Month: May 2019

The Nashville Analytics Summit will be on us before we know it. This special gathering of data and analytics professionals is scheduled for August 20th and 21st, and should be bigger and better than ever. From my first experience with the Summit in 2014, it has consistently been a highlight of my year. My first Summit took place at the Lipscomb Spark Center meeting space with about a hundred attendees. Just a few years later, we’d grown to more than 450 attendees and moved into the Omni Hotel.

Mark it on your calendar. I’ll give you five reasons why it is a can’t-miss event if you work with data:

1. We’ve invited world-renowned keynote speakers like Stephen Few and Thomas Davenport. You won’t believe who we are planning to bring in this year.
2. There isn’t a better networking event for analytics professionals in our region. Whether you’re looking for talent or looking for the next step in your career, you’ll meet kindred spirits, data lovers, and innovative businesses. For two years in a row, we have hired Juice interns directly from conversations at the Summit. 
3. It’s for everyone who works with data. Analyst, Chief Data Officer, or Data Scientist… we’ve got you covered. There are technical workshops and presentations for the hands-on practitioner and case studies and management strategies for the executive. We’re committed to bringing you quality and diverse content.
4. It’s a “Goldilocks” conference. Some conferences go on for days. Some conferences are a sea of people, or too small to expand your horizons. The Analytics Summit is two days, 500-something people, and conveniently located in the cosy confines of the Omni Hotel. It is easy to meet new people and connect with people you know.
5. See what’s happening. Nashville has a core of companies committed to building a special and innovative analytics community. We have innovators like Digital Reasoning, Stratasan, and Juice Analytics. We have larger companies making a deep commitment to analytics like Asurion, HCA, and Nissan. The Summit is the best chance to see the state of our thriving analytics community.

Now that you’re convinced you can’t miss out, you’re may wonder what to do next. First, block out your calendar (August 20 and 21). Next, find a colleague who you’d like to go with. Want to be even more involved? We invited dozens of local professionals to speak at the Summit. You can submit a proposal to present. 

Finally, if you don’t want your company to miss out on the opportunity to reach our entire analytics community, there are still slots for sponsors.

I hope to see you there.

learn more and register

Originally Posted at: Let’s Meet Up at the Nashville Analytics Summit by analyticsweek

Big data is big news, but it’s still in its infancy. While most enterprises at least talk about launching Big Data projects, the reality is that very few do in any significant way. In fact, according to new survey data from Dimensional, while 91% of corporate data professionals have considered investment in Big Data, only 5% actually put any investment into a deployment, and only 11% even had a pilot in place.

Big data is big news, but it’s still in its infancy. While most enterprises at least talk about launching Big Data projects, the reality is that very few do in any significant way. In fact, according to new survey data from Dimensional, while 91% of corporate data professionals have considered investment in Big Data, only 5% actually put any investment into a deployment, and only 11% even had a pilot in place.

Real Time Gets Real

ReadWrite: Hadoop has been all about batch processing, but the new world of streaming analytics is all about real time and involves a different stack of technologies.

Langseth: Yes, however I would not entangle the concepts of real-time and streaming. Real-time data is obviously best handled as a stream. But it’s possible to stream historical data as well, just as your DVR can stream Gone with the Wind or last week’s American Idol to your TV.

 This distinction is important, as we at Zoomdata believe that analyzing data as a stream adds huge scalability and flexibility benefits, regardless of if the data is real-time or historical.

RW: So what are the components of this new stack? And how is this new big data stack impacting enterprise plans?

JL: The new stack is in some ways an extension of the old stack, and in some ways really new.

Data has always started its life as a stream. A stream of transactions in a point of sale system. A stream of stocks being bought and sold. A stream of agricultural goals being traded for valuable metals in Mesopotamia.

Traditional ETL processes would batch that data up and kill its stream nature. They did so because the data could not be transported as a stream, it needed to be loaded onto removable disks and tapes to be transported from place to place.

But now it is possible to take streams from their sources, through any enrichment or transformation processes, through analytical systems, and into the data’s “final resting place”—all as a stream. There is no real need to batch up data given today’s modern architectures such as Kafka and Kinesis, modern data stores such as MongoDB, Cassandra, Hbase, and DynamoDB (which can accept and store data as a stream), and modern business intelligence tools like the ones we make at Zoomdata that are able to process and visualize these streams as well as historical data, in a very seamless way.

Just like your home DVR can play live TV, rewind a few minutes or hours, or play moves from last century, the same is possible with data analysis tools like Zoomdata that treat time as a fluid.

Throw That Batch In The Stream

Also we believe that those who have proposed a “Lambda Architecture,” effectively separating paths for real-time and batched data, are espousing an unnecessary trade-off, optimized for legacy tooling that simply wasn’t engineered to handle streams of data be they historical or real-time.

At Zoomdata we believe that it is not necessary to separate-track real-time and historical, as there is now end-to-end tooling that can handle both from sourcing, to transport, to storage, to analysis and visualization.

RW: So this shift toward streaming data is real, and not hype?

JL: It’s real. It’s affecting modern deployments right now, as architects realize that it isn’t necessary to ever batch up data, at all, if it can be handled as a stream end-to-end. This massively simplifies Big Data architectures if you don’t need to worry about batch windows, recovering from batch process failures, etc.

So again, even if you don’t need to analyze data from five seconds or even five minutes ago to make business decisions, it still may be simplest and easiest to handle the data as a stream. This is a radical departure from the way things in big data have been done before, as Hadoop encouraged batch thinking.

But it is much easier to just handle data as a stream, even if you don’t care at all—or perhaps not yet—about real-time analysis.

RW: So is streaming analytics what Big Data really means?

JL: Yes. Data is just like water, or electricity. You can put water in bottles, or electricity in batteries, and ship them around the world by planes trains and automobiles. For some liquids, such as Dom Perignon, this makes sense. For other liquids, and for electricity, it makes sense to deliver them as a stream through wires or pipes. It’s simply more efficient if you don’t need to worry about batching it up and dealing with it in batches.

Data is very similar. It’s easier to stream big data end-to-end than it is to bottle it up.

Article originally appeared HERE.

Source by analyticsweekpick

Big Data is less about the data itself and more about what you do with the data. The application of statistics and statistical principles on the data helps you extract the information it contains. According to Wikipedia, statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. The American Statistical Association defines statistics as “the science of learning from data, and of measuring, controlling, and communicating uncertainty.”

Statistics is considered to be one of the three primary pillars of the field of data science (the other two are content domain knowledge and computer science skills). While content domain expertise provides the context through which you identify the relevant questions to ask, computer science skills help you get access to the relevant data and prepare them for analysis, statistics helps you interrogate that data to provide answers to your questions.

The Rise of Statistics

We have a lot of data and are generating a lot more of it. IDC says that we created 2.8 zettabytes in 2012. They estimate that number will grow to 40 zettabytes by 2020. It’s not surprising that Hal Varian, chief economist at Google, in 2009, said that “the sexy job in the next 10 years will be statisticians.” Statistics, after all, helps make sense of and get insight from data. The importance of statistics and statistical thinking in our datafied world can also be found in this excellent slideshare by Diego Kuonen, a statistician.

Statistical skills are receiving increasing attention in the world of business and education. LinkedIn found that statistical analysis and data mining was the hottest skill in 2014 (see Figure 1).

Many companies are pursuing statistics-savvy people to help them make sense of their quickly-expanding, ever-growing, complex data. Job postings on Indeed show that the number of data science jobs continue to grow (see Figure 2).

University students are flocking to the field of statistics. Of the STEM Professions, statistics has been the fastest growing undergraduate degree over the past four years (see Figure 3).

The Fall of Statistics

The value of statistics is evident by the increase in number of statistics degrees and the Big Data jobs requiring statistical skills. These are encouraging headlines, no doubt, as more businesses are adopting what scientists have been using to solve problems for decades. But here are a few troubling trends that need to be considered in our world of Big Data.

McKinsey estimates that the US faces a shortage of up to 190,000 people with analytics expertise to fill these data science jobs as well as a shortage of 1.5 million people to fill managerial and analyst jobs who can understand and make decisions based on the data. Where will we find these statistics-savvy people to fill the jobs of tomorrow? We may have to look outside the US.

In a worldwide study on 15-year-old students’ reading, mathematics, and science literacy (the Program for International Student Assessment; PISA), researchers found that US teenagers, compared to children of other countries, ranked 27th (out of 34 countries) in math literacy (see Figure 4), many countries having significantly higher scores than the US. According to the NY Times, while 13% of industrialized nations reached the top two levels of proficiency in math,  just 9% of US students did. In comparison, 55% of students from Shanghai reached that level of proficiency. In Singapore, 40% did.

Even the general US public is showing a decreased interest in statistics. Using Google Trends, I looked at the popularity of the term, statistics, among the general US public, comparing it with “analytics” and “big data.” While the number of searches for “big data” and “analytics” has increased, the number of searches of “statistics” has decreased steadily since 2004.

Summary and Major Trends

Statistics is the science of learning from data. Statistics and statistical thinking helps people understand the importance of data collection, analysis, interpretation and reporting of results.

In our Big Data world, statistical skills are becoming increasingly important for businesses. Companies are creating analytics-intensive jobs for statistics-savvy people, and universities are churning out more graduates with statistics degrees. On the other hand, there is expected to be a huge talent gap in the analytics industry. Additionally, the math literacy of US students is very low compared to the rest of the world. Finally, the US general public’s interest in statistics has been decreasing steadily for about a decade.

Knowledge of Statistics Needed for Both Analysts and Consumers

Statistics and statistical knowledge are not just for people who analyze data. They are also for people who consume, interpret and make decisions based the analysis of those data. Think of the data from wearable devices, home monitoring systems and health records and how they are turned into reports for fitness buffs, homeowners and patients. Think of CRM systems, customer surveys, social media posts and review sites and how dashboards are created to help front-line employees make better decisions to improve the customer experience.

The better the grasp of statistics people have, the more insight/value/use they will get from the data. In a recent study, I found that customer experience professionals had difficulty estimating size of customer segments based on customer survey metrics. Even though these customer experience professionals commonly use customer survey metrics to understand their customers, they showed extreme bias when solving this relatively simple problem. I assert that they would likely benefit (make fewer errors) if they understood statistics.

To get value from the data, you need to make sense of it, do something with it. How you do that is through statistics and applying statistical thinking to your data. Statistics is a way of helping people get value from their data. As the number of things that get quantified (e.g., data) continues to grow, so will the value of statistics.

The Most Important Thing People Need to Know about Statistics

Statistics is the language of data. Like knowledge of your native language helps you maneuver in the world of words, statistics will help you maneuver in the world of data. As the world around us becomes more quantified, statistical skills will become more and more essential in our daily lives. If you want to make sense of our data-intensive world, you will need to understand statistics.

I’m not saying that everyone needs an in-depth knowledge of statistics, but I do believe that everybody would benefit from knowing basic statistical concepts and principles. What is the most important thing you think people need to know about statistics and why? I would love to hear your answers in the comments section. Here is my take on this question.

 

Source by bobehayes

CHENNAI: New areas such as big data and predictive analytics are emerging as the most coveted in the Indian recruitment space and also accelerating the need for a highly sophisticated workforce, says TeamLease Services’ employment outlook report for the half year period from April to September 2015.

The report expects that demand for the analytic skills is likely to far outstrip supply. The skills required for a data analytics function typically relate to mathematics and statistics, besides programming skills and an ability to go through lines of data generated by businesses to unearth valuable patterns.

According to TeamLease, other key trends that are likely to dominate recruitment industry over the next six months include increasing demand for Information Technology (IT), engineering and other blue collar jobs, emergence of startups as key hirers and the increased adoption of Recruitment Process Outsourcing (RPO).

The report states that both business and employment outlook is likely to dip marginally in the April-September period, indicating a consolidation by India Inc. While business sentiment is expected to witness a one point drop, employment outlook is likely to be down by two points.

“However, the cautiousness seems to have not dampened job growth and it remains strong at 11.3%, although lower than last half year, it is significantly better than the previous year,” TeamLease said in a release.

Industry-wise analysis showed that retail (led largely by online) and manufacturing/engineering, which clocked a three and four point increase in business and employment outlook respectively, are pushing the overall sentiment upwards while telecom seems to be the laggard.

Geographically, Mumbai and Delhi continue to showcase robust business and hiring activity, while Chennai’s growth is restricted only to business.

Pune, with a three point increase in hiring, is on a hiring spree, the report says. It expects that hiring from tier 2 towns will fall by two points in the current half year.

Functionally, the report says sales & marketing seems to have lost its sheen in the employment market with the focus landing on IT and engineering roles.

“As the economic fundamentals that drive business and hiring sentiments last half year continue to exist, the current dip is more a course correction than a downturn. The pro-industry announcements and easing of norms coupled by the resurgence in the GDP growth will definitely pull the hiring back onto the growth trajectory,” Kunal Sen, senior vice-president of TeamLease Services, said in a statement.

The report stresses on the growing requirement for talent in the field of delivery/logistics, facility management, mobile applications as well as data science and lists content curator, dental hygienist and valuation and market risk analysts as a few sought after skills.

To read the original article from The Times of India, click here.

Originally Posted at: Big data and predictive analytics likely to dominate recruitment, TeamLease report says by analyticsweekpick

Data quality is the foundation upon which data-driven culture rests. Before upper level management can embrace data-centric processes, before analytics and reliance on big data becomes pervasive, and before data stewardship can truly extend outside IT’s boundaries to become embraced by the business, organizations must trust their data.

Trustworthy data meets data quality measures for…

• Parsing
• Cleansing
• Profiling
• De-duplicating
• Modeling

…and is the reliable, consistent basis for accurate analytics and the value data provide to optimize business processes.

By ensuring data quality, organizations are laying the foundation for becoming data driven both explicitly and implicitly. Explicit manifestations of this shift include an increased reliance on data, greater valuation of data as an asset, and an entrenchment of data as a means of optimizing business. Implicitly, the daily upkeep of data-driven processes become second-nature as aspects of data stewardship, provenance, integration, and even modeling simply become due diligence for everyone’s job.

According to Tamr head of product and strategy Nidhi Aggarwal, the quintessential manifestation of a data-centered culture may be reflected in another way that delivers even greater pecuniary benefits—the utilization of all enterprise data. “People talk about the democratization of analytics and about being truly data driven,” Aggarwal commented. “You cannot do that if you’re only using 20 percent of your data.”

Cognitive Data Science Automates Data Quality Measures
Data quality is largely conceived of as the output of assiduous data preparation. It is effectively ensured via the deployment of the machine learning technologies that are responsible for automating critical facets of data science: particularly the data cleansing and preparation that can otherwise become too laborious. Contemporary platforms for data quality establish machine learning models that map data of all types to specific measures for quality—and which can also include additional facets of data preparation, such as transformation. “Our machine learning models are able to do that really fast, really cheap, and improve it over time as we see more and more data,” Aggarwal noted.

With machine learning, quality measures begin by mapping relevant data sources to one another to determine how their attributes relate. The cognitive prowess of these models are demonstrated in their ability to sift through individual records for these data sources (which may be bountiful). In doing so, they identify points of redundancy, relationships between data, names and terms, how recent data are, and many other facets of data quality. “It’s very difficult for a human to do that,” Aggarwal said. “With machine learning, by doing statistical analysis, by looking at all of the attributes, by looking at these rules that some domain experts provide to the models, by looking at how the humans answered the questions that we presented as samples to them, it makes decisions about how these things should be de-duplicated.”

Reinforcing Trust with Provenance and Natural Language Processing
Competitive preparation platforms that facilitate data quality temper the quality measures of cognitive computing with human involvement. The result is extremely detailed data provenance which reinforces trust in data quality, and which is easily traced for the purposes of assurance. The decisions that domain experts make about how sources are unified and relate to each other for specific data types—which is critical to establishing data quality—are recorded and stored in platforms for traceability. Thus, there is little ambiguity about who made a decision, when, and what effect it had on the underlying machine learning model for how data was unified and defined to establish data quality. Natural Language Processing is involved in the data quality process (especially with unstructured text) by helping to reconcile definitions, different terms, and commonalities between terms and how they are phrased. The pivotal trust required for becoming data-driven is therefore facilitated with both machine learning and human expertise.

Metadata and Evolving Models
The granular nature of a machine learning, human tempered approach to data quality naturally lends itself to metadata and incorporating new data sources into quality measures. Metadata is identified and compared between sources to ensure unification for specific use cases and requisite data quality. The true value of this cognitive approach to data quality is evinced when additional data sources are included. According to Aggarwal: “People can do this manual mapping if they only wanted to do it manually once. But the trouble is when they have to add a new data source, it’s almost as much effort as doing it the first time.” However, the semantic technologies that form the crux of machine learning are able to incorporate new sources into models so that “the model can actually look at the new data set, profile it really quickly, and figure out where it maps to all the things that it previously knows about” Aggarwal said.

More significantly, the underlying machine learning model can evolve alongside data sets that are radically dissimilar from its initial ones. “Then the model updates itself to include this new data,” Aggarwal mentioned. “So when a new data set comes in further down the line, the chances are that it will be completely new and that the models don’t align with it go lower and lower every time.” The time saved from the expedited process of updating the models required for data quality underscore the agility required to further trust data when transitioning to becoming data driven.

Using All Data
When organizations are able to trust their data because of the aforementioned rigorous standards for data quality, they are able to incorporate more data into business processes. The mapping procedures previously outlined helps organizations to bring all of their data together and determine which of it relates to a specific uses case. The monetary boons of incorporating all enterprise data into business processes is exemplified with a use case from the procurement vertical. Were a company attempting to determine how many suppliers it had and whether it was getting the best payment terms from them, those that were not data savvy could only use a finite amount of their overall data—limited to particular business units—to determine this answer. Those that were truly data-driven and able to incorporate all of their data for this undertaking could incorporate the input of greater amounts of business units and, according to Aggarwal, who encountered this situation with a Tamr customer:

“There were wildly different payment terms for the same supplies. When we dug into what parts they were buying from the suppliers and at what prices across the different business units, there were sometimes 300X differences in the price of the same part.” Unifying one’s data for uniform quality measures is integral to identifying these variances, which translates into quantifiable financial advantages. “An individual decision might save them a few hundred dollars here and there,” Aggarwal remarked. “Collectively, optimizing their decisions every single day has saved them millions and millions of dollars over time. That’s the power of bringing all data together.”

Citizen Stewardship and Business Engagement
The pervasiveness of data reliance and the value it creates for decision-making and business processes is intrinsically engendered through the trust gained from a firm foundation in data quality. By utilizing timely, reliable, data that is consistent in terms of metadata, attributes, and records management, organizations can transition to a datacentric culture. The products of such a culture are the foregoing cost advantages businesses attributed to improved decision-making. The by-products are streamlined data preparation, improved provenance, upper level management support, aligned metadata, and an appreciation of data’s value and upkeep on the part of the business users who depend on it most.

Aggarwal commented that increased data quality processes facilitated by machine learning and human oversight result in: “A broader dialogue about data in terms of stewardship. Today stewardship is in the hands of IT people basically who don’t have business context. What [we do] is take that stewardship and engage the business people who actually know something about the data much sooner in the process of data quality. That’s how they get to higher data quality, faster.”

And that’s also how they become data driven, faster.

Originally Posted at: The Blueprint for Becoming Data Driven: Data Quality

There is a huge debate on what constitutes the Big Data Analytics tool of the future and many jump in the race to try their flavor of solutions or problem solving techniques that address many critical use cases at play in Big Data laden businesses. While new businesses are working at it, what constitutes a good fundamental theory on product design strategy that could help create something for the future – Solutions with an ability to stay competitive and relevant in the current times.

On the search for some thoughts, I stumbled upon the video of Christopher Lynch from Atlas Venture (@AnalyticsWeek Boston’s First Unconference Finance/Insurance track keynote). He made some interesting points on what constitutes an interesting focus area for new opportunities. You could see the video attached below(Click the video below to watch the specific bit, I would also recommend watching the entire bit as it has lots of great points on current big data ecosystem). He touched on 3S’s Simplicity, Scalability and Security as 3 fundamental areas for big data analytics companies. He certainly has an interesting perspective and surely provides a good coverage on the current disruptive opportunity areas. I’ve some coinciding thoughts briefly mentioned in the ebook Data Driven Innovation – A Primer(download free here). I briefly touched about 3S’s that we should use in our products to help cause much needed disruption in big data space. My laundry list was Small, Simple and Scale. So, it’s great to have 2 of 3 areas that Chris also slated.

3S’s that I think will shape the future of Big Data Analytics and Why:

1. Small: Yes, Big Data is Big but the solution should be small. Reducing the scope of the product to the one magical thing that could solve a potential use case. Wearing a system’s architect hat, one could easily vote for it as small solutions tends to scale well and are more often than not simple to understand. Small is where the most tough part goes in the planning. When you’ve heard that 80% is planning and 20% is execution, it is safe to say that 80% is / should be spend on making the solution smaller. A quick bite size for easy adaptability.

2. Simple: This is a no brainer in the world of software engineering. Simplicity always triumphs ginormous complicated product. Sure, complexity sells but as a service and not as a product. Who has not heard about the quote “if you can’t explain it simply you don’t understand it well enough?”. This is applicable to a good system design and hence a good big data product design. Simple solutions are often understood quickly and therefore meet easy adoption, hence better sales. In fact, it could be safe to say that simplicity is the most important aspect of the 3 listed here.

3. Scale: This is surely a freebie if you get the first two right, but there have been times when a simple and small solution failed to scale. Scalability is another good area of focus for disruption. A good unit size simple tool could be replicated over and over. This will induce the element of scalability in the tools. A good system should be able to grow with the company it is helping to grow. A tool that does not travel for a long ride with a company will often see a diminishing adoption right at the beginning. A great hopeful thought is that this point is easiest to achieve if above 2 points are taken into consideration. Scalability is important for adoption among big businesses who deal with big blobs of data.

I would certainly agree to Chris’s point of importance of security in current tools for easy adoption in enterprise world and I should probably add it as my 4th S as well. So, yes, all powers to you and congratulations on your disruptive platform if you’ve build your science around those 4S’s. World needs your product and Big Data Analytics world is craving for disruption from the tools that only serve to the 1% and rest 99% only wait and watch for the tools to get to their capability levels, else they have to up their game and buy into ocean of tools which are complicated, super sticky and failure is expensive.

Till that day arrives, all we’ve to do is write in front of us: Simple, Small & Scalable and keep them in mind as you build a solution.

Here’s the video (If you don’t have time? Skip to 4m 10sec down):

Originally Posted at: 3 S for Building Big Data Analytics Tool of the Future