BIG DATA

             Today, however,customers expect a company to know why they are buying. Or why they are n't, Because when company knows what motivates customers,it can serve them better.the good news is such data exists, just not in the columns, rows, reports and purchase histories we're used to. It's called Big Data, and it comes from tweets, videos,click streams and other unstructured sources. It is the data of desire.And today we have the technology and tools to make sense of it.

Enter Smarter Analytics from IBM-software, systems and strategies that help companies combine their own enterprise data with their customers' unstructured data to see a fuller picture. A big data platform, paired with predictive and sentiment analytics, allows organizations to correlate, for exampe, sales records with social media mentioned for more relevant insights.  So now, instead of learning which customer has lost, a company can learn which customers it might lost and present timely offers or products motivating those customers to stay. Using IBM smarter analytics to identify which customers were most likely to switch to another communications carrier, XO communications was able to predict likely customer defections within 90 days, reducing by 35 percent the first year.
With IBM smarter analytics, companies are gathering big data and using it to ask- and answer-smarter questions about what their customers really want.

                           

Characteristics of Big Data:

Volume: Enterprises are awash with ever-growing data of all types, easily amassing terabytes even petabytes of information. Turn 12 terabytes of Tweets created each day into improved product sentiment analysis.Convert 350 billion annual meter readings to better predict power consumption.

Velocity: sometimes two minutes is too late. for time sensitive processes such as catching fraud, big data must be used as it streams into your enterprise in order to maximize its value.Scrutinize 5 million trade events created each day to identify potential fraud.Analyze 500 million daily call detail records in real-time to predict customer churn faster.

Variety: Big data is any type of data structured and unstructured data such as text, sensors,logfiles, video, audio and more new insights are found when analysing these data types together.Monitor 100’s of live video feeds from surveillance cameras to target points of interest.Exploit the 80% data growth in images, video and documents to improve customer satisfaction.

Big Data Usage

       the real issue is not that you are acquiring large amounts of data (because we are clearly already in the era of big data). It's what you do with your big data that matters. The hopeful vision for big data is that organizations will be able to harness relevant data and use it to make the best decisions.Technologies today not only support the collection and storage of large amounts of data, they provide the ability to understand and take advantage of its full value, which helps organizations run more efficiently and profitably. For instance, with big data and Big data analytics. it is possible to:

• Analyze millions of SKUs to determine optimal prices that maximize profit and clear inventory.
• Recalculate entire risk portfolios in minutes and understand future possibilities to mitigate risk.
• Mine customer data for insights that drive new strategies for customer acquisition, retention, campaign optimization and next best offers.
• Quickly identify customers who matter the most.
• Generate retail coupons at the point of sale based on the customer's current and past purchases, ensuring a higher redemption rate.
• Send tailored recommendations to mobile devices at just the right time, while customers are in the right location to take advantage of offers.
• Analyze data from social media to detect new market trends and changes in demand.
• Use clickstream analysis and data mining to detect fraudulent behavior.
• Determine root causes of failures, issues and defects by investigating user sessions, network logs and machine sensors.

Big data Challenges

• Data acquisition
• Storage
• Processing
• Data transport and dissemination
• Data management and curation
• Archiving
• Security
• Workforce with specialized skills
• Cost of all of the above

    Data acquisition is the question of how we get data, It leads into the next two challenges, data storage and processing, which also are linked to each other. Storage is especially challenging because there are many different kinds of data that needs to be stored. You have long-term storage, and then there’s intermediate-term storage. Then data processing comes in because  you also need to worry about the volatile storage: how you keep this information in RAM when you’re doing data processing, in ways that make your data processing more efficient.

    Data transport and dissemination is an area where networking technologies are pushing some of the advances that we need. It is the challenge of getting the data from the place where it is analyzed, to the place where it is used by the people who need it, and in cases when real-time data is needed, this is critical.

      Data management is a short-term issue, but also a long-term issue, since some data sets will be used and reused at other points in the future.As for archiving, that while this is similar to data storage, there are approaches to doing data storage for archival purposes that are more cost-effective than the every-day types of storage.This makes big differences in operational costs.

    Security is an obvious challenge, but it is not just that you want your data to remain confidential; very often, one of the key issues in many of these mission areas is more one of integrity you just want to make sure your data is not being compromised, and that you know that it remains correct and accurate.

   As these challenges begin to be hammered out, big data technologies will become more sophisticated, which means there will be a need for  a more specialized and highly skilled workforce to help us deal with big data.

     Many organizations are concerned that the amount of amassed data is becoming so large that it is difficult to find the most valuable pieces of information.

• What if your data volume gets so large and varied you don't know how to deal with it?
• Do you store all your data?
• Do you analyze it all?
• How can you find out which data points are really important?
• How can you use it to your best advantage?

Until recently, organizations have been limited to using subsets of their data, or they were constrained to simplistic analyses because the sheer volumes of data overwhelmed their processing platforms. What is the point of collecting and storing terabytes of data if you can't analyze it in full context, or if you have to wait hours or days to get results? On the other hand, not all business questions are better answered by bigger data.

Incorporate massive data volumes in analysis. If the answers you are seeking will be better provided by analyzing all of your data, go for it. The game-changing technologies that extract true value from big data – all of it – are here today. One approach is to apply high-performance analytics to analyze the massive amounts of data using technologies such as grid computing, in-database processing and in-memory analytics.

Determine upfront which big data is relevant. Traditionally, the trend has been to store everything (some call it data hoarding) and only when you query the data do you discover what is relevant. We now have the ability to apply analytics on the front end to determine data relevance based on context. This analysis can be used to determine which data should be included in analytical processes and which can be placed in low-cost storage for later availability of storage.