Make Better Decisions And Strategic Business Moves
If you want to find actionable insights you need to segment your data.
Only then will you understand the behavior of micro-segments of your customers, which in turn will lead you to actionable insights because you are not focusing on the whole but rather on a specific.
Cluster techniques transform data into insights.
Cluster techniques are a powerful tool to embed insights by generating segments that can be readily understood and acted upon.
These methods make it possible for decision-makers to identify customers having similar purchases, payments, interactions and other behavior, and to listen to customers’ unique wants and needs about channel and product preferences.
Social Media Sentiment analysis
The flood of posts that flow through social media like Facebook, Twitter, Instagram and others is the classic example of big data. Today, companies are expected to monitor what people are saying about them in social media and respond accordingly avoiding therefore to lose customers.
Social media analytics tools help organizations understand trending topics. Trending topics are subjects and attitudes that have a high volume of posts in social media. Sentiment analysis (or opinion mining), uses social media analytics tools to determine attitudes toward a product, idea, and so on.
Real-time Twitter trend analysis is a great example of an analytics tool, because the hashtag subscription model enables you to listen to specific keywords (hashtags) and develop sentiment analysis of the feed.
Big data analytics can help your company to optimize the price you charge your customers by analyzing sales under various historic market conditions.
Big data is essential helping your organization identify potential opportunities to streamline operation and maximize profits.
Big data can process huge amonts of data in real time thus empowering decision makers with real time actionable information.
An alert can be triggered enabling your managers to take immediate action to correct the problem, before it become more costly to your company.
Big data can also make recommendations to your customers by analyzing historical data, therefore increasing upsell or cross-sell opportunities.
Big Data is the frontier of a firm’s ability to store, process, and access all the data it needs to operate effectively, make decisions, reduce risks, and serve customers.
- Store. Can you capture and store the data?
- Process. Can you cleanse, enrich, and analyze the data?
- Access. Can you retrieve, search, integrate, and visualize the data?
Big data is often characterized by 3Vs: the extreme volume of data, the wide variety of data types and the velocity at which the data must be processed.
Big data analysis expands into fields like machine learning and artificial intelligence, where analytical processes mimic perception by finding and using patterns in the collected data.
In order to take advantage of Big Data to gain a competitive advantage a big data architecture is of paramount importance.
Big data differs from traditional data architectures. Big data architecture is designed as follows:
- Bottom-up approach
- Handle the ingestion, processing, and analysis of data that is too large or complex for traditional database systems.
- Big data introduces new data sources such as social media content and streaming data.
- The enterprise data warehouse becomes a source for big data.
- This requires new type of data persistence and processing capabilities (advanced analytics)
Big data solutions typically involve one or more of the following types of workload:
- Batch processing of big data sources at rest.
- Real-time processing of big data in motion.
- Interactive exploration of big data.
- Predictive analytics and machine learning.
When working with very large data sets, it can take a long time to run the sort of queries that clients need. These queries can’t be performed in real time and thus introduces latency. To address this problem is to combine real time data and batch analytics data.
The lambda architecture, first proposed by Nathan Marz, addresses the problem by creating two paths for data flow. All data coming into the system goes through these two paths:
A batch layer (cold path) stores all of the incoming data in its raw form and performs batch processing on the data. The result of this processing is stored as a batch view.
A speed layer (hot path) analyzes data in real time. This layer is designed for low latency, at the expense of accuracy.
The batch layer feeds into a serving layer that indexes the batch view for efficient querying. The speed layer updates the serving layer with incremental updates based on the most recent data.
A drawback to the lambda architecture is its complexity. Processing logic appears in two different places — the cold and hot paths — using different frameworks. This leads to duplicate computation logic and the complexity of managing the architecture for both paths.
The kappa architecture was proposed by Jay Kreps as an alternative to the lambda architecture. It has the same basic goals as the lambda architecture, but with an important distinction: All data flows through a single path, using a stream processing system.
There are some similarities to the lambda architecture’s batch layer, in that the event data is immutable and all of it is collected, instead of a subset. The data is ingested as a stream of events into a distributed and fault tolerant unified log. These events are ordered, and the current state of an event is changed only by a new event being appended. Similar to a lambda architecture’s speed layer, all event processing is performed on the input stream and persisted as a real-time view.