenses for the cloud. Base this on additional cloud services you’ll need, increased cloud usage, as well as scalability.

5. Timely reviews

By performing audits of the cloud usage and resource, teams can get better control over this matter. A CCM tool can perform audits every couple of months and hand out reports to the company about cloud usage.

These reports are shared within organizations and used to categorize expenses and drop under-utilized resources in the cloud.

6. Memory and storage management

Another great benefit of using a CCM tool is that it helps you manage your storage. With it, you can detect unused data, manage regular data backups, and check the usage of the cloud.

Going through everything stored in your cloud can take forever, but not with these tools. With these tools, organizations can find and discard data that is useless and save a lot of money in the process. It’s amazing how much is stored in clouds without actual use. The right cloud cost management tool can point you in the direction of things you don’t need and remind you to cut them loose.

7. Multi-cloud synchronization

Chances are, your business is using more than one cloud service to store its data. After a while, your data is scattered across different clouds and you cannot find or manage information as easily as you should. When you’re handling more clouds, it gets much harder to optimize the costs or reduce the expenses.

But, not if you have a good CCM tool.

A good CCM tool offers synchronization between at least the most-used cloud services out there. This will allow you to optimize your costs across different clouds, figure out where you can save some money, and find a way to organize your cloud usage more efficiently.

The best time to optimize your cloud is now!

Who would have thought that clouds will become such a big part of our business operations? If you are using clouds for your business, investing in a cloud cost management tool is the next smartest step to take. Thanks to a small investment in a good tool, you can save a fortune on cloud costs.

AI Platforms – A Comprehensive Guide

July 26, 2021/0 Comments/in Tool Introduction /by Jaimin Dave

A comprehensive guide compiled to introduce readers to AI platforms, their types, and benefits. A concluding section to discuss AI platform selection strategy with Attri’s Best of Breed approach to build AI platforms.

Don’t you think that this century is really fortunate? In my opinion, the answer is yes; we witnessed technological transformations and their miracles that created substantial changes in our lifestyle. While talking about these life-changing technological revolutions, AI or artificial intelligence deserves a front seat due to its incredible contribution and capabilities. Now everyone knows AI has limitless potential simply from creating funny faces in mobile to taking informed and intelligent business decisions. In the last 50 years, we have progressed by leaps and bounds to give machines the ability to understand, help and mimic us.

Artificial intelligence enables machines to imitate human intelligence across a variety of domains ranging from problem-solving and reasoning to General Intelligence and in-depth knowledge representation. With tremendous progress in AI, another enabler came into existence and received attention—AI platforms. AI-platform is a layer that integrates all the tools and processes required to build, deploy and monitor ML models. In this article, we shall go through the various aspects of AI platforms covering a range of topics like AI Platform types, the benefits such platforms entail, selection strategy in detail as well as a brief look into Attri’s industry contribution with an Open AI Platform.

Diving Deeper With AI Platforms

The AI Platform acts as a layer over your current AI infrastructure and integrates all the tools and processes required to develop ML models. It provides you the flexibility to integrate all your ML models under a single roof. With this flexibility, you can create and deploy several ML models over the platform. Further, you can even monitor these models to confirm that they are serving their intended purpose. AI platform makes your AI adoption easy by attaining the following requirements–

Use of vast data to develop ML solutions.
Ensure transparency and reproducibility within a project
Accelerate collaboration and governance within teams
Ensure scalability for ever-growing machine learning demands

An ideal AI platform should ensure the following features for better addressing different challenges.

Seamless access control: Ensure robust access control to team members in order to conquer the challenge of centralized data access with AI projects.
Excellent monitoring: Integrate top-notch observability practices while developing ML models.
Data and technology-agnostic integration: Seamless experience to enterprises with infrastructure set up responsibility handed over to platform providers
All-inclusive Platform: Single platform to facilitate all underlying tasks from data preparation to model deployment
Continuous Improvement: Ability to produce and deploy models as a reproducible package and thereby integrate changes with models that are already in production
Rapid Processing: Faster data preparation and powerful visual interfaces

AI Platform Classification

With loads of AI platform providers available in the market, AI platform classification becomes a tough job, as it requires thinking separately on each platform’s offerings, its features, and cost factors. Also, you need to check whether AI solutions are open source AI platforms or proprietary offerings.

We have decided to present an AI platform classification based on its striking features and offerings. With this, we have classified AI platforms across three main classes—

AI cloud-based platforms
AI conversational platforms
No code AI platforms

Cloud based AI Platforms

All major cloud providers offer cloud-based AI platforms to boost businesses with AI capabilities. With cloud AI platforms, enterprises can leverage cloud providers’ matchless technical expertise to overcome affordability and data requirement challenges associated with AI implementation. Cloud-based AI offerings benefit businesses with economic AI solutions, defined and pre-packaged services, lower risks, and modern technology.

Amazon Web Services

AWS offers a comprehensive set of AI solutions to conquer major hurdles in the AI adoption journey of businesses. AWS has been recognized as the topmost cloud AI partner with its broad capable portfolio. AWS pre-trained models cater to diverse use cases like forecasting, recommendations, computer vision, language interpretation, customer engagement, and safety for deploying ML models at scale. Amazon also provides text analytics, NLP, chatbots, and document analysis solutions. Fully managed AWS packages amplify your experience with minimum resource requirements and wizard-based friendly model development experience. Hence, AWS is one of the top cloud AI partners that cater to your AI adoption needs.

Google cloud

The Google Cloud Platform (GCP) is a Google offering for cloud-driven computing services devised to support multiple use cases such as hosting containerized applications, massive-scale data analytics platforms, and even applying ML and AI for business use cases. Google AI Platform is a Google Cloud offering that helps build, deploy and manage machine learning models in the cloud.

Google leverages enterprise AI experience through its consumer-facing products. Google helps improve customer satisfaction through Contact Center AI. Google offering DialogFlow CX is used to create advanced chatbots that handle customer messaging, response, and voice recognition. Digiflow is applied to create virtual agents for messaging services, mobile apps, and IoT devices.

Google’s Cloud Vision API is beneficial to recognize objects, logos, and landmarks within content or images. Google provides Natural Language API to bring more clarity in content classification, entities, syntax, and sentiments. Further, Google speech API helps in converting audio to text and recognizing 110 languages.

Google’s Cloud ML services facilitate better decision-making with end-to-end ML solutions. Google offers an all-inclusive ML development platform that enables effective decision-making backed by explainable AI, continuous evaluation, data labeling, pipelines, training, and what-if tool. This platform is based on the TensorFlow framework and it enables building predictive models for various scenarios.

Kubeflow is a Cloud-Native and open-source platform that helps you build portable ML pipelines that can be executed on-premises or on the cloud. With this, you can access Google technologies like TPUs, TensorFlow, and TFX tools as you deploy your ML models in production.

For expert ML developers, Google provides an Open Source AI platform with TensorFlow models that are trained for various scenarios. It offers an excellent prediction service using trained models.

Microsoft Azure

Similar to Amazon Web Services, Microsoft Azure ML capabilities are based on its real-time and live applications. Azure provides superior machine learning capabilities to develop, train, and deploy machine learning models through Azure Machine Learning, Azure Databricks, and ONNX.

Azure Machine Learning

A Python-based ML service to facilitate automated machine learning.

ONNX

An open-source model format enables machine learning through various frameworks and hardware platforms of the user’s choice.

Azure Cognitive Search

Formerly known as Azure Search,this is the only cloud search service that allows built-in AI capabilities to explore content effectively at scale. Microsoft empowers the user with cognitive search services like text analytics, translation, document analytics, custom vision, and Azure Machine Learning solutions.

IBM Cloud

IBM has brought Watson studio a data analysis application to accelerate innovation and ML-centric practices in business. IBM Cloud AI Platform offers 170 services with more emphasis on data-speech conversions and analytics. Watson Studio offers an all-inclusive suite to work with data and train, build and deploy ML models.

An innovative giant IBM also brought AI based learning platform recently to aid academic stakeholder like students, researchers and teachers.

AI Conversational Platforms

Conversational AI opens new doors for automated conversations between an enterprise and its customers. These conversations include messaging or voice-based communication platforms to enable text or audio-based conversation.

Conversational platforms leverage your customer experience with a range of applications such as follow-up, guidance, or the resolution of customer queries and round-the-clock support. These platforms are beneficial to drive more leads, increase conversions by cross-selling and upselling, promotional efforts, customer research, queries resolution and customer feedback handling, etc.

AI technology helps systems to mimic human conversations to a certain level and with great accuracy. An AI offering- Natural Language processing is used to shape these conversations by understanding intent, text, speech, and languages.

Intelligent Virtual Assistants

The intelligent virtual assistants represent an advanced level of Conversational AI and their discussion is incomplete without a mention to Siri and Alexa. Most popular intelligent virtual assistants include Siri by Apple, Alexa by Amazon, Google Assistant, and Bixby by Samsung. While Alexa performs as a voice assistant for the home, Siri and Bixby stand as mobile assistants with numerous operations support like navigation, text-to-speech, response to weather, quick reply, and address search.

SAP Conversational AI

SAP Conversational AI is one of the leading conversational AI platforms. With its friendly UI and multiple versioning, it offers a better experience of mimicking human conversations. SAP Conversational AI Platform uses NLP to facilitate developing chatbot that works more humanely and serves your customers 24*7. Its striking features include—

Simple integration
NLP capabilities
Analytics tools to help you
Multi-language support

Clinc

A powerful self-learning Conversational AI Platform enriched with NLP capabilities and machine learning. It secures top position in the Conversational AI Platform list due to its learning from previous conversations and improving responses over time. Its feature set include—

No technical expertise required
Self-learning abilities
NLP capabilities

Kore.ai

An enterprise-grade Conversational AI Platform to cater to your consumer as well as staff needs. It helps to build a virtual chatbot for any suitable platform without compromising the safety and security standards. Its major features cover—

The high degree of customization for chatbots
Comprehensive analytics with FAQs and alerts
Simple integration with ML models and channels
Flexible deployment
Supported with a multi-pronged NLP engine

Mindmeld

It is an excellent option as a Deep-Domain Conversational AI Platform with NLP capabilities. It can be used for both text-based and voice-based virtual assistants. This platform effectively caters to multiple industries and their numerous use cases. Check its striking features list—

Open-source platform
NLP capabilities
Supports discovering on-demand video or music
Quick chat-based transactions

No Code AI Platforms

As discussed above, AI platform classification necessitates platform considerations from various perspectives. We are introducing another category of AI platforms—No Code AI Platforms. The motivation behind introducing these platforms is to encourage enterprise AI adoption while keeping AI implementation costs low and minimizing dependencies on skilled professionals. Many IT giants are now offering no-code AI Platforms to enterprises for their AI adoption.

Google ML Kit

Google ML Kit comes with Android and iOS and it facilitates the integration of functions with lesser codes or with minimum knowledge of machine learning algorithms. This open source AI Platform supports different features such as text recognition, face detection, and landmark recognition.

RapidMiner Studio

RapidMiner Studio enables powerful data analytics with drag and drop features. Rapidminer Studio allows easy integration with databases, warehouses, social media for easy data access by authorized persons.

ML Platform Selection Strategy

Having discussed so many types of ML platforms, their features, and offerings, the next question is–how to select the best ML Platform for an enterprise AI adoption. Well, to answer this Million-Dollar question, we need to consider a few key aspects, such as

Who will use and benefit from the AI Platform? It is required to find out AI platform users here, the data science team, analytics team, developers, and how the platform will benefit each stakeholder.
The next aspect is to explore the skill levels of AI platform users, are they competent to handle ML development and analytics requirements with years of experience
Proficiency of users with programming languages
The next point in finalizing the AI platform strategy is to conclude code-first or code-free approaches to streamline AI workflows. This aspect can be studied by thinking about different attributes such as data preparation ease, feature engineering automation, ML algorithms, Model Deployment ease, and platform integration aspects.

Once you come up with answers to these queries, you will be able to finalize the best AI Platform Selection strategy for your enterprise. It can be a unique cloud platform, or even it can be a hybrid solution with a “best-of-breed” approach.

All-in-one platform strategy involves getting one end-to-end platform for the entire AI project lifecycle from raw data prep to ETL to building and operationalizing models followed by monitoring and governance of systems.

The best-of-breed approach allows using the preferred and custom tools for each phase of the lifecycle and aligning these tools together to build a customized platform solution for AI adoption.

This approach offers an excellent AI platform solution for organizations looking for flexible, inexpensive, change-oriented AI solutions and having a DIY spirit. With this mix-and-match approach, you can combine APIs offered by different cloud platforms and deliver AI solutions that cater to your AI use cases. Organizations using the best-of-breed approach are more comfortable with technology shifts with their abilities to use, adopt and swap out tools as requirement changes.

Business Process AI Transformation Simplified With Attri’s Open AI Platform

At Attri, we provide AI platform solutions to diverse industry verticals. With our flagship Open AI Platform, we heighten your AI adoption experience with a rich array of platform features like—

Customizable best-of-breed architecture
Utilize existing infrastructure
AI as a platform solution
Reduced effort in migrating to a new technology
Centralized Monitoring and Governance
Explainable and Responsible AI

We help you achieve your business process transformation goals with our unique AI offerings such as Open AI Platform and Open AI solutions.

Our AI platform assures multiple benefits to your enterprise while keeping AI adoptions costs low and ensuring faster AI implementations. We can summarize the benefits of Attri Open AI Platform as under–

No efforts in reinventing complete AI suites

Attri’s AI Platform integrates multiple AI services and eliminates the need for reinventing complete AI suites. The platform delights enterprises with scalability, the ability to reuse current infrastructure, and customizable architecture.

Accelerated Go To Market

Attri’s Open AI Platform ensures accelerated GTM with a sincere approach to testing, reviewing, and finalizing reference templates for different industries.

No vendor lock-in

With Open AI Platform, we bring client-friendly policies such as no vendor lock-in and flexibility to choose their preferred tools and technology.

High reliability

We keep our AI Platform highly reliable with a comprehensive testing approach. We also meet the growing requirements of enterprises by ensuring high scalability with our open AI platform.

Get connected with us for your enterprise AI adoption requirements.

Know more about our Open AI Platform…

What Is Data Lake Architecture?

June 24, 2021/0 Comments/in Data Engineering, Data Warehousing /by Shannon Flynn

The volume of information produced by everyone in the world is growing exponentially. To put it in perspective, it’s estimated that by 2023 the big data analytics market will reach $103 billion.

Finding probable solutions for storing big data is a challenge. It’s no easy task to hold enormous amounts of information, clean it and transform it into understandable subsets — it’s best to take one step at a time.

Some reasons why companies access their big data is to:

Improve their consumer experience
Draw conclusions and make data-driven decisions
Identify potential problems
Create innovative products

There are ways to help define big data. Combining its characteristics with storage management methods help experts make their clients’ information digestible and understandable. Cue data lakes, which are repositories for big data in its native form.

Think of an actual lake with multiple water sources around the perimeter flowing into it. Picture these as three types of data: structured, semi-structured and unstructured. All this information can remain in a data lake and be accessed in its raw form at any time, making it an attractive storage method.

Here’s how data lakes are created, some of their components and how to avoid common pitfalls.

Creating a Data Lake

One benefit of creating and implementing a data lake is that structuring becomes much more manageable. Pulling necessary information from a lake allows analysts to compare and contrast data and communicate any connections between datasets to their client.

There are four steps to follow when setting up a data lake:

Choosing a software solution: Microsoft, Amazon and Google are cloud vendors that allow developers to create data lakes without using servers.
Identifying where data is sourced: Where is your information coming from? Once sources are identified, determine how your data will be cleaned or transformed.
Defining process and automation: It’s vital to outline how information should be processed once the data lake ingests it. This creates consistency for businesses.
Establishing retrieval governance: Choosing who has access to what types of information is crucial for companies with multiple locations and departments. It helps with overall organization. Data scientists, for this reason, primarily access data lakes.

The next step would be to determine the extract, transform and load (ETL) process. ETL creates visual interpretations of data to provide context to businesses. When information from a data lake is sent to a warehouse, it can be analyzed.

Components of a Data Lake

Here is what happens to information once a data lake is created:

Collection: Data comes in from various sources.
Ingestion: Data is processed using management software.
Blending: Data is combined from multiple sources.
Transformation: Data is analyzed and made sense of.
Publication: Data can be used to drive business decisions.

There are other aspects of a data lake to keep in mind. These are the critical components that help provide business solutions:

Security: Data lakes require security to protect information — they do not have built-in safety measures.
Governance: Determine who can check on the quality of data and perform measurements.
Metadata: This provides information about other data to improve understanding.
Stewardship: Choose one or more employees to take on the responsibility of managing data.
Monitoring: Employ other software to perform the ETL process.

Big data lends itself to incorporating multiple processes to make it usable for companies. The volume of information one company produces is massive — to manage it, experts need to consider these components and steps when building a data lake.

What to Avoid When Using Data Lakes

The last thing people want for their data lake is to see it turn into a swamp. When big data is processed incorrectly, its value decreases, making it useless to the business sourcing it.

The first step in avoiding a common pitfall is to consider the sustainability of the data lake. Planning processes are necessary to ensure it’s secure, and governing and regulating incoming information will allow for long-term use.

A lack of security causes another problem that can arise in data lakes. Safety measures must be implemented. Because enterprises will build data lakes for different purposes, it’s easy for information to become unorganized and vulnerable to hacking. With security, the likelihood of data breaches decreases, and the quality of data remains high.

The most important thing to remember about data lakes is the planning stage. Without proper preparation, they tend to be overwhelming due to their size and complexity. Taking the time and care to establish the processes ahead of time is vital.

Using Data Lake Architecture for Business

Data lakes store massive amounts of information to be used later on to create subsets, analyze metadata and more. Their advantages allow businesses to be flexible, save money and have access to raw information at all times.

How Microsoft Azure Is Impacting Financial Companies

June 16, 2021/0 Comments/in Cloud, Insights, Use Cases /by Renata Kalsbeek

Microsoft Azure has taken a large chunk of the cloud marketplace, transforming companies with the speed and security of the cloud. Microsoft has over the years used Azure to cushion companies against risk, deal with fraud and differentiate their customer experience.

With Microsoft Cloud App Security, customers experience 75% automatic threat elimination because of increased visibility and automated threat protection. With all these and more amazing benefits of using Azure, its market share is bound to increase even more over the coming years.

Image Source

Financial companies have not been left behind by the Azure bandwagon. The financial industry is using Microsoft Azure to enhance its core functions— invest money by making informed decisions, and minimize risk while maximizing returns.

Azure facilitates these core functions by helping with the storage of huge amounts of data— some dating back to decades ago—, data retrieval and data security.

It also helps financial companies to keep up with regulatory compliance.

Microsoft Azure is not the only cloud services provider. But here’s why it is the most outstanding when it comes to helping financial companies achieve their business goals.

Azure Offers Hybrid and Multi-Cloud Computing for Financial Companies

The financial services industry is extremely dynamic. Organizations offering financial services have to constantly test the market and come up with new and innovative products and services.

They are also often under pressure to extend their services across borders. Remember they have to do all of this while at the same time managing their existing customers, containing their risk, and dealing with fraud.

Financial regulations also keep changing. As financial companies increasingly embrace new technology for their services— including intelligent cloud computing— and they have to comply with industry regulations. They cannot afford to leave loopholes as they take on their journey with the cloud.

The financial services industry is highly competitive and keeps up with modernity. These companies have had to resort to the dynamic hybrid, multi-cloud computing, and public cloud strategies to keep up with the trend.

This is how a hybrid cloud model works— it enables existing on-premises applications to be extended through a connection to the public cloud.

This allows financial companies to enjoy the speed, elasticity, and scale of the public cloud without necessarily having to remodel their entire applications. These organizations are afforded the flexibility of deciding what parts of their application remains in an existing data center and which one resides in the cloud.

Cloud computing with Azure allows financial organizations to operate more efficiently by providing end-to-end protection to information, allowing the digitization of financial services, and providing data security.

Data security is particularly important to financial firms because they are often targeted by fraudsters and cyber threats. They, therefore, need to protect crucial information which they achieve by authenticating their data centers using Azure.

Here’s why financial companies cannot think of doing without Azure’s hybrid cloud computing even for just a day.

Photo by Windows on Unsplash

The ability to expand their geographic reach

Azure enables financial companies to establish data centers in new locations to meet globally growing demand. This allows them to open and explore new markets. They can then use Azure DevOps pipelines to maintain their data factories and keep everything consistent.

Consistent Infrastructure management

The hybrid cloud model promotes a consistent approach to infrastructure management across all locations, whether it is on-premises, public cloud, or the edge.

Increased Elasticity

Financial firms and banks utilizing Azure services can respond with great agility to transactional changes or changes in demand by provisioning or de-provisioning as the situation at hand demands.

In cases where the organization requires high computation such as complex risk modeling, a hybrid strategy allows it to expand its capacity beyond its data center without overwhelming its servers.

Flexibility

A hybrid strategy allows financial organizations to choose cloud services that fall within their budget, match their needs, and suit their features.

Data security and enhanced regulatory compliance

Hybrid and multi-cloud strategies are a superb alternative for strictly on-premises strategies when one considers resiliency, data portability, and data security.

Reduces CapEx Expenses

Managing on-premises infrastructure is expensive. Financial companies utilizing Azure do not need to spend large amounts of money setting them up and managing them.

With the increased elasticity of the hybrid system, financial organizations only pay for the resources they actually use, at a relatively lower cost.

Financial Organizations Have Access to an Analytics Platform

As we mentioned earlier, financial companies have the core function of making financial decisions in order to invest money and gain maximum returns at the least possible risk.

Having been entrusted with their customers’ assets, the best way to ensure success in making profits is by using an analytics system.

Getting the form of analytics that helps with solving this investment problem is the kind of headache that does not go away by taking a tablet of ibuprofen and a glass of water— integrating data is not an easy task. Besides, building a custom analytics solution from scratch is quite expensive.

Luckily for financial companies, Azure has a dedicated analytics platform for the financial services industry. It is custom-made just for these types of organizations.

Their system is quite intuitive and easy to use. Companies not only get to save the resources they would have otherwise used to build a custom solution, but they get to learn about their investment risks and get instant results at cloud speed.

They can mitigate against negatively impactful market occurrences and gain profits even when operating in adverse market conditions.

Image by Headway on Unsplash

Financial Companies Get Advanced Data Management

Good analytics goes hand-in-hand with a great data management system. Financial companies need to have good data, create an organized data warehouse, and have a secure data storage system.

In addition to storing your data, Microsoft Azure ensures your storage can be optimized to support advanced applications, for example, machine learning and forecasting.

Azure even allows you to compress and store documents for long periods of time when you write the data to Microsoft Azure Blob Storage. These documents can be retrieved anytime when the need arises for auditors’, regulators’, and lawyers’ perusal.

Conclusion

Microsoft has over time managed to gain the trust of many industries, the financial services industry inclusive. Using its cloud computing giant, Azure, it has empowered these companies to carry out their functions efficiently and at the lowest cost and risk possible.

Azure’s hybrid cloud computing strategy has made financial operations flexible, opened doors for financial companies to establish their services in multiple locations, and provided them with consistent infrastructure management, among many other benefits.

With their futuristic model and commitment to growth, it’s only prudent to assume that Microsoft Azure will continue carrying the mantle as the best cloud services provider in the financial services industry.

Interview: Does Business Intelligence benefit from Cloud Data Warehousing?

May 13, 2019/0 Comments/in Big Data, Cloud, Data Engineering, Data Security, Data Warehousing, Database, Insights, Interviews, Sponsoring Partner Posts /by Benjamin Aunkofer

Interview with Ross Perez, Senior Director, Marketing EMEA at Snowflake

Read this article in German:
“Profitiert Business Intelligence vom Data Warehouse in der Cloud?”

Does Business Intelligence benefit from Cloud Data Warehousing?

Ross Perez is the Senior Director, Marketing EMEA at Snowflake. He leads the Snowflake marketing team in EMEA and is charged with starting the discussion about analytics, data, and cloud data warehousing across EMEA. Before Snowflake, Ross was a product marketer at Tableau Software where he founded the Iron Viz Championship, the world’s largest and longest running data visualization competition.

Data Science Blog: Ross, Business Intelligence (BI) is not really a new trend. In 2019/2020, making data available for the whole company should not be a big thing anymore. Would you agree?

BI is definitely an old trend, reporting has been around for 50 years. People are accustomed to seeing statistics and data for the company at large, and even their business units. However, using BI to deliver analytics to everyone in the organization and encouraging them to make decisions based on data for their specific area is relatively new. In a lot of the companies Snowflake works with, there is a huge new group of people who have recently received access to self-service BI and visualization tools like Tableau, Looker and Sigma, and they are just starting to find answers to their questions.

Data Science Blog: Up until today, BI was just about delivering dashboards for reporting to the business. The data warehouse (DWH) was something like the backend. Today we have increased demand for data transparency. How should companies deal with this demand?

Because more people in more departments are wanting access to data more frequently, the demand on backend systems like the data warehouse is skyrocketing. In many cases, companies have data warehouses that weren’t built to cope with this concurrent demand and that means that the experience is slow. End users have to wait a long time for their reports. That is where Snowflake comes in: since we can use the power of the cloud to spin up resources on demand, we can serve any number of concurrent users. Snowflake can also house unlimited amounts of data, of both structured and semi-structured formats.

Data Science Blog: Would you say the DWH is the key driver for becoming a data-driven organization? What else should be considered here?

Absolutely. Without having all of your data in a single, highly elastic, and flexible data warehouse, it can be a huge challenge to actually deliver insight to people in the organization.

Data Science Blog: So much for the theory, now let’s talk about specific use cases. In general, it matters a lot whether you are storing and analyzing e.g. financial data or machine data. What do we have to consider for both purposes?

Financial data and machine data do look very different, and often come in different formats. For instance, financial data is often in a standard relational format. Data like this needs to be able to be easily queried with standard SQL, something that many Hadoop and noSQL tools were unable to provide. Luckily, Snowflake is an ansi-standard SQL data warehouse so it can be used with this type of data quite seamlessly.

On the other hand, machine data is often semi-structured or even completely unstructured. This type of data is becoming significantly more common with the rise of IoT, but traditional data warehouses were very bad at dealing with it since they were optimized for relational data. Semi-structured data like JSON, Avro, XML, Orc and Parquet can be loaded into Snowflake for analysis quite seamlessly in its native format. This is important, because you don’t want to have to flatten the data to get any use from it.

Both types of data are important, and Snowflake is really the first data warehouse that can work with them both seamlessly.

Data Science Blog: Back to the common business use case: Creating sales or purchase reports for the business managers, based on data from ERP-systems such as Microsoft or SAP. Which architecture for the DWH could be the right one? How many and which database layers do you see as necessary?

The type of report largely does not matter, because in all cases you want a data warehouse that can support all of your data and serve all of your users. Ideally, you also want to be able to turn it off and on depending on demand. That means that you need a cloud-based architecture… and specifically Snowflake’s innovative architecture that separates storage and compute, making it possible to pay for exactly what you use.

Data Science Blog: Where would you implement the main part of the business logic for the report? In the DWH or in the reporting tool? Does it matter which reporting tool we choose?

The great thing is that you can choose either. Snowflake, as an ansi-Standard SQL data warehouse, can support a high degree of data modeling and business logic. But you can also utilize partners like Looker and Sigma who specialize in data modeling for BI. We think it’s best that the customer chooses what is right for them.

Data Science Blog: Snowflake enables organizations to store and manage their data in the cloud. Does it mean companies lose control over their storage and data management?

Customers have complete control over their data, and in fact Snowflake cannot see, alter or change any aspect of their data. The benefit of a cloud solution is that customers don’t have to manage the infrastructure or the tuning – they decide how they want to store and analyze their data and Snowflake takes care of the rest.

Data Science Blog: How big is the effort for smaller and medium sized companies to set up a DWH in the cloud? Does this have to be an expensive long-term project in every case?

The nice thing about Snowflake is that you can get started with a free trial in a few minutes. Now, moving from a traditional data warehouse to Snowflake can take some time, depending on the legacy technology that you are using. But Snowflake itself is quite easy to set up and very much compatible with historical tools making it relatively easy to move over.

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