Hadoop Matters Blog

Big Industries' blog

Hadoop and Cassandra: Happy Together

Apache Cassandra

Apache Cassandra is a NoSQL database ideal for high-speed, online transactional data, while Hadoop is a big data analytics system that focuses on data warehousing and data lake use cases.

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Big Data for Operational Analytics

operational analytics

Manufacturing, operations, service or product executives know all too well the intense pressure to optimize asset utilization, budgets, performance and service quality. It’s essential to gaining a competitive edge and driving better business performance.

The question is, how can these goals be achieved? By quickly delivering high-impact data projects that help them achieve their goals. Armed with the right solutions, they can analyze product availability and predict product failures before they occur, optimize existing infrastructure to increase up-time, and reduce operational and capital expenditures. And they can better meet service level agreements by proactively identifying and fixing potential issues before real problems occur.

The key is unlocking insights buried in log, sensor and machine data – insights like trends, patterns, and outliers that can improve decisions, drive better operations performance and save millions of dollars. Servers, plant machinery, customer-owned appliances, cell towers, energy grid infrastructure, and even product logs – these are all examples of assets that generate valuable data. Collecting, preparing and analyzing this fragmented (and often unstructured) data is no small task. The data volumes can double every few months, and the data itself is complex – often in hundreds of different semi-structured and unstructured formats.

Why Big Data Analytics?

Running a Data Discovery and Visualization Tool against Hadoopis the answer. It’s so powerful because it enables you to combine, integrate and analyze all of your data – regardless of source, type, size, or format. For example, you can quickly grab structured data such as CRM, ERP, mainframe, geo location and public data and combine them with unstructured data such as network elements, machine logs, and server and web logs. And then, using the right analytical tools, you can use this data to detect outliers; run time series and root cause analyses; and parse, transform and visualize insights from your data.

For example, you can use customer and device usage across networks to identify high-value usage. Or correlate operational, usage and cost data across operations to identify low-value segments. You can integrate and analyze historic machine data and failure patterns to predict and improve mean time-to-failure – or ERP purchase data and supplier data to optimize supply chain operations. And you can use sensor and machine data to identify and resolve network bottlenecks. The possibilities are endless.

source: Datameer

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How will Big Data Change Sales & Marketing in 2016?

source Datameer

As a CMO, digital marketing, or customer loyalty executive responsible for optimizing customer acquisition and loyalty campaigns, you need greater visibility into the customer buying journey. Why? Because deeper, data-driven customer insights are critical to tackling challenges like improving customer conversion rates, personalizing campaigns to increase revenue, predicting and avoiding customer churn, and lowering customer acquisition costs.

But consumers today interact with companies through lots of interaction points – mobile, social media, stores, e-commerce sites, and more – which dramatically increases the complexity and variety of data types you have to aggregate and analyze. Think Web logs, transaction and mobile data. Advertising social media and marketing automation data. Product usage and contact center interactions. CRM and mainframe data. And even publicly available demographic data.

When all of this data is aggregated and analyzed together, it can yield insights you never had before – for example, who are your high-value customers, what motivates them to buy more, how they behave, and how and when to best reach them. Armed with these insights, you can improve customer acquisition and drive customer loyalty.

Why Big Data Analytics?

Big data analytics is the key to unlocking the insights from your customer behavior data – structured and unstructured. It empowers you to combine, integrate and analyze all of your data at once – regardless of source, type, size, or format – to generate the insights needed to drive customer acquisition and loyalty.

For example, imagine being able to use insights about the customer acquisition journey to design campaigns that improve conversion rates? What if you could identify points of failure along the customer acquisition path – or the behavior of customers at risk of churn so you could proactively intervene and prevent losses? How would it help if you could understand high-value customer behavior beyond profile segmentation (for example, what other companies they shop from, so you can make your advertisements even more targeted)?

To precisely understand your customers and their customer journey, you need a way to integrate data from every channel – structured and unstructured – and analyze it all at once for an integrated customer view and holistic insights. Most importantly, big data enables you to do iterative data discovery that leads to insights you never had before, questions you never knew to ask.Big data analytics can help you achieve this. With these technologies, you can bring together all of your structured and unstructured data into Hadoop and analyze all of it as a single data set, regardless of data type. The analytical results can reveal totally new patterns and insights you never knew existed – and aren’t even conceivable with traditional analytics. The possibilities are endless.

 

Source: Datameer

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Operational Data Store: First Step towards an Enterprise Data Hub

operational_data_store-hadoop

Learn how an Enterprise Data Hub, powered by Apache Hadoop forms the ideal solution for customers with data stored in various locations. An Operational Data Store (ODS) aggregates data from multiple sources to be combined, cleansed and prepared for downstream operational and analytical use.

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