Big data is everywhere, and small businesses and enterprises alike are making strides in transforming business outcomes through effective big data analytics. For today’s marketing and IT professionals, big data analytics is rapidly becoming an essential yet multi-faceted skill, and those who master big data analytics play a critical role in transforming their companies into data-driven organizations.
Learning about big data analytics is an ongoing process, and there are a variety of routes professionals and students can take to become experts in the field. From four-year degrees and two-year master’s-level programs to free online courses and tutorials, the path to becoming a big data analytics master may be formal or self-led.
With big data analytics becoming a sought-after skill in business in nearly every vertical, both students and existing professionals are seeking ways to enhance their knowledge and build their skills to provide more value to their current or future employers. We’ve compiled this list of 51 expert tips to provide insight into the paths to mastering big data analytics, essential languages and skills to learn, and tips for making the best use of data to drive decision-making.
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Why Master Big Data Analytics?
1. Big data creates career advancement opportunities for IT and other professionals. “Big data is definitely creating tremendous opportunities for the IT pros that know and understand it. That could be in a new role such as a data engineer or simply in a revision of an existing job description — one that makes you more versatile and less dispensable to your employer and will likely generate unexpected opportunities down the road.
“Where do you add these magical skills, especially if your employer isn’t offering training in them? The Internet, of course. Education and skills training has experienced its own share of change lately, and there’s plenty of upside for the knowledge-thirsty IT pro: Loads of readily available, online classes for developing new skills across the technical spectrum. Best of all, many of these learning opportunities come at no cost to students — so the only thing you’re really putting on the line is your time and energy. Admittedly, those are not finite resources — but you can tackle new learning and career advancement chances with minimal risks.” – Kevin Casey, 10 Big Data Online Courses, InformationWeek; Twitter: @InformationWeek
2. Making sense of vast amounts of unstructured data is a necessity for modern organizations. “The recent explosion of social media and the computerization of every aspect of economic activity resulted in the creation of big data: mountains of mostly unstructured data in the form of web logs, videos, speech recordings, photographs, e-mails, and tweets. In a parallel development, computers kept getting ever more powerful and storage ever cheaper. Today, we have the ability to reliably and cheaply store huge volumes of data, efficiently analyze them, and extract business and socially relevant information.” – Big Data Analytics, Harvard University; Twitter: @Harvard
3. Big data analytics skills are useful in practically any setting, including learning environments. “With the digital age we have seen the exponential growth of data and with it the potential to analyse data patterns to assist in determining possible factors that may improve the learner’s success. However, the challenge is to determine which data are of interest. We are now in an era where gaining access to data is not the problem; the challenge lies in determining which data are significant and why. There are many data mining tools available to help with the analysis, but in the past they were targeted on structured data. Today we have so much data that come in an unstructured or semi-structured form that may nonetheless be of value in understanding more about our learners.” – – Patricia Charlton, Manolis Mavrikis, and Demetra Katsifli, The Potential of Learning Analytics and Big Data, Ariadne
4. Big data is changing the way marketing and IT professionals are trained. “In my mind, big data isn’t a new specialty or suite of tools we have to train people into, as much as it’s a new organizational reality that everyone will need to adjust to occupationally.
“How we train marketing people will change. How we train IT people will change. How we train supply chain people will change. And so on. Even how we train executives will change. Everyone across the board needs more formal training in statistical analysis, and it should start early in the education process. It would be valuable to develop interdisciplinary curricula around the emerging concept of “data science” as a way of blending elements of math and statistics and computer science.
“Looking across the organization, some occupational roles will require additional computer and statistical programming skills, other roles will require new data management and data cleaning skills, and yet other roles will require skills in data visualization and interpretation.” – Michael Rappa, founding director of the Institute for Advanced Analytics at North Carolina State University, as quoted by Patrick Thibodeau in Q&A: What’s needed to get a big data job?, ComputerWorld; Twitter: @ComputerWorld
5. Machine learning enables organizations to draw from past experiences to predict outcomes. “Machine learning is to big data as human learning is to life experience: We interpolate and extrapolate from past experiences to deal with unfamiliar situations. Machine learning with big data will duplicate this behavior, at massive scales.
“Where business intelligence before was about past aggregates (‘How many red shoes have we sold in Kentucky?’), it will now demand predictive insights (‘How many red shoes will we sell in Kentucky?’). An important implication of this is that machine learning will not be an activity in and of itself … it will be a property of every application. There won’t be a standalone function, ‘Hey, let’s use that tool to predict.'” – Peter Levine, Machine Learning + Big Data: Predictive Analytics (and where do Hadoop and Spark come in?), Andreessen Horowitz; Twitter: @a16z
6. Machine Learning as a Service (MLaaS) applications extend the capabilities of big data, but knowledge of big data analytics proves useful for data scientists and other professionals to determine how companies can best utilize data and data-mining software to translate raw numbers into actionable insights. “In many ways, Machine Learning functions at the epicenter for a number of different facets of Big Data analytics. Its pivotal role only increases with the availability of MLaaS, which helps to democratize this subset of predictive analytics and enhance the roles of laymen and experts alike. As one of the enablers of the IoT, Machine Learning has a secure place in the future of Big Data. Its capacity to create timely action from analytics makes it essential to Big Data applications.” – Jelani Harper, Improving Big Data Analytics with Machine Learning-as-a-Service, Dataversity; Twitter: @Dataversity
7. Big data analytics knowledge is useful in many roles, including the C-suite. “Big data and analytics represent a huge opportunity for today’s business leaders. Leverage the value and insights of the work done in data science to transform your organization.
“Big data and analytics is not a technology or data science problem; it’s a leadership problem that can and must be solved by leaders. Executives equipped with a working knowledge of data science can massively improve marketing, create operating efficiencies, build new business models, disrupt the competitive status quo of industry and spark innovation.” – Leading with Big Data and Analytics, Northwestern Kellogg Executive Education; Twitter: @KelloggExecEd
8. Data sets have the potential to solve key business problems, but the size, complexity, and diversity of raw data makes it challenging for many companies to make use of data in meaningful ways. “Data is everywhere and can be obtained from many different sources. Digital data can be obtained from social media, images, audio recordings and sensors, and electronic data is quite often available as real-time data streams.
“Many of these datasets have the potential to provide solutions to important problems, and advice in making decisions in health, science, sociology, engineering, business, information technology, and government.
“However, the size, complexity, quality and diversity of these datasets often make them difficult to process and analyse using standard statistical methods, software or equipment.” – Big Data: from Data to Decisions, FutureLearn; Twitter: @FutureLearn
9. Technology readily supports the collection, storage, and security of big data, but the real opportunity lies in big data analysis. “Great strides have been made in the gathering, storage and security of big data, but the real opportunity is in the data analysis.
“Its application is virtually limitless, and will shape the world we live in—from the way we interact on a personal level to how businesses and governments evolve.
“The increased use of big data in virtually every sector has created a talent gap for data analysts.” – What is Big Data?, UMUC; Twitter: @UMUC
10. Managers must learn tools for analyzing and visualizing information to communicate with data scientists. “Theos Evgeniou, professor of decision sciences and technology management at INSEAD, believes that big data is reshaping management.
“‘Think of the impact excel had on managers’ daily jobs. The impact from big data may be a similar, or bigger, leap forward in terms of how it will change people’s jobs,’ he says.
“‘Much like the introduction of excel required managers to develop basic technical and quant skills, going to the next stage will require even more quant and technical skills.
“‘Managers will need to learn new tools to analyze and visualize information, and also develop their ability to better communicate with data scientists.'” – Seb Murray, 10 B-School Experts Share Top Big Data Analytics Tips, BusinessBecause; Twitter: @businessbecause
11. Effective big data analytics improves corporate decision-making. “Companies are struggling with how to efficiently and cost effectively collect and store this fast-growing data. But the real benefit lies in being able to analyze it in ways that can improve product quality, speed decision-making, boost customer service, and optimize business processes. And it works; according to a Dell survey, 89 percent of companies with big data initiatives report significant improvements in corporate decision-making. A report by McKinsey Global Institute estimated that retailers using data analytics across their organizations at scale could increase their operating margins by more than 60 percent, and that healthcare organizations could reduce costs by 8 percent by leveraging data analytics.” – Karen D. Schwartz, Three Tips for Optimizing Big Data Analytics, WindowsITPro; Twitter: @WindowsITPro
12. To get the most out of big data, companies must make big data a central business tenet. “Rearden Commerce CTO Phil Steitz succinctly sums up the single most important driver of big data success: You must integrate analytics and data-driven decision making into the core of your business strategy.
“‘If ‘big data’ is just a buzzword internally, it becomes a solution looking for a problem,’ Steitz says.
“For Rearden Commerce, whose e-commerce platform leverages big data and other resources to optimize the exchange of goods, services, and information between buyers and sellers, the concept of ‘absolute relevance’ — putting the right commercial opportunity in front of the right economic agent at the right time — is key.
“‘It is an example of this kind of thinking originating and centrally driving strategy at the top of the house,’ Steitz says.
“Part of this approach includes developing a small, high-powered team of data scientists, semantic analysts, and big data engineers, then opening a sustained, two-way dialog between that team and forward-thinking decision makers in the business, Steitz says.
“‘The biggest challenge in really getting value out of contemporary analytics and semantic analysis technologies is that the technologists who can really bring out what is possible need to be deeply engaged with business leaders who ‘get it’ and can help winnow out what is really valuable,’ Steitz says.” – Bob Violino, 5 strategic tips for avoiding a big data bust, InfoWorld; Twitter: @infoworld
13. Even with the growth of self-service analytics platforms and tools that streamline data mining and analysis, the human element remains a critical piece of the puzzle. “The ability to derive value from the information generated by networks, devices and subscribers is reliant on a dramatic, almost counter-cultural shift (for telcos) in corporate strategy and internal processes. Telcos ‘need to be able to prioritize and make decisions — neither of these happen today as [telcos] don’t have the right structure to do this,’ said Peter Crayfourd, a former senior customer experience executive at France Télécom – Orange, who is now an independent consultant.
“‘The human aspect is as important, if not more important, than the technology,’ stated Belgacom Business Intelligence Manager Wim Castuer at the end of his presentation. He outlined how the Belgian incumbent reorganized and brought together its IT and business/marketing teams in an effort to figure out how to get from ‘data spaghetti to [a] structured data model.'” – Ray Le Maistre, The Big Data Challenge: 10 Tips for Telcos, Light Reading; Twitter: @Light_Reading
Get an Education in Big Data Analytics
14. Consider a two-year Master’s degree program focused on Big Data analytics. “It’s well documented that there’s a big data talent gap, but what’s being done about it? What’s needed is knowledge and experience. On the first front, hundreds of colleges and universities worldwide are gearing up business analytics, machine learning and other programs aimed at analysis of data in a business context.” – Doug Henschen, Big Data Analytics Master’s Degrees: 20 Top Programs, InformationWeek; Twitter: @InformationWeek
15. Colleges and universities often provide course materials and even full course offerings for free online. “Increasingly colleges and universities are putting courses online where they can be studied for free. You may not get a degree at the end, but that might not be important. IBM big data evangelist James Kobielus said in 2013 ‘academic credentials are important but not necessary for high-quality data science. The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.'” – Bernard Marr, How To Learn Big Data – For Free!, LinkedIn; Twitter: @BernardMarr
16. If a four-year degree or two-year graduate program aren’t in the cards, you can supplement your existing education and experience with a certificate program in big data analytics. “Data and big data analytics are becoming the life’s blood of business. Data scientists and analysts with expertise in the techniques required to analyze big data and engineers and developers who know their way around Hadoop clusters and other technologies, are hard to come by. If you’re looking for a way to get an edge — whether you’re job hunting, angling for a promotion or just want tangible, third-party proof of your skills – big data certification is a great option. Certifications measure your knowledge and skills against industry- and vendor-specific benchmarks to prove to employers that you have the right skillset. The number of big data certs is expanding at a rapidly.” – Thor Olavsrud, 13 big data certifications that will pay off, CIO; Twitter: @CIOonline
17. Online courses arm students with knowledge of specific languages or other niche skills to enhance big data analytics skills, and introductory courses are also available for those first gaining foundational knowledge of big data analytics. “With the involvement of Big Data Technologies in almost every sector of business, there are anticipated to be millions of Big Data jobs vacancies in government and other sector of business. That’s the reason why everyone is intrigued to learn Big Data as there are lot of unfilled vacancies and a lucrative career ahead. Now the question is ‘How to Learn Big Data Analytics?’ Obviously you can’t go to back to the college to get a degree and relevant experience, but there are alternatives.
“Keeping the scope of this industry in mind, many universities and colleges have started to putting their Big Data courses online for the convenience of aspirants. However, these courses aren’t completely free, one has to pay a particular amount depending on the course. There are few different prerequisites for different courses that a person should have knowledge of before grabbing the insight details of the course.” – Ayush Sharma, How to learn Big Data Analytics?, Big Data Science Training
18. Free online courses that enable you to advance your knowledge in big data analytics are readily available. “Data is the foundation of the Digital Age. Learn how to organize, analyze and interpret these new and vast sources of information. Free online courses cover topics such as machine learning, baseball analytics, probability, randomization, quantitative methods and much more.” – Data Analysis & Statistics Courses, edX; Twitter: @edXOnline
19. Find a mentor and study their best tips and tricks. “I am very impatient when it comes to acquiring knowledge. I don’t want to wait for years. I don’t want to do hit and trial or fail first to learn things the hard way.
“I want to learn everything right now, if possible. I believe in head starting, by learning from the best in the industry.
Why repeat the mistakes which others have made before you? It doesn’t make any sense.
Learn from other people’s mistakes, avoid them and make your own original mistakes.
“You need to make mistakes in order to grow.
“So the first step in becoming a ninja in data reporting or in anything is finding the right mentor and making mistakes under his/her supervision. You will learn a ton, every time you make a mistake in the presence of your mentor.
“I did this by finding ‘Avinash Kaushik’ and by learning the very best skills, tricks and tips from him.” – Himanshu Sharma, How to become Champion in Data Reporting, OptimizeSmart; Twitter: @OptimizeSmart
20. Look to trendsetters. “Some universities are taking a big leap forward into analytics, and faculty can gain valuable insight by researching these schools’ programs. Michigan State University and West Virginia University, for example, offer a Master of Science degree in Business Analytics. Faculty from numerous functional areas—accounting, finance, economics, supply chain management, marketing, and management information systems—helped create WVU’s program, which teaches students how to collect, analyze, and interpret data in an ethical and strategic fashion, said WVU accounting associate professor Ludwig Christian Schaupp, Ph.D.
“The University of Mississippi offered a special session this past summer called ‘Data Analytics for Accountants,’ and this spring accounting faculty plan to incorporate concepts from the session into their Accounting Systems Seminar.” – Cheryl Meyer, 8 tips for teaching Big Data, American Institute of CPAs; Twitter: @AICPA
Essential Languages and Skills to Master
21. There are several essential tools of the trade anyone interested in a career in big data analytics should master. “SAS, SPSS, R, and SQL. Start with any tool that you can get access to. Sometimes you will be surprised to find that a Tool that you thought did not exist in your organization actually does. In one of my previous jobs, when I was busy negotiating with SAS for licenses for my team, a colleague of mine, who was an Actuary told me that he had seen a SAS session in one his team member’s PC, sometime back. I followed up with that team member and we found that we had a SAS server already in place waiting to be used!
“Learning is not about knowing everything, but learning substantial portions thoroughly and gaining sound knowledge about what you learn. I would much prefer a candidate who knows a lot about how to run a regression in SPSS, than a person who has half baked knowledge (knows a little bit about CHAID, done a little bit of regression, knows a little bit of SAS and a little bit of SPSS) If you can master one tool and a few modules/techniques of the tool, then you stand a better chance of getting a job and also of being able to get a job done.
“Pick up a tool that is available easily to you and start learning it – SAS, SPSS, R (now available as open source).
“I do not recommend using pirated software though they are now openly available in the market.” – Snehamoy Mukherjee, 5 Tips to build a Career in Analytics and Big Data!, LinkedIn; Twitter: @snehamoym
22. Learn Python, but don’t stop there. “As described in ‘R or Python? Consider learning both‘ we don’t recommend that you only learn Python and forget about the rest. However, learning Python is one of the best things you can do for your career. There are good reasons why Python is being adopted so widely by computer scientists, and why it’s a data analysis tool of choice for so many, the main one being the ease of learning and using Python.” – Martijn Theuwissen, DataCamp, Comprehensive Guide to Learning Python for Data Analysis and Data Science, KDNuggets; Twitter: @kdnuggets
23. Deep learning, coupled with big data analytics, are high-focus areas within the broader field of data science. “Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simpler abstractions formulated in the preceding level in the hierarchy. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized.” – Maryam M Najafabadi, Flavio Villanustre, Taghi M Khoshgoftaar, Naeem Seliya, Randall Wald, and Edin Muharemagic, Deep learning applications and challenges in big data analytics, Journal of Big Data via Springer Open
24. New technological and methodological solutions are needed to analyze big data. “Everyone has heard of big data. Many people have big data. But only some people know what to do with big data when they have it.
“So what’s the problem? Well, the big problem is that the data is big—the size, complexity and diversity of datasets increases every day. This means that we need new technological or methodological solutions for analysing data. There is a great demand for people with the skills and know-how to do big data analytics.” – Big Data: Statistical Inference and Machine Learning, FutureLearn; Twitter: @QUT, @FutureLearn
25. Hadoop is widely used in big data analytics, and there are ample opportunities for learning this open-source software framework. “Hadoop, an Apache open source software framework for storing and crunching big data sets across clusters of machines, is hitting the big time. Transparency Market Research forecasted last fall that the Hadoop market could grow from $1.5 billion USD (2012 figure) to $20.8 billion USD by 2018. Growth on that scale precipitates the need for many more able bodies to develop,manage and administer all of those Hadoop implementations.
“Given ample time and a penchant for the topic, a lot of people in the field feel that you can learn what you need to know about Hadoop through self-study. For those folks, browsing the plethora of documentation on the Apache Hadoop website is a good starting point. You can also download the open source Hadoop release, giving you the opportunity to turn the knobs and explore at your own pace. Administrators and developers who prefer a more structured learning experience can take advantage of free online training courses that can get you to your goal a lot faster.” – Ed Tittel, 8 Free Hadoop Online Training Resources, Tom’s IT Pro; Twitter: @tomsitpro
26. Understand the possibilities of big data analytics. “Data may originate from many disparate sources, including:
- scientific instruments;
- digitally-authored media, including text, images, audio, and emails;
- streaming data from weblogs, videos, financial/commercial transactions;
- from ubiquitous sensing and control applications in engineered and natural systems;
- social interactional data from social networks, twitter feeds and click streams; or
- scientific data from large-scale surveys, and brain research.
“The data can be temporal, spatial, or dynamic; structured or unstructured; and the information and knowledge derived from data can differ in representation, complexity, granularity, context, quality, provenance, reliability, and trustworthiness. This phenomenal growth means that you must not only understand big data in order to decipher the information that truly counts, but also understand the possibilities of big data analytics.” – Introduction to Big Data Analytics, University of Massachusetts Boston; Twitter: @UMassBoston
27. It’s imperative to know how to decipher the quality of your data. “It may be very hard to spot signals if they’re constantly obscured by noise from bad data. Anyone who has worked in analytics knows that no data is 100% perfect. Rather than distrusting all of your data and flying blind, it’s better to determine what data is still useful. It’s like looking in the fridge to see what ingredients aren’t spoiled, and then filtering your meal options accordingly. I’ve used this approach in consulting engagements where I’ve noted what data appeared to be broken or missing, and then worked within the constraints of the data that was available. I won’t say it’s not frustrating to find contaminated or missing data, but it doesn’t need to block or end your quest for insights.” – Brent Dykes, Analyzing Big Data: 8 Tips For Finding The Signals Within The Noise, Forbes; Twitter: @analyticshero
28. Learn (and use) the right data management platforms for the right purpose. “Incorporating Hadoop as the foundation of a company’s data storage and processing platform does not mean that other platforms are replaced. It is a leave and layer strategy to effectively use Hadoop, noSQL,streaming, massive parallel processing, relational, data-warehousing, business intelligence and application specific platforms for the right purpose but the data asset that you can create by having all data regardless of structure or size land in Hadoop gives organizations a chance at rapid innovation.” – 3 Tips on Data Storage Management, ThinkBig; Twitter: @thinkBigA
29. Data security is a top priority for customer-centric organizations. The best big data analytics pros understand the importance of protecting data and the strategies for doing so in addition to their knowledge of data mining and analysis. “Retailers are responsible for protecting not only their own information, but the information of their customers as well. And they are faced with a diverse array of threats that are creating new potential vulnerabilities, such as theft of customer information and credit card data. As a result, many consumers now view data security as a differentiator and will change shopping habits based on the level of security that’s in place.
“Since so much is at risk, it’s not enough for retailers to take a point product approach to data security. Trying to protect people, data and information using pieced together set of firewalls, intrusion detection devices and encryption schemes can leave gaping holes that hackers worldwide can easily exploit.” – Tim Appleby, Six Security Tips for Retailers in the Age of Big Data, Security Intelligence; Twitter: @appsnc
30. Know why data cleansing is important and how to cleanse your company’s data. “With big data all the range at the moment, many IT leaders seem to be forgetting the most basic price of admission to the big data world, clean data.
“Your big data analytics are only ever going to be as good as the data that goes into it. So if you are burdened with incomplete or inaccurate data, fix it first.
“As you can probably imagine, it is quite a long slog to link, match, cleanse, and transform data across systems. However, it is necessary to connect and correlate relationships, hierarchies, and multiple data linkages, or your data can quickly spiral out of control.
“Saying that, it’s a much better idea to look at preventive measures in addition to one-off cleaning methods. Maintaining a closer relationship with contact data right from the point of entry is critical. Consistency can be enforced, and the consequences of poor formatting will be improved.
“Capturing addresses correctly the first time is therefore crucial. Any subsequent processing will never match the quality of an address captured correctly in the first place. Most of the time, a customer’s address arrives as part of a purchase, which makes the capturing process even more important. Not only are you about to find out who this consumer (perhaps a repeat customer) is, but the address will also be essential to validate payment and, of course, to deliver any physical goods to the right place.” – Jamie Turner, Four Tips for Big Data, PCA Predict; Twitter: @PCApredict
31. Learning the R programming language proves a useful skill for today’s data scientists and data analysts. “The R programming language makes it easy for a business to go through the business’s entire data. What the language does is it scales the information so that different and parallel processors can work upon the information simultaneously. When using a regular R package, most computers do not generally have sufficient memory to handle high amounts of data. However, the R programming language offers ScaleR, which will repurpose the information into smaller chunks so that the information can then be processed on various servers at the same time. In other words, ScaleR makes it easy to divide a huge database across different nodes. This allows users of the programming language to make analyses of statistical information in a very sophisticated manner. Furthermore, the language also makes it possible for programmers to easily perform periodic checks on the information as it is being processed. This benefits businesses because they can use high amounts of data and fine-tune it to do more sophisticated analyses.” – Eshna Verma, A Quick Guide to R Programming Language for Business Analytics, SimpliLearn; Twitter: @simplilearn
32. Developing an iterative process enables you to derive value from your data. “As you look to get value from your data, having an iterative process is crucial. Data insights can be incredibly valuable, but they can’t be extracted overnight. Allow your data teams to focus on small wins, but make sure you’re guiding them toward the bigger picture.
“Perfecting your process can take years, but you don’t need a perfectly refined process to get actionable results. Constant improvement is key. Give your data teams the freedom to innovate new ways to look at data, rather than repeating the same processes that may not work for your company.
“But gaining insights from your data is just the first step. To get the most out of your data, you need to create a process for taking action based on those insights to derive true value from them.” – Travis Oliphant, How To Make Big Data Insights Work For You, BusinessIntelligence.com; Twitter: @BIdotcom
Tips for Mastering Big Data Analytics
33. If you’re a business or marketing professional without an in-depth knowledge of the technical jargon typically used in big data analytics tutorials and courses, you can still master big data analytics if you know where to look for the right learning materials. “Intrigued by analytics? Wish you knew more about it? A lot of people search for information, and land on sites that are, well, too geeky. They’re aimed at programmers, people who pride themselves on knowing all the intricacies of their favorite software, or (eek!) math majors. These are not good source for business people aiming to get a grip on the topic.
“Maybe you’ve come across ESPN ’s FiveThirtyEight. This is the right kind of reading for you. These articles, written in normal human English (ok, much better than normal), can be read and understood by any educated adult. Great. Still, there’s a much wider range of analytics topics, and viewpoints, on the web that business readers can understand and put to good use. It’s a matter of knowing where to look.” – Meta S. Brown, 6 (OK, 7) Big Data and Analytics Learning Resources That Business People Can Understand, Forbes; Twitter: @metabrown312
34. Don’t focus on technology but on the questions you want to answer for your company. “The potential of Big Data is in its ability to solve business problems and provide new business opportunities. So to get the most from your Big Data investments, focus on the questions you’d love to answer for your business. This simple shift can transform your perspective, changing big data from a technological problem to a business solution.” – Big Data Analytics: How an agile approach helps you quickly realize the value of big data, ThoughtWorks; Twitter: @ThoughtWorks
35. Know what problems you’re aiming to solve. “Traditionally, big data has been described by the ‘3Vs’: Volume, Variety, Velocity. What is a real analytics problem that is best solved using big data tools? What kind of metrics do you want to capture? The most common use cases today involve scraping large volumes of log data. This is because log data tends to be very unstructured, can come from multiple sources, and especially for popular websites, can be huge (terabytes+ a day). Thus having a framework for performing distributed computing tasks is essential to solve this problem.” – Chris Schrader, Business Intelligence Consultant, How do I learn big data?, Quora
36. From improving customer retention to identifying students most likely to succeed and boosting graduation rates, big data analytics is being put to use by companies and organizations of every type to maximize results. After gaining knowledge of big data analytics, find innovative ways to put big data to use and transform business results. “According to the Washington Post, Virginia Commonwealth University recently helped to close a gap in student support services by leveraging data to zero in on sophomores and juniors who were at risk of not graduating, despite middle-of-the-road GPAs. The report states that, within a single semester, VCU saw a 16 percent increase in the number of students who successfully completed courses.
“University of Tennessee at Chattanooga CIO Tom Hoover told me recently that, as his institution started to use analytics to improve graduation rates, they discovered some interesting things: ‘We started to examine the graduation rates for our nursing students and found something very interesting. We determined that one of the stumbling blocks that our students were having difficulty with was not a chemistry or biology class, but rather an English class. That English class was actually the class that was forcing them to choose another major when we dug deeper to look at the data.'” – Nicci Fagan, The Power of Big Data and Learning Analytics, EdTech Magazine; Twitter: @EdTech_HigherEd
37. Tap into sources of data that you’re not currently utilizing. If something can be measured, data is likely already being collected – but that doesn’t mean organizations are using it to gain valuable business insights. “If something can be measured, then in all likelihood a vast archive of data is already being compiled—and it is growing daily. Often, the data is unprocessed, waiting for someone to analyze it and discover new, valuable knowledge about the world.
“This is the role of data analytics, a powerful set of tools for making sense of datasets of all sizes—from a personal exercise log to the massive collections of “big data” that define our information age. From science to sales, from sociology to sports, data analytics is unraveling the fascinating secrets hidden in numbers, patterns, relationships, and information of every kind.
“Consider these examples:
- Cell phone science: If you are an avid user of your cell phone, try downloading several months of your calling data. You may see daily and long-term patterns in your usage that surprise you. Plus, any changes in your routine, such as a vacation, will show up prominently.
- Hardball analytics: The book and film Moneyball tell how the Oakland A’s overcame one of the smallest budgets in major league baseball to assemble a division-winning team. The secret? Managers used overlooked data analytics to hire undervalued, high-performing players.
- Presidential prediction: In the 2012 presidential election, statistician Nate Silver and a few others correctly predicted the winner of all 50 states and the District of Columbia. Here, weighting criteria make it possible to analyze data collected by hundreds of pollsters from thousands of distinct polls.
“In our age of accelerating progress in so many fields, it’s easy to lose sight of the underlying innovation that makes this revolution possible. In case after case, the big breakthrough comes from data analytics, the mathematical magic that turns undigested information into life-transforming insights and advances.” – Professor Tim Chartier, Ph.D., Big Data: How Data Analytics Is Transforming the World, The Great Courses; Twitter: @TheGreatCourses
38. Collecting and storing data is only one piece of the puzzle, it’s the analysis of raw data that offers transformational business insights. “While ‘Analytics’ as a term has been around for some time now, ‘Big data’ is a more recent phrase. It has come into existence because of the sheer volume of data that is being generated today in almost every aspect of our lives. All this is creating a need to manage this data differently. When put to good use, Data allows analysts to spot trends, extract insights and make predictions. . Businesses have realized that analysing this huge volume of data can give extremely valuable insights that can help them drive revenues or profits or customer loyalty.” – Analytics, NIIT Cloud Campus; Twitter: @NIITLtd
39. Focus on predicting the future rather than looking into the past for simply generating historical reports. “How should we best architect our enterprise stack to gain value from Big Data in terms of Hadoop, complex event processing, NoSQL and traditional data warehouses? Should we host our data on-premise or on the cloud?
“These are fair questions to ask, but they don’t get to the core of why Big Data is a big deal. Only with advanced analytics, and specifically machine learning, can companies truly tap into their rich vein of experience and mine it to automatically discover insights and generate predictive models to take advantage of all the data they are capturing. This advanced analytics technology means that instead of looking into the past for generating reports, businesses can predict what will happen in the future based on analysis of their existing data. The value of machine learning is rooted in its ability to create accurate models to guide future actions and to discover patterns that we’ve never seen before.” – Martin Hack, Use Data to Tell the Future: Understanding Machine Learning, Wired; Twitter: @WIRED
40. Decipher the most relevant data from irrelevant noise, but avoid creating a silo effect by narrowing your focus too much. “Problem: Big Data comes from many disparate sources. Putting it all together is challenging. You need to normalize and analyze huge sets of data. However, pre-determining what’s important creates a silo effect – looking only at a narrow view.
“Solution: ‘Correlations and patterns from disparate, linked data sources yield the greatest insights and transformative opportunities’ – Gartner. Use a robust ETL system to take any possible data into account and logically analyze what is and what isn’t relevant. Avoid misconceptions due to lack of information. Findings are often quite surprising.” – Five Tips to Improve Your Data Analysis, ITBusinessEdge; Twitter: @ITBusinessEdge
41. Consider your end goal and ask the right questions. “The most valuable insight into business performance is achieved by pre-determining exactly what information is needed and then asking your data specific questions. Many companies implement big data solutions and expect insight without first deciding what they need to know. Vague questions will not receive clear answers.” – Duncan Macrae, Top 10 Tips To Ensure Big Data Analytics Success, TechWeek Europe; Twitter: @TechWeekEurope
42. Collect more data for richer data sets. “You should collect more data . . . and then be good about storing and saving the data you do collect.
“In order words, don’t sloppily discard or carelessly lose or foolishly throw away the data we already collect or have. That data could be priceless. And if it isn’t priceless today, who knows? It might be at some point in the future.
“Face it. The richer the data set, the better the chances some cool insight will jump out at you.” – from Excel Data Analysis For Dummies, 3rd Edition, 10 Tips for Better Big Data Analysis, Dummies.com; Twitter: @ForDummies
43. Avoid shiny objects. “Organizations have access to more data than ever, from sensors to smart phones, and the pressure to do something big is bigger than ever. It’s easy to get caught up in the hype, especially around shiny new technologies.
“But finding success with big data is not easy, and it’s not something that can be done overnight by simply adopting technology. Today’s big data leaders have been working at analytics for 10 years or more, and already have the foundations in place that lead to success.
“‘A lot of times, laggards look at silver bullets,’ says Ron Bodkin, the founder of ThinkBig, a data analytics services firm bought by Teradata last year. ‘They hope they’ll adopt a technology and all their problems will be solved, which of course never happens.'” – Alex Woodie, 10 Tips for Beginning Your Big Data Journey, Datanami; Twitter: @datanami
44. Choose the right software tools, but remember that software isn’t a total replacement for the human element. “While software can abstract the underlying complexity of connecting to data sources or executing an analysis, software is not a complete substitute for human intellect. Although predictive and prescriptive analytics can help, humans still need to understand how they can use data to benefit the business. In addition, specialized knowledge is still necessary to solve unusually difficult problems.
“Wherever a company is on its journey, there is no shortage of tools from which to choose. But even with ‘the right’ tools in place (which varies from organization to organization), businesses can still fail to realize the potential business value of their efforts because they still have organizational obstacles to overcome, not the least of which is balancing the pace of business and technological innovation with a corporate structure and mindset that can support it.” – Lisa Morgan, 11 Tips For Successful Self-Service BI And Analytics, InformationWeek; Twitter: @InformationWeek
45. Put customers at the center. “All businesses interact with customers. But we often look at these interactions in aggregate instead of by type of customer. A good place to start for any organization is to take their existing data and look for ways to pivot it to be around the customer. For example, you may know your sales, but do you know your sales data for just first time buyers? What are they buying? Or you may know your mobile app traffic, but how much is from men versus women? The first key step in this process is to also organize your data by customer instead of just by product line, channel, brand, or store. This customer-centric approach will build the foundation for more sophisticated analysis in the future like predictive modeling.” – Zoher Karu, Big-data tips for small businesses from eBay’s head of data, VentureBeat; Twitter: @VentureBeat
46. Don’t collect data for the sake of collecting it; have a strategic plan. “Farouk Ferchichi at Toyota Financial Services says: ‘Ensure there is a purpose you understand of why analytics is valuable to the organization. Purpose can be a business sponsor like discovering new ways (i.e. products, markets, etc.) to increase revenue, retention, profit, or control costs. So ask the tough questions and align with executives mandates.'” – Farouk Ferchichi at Toyota Financial Services, as quoted by Alesia in Top Ten Big Data Analytics Tips, Data Science Central; Twitter: @DataScienceCtrl
47. Find your story and cast of characters. “Big Data allows you to dig into customer activities and profiles and build detailed persona. It allows you to trace the customer journey and build a story out of the data points and behavioral choices. Marketers can now build customer profiles with 10-15 trait categories per type, and more. This allows new insights into affinity, preferences, tastes, needs and wants – in real time. Anonymization of data can help deal with concerns of customer privacy.” – Madanmohan Rao, Big Data: 15 Tips for Success, YourStory; Twitter: @YourStoryCo
48. Do your homework to gain trust from key stakeholders for implementing big data analytics in your organization. “It almost seems counterintuitive that you can use data to build something so intangible and downright emotional as trust.
“Regardless of the department, whether it is marketing or sales, corporate executives are always concerned about people, money, and time.
“To approach leaders about these matters, you have to prove that you have the same objectives as they do.
“Be prepared and ready to show how you are using analytics to help your own department, your colleagues, and your internal and external customers.
“Otherwise, people will be less inclined to believe you.
“‘If they can bear in mind that you both share similarities, the trust can be nurtured,’ says Tomasz Wyszynski, director of data analytics at Schneider Electric.” – John Kelly, Eight tips for getting corporate buy-in for data analytics, Econsultancy; Twitter: @Econsultancy
49. Keep it simple. “OK, most businesses can recognize they need to be able to make more informed choices. But they are faced with a mountain of unknowns – not just the sheer volume of the data itself, but more basic questions like where the data is located, who in the company needs it, and what they are missing in terms of technology to begin to make it actionable.
“That’s where it can go off the rails for SMEs. They find out about dozens of possible Big Data and analytics tools they could use and lose interest fast due to the hefty price tag, the number of personnel needed to implement it or the length of time it will take to deploy the systems.” – Drew Robb, Big Data Success: 10 Tips for SMEs, Enterprise Apps Today; Twitter: @EntApps2Day
50. Make BI an integral component of your company’s marketing strategy. “Raw data does not have any influence on a business until it is assessed, analyzed and made into useful information that enables the company to understand their customer demographics. One of the biggest challenges that companies face is to derive monetary value from their data. This can be assured by effective business intelligence and analysis.
“Many businesses make the common mistake of piling up collected data then start planning for analytics. This usually just makes the business store useless data for a long time. Businesses should instead take the first step in data analysis with the small set of information that they start with, and grow in the data analytics and data base in a parallel manner. This is a great way of making data management a more pleasant task for the company’s employees or DBA experts.
“No business has an excuse of excluding BI in their marketing strategy, with the available tools and options in the market. Experts and manufacturers are making the process simpler and more user friendly by the day.” – Jack Dawson, Basic Tips of Data Analytics for the Effective Business Leader, Dataversity; Twitter: @Dataversity
51. Assigning meaning to data is the key to deriving the most value from your analytics. “Too many big data projects start by searching for the right technology, then dumping a mass of data into it and trying to work out how it can help the business. That’s the wrong way round. It’s like buying land in a distant country without getting it surveyed first and then trying to work out what to do with the land.
“Some retailers are trying to track customers moving round their stores via their mobile phones and RFID tags. They’re putting ‘beacons’ on products and shelves, and using their RFID tags to pick up a mobile phone signal. Consequently, one of the excuses for failure of RFID projects was that they were swamped by the data.
“But this is a poor excuse. These projects are still failing even though retailers have numerous big data tools to choose from that purport to be perfectly capable of handling the data volumes. The problem lies in assigning meaning to data. It’s both an intellectual and a technical problem.” – Ben Rossi, The big data phenomenon is broken: 5 tips for doing analytics the right way, InformationAge; Twitter: @InformationAge