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Marketing, Telecom, Healthcare, IT services, FinTech, supply chain management : Which one is bullish on data science?

data science

Leap of faith has been coming from almost all sectors. Marketing, e-Commerce, Telecom, Healthcare, IT services,FinTech, Blockchain and supply chain management are apparently more strong than others at present as these sectors have huge customer base and have many competitors. They have been trying anything and everything to delight and retain customers, to maximize revenue by data-driven upsell and cross-sell strategies, to understand trends & patterns and to make the best possible strategies . Every business entity is eventually going to adopt big data analytics to survive and prosper in the market.

Almost everyone is optimistic on big data analytics/ data science. Return on investment on data science is very high and perhaps, that is why every business entity and sub-entity want to leverage big data analytics – a combination of data science and big data technologies. Everyone wants to streamline business, minimise the wastage & cost ,maximise the revenue, profit and customer delight in the competitive world. This can happen only when we start automating the process and promote data-driven decision making process.

If you want to know what customers are talking about your product/service/event, please use this API Social Media Sentiment Tracker

If you want to know how healthcare is using data science, please use this API 

If you want to know how Telecom is using data science/network visualization, please use this API

If you want to know how supply chain management is utilizing data science, please use this article

If you want to know how Blockchain/Bitcoin is utilizing data science, please use this API

If you want to know more about the perspective of a data scientist , please use this article

 

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Silent Drains: How Poor Data Observability Costs Enterprises Millions

Let’s rewind the clock for a moment. Thousands of years ago, humans had a simple way of keeping tabs on things—literally. They carved marks into clay tablets to track grain harvests or seal trade agreements. These ancient scribes kickstarted what would later become one of humanity’s greatest pursuits: organizing and understanding data. The journey of data began to take shape.

Now, here’s the kicker—we’ve gone from storing the data on clay to storing the data on the cloud, but one age-old problem still nags at us: How healthy is that data? Can we trust it?

Think about it. Records from centuries ago survived and still make sense today because someone cared enough to store them and keep them in good shape. That’s essentially what data observability does for our modern world. It’s like having a health monitor for your data systems, ensuring they’re reliable, accurate, and ready for action. And here are the times when data observability actually had more than a few wins in the real world and this is how it works

How Data Observability Works

Data observability involves monitoring, analyzing, and ensuring the health of your data systems in real-time. Here’s how it functions:

  1. Data Monitoring: Continuously tracks metrics like data volume, freshness, and schema consistency to spot anomalies early.
  2. Automated data Alerts: Notify teams of irregularities, such as unexpected data spikes or pipeline failures, before they escalate.
  3. Root Cause Analysis: Pinpoints the source of issues using lineage tracking, making problem-solving faster and more efficient.
  4. Proactive Maintenance: Predicts potential failures by analyzing historical trends, helping enterprises stay ahead of disruptions.
  5. Collaboration Tools: Bridges gaps between data engineering, analytics, and operations teams with a shared understanding of system health.

Real-World Wins with Data Observability

1. Preventing Retail Chaos

A global retailer was struggling with the complexities of scaling data operations across diverse regions, Faced with a vast and complex system, manual oversight became unsustainable. Rakuten provided data observability solutions by leveraging real-time monitoring and integrating ITSM solutions with a unified data health dashboard, the retailer was able to prevent costly downtime and ensure seamless data operations. The result? Enhanced data lineage tracking and reduced operational overhead.

2. Fixing Silent Pipeline Failures

Monte Carlo’s data observability solutions have saved organizations from silent data pipeline failures. For example, a Salesforce password expiry caused updates to stop in the salesforce_accounts_created table. Monte Carlo flagged the issue, allowing the team to resolve it before it caught the executive attention. Similarly, an authorization issue with Google Ads integrations was detected and fixed, avoiding significant data loss.

3. Forbes Optimizes Performance

To ensure its website performs optimally, F