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The Problem/Challenge Business Encounter

Traditionally, businesses grappled with connectivity challenges due to limited data. For example, relying on call data alone made it difficult to understand customer usage patterns, leading to inflexible and inefficient service offerings. This limited data hindered effective decision-making, resulting in inefficiencies in operations, subpar customer experiences, and missed growth opportunities. The need for a transformative solution was evident, paving the way for the rise of telecom analytics.

The Solution: Telecom Analytics

Telecom analytics involves the collection, analysis, and visualization of data, offering a comprehensive solution to the challenges faced by businesses. By partnering with a company that develops software for telecom companies, organizations can leverage state-of-the-art tools and expertise to:

• Optimize their networks: Identify and resolve network issues, leading to improved performance and reliability.
• Predict customer churn: Anticipate customer loss and take proactive action to retain valuable customers.
• Execute personalized marketing campaigns: Tailor offerings and promotions based on individual customer needs and preferences.

This newfound capability empowers businesses to make data-driven decisions, fostering a more agile and responsive operational environment.

The Benefits of Data-Driven Connectivity

The advantages of leveraging telecom analytics are manifold, impacting various aspects of business operations:

• Increased operational efficiency and cost reduction: Streamline processes and identify areas for cost savings.
• Elevated customer experience and satisfaction: Provide personalized services and address customer needs more effectively.
• More informed decisions: Make data-backed choices for better strategic planning and resource allocation.
• Uncovered new revenue opportunities and market insights: Identify untapped markets and develop targeted offerings based on data-driven insights.

The era of data-driven connectivity is synonymous with heightened competitiveness and sustainable growth.

The Future of Telecom Analytics

Looking ahead, the future of telecom analytics is even brighter. Artificial intelligence (AI), machine learning, and big data are poised to play pivotal roles, unlocking new dimensions of insights and efficiency. As these technologies advance, businesses can anticipate:

• More sophisticated applications in network management: AI can automate tasks, allowing for faster response times and improved resource allocation.
• Advanced predictive analytics: Machine learning can identify complex patterns and predict future trends with greater accuracy.
• Enhanced personalized services: Big data enables the creation of highly personalized offerings tailored to individual customer needs.

These advancements solidify telecom analytics as a cornerstone of the industry, driving innovation and shaping the future of connectivity.

Conclusion

The telecom revolution is in full swing, fueled by the transformative power of data-driven connectivity. Businesses that embrace telecom analytics gain a competitive edge by turning data into actionable insights. As we navigate the evolving landscape of telecommunications, the future looks promising, with innovation and connectivity going hand in hand. Take the first step towards a connected future – schedule a consultation with a telecom analytics expert today to learn how to leverage data-driven insights for your business.

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In this post, I'll outline where I think we're getting it wrong … causing us to churn out a lot of graduates who don't necessarily have the skills they need to succeed in the real world. While my observations are based on my knowledge of the discipline of data science and my personal experience in the business world, I believe that the same concerns apply much more broadly.

Some Real-World Examples To Illustrate The Problem

A year ago, I was stuck in an airport due to a flight delay. There was a group of 6 – 7 students from a highly respected university that is universally considered a “top tier” institution. As a result, the grades and test scores required to gain admission mean that only the highest achievers will be accepted. I ended up chatting with the students for 15 or 20 minutes and they all seemed genuinely nice and bright.

After we moved on, I turned to my travel partner and commented that while they seemed nice and smart, there was only one that I would consider hiring. Most of the students were awkward, didn't make much eye contact when talking, and didn't exude any level of confidence in a social setting. I certainly couldn't have put them in front of a client or an internal executive.

In another instance I was watching a high school sports award ceremony. Multiple team members went on stage for their awards and smiled, shook hands with the coach, and faced the audience for pictures. Then the student who received the “best grades” award walked up with his eyes down, did a limp and brief handshake with the coach, and then stared at the ground for the picture. Like the students in the airport, he was awkward and didn't exude any level of social confidence even though his grades and, presumably, test scores were top notch.

These Weren't Isolated Instances – They Are A Product Of Today's System

The above examples didn't happen by chance, and I've seen similar situations many times. The students in the examples are what we are optimizing for. With our massive focus on grades and test scores, we reward students who do what it takes to maximize their grades and test scores. In their eagerness to succeed, many students fail to focus on being well rounded and learning to interact with others. While we push students to optimize their grades, we simultaneously and unintentionally push many of them to stunt their growth in other areas like social skills and emotional intelligence. I often see teenagers sitting together but not talking. They are on their phones constantly and sometimes even messaging those right next to them via their phone instead of talking to them directly.

If we optimize for grades and test scores, we'll graduate students who have great grades and test scores, but who don't necessarily develop those other traits. Worse, many of those students may not even recognize that those traits are necessary and desirable once they hit the workforce. One of the most common topics raised by analytics and data science executives on my webcast is the fact even in a highly technical field like data science, things like soft skills, communication skills, and emotional intelligence are critical for success. They are also hard to find since so many people in our field are focused purely on the technical aspects of our work.

How Do We Fix It?

I doubt we can go back to the days when an in-person interview was part of a university admission process, but there are steps we can take. Some time ago, I talked about the idea of apprenticeships for technical fields. Those would take place outside of, but alongside, a student's academic program. However, there are steps that schools and universities of all levels can take to help produce more well-rounded students. A few ideas:

• Require team-based project courses starting even before high school so that students learn to work with others effectively in a team environment.
• Require students to present and explain their work, both in writing and verbally, to their peers, teachers, and others in the community in a variety of settings.
• Make joining clubs, playing sports, or other group social activities a requirement rather than an option.
• Teach courses focused on 1) communicating effectively, 2) listening to, and understanding, the viewpoints of others from different fields and with different skills, and 3) handling and managing conflict.
• Stress to students that while grades and test scores are important, they aren't the only goals they should focus on in school or in life.

I like to think that progress is being made. I know that at our program at Kennesaw State University, we push experiential learning and have some innovative courses – both released and under development – that focus heavily on those soft skills. But, we still have a long way to go as does most of today's educational system.

Perhaps what's needed most is for the customers of the education system – employers, parents, and students – to ask for change. As long as we allow educational success to be focused mostly on maximizing grades and test scores, we'll continue to often achieve a local maximum of student preparedness for the real world rather than a more well-rounded global maximum. Students and their careers would be well served if we change the outcomes we optimize for.

Originally posted in the Analytics Matters newsletter on LinkedIn

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Choosing the Right Hosting Model

The hosting model you pick can make or break the bank in analytics. Each hosting model for analytic databases has unique cost implications. Here's a snapshot of the options:

• ‘Buy the Box' Model: Ideal for unpredictable customer analytics. It offers cost-effective computing but tends to have higher storage costs due to block storage usage.
• Snowflake's Virtual Data Warehouse Model: This model suits enterprises looking for a comprehensive, all-in-one analytics solution. It's known for higher compute costs but offers a robust, general-purpose database.
• BigQuery's On-Demand Query Model: BigQuery is particularly cost-effective for sporadic query loads but can become expensive with extensive data scans. Its on-demand nature makes it suitable for varying analytic demands.

If you're interested in reading a more detailed analysis of the cost structure and dynamics of each model, especially regarding compute expenses, you should check out this Hackernoon feature published by Altinity Inc.

How to Get a Good Deal on Cloud Analytics: Advanced Cost-Optimization Strategies

A reasonable cloud analytics pricing should be affordable and scalable in line with your business growth. It should be devoid of charges for unused resources and free of hidden costs like data transfer fees. Beyond the basic platform choices, the following advanced strategies can help in optimizing your cloud expenses:

• Decouple and Scale: Opt for services that offer separate storage and compute to ensure flexible scaling and cost management, especially critical for persistent analytics workloads.
• Compressed Storage Billing: Choose providers like Snowflake and ClickHouse that bill for compressed storage, allowing you to harness cost efficiencies. If you are not quite familiar with Clickhouse then check out this gentle introduction.
• Query Optimization: On platforms like BigQuery, refine your query design to minimize data scans, which can lead to significant cost savings.
• Hybrid Storage: Employ a blend of block and object storage solutions to strike the right balance between performance and cost.
• Auto-Scaling: Utilize auto-scaling compute resources to align performance with the ebb and flow of your operational demands without overspending.
• Economical Long-Term Storage: For seldom-accessed data, turn to cost-saving long-term storage options like Amazon Glacier or Google Coldline.
• Negotiate Discounts: Proactively seek out discounts for substantial monthly expenditures, focusing on compute resources where possible.
• Leverage Marketplaces: Make purchases through cloud marketplaces to potentially reduce overall costs in line with your service agreements.

How to Get an Even Better Deal: Build with Open-Source

When default cloud services don't quite fit the bill, for example, when you need a GDPR-compliant analytics solution, a custom Kubernetes-based approach is a smarter strategic pivot. This method forms the foundation of what's called a Modern Analytics Stack, which is highly adaptable for stringent compliance and specific operational demands.

You can harness Kubernetes, a powerhouse for orchestrating containerized applications, to construct a robust, scalable foundation for your modern analytics stack. This isn't just about infrastructure; it's about crafting a toolset that bends to your will, not the other way around. By using open-source databases optimized for specific tasks, such as ClickHouse for real-time analytics, you can tailor your stack to your application's requirements.

Step 1: Choose Managed Kubernetes

Jumpstart your journey with a managed Kubernetes service. It's like having a team of experts running the background operations so you can concentrate on your app. And it's affordable – take Amazon EKS, which is about $72 a month.

Step 2: Select the Right Database

Next, you're selecting an open-source database. For analyzing data on the fly, ClickHouse is your go-to. It's purpose-built for speed and efficiency, especially if you're dealing with real-time data.

Step 3: Use a Kubernetes Operator

Now, you're choosing the right tool for the job, ensuring your database can keep up with the speed of your data. With Kubernetes, managing your database becomes a breeze when you utilize an operator. Time to meet the Altinity Operator for ClickHouse on GitHub. This isn't just a tool; it's your command center for database deployment and maintenance. You just feed it a simple YAML file – a set of instructions – and it sets up your database just like that.

Step 4: Set Up Observability

Monitoring and observability aren't just afterthoughts. You integrate Prometheus to keep tabs on your operations and Grafana to visualize the story your data tells. They work together to let you see what's happening under the hood of your app, with detailed graphs and real-time data.

Step 5: Implement GitOps with Argo CD

Argo CD is your bridge between the code in your GitHub and your live app. With Argo CD, you're not just deploying code; you're deploying confidence. Your infrastructure becomes as manageable as a git repository. It takes your changes and updates your app across Kubernetes clusters automatically or with a simple command.

And that is it! You've got a modern, agile analytics stack. It's a setup that's easy to change, easy to scale, and easy to keep an eye on – all while being light on your wallet. Plus, with tools like Argo CD, you can update your app with just a push to GitHub. Following these steps, you're not just building a stack; you're architecting a solution. Kubernetes‘ adaptability meets the precision of open-source tools, all orchestrated through the rhythm of GitOps.

In short, this is a cost-effective, scalable way to build an analytics app that grows with you, powered by the community-driven innovation of Kubernetes and ClickHouse.

We have an excellent hands-on demo by Robert Hodges showcased in the webinar which this article is derived from. If you're specifically interested to see the demo, then go straight to the timestamp 40:30

Conclusion

Kubernetes might seem daunting, but it's actually a clear-cut way to a solid app foundation. Managed services like Amazon EKS streamline its complexity. ClickHouse excels in real-time analytics, and with the ClickHouse Operator, deployment becomes a breeze. Tools like Prometheus and Grafana give you a window into your system's health, while Argo CD and GitOps practices link your codebase directly to deployment, automating updates across environments.

If you hit a snag or need to expand your stack, Altinity's ClickHouse support and the Altinity.Cloud platform offer the guidance and resources to simplify the process, ensuring your project's success with less hassle.

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• Red carpet entrance
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Claim your space along the red carpet and purchase your ticket to join your Data & Analytics peers at the social event of the season! Register for The Corinium Awards Gala here: https://cdao-fall.coriniumintelligence.com/corinium-awards-ticket

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• CDAOs, CDOs, CAOs, CTOs, CIOs, CEOs
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What to expect:'

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To register, visit: https://cdao-gov.coriniumintelligence.com/register-pricing Use promo code: DATAFLOQ for 30% discount.'

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• CDAOs, CDOs, CAOs, CTOs, CIOs, CEOs
• Data Scientists
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What to expect:'

• 40+ speakers
• Panel Discussions
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To register, visit: https://cdao-canada-ps.coriniumintelligence.com/register-pricing Use promo code: DATAFLOQ for 30% discount'

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