Modernize Your Data Warehouse

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Entrepreneur reports that by 2020 every person online will create 1.7 megabytes of new data every second, and according to a study on  Computerworld, by that same year $72 billion will be spent on big data hardware, software and professional services. Data is growing and its coming in many forms both structured and unstructured, and companies are looking for the tools to help them manage and utilize it.

Big data can provide a more complete picture of your customer, marketplace or product/service. But before you can take advantage of big data, you must first have the right systems and tools in place.  Now is the time to consider your own infrastructure to make sure the right workloads are working on the right technologies.  In fact, it may be time for your company to take a deep look into how your data warehouse architecture can be modernized, so you are ready to move ahead with big data analytics projects.

Modernizing your data warehouse can increase your ability to deploy new workloads and handle new data sources.  You can also simplify your operations. Modern appliances can be integrated with your data warehouse, filling service gaps and making deployment and management of data easier.  Finally, a whole new set of tools for big data analysis are available. These analytics tools can integrate into your system allowing you to crunch massive amounts of data to form actionable insights.

So how do you get started on the right path? A trusted partner can help you develop a plan for the changes needed to modernize your data warehouse architecture and can help identify the right big data analytics tools for your company. Evolving Solutions takes the time to fully assess your data and business objectives, develop customized software solutions based on your needs, and we provide ongoing support.  We take the time to understand your goals and help you to accomplish them. Contact us today to start discussing how to modernize your data warehouse.

Industry Spotlight: Banking & Big Data

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Big data use has been expanding across a variety of industries. Today let’s focus on the banking industry and how it is using big data.

Business Insider reports that some of the biggest banks in the world hope to use the information found on social media channels like Facebook, Twitter and Instagram as part of a customer’s credit history or in place of one when none is available.  Of course, developing a system to handle this type of data does not only bring technology and system challenges but also regulation and privacy challenges. Still, many feel tapping into social media would allow the industry to get amore complete picture of the consumer’s financial status and credit.

Bernard Marr spotlights Citibank’s use of big data in a Forbes article.  Citibank is taking a “data-led” approach to its decision making and using big data analytics across areas such as customer retention and acquisition, customer service and compliance and fraud. Citibank has over 200 million customer accounts and operates in hundreds of countries which all equates to a lot of data to handle and store. Adopting big data tools and processes has also helped Citibank to utilize data better and manage and store data more effectively. Mr. Marr writes, “At Citi, model testing allows for a holistic understanding of innovative use cases by deconstructing data at its most granular level as well as synthesizing structured and unstructured data sources.”

The banking industry is making inroads with big data to better serve its customers. It continues to look for solutions that strike a balance between regulations and privacy and solutions that support the vast amounts of transactional and customer data it already has with new data sources.

Industries Exceling with Big Data

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“Two and a half quintillion bytes or 2,500,000,000,000,000,000 bytes. That’s how much data humanity generates every single day. And the amount is increasing; we’ve created 90% of the world’s data in the last two years alone,” writes Michael Belfiore for IBM’s Watson Blog.

Many businesses struggle with this ever increasing data, especially growing unstructured data, but other businesses in an assortment of industries are succeeding by using cognitive computing technology to turn big data into big insights.  Mr. Beliofe provides a summary of several industries succeeding with big data:

Telecommunications – cognitive computing tools are being used to index documents, images and manuals giving call center agents access to more actionable data to better solve customer issues. Every second saved in call time is $1 in cost savings.

Finance – an auto finance firm is using big data to develop better customer insights that not only allow them to serve customers better but also to improve data security.

Healthcare – by being able to better capture and analyze unstructured data, one health system is using this data to help identify patients with risks for chronic diseases to improve treatments and reduce readmission.

Fitness – the rise of mobile and wearables has led to a fitness app that tracks your performance and progress and coaches you through your workouts to meet fitness goals.

Retail – a clothing retailer has used big data to drive a better in-store experience using sensors and Wi-Fi data to track customer behavior while in the store.

Travel – one airline is using mobile to access customer preferences, travel history and even allergies to provide more personalized service in-flight.

In each of these examples, cognitive computing technology allows companies to harness big data to create better customer service experiences.

IBM Shares The 3 “Cs” of Big Data

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There are the “Vs” of big data: volume, variety, velocity and veracity, but also Chris Nott of IBM in a recent article introduces us to the three “Cs” of big data. What are they? Confidence, context and choice. Today let’s look at his explanations on what each of the “Cs” mean.

Confidence.  Big data comes from many sources and most of the time to be useful it must be combined. Combining data is not an exact science.  There can be different data formats, definitions and variations in which the data is managed or stored. But, Mr. Nott points out a “confidence level” can be assigned so that leaders making decisions with the data can judge the quality and risk associated with the results. He adds, “the level of confidence that is acceptable is a judgment that a business needs to make based on the risk and effect of actions. And that judgment is a balance between what might result from poor decisions that arise from inaccurate data and the cost of making improvements in the provision of data.”

Context.  Big data grows fast and moves fast within an organization. Context is important not only for delivering the right information to the right person but also for granting the right access to the right person. Mr. Nott writes, “Understanding context requires understanding who is asking the question and why. And part of that grasp includes the role of the person, where that person is asking the question, what the questioner is trying to do and the purpose to which the results will be applied.”

Choice. There are a variety of tools and platforms available to crunch big data. IT must examine each tool and determine if it fits the purpose and needs of the business user. This “choice” is not one-size-fits-all and should be weighed against each groups’ needs as well as the organization’s governance policies so users have confidence in the platform choice that is offered.

The 3 “Cs” of big data help users develop a trust level with the data by first allowing them to understand the risks (confidence), then knowing the data is being delivered with their needs in mind (context) and finally being confident that they are utilizing a reliable set of tools.

3C's of Big Data - IBM
source: ibmbigdatahub.com

What’s Next for Big Data in Healthcare

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“Big Data will leave no sector untouched as it continues to change the way we think about everything from sales to human resources, and medicine and healthcare are no different,” writes Bernard Marr for Forbes.

Handling data is nothing new to the healthcare industry.  But, in recent years, increased abilities to share and access data plus new data from sensors and wearables has created not only more data but better data according to Mr. Marr.  In his article he outlines several ways big data will make an impact:

Prevention.  Mr. Marr writes, “smartphones and other popular smart devices including Jawbone, Fitbit and others, now have the capacity to help people track their progress towards a healthier lifestyle. Apps and devices to help track and monitor physical fitness but also chronic ailments like diabetes, Parkinson’s and heart disease are also being developed.”  Not only do these devices track more data but this data can be more reliable than traditional patient led tracking and reporting methods.

Diagnosis. Improvements are being made to how big data is stored and shared in the healthcare industry with the goal of  bringing medical providers more access. Systems, such as IBM’s Watson, are also looking at test results, recognizing patterns and learning in order to aid in diagnostics and improve early detection.

Treatment.  To get to more personalized medicine and better patient  treatments you first have to start big, as in big data.  The results of big data analytics, predictive modeling and new systems crunching vast amounts of information will help to better inform doctors about the needs of each patient.

Big data is definitely a “game changer” for the healthcare industry both now and in the future.