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Such understanding and predictive abilities allow much quicker response to market changes and supports LagiSatu. It changes the business position from being reactive towards a more proactive stance. With the implementation of BDA practices, the hardware infrastructure for data processing can be reduced and the speed significantly increased. BDA also allows Lagisatu. A substantial amount of cost-savings can be brought about for the company. Overall, LagiSatu. BDA also supports the company in processing years of historical data, providing its marketing team with the ability to follow user behaviour trends.

This allows the team to strategize and to improve its customer experience better than other players. The Future: From data centres to software architecture, scalability is at the heart of Lagisatu.


Doing business now and in the future means harnessing the explosive growth of data. The mobile data generation, real-time connectivity and digital businesses have changed the nature of the game when it comes to protecting data assets. As a result, BDA has an increasingly important role to play in data security, which the company wants to intensify in the future. It also wants to use BDA to transform intrusion detection, differential privacy and malware countermeasures. Toggle navigation. May 9, News. Login Subscribe.

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Second, we are seeing a lot more data outside the organization — some available publicly free of cost, some based on paid subscription, and the rest available selectively for specific business partners or customers. This includes information available on social media sites, product literature freely distributed by competitors, corporate customers' organization hierarchies, helpful hints available from third parties, and customer complaints posted on regulatory sites.

Many organizations are trying to incentivize customers to create new data. For example, Foursquare www.

It provides me with points for each visit and rewards me with the "Mayor" title if I am the most frequent visitor to a specific business location. For example, every time I visit Tokyo Joe's — my favorite nearby sushi place — I let Foursquare know about my visit and collect award points. Presumably, Foursquare, Tokyo Joe's, and all the competing sushi restaurants can use this information to attract my attention at the next meal opportunity.

Sunil Soares has identified five types of Big Data: web and social media, machine-to-machine M2M , big transaction data, biometrics, and human generated. Here are some examples of Big Data that I will use in this book:. Why is Big Data different from any other data that we have dealt with in the past? Some analysts have added other V's to this list, but for the purpose of this book, I will focus on the four V's described here. Most organizations were already struggling with the increasing size of their databases as the Big Data tsunami hit the data stores. According to Fortune magazine, we created 5 exabytes of digital data in recorded time until In , the same amount of data was created in two days.

By , that time period is expected to shrink to just 10 minutes.

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A decade ago, organizations typically counted their data storage for analytics infrastructure in terabytes. They have now graduated to applications requiring storage in petabytes. This data is straining the analytics infrastructure in a number of industries. For a communications service provider CSP with million customers, the daily location data could amount to about 50 terabytes, which, if stored for days, would occupy about 5 petabytes.

In my discussions with one cable company, I learned that they discard most of their network data at the end of the day because they lack the capacity to store it. However, regulators have asked most CSPs and cable operators to store call detail records and associated usage data. There are two aspects to velocity, one representing the throughput of data and the other representing latency.

Let us start with throughput, which represents the data moving in the pipes. The amount of global mobile data is growing at a 78 percent compounded growth rate and is expected to reach To analyze this data, the corporate analytics infrastructure is seeking bigger pipes and massively parallel processing.

Latency is the other measure of velocity. Analytics used to be a "store and report" environment where reporting typically contained data as of yesterday — popularly represented as "D For example, Turn www. In the s, as Data Warehouse technology was rapidly introduced, the initial push was to create meta-models to represent all the data in one standard format.

The basic premise was narrow variety and structured content. Big Data has significantly expanded our horizons, enabled by new data integration and analytics technologies. A number of call center analytics solutions are seeking analysis of call center conversations and their correlation with emails, trouble tickets, and social media blogs.

The source data includes unstructured text, sound, and video in addition to structured data. A number of applications are gathering data from emails, documents, or blogs.

For example, Slice provides order analytics for online orders see www. Its raw data comes from parsing emails and looking for information from a variety of organizations — airline tickets, online bookstore purchases, music download receipts, city parking tickets, or anything you can purchase and pay for that hits your email. How do we normalize this information into a product catalog and analyze purchases?

Another example of enabling technology is IBM's InfoSphere Streams platform, which has dealt with a variety of sources for real-time analytics and decision making, including medical instruments for neonatal analysis, seismic data, CDRs, network events, RFID tags, traffic patterns, weather data, mainframe logs, voice in many languages, and video.

Unlike carefully governed internal data, most Big Data comes from sources outside our control and therefore suffers from significant correctness or accuracy problems. Veracity represents both the credibility of the data source as well as the suitability of the data for the target audience. Let us start with source credibility. If an organization were to collect product information from third parties and offer it to their contact center employees to support customer queries, the data would have to be screened for source accuracy and credibility. Otherwise, the contact centers could end up recommending competitive offers that might marginalize offerings and reduce revenue opportunities.

A lot of social media responses to campaigns could be coming from a small number of disgruntled past employees or persons employed by competition to post negative comments. For example, we assume that "like" on a product signifies satisfied customers. What if the "like" was placed by a third party?

We must also think about audience suitability and how much truth can be shared with a specific audience. The veracity of data created within an organization can be assumed to be at least well intentioned. However, some of the internal data may not be available for wider communication. For example, if customer service has provided inputs to engineering on product shortcomings as seen at the customer touch points, this data should be shared selectively, on a need-to-know basis. Other data may be shared only with customers who have valid contracts or other prerequisites.

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Over the past year, the Information Agenda team has been asked to conduct a number of Big Data Analytics workshops. The three most common questions have been as follows:. How does Big Data change our analytics organization and architecture? Most of the material included in this book was collated in response to answering these questions. First, why is Big Data Analytics becoming so important, and what can we do with it?

The book projects major trends behind the rise of Big Data and shows typical use cases tackled by Big Data Analytics, where leading organizations are already seeing major benefits. Second, the book lists major components of Big Data Analytics and introduces an integrated architecture — Advanced Analytics Platform AAP — that combines Big Data Analytics with the rest of the analytics infrastructures and integrates with business processes.

It shows how these components work together in the AAP to provide an integrated engine that can combine Big Data with traditional Data Warehouse and Business Intelligence to provide an overall solution. Third, the book provides a glimpse at implementation concerns and how they must be tackled. How do we establish a roadmap and implement key pilot programs to gather momentum and persist to create a game-changing vision?