Big Data Analytics and Variety

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Chris Gillar of IBM Big Data writes, “when you choose analytics solutions, you’ll likely have many diverse needs, all of which either need to be addressed now or will someday need to be addressed. Moreover, because even a single platform can offer considerable variety, getting the variety you need all at once has never been simpler. But you must also take care that the variety offered is the right variety.”

In a recent article he discusses the importance of choice in your big data analytics solution.  He identified five common needs and points out businesses will accommodate these needs in different ways which calls for a platform that offers variety and choice. Here are the common capability needs:

  • Support of a Hadoop-based solution.  With open source frameworks becoming more popular there are also certain capabilities that your company may or may not have that need to be there. Mr. Gillar highlights the need for strong SQL on Hadoop and full integration and support for R, text analytics and more
  • Support business intelligence and performance management whether on-premise, cloud or hybrid
  • Support predictive and prescriptive analytics. Mr. Gillar writes, “But you must look for capabilities such as the ability to perform analytics regardless of whether data is at rest or in motion, as well as the ability to automate data preparation. These two features in particular simplify analytics and make it inclusive”
  • Support of real-time analysis and solution development. Data is moving fast and your company needs to be able to analyze and react to it.  Your big data solution should offer a variety of capabilities so you can choose what makes most sense for your company
  • Support business users. Your solution must not only be powerful behind-the-scenes but it must also be usable by non technical employees across the organization

Seeking out a big analytics solution that brings you variety and choice will better allow you to meet your company’s needs now and in the future.

Turning Data Insights into Results

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Businesses of every size struggle with turning data analytics and insights into actionable results. As more businesses jump in with big data this problem only grows. IBM’s Institute for Business Value found “nine levers” that are present in businesses who are able to turn volumes of data into actions that bring value to their operations and customers. These levers or capabilities are:

  • Culture – availability and use of data and analytics within an organization
  • Data – structures and formality of the organization’s data governance process and the security of its data
  • Expertise – Development of and access to data management and analytics skills and capabilities
  • Funding – Financial rigor in the analytics funding process
  • Measurement – Evaluating the impact on business outcomes
  • Platform – Integrated capabilities delivered by hardware and software
  • Source of Value – Actions and decisions that generate results
  • Sponsorship – Executive support and involvement
  • Trust – Organizational confidence

These levers were then measured against their impact to creating value. Not all deliver the same boost but instead are divided into three categories:

  • Enable –  foundations for value creation
  • Drive – needed to realize the value from data
  • Amplify – needed to boost value creation

Below is a graphic depicting where each lever measures up:

Big Data Success

As your company dives deeper into big data, now is the time to evaluate and plan for turning that data into actionable insights. Which of the nine levers does your company have in place, which need work? The research is quick to point out that each lever does not work in its own silo. Instead, they are interrelated and when together facilitate organizations to drive value from their data.

Interested in diving deeper? Check out this Big Data Analytics Blueprint research from IBM:


Big Data: Make Room for Useful Data

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Mary Shacklett writes for Tech Republic, “Data stores continue to be overwhelmed by big data, so why don’t data center managers get rid of excess big data that isn’t of use?” Data whether for use today or maybe tomorrow still takes up space and costs money to maintain, but the answer on what to do about it isn’t always quite as cut and dry.

Ms. Shacklett shares Gardner’s definition for “dark” data, “the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing).” Gardner further explains, “Similar to dark matter in physics, dark data often comprises most organizations’ universe of information assets. Thus, organizations often retain dark data for compliance purposes only. Storing and securing data typically incurs more expense (and sometimes greater risk) than value because often organizations don’t classify it or intend to use it.”

Of course, just because you have dark data doesn’t mean it is all for the virtual “garbage”. Here are some tips to make sense of it as well as keep your big data storage solutions healthy:

  • Data filtration. Once you have defined the types of data you need now and may need in the future, there are various tools that can help filter through the data coming in to categorize for storage treatments
  • Export data. You also have the option to choose a cloud service provider to host certain data
  • Define data retention policies. Just as you would with internal data, be diligent about creating retention policies specific to big data

Big data is challenging both because an organization must determine how to extract analysis and insights, and it must also determine how to manage the back-end storage and maintenance. You need useful, business-impacting information on-hand and ready go, but utilize the tips above to sort through and handle the rest of the data for more effective operations.

Big Data Strategy Best Practices

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Are you looking for best practices to help guide your big data strategy? We have compiled some ideas from industry publications below.

First, David Kelly of Forbes recently wrote about these three big data strategy best practices:

  • Big data should be viewed as a corporate asset not belonging to just one group but viewed as part of the whole. Ask how this data could benefit the organization first and foremost
  • Take steps to change your data culture. Mr. Kelly writes, “Organizations need to foster attitudes that value creativity, experimentation and taking data-informed risks. Business and IT leaders need to be willing to challenge, adapt and refine both their strategy and execution plans based on data and practice fact-based decision making”
  • Data needs to lead to action.  Organizations can collect data but it also needs to be actionable. Insights must also be delivered to the departments and employees who can use it

Lisa Morgan of Information Week writes, “getting the most from data requires information sharing across departmental boundaries. Even though information silos remain common, CIOs and business leaders in many organizations are cooperating to enable cross-functional data sharing to improve business process efficiencies, lower costs, reduce risks, and identify new opportunities.” Her article points to interdisciplinary problem-solving as the next step in successful big data use.  Beyond just math and statistics experts, this type of problem solving brings in experts from different fields of study to help analyze the data. Ms. Morgan reports that this type of work is common in the academic world but only starting in the business world.

Share what is working best for your company as you formulate your own big data plan.

Getting Started: Big Data for Small Business

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As a small business owner, big data may seem like a technology only large enterprise companies can achieve but actually businesses of all sizes can take advantage of big data analytics in some form.  Author Bernard Marr writes on LinkedIn, “Big data might seem like it’s something that only big business can make use of. When people first hear that massive volumes of information are being used to fight terrorism, cure cancer or predict the spread of Ebola, it sounds expensive, difficult and time-consuming. But that doesn’t have to be the case.” Whether you plan to build your own systems or tap into a service provider, Mr. Marr points out that a key first step is to make sure you incorporate business analytics into your operations in an “intelligent way.”

How to do this? Mr. Marr recaps his SMART data principle:

  • Start with a strategy
  • Measure metrics and data
  • Apply analytics
  • Report results
  • Transform your business

You don’t have to use big data to solve world problems but to use big data you need a solid plan in place. First understand why and what you need the data for. Ask yourself questions around what problem do I want to solve, what data do I need, what data do I have, how can I use the results my data produces?

You should also look at your people resources – how will the results of the analysis impact their jobs, will they have the tools necessary to take action on the data results, do they have the know-how? Remember whether you are a small business or an enterprise, big data is a combination of technology, strategy, implementation and people.

Big Data in Practice: Common Hurdles

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Today let’s examine two hurdles that might come along when putting big data into practice.

First up a common big data hurdle many organizations are still trying to overcome – preparing their people for increased data analysis and increased volumes of data. Katherine Noyes of CIO writes that data has taken a more central role in decision making and this means that more people in your organization need data skills. Not everyone needs to be a data scientist but gaining the skills necessary to do their jobs better is key. An industry expert in Ms. Noyes article recommends first asking and defining, “what data analysis skills and tools will help a particular role perform better?” By starting there you can start to formulate a plan to make sure that employees at all levels are prepared.

Jill Dyche also of CIO brings up another interesting big data hurdle in a recent opinion article. How does an organization monetize/value data. She poses the question in her article: is that really the right question to ask and tackle? Instead, could your data value question really mean something else or could launching a data valuation project really get in the way of real big data progress? Her are a few other questions she points out that you should perhaps consider first before monetizing data:

  • What data do we have? First make sure you have a clear inventory of the data you have and how it originates and where it is located and how is it managed and used
  • How do we prioritize data? Does your organization treat every piece of data the same? Do you want to invest in big data but can’t get your arms around where to start. Discussion and action around data prioritization should be first and foremost before you tackle valuation
  • How do we become a digital business if we don’t get our arms around our data? Ms. Dyche points out that going down the road of data valuation will not make you digital-ready. Instead real work on systems and processes will ready a company

Ms. Dyche recommends when monetizing your data do it within the context of its role in solving a problem or meeting a strategic goal. She adds, “find a problem your company wants to solve, quantify the value of solving it, and illustrate data’s role in that solution.”

Share your thoughts – what big data hurdles does your organization face?

Getting Big Data Ready

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Are you ready for big data? David Kelly of Forbes writes, “jumping on the big data bandwagon can backfire in a very expensive way without a concise blueprint for leveraging, acting on and benefitting from the information.”  So how can you start to prepare?  Today, we will look at tips for developing your strategy as well as how to prepare your team.

First, create a strategy, a clear roadmap, that defines how you plan to implement and use big data as well as what outcomes to expect. Questions to ask:

  • What are my priorities and how will big data serve those?
  • How can we test the waters before plunging in too far?
  • How will big data inform my business decisions?
  • How will my organization handle and process more data volume?
  • What technology solutions are available and what will work best?

Mr. Kelly adds, “in addition, organizations should foster an operational mindset for practicing data-based decision making. They need a company-wide culture that encourages and rewards data usage, positive outcomes and value delivered to the business.” Sujan Patel contributor to Forbes shares these tips to help prepare your team to be more data-driven:

  • Start small. Define and tackle a limited big data project to test the waters and gain experience
  • Address talent issues. Don’t wait and expect trial by fire to work itself out. Instead identify, first, where you are shorthanded in skills and experience and explore opportunities to gain the skills needed
  • Make insights useable. Ensure you produce actionable insights and be open to granting access to multiple types of users, big data just like any data must have a business value

A clear strategy and your people, just like with other business and technology initiatives these two factors are key to define and develop if you wan to turn your big data vision into a successful reality.

Tips to Unlock Big Data Insights

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It is common, according to Bernard Marr of IBM’s Big Data Hub blog, to think that big data success is only about size.  Mr. Marr  points out, “By simply focusing on the size of your data you run the risk of becoming data rich and insight poor. You have huge volumes of information at your fingertips, but no idea what it all means or what to do with it.”  To benefit from  big data you have to gain valuable insights that transform your business, not just amass more data.

Mr. Marr outlines his five-step “Smart Data Framework” to help you best unlock big data insights:

  • Start with a strategy. Determine the problems you want to solve with big data and define the specific questions you have and the data you need to answer
  • Measure metrics and data. Determine where and how you can collect the data you need.
  • Apply analytics. Much of the growth in big data is fueled by unstructured data which can be “messy.” Today there are a number of tools out there that you can use to manage unstructured data
  • Report results. Tell a story suggests Mr. Marr, “if you use data visualization and narratives to tell that story in a focused and interesting way, it’s far more likely people will understand what you are trying to do, and be as motivated as you are yourself about implementing data-driven change.”
  • Transform your business. Keep in mind that change is the end goal and the data is there to help lead the charge

Notice how if you take the first letter from every bullet above you get the word SMART. Just like with data analysis and storage of the past, today, “smart” use of data whether big or small continues to be important.

Do you want to gain more big data experience and discuss the infrastructure considerations for your business in a Hadoop cluster environment? Join us April 23rd for our Big Data Hands-on Workshop with the Evolving Solutions technical team and experts from IBM.

Industry News Round-up: Next-Gen Big Data Analytics

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To keep you on top of the latest and greatest big data technology news, we’ve rounded-up a list of topics in the news. First, read about key priorities that will help facilitate next-generation big data analytics, then why big data can help non-profits and, finally, big data and Oscar picks.

The Big 4 Priorities for Next-Gen Big Data

Scott Gnau of Forbes writes about preparing for the future of big data, “specific advice is tricky, given the variety of applications and use cases out there. So here’s my own attempt to distill things down to four priorities when implementing tomorrow’s big data architectures. Don’t think of these as separate buckets, though. Consider them more like a set of closely interrelated priorities, like signposts along the road to success in creating your own next generation analytics.”  With big data changing fast, he recommends keeping these priorities in mind:

  • Flexibility
  • Access
  • Governance
  • Context

Flexible architectures will help you handle the diverse nature of big data.  Providing access  not only to the data scientists but also to business users is important. When you empower more people, you also need to have governance in place to regulate. Finally, context not only to keep data clean but in some cases protect data privacy.

Big Data and Nonprofits

Nonprofit organizations are also looking to use big data reports Ricky Rlbelro for BizTech Magazine.  Nonprofits can not only utilize big data to build and retain membership or to find and attract new donors, but they can also use big data to determine how their services are being perceived in the public and potentially spot issues before they become too big.

Big Data’s Oscar Picks

So the Oscars have wrapped up for another year. We can debate the merits of the winners and wonder why others didn’t make the cut, but how did big data do with its predictions this year. Steven Zeitchik and Oliver Gettell report for the LA Times, “in recent years, Big Data has attempted to turn art into science. This year such efforts are more prolific than ever — and more relevant, with the battle for best picture.” All three of the data companies interviewed for the article had “Birdman” in the lead for Best Picture which did in fact win. The companies took different approaches. One analyzed the voting body and others used combinations of data from past award ceremonies for their predictions. Just another example of how big data is growing in use – mixing art and science in this case.

Industry News Round-up: Big Data and Automakers

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To keep you on top of the latest and greatest big data technology news, we’ve rounded-up a list of topics in the news this week. First, read about how automakers would like to use big data and then is it SQL or no SQL – that is the question.

Ford Jumps on the Big Data “Wagon”
Larry Dignan for ZDNet reports on a recent keynote address by Ford CEO Mark Fields at CES 2015. Turns out Ford is looking for new  and innovative ways to utilize the data it collects.

Mr. Dignan writes, “Ford is obviously aiming beyond the vehicle cockpit. Ford is aiming to become the Apple of the auto industry. It’s not the hardware or software that makes the sale. It’s the integration and intelligence between the hardware and software.”  He also adds Ford is a good example of how every company is turning into a technology company due to data, big data.  Ford has started to experiment with the massive vehicle and driver data it collects. Early plans include analyzing car sharing, vehicle intelligence, driving patterns and insurance to not only improve vehicle automation but also to create smarter roads and cities.

Mr. Dignan notes one challenge that Ford will face in these new initiatives will be connecting big data to the human experience.  What do you think?

Big Data Flip Flop – SQL

Bill Franks writes for Forbes about the current debate buzzing around SQL and non-relational tools.  He sees both SQL and non-relational working side by side in a balanced approach as the best way to approach big data, “Where we will all land is a balanced approach where SQL remains a critical tool for analyzing data, and non-relational analysis is also utilized when appropriate. After all, the goal should always be to solve our business problems in the most efficient way possible.”

He also underscores that non-relational tools are not new.  His opinion is that many businesses just went too far with SQL trying to make it fit every situation. “Just keep in mind that non-relational options have always been available. It isn’t that there was no need for non-relational processing during the first decade of the 21st century.  Rather, companies moved too far toward SQL.  In a case of massive opinion flip-flop, there is now a large movement to enable SQL-like functionality on a wide variety of non-relational platforms, such as Hadoop.”

How does your company use SQL and non-relational tools to answer business questions? Share your thoughts on this debate.