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

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.

Survey Says: Big Data

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Big data management remains a topic at the table for most IT teams. Whether you are preparing a project, in the middle or on to that “next step”, utilizing data – structured or unstructured – to form actionable insights is key to your company’s long term viability and success. Today, let’s look at an article by Thor Olavsrud on CIO that covers a recent big data survey of enterprise executives.  What do the executives feel is working?  What challenges do even enterprise-sized businesses face?


First, what is working?

  • Strategy – 94% of the executives surveyed are confident their big data strategy is headed in the right direction
  • Motivation – 98% of the executives believe they rightfully encourage their employees to ground business decisions in “data and evidence”
  • Investment – 81% of those surveyed will increase their investments in talent, tools and technologies that will help the company leverage big data

What are the challenges?

  • Data Silos – 41% still report that the data their companies have is too siloed to be easily accessed
  • Access – 37% of survey respondents find it can take one day or more to access big data sources to analyze
  • Legacy Systems – 59% of executives surveyed find their data storage systems require too much processing time to meet today’s requirements

This survey demonstrates the importance of expanding your big data planning discussions to consider the operations and the daily management.  What are the potential  pain points  when accessing or using the data?  Do current systems or processes limit in any way and what is the solution and what can be done to overcome?  Big data management includes not only the end user capabilities but also those in the middle managing the operation and the systems already in place (or needed) to support.

CIO Big Data Obstacles

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“Most CIOs and other senior IT executives believe their companies are not prepared to make full use of the waves of data coming into their organization” Bain Insights leaders Steve Berez, Paul Callahan and Rasmus Wegener report for Forbes.

Big data, when accessible, can lead to more meaningful and actionable insights, but there is also the management and processing of that data behind-the-scenes and that is the place where many companies feel they are not quite ready.  Bain Insights finds that consolidating and cleaning data, simplifying access and rights management and improving access to external data sources are all commonly cited by CIOs as big data obstacles.  These are pretty specific but there are also cultural obstacles that are playing a role and sometimes even preventing some companies from working towards using big data.

Here are the culture or mindset obstacles that companies face with big data as identified by Bain Insights:

  • Owners of the data (business) and stewards of the data (IT) still make decisions with little interaction or understanding of the other group’s needs
  • IT focuses on storing and securing data versus focusing on getting data to the right hands for gaining insights
  • The company continues to apply old processes to new challenges and needs

How to overcome? One recommendation is to form closer relationships between business and technology teams. Once a relationship is established, balance business priorities with the technology investments that help productivity long-term.  DataOps is the term Bain Insights uses, “a DataOps mentality focuses the organization on improving the way that business leaders, data scientists and IT managers work together to discover insights.”

Share what big data obstacles your company faces? Does your company also have to make a mindset shift in order to capitalize on big data?

Big Data Predictions

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Bernard Marr compiles some of the top big data predictions for the year from leading analysts and firms in a recent Forbes article. Let’s take a look at those today.

  • Big data growth. Mr. Marr writes, “There’s absolutely no question that we will continue generating larger and larger volumes of data, especially considering that the number of handheld devices and Internet-connected devices is expected to grow exponentially.”
  • New tools for analyses. Emerging tools like Spark will compliment SQL
  • Decision making. Data analysis leading to real-time decisions will continue to be important
  • Machine learning. Gartner reported this as a top strategic trend for 2016
  • Privacy. Mr. Marr cites a Gartner report, which predicts by 2018 50% of business ethics violations will be related to data. This makes managing privacy controls and procedures a top concern for businesses
  • Staffing. A shortage of the types of workers needed to handle big data – from scientists and analysts to architects –  will continue
  • Data-as-a-service. Mr. Marr finds that Forrester is predicting that more companies will attempt to monetize their data
  • Cognitive computing. We see this word popping up more and more in the media and Mr. Marr believes businesses will start to realize its a natural fit with analytics
  • Data, data. “All companies are data businesses now” this statement is from Forrester and relates to the fact that more and more businesses are looking for ways to capitalize on their data

Mr Marr shares these closing thoughts, “Only time will tell which of these predictions will come to pass and which will merely pass into obscurity.” He points out that the key take away here is that big data is here to stay and will continue to grow.

Share your own thoughts on these big data predictions and what is happening in your company.

Super Bowl, Super Data

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With Super Bowl 50 played and the Denver Broncos enjoying their victory, today let’s take a look at the tech side of things. In particular, how big data and wearables are making an impact on football.  Bernard Marr breaks down the different ways football can use big data to gain new insights in a recent Forbes article:


Real-time data

From sensors in stadiums, helmets and pads, coaching staff can get access to real-time position data. Mr. Marr points out, “[data] can even help indicate when a player may have suffered a damaging hit to the head.”  NFL has given coaches access to tablets during the game to process data and plays.

Predicting Outcomes

Mr. Marr writes, “For the biggest football game of the year — and the biggest sporting event of any kind in the U.S. — you can bet that people are betting on their ability to predict the outcome of the game.”  As more data is collected people have moved to better computer algorithms for predicting game results. On the game side, big data and wearables can even help predict how weather conditions may affect a play.


A Super Bowl article that doesn’t mention advertising would not be considered complete. As the cost for a 30 second television ad spot continues to rise – Mr. Marr reports this year’s costs was $5 million per 30 second spot – marketers are turning to better data analytics and the increased abilities to use social data to make better use of their ad spend.

So next year when you tune into your favorite NFL team, remember big data is making an impact on the big game.

2016: Predictions for Big Data

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As we approach the final month of 2015, let’s take a look at what is in the news for big data trends.

Growth through 2019

Thor Olavsrud reports for CIO that “the market for big data technology and services will grow at a compound annual growth rate (CAGR) of 23 percent through 2019” according to IDC. IDC looks at growth in big data infrastructure, software and services and finds that software, which includes information management, discovery and analytics and app software, will grow the most, by 26% in the next five years.  Also, growth in professional and support services will account for half of all big data spending through 2019.

Industry Growth

Mr. Olavsrud’s article also takes a look at big data trends by industry. IDC found that top spenders on big data technology and services to-date are:

  • Discrete manufacturing
  • Banking
  • Process manufacturing

In the coming years IDC expects fast growth in the securities and investment, banking and media sectors. According to a Forbes article by Louis Columbus the top five industries for hiring big data professionals are (the percentage represent the number of total advertised positions in the market):

  • Professional, Scientific & Technical Services – 30%
  • Information Technologies – 19%
  • Manufacturing – 18%
  • Finance and Insurance – 10%
  • Retail Trade – 8%

You can see manufacturing and finance not only emerging as large drivers of  big data spending but also big data hiring.

In other industry news, big data will continue to play a role in the medical industry. Paddy Padmanabhan reports for IBM Big Data Hub current big data trends in healthcare include using analytics and data for disease detection and prevention, prevention of system fraud and abuse and improved population health management. In the news, you can also see the term precision medicine more regularly as well as new partnerships developing in the industry that incorporate big data into every day medicine and research.

Overall, big data growth is expected to be strong. Analysts believe that the market will begin to mature in the coming years but big data will continue to be a key element for company’s to remain competitive.