The average salary of a data scientist in the U.S. is nearly $130,000, a figure that’s bound to climb as the shortage of people with the requisite skills persists. With that kind of investment at stake, any company would want to get the maximum value out of its skills investment, but by most accounts, data scientists typically spend 80% of their time on the routine and monotonous tasks of finding and organizing data.
They have no choice. Corporations have adopted data lakes enthusiastically, but without good governance and quality control procedures, those data lakes quickly become data swamps. Duplication, inconsistency, omissions, data quality issues, format incompatibilities, acceptable use policies, and permission problems are just some of the obstacles data scientists must navigate to whip information into shape so they can do the analyses and find the insights that matter to the business.
And that’s if they can find the data in the first place. In many organizations, silos have grown up over the years that make important data difficult or impossible to track down. Even if data scientists can locate the right information, they may wait weeks for the owners to make it available. Then begins the laborious task of correcting errors, harmonizing formats, filling in gaps, and resolving conflicts. It’s not surprising that this grunt work can consume most of an expensive data scientist’s time.
Why Data Catalogs Are the Solution
Organizations that are serious about data science need to be serious about data catalogs. Today’s technology enables machines to discover and classify data wherever it lives in the organization. And machine learning technology makes catalogs smarter as they work. With a little help from a human to resolve questions and inconsistencies, data catalogs can quickly learn to make their own decisions without human intervention.
A good rule of thumb is to assume that 80% of the effort is going to center around data-integration activities… A similar 80% of the effort within data integration is to identify and profile data sources.
— Boris Evelson, Forrester Research, March 25, 2015
Data catalogs help data scientists in areas other than just information discovery. They’re one of the best ways to identify duplicate or inconsistent information, cutting down on a laborious human task. Tags applied automatically or by humans through crowdsourcing can help data scientists decide if a given dataset is useful or extraneous without requiring them to dig into the data itself. The catalog can also indicate permissions and data governance standards that tell whether it’s OK to use a given set of records.
How Catalogs Ease the Burden on Data Scientists
Data swamps present a formidable challenge to data scientists. Without a clear definition of data types, intended usage, and quality rating, scientists are left to make their best guess about what to use and what to disregard.
Unfortunately, poor data quality is a rampant problem. Experian’s 2017 Global Data Management Benchmark Report found that fewer than half of the organizations surveyed trust their data to make important business decisions. The most frequently cited cause of poor data quality is human error, such as sloppy data entry. Then there is poorly identified data. For example, a string of eight digits may be a partial phone number, a Social Security number, an account number, or a date. A smart data catalog can discover and tag the information that’s most relevant to the task, eliminating guesswork and the risk of bad decisions.
Copy sprawl is another challenge. In a perfect world, organizations would have only one “golden copy” of their data, but the reality is that duplication is rampant in most organizations. Sales managers want customer data to populate their customer relationship management systems. Marketing wants it for a lead nurturing program. The support team wants it to build their service history database.
International Data Corp. has estimated that up to 60% of storage capacity in a typical enterprise consists of these kinds of copies, but fewer than 20% of organizations have copy-management standards. Gartner analyst Dave Russell estimates many companies keep between 30 and 40 copies of business data for purposes ranging from backups to regulatory compliance.
As each group gets its own extract of production data, the costs and risks grow. Updates to one copy aren’t reflected in the others, creating discontinuity. No one knows what the truth is, which makes analyzing data for critical business decisions a risky affair.
An enterprise data catalog brings order out of this chaos by “fingerprinting” data and tagging backups and extracts so that there’s never any confusion about which copies are valid. A catalog doesn’t prevent copies from being made, but it can designate ownership, flag data that’s been modified, and even specify rules about how those copies can be used.
Stricter Privacy Rules Make Data Catalogs Even More Important
The need for a data catalog will become even more pronounced as new privacy rules take effect in Europe and elsewhere. These regulations place strict limits on how personal data may be used for purposes like profiling and segmentation. Information may need to be anonymized or deleted depending on the permissions that have been granted by the subject individual. This directly impacts the types of data science applications that can be used.
For example, a marketing organization may want to target promotions at individual households. Residents who have given permission for such contact may receive customized offers, while those who haven’t may receive only general promotions or may not be contacted at all. A data catalog can specify at a fine level of granularity what kinds of information may be used for targeting, thereby avoiding large fines for the company. The data scientist is protected when legitimate usage is defined by the data catalog.
Data catalogs set the ground rules for how data is stored and labeled across an organization. This is particularly useful for companies that have grown rapidly through mergers and acquisitions, a phenomenon that tends to stoke the data silo problem. Introducing a catalog gives those companies a chance to get a clean start with a unified view that applies to all data.
When you do the math, the benefits of a data catalog quickly exceed the costs. For example, if a catalog can save 30% of a data scientist’s time that’s currently wasted on searching and prepping, that’s $40,000 per year. And that’s not even taking into account the business benefits of having that person working in a satisfying, challenging job doing what you hired him or her for.
Big data has changed healthcare more than almost any other industry. Over the past decade, the majority of discussions about big data in health care have focused on improving outcomes and spurring medical innovation. Health economists estimate that the impact of big data will increase the size of the healthcare industry by $300 million a year or more. Less discussion has focused on changing relationships between healthcare providers, their colleagues and patients.
Healthcare experts are finally starting to evaluate the impact that big data has on healthcare relationships. Among other topics of interest, they are debating the role patients will have in making future healthcare decisions. There are a number of reasons that it is changing the relationship between healthcare providers and patients. Patients will be more informed and take a more proactive role in their treatment.
In some ways, big data is giving patients more control over their healthcare decisions. Here are some reasons it is changing the dynamic.
Making patients more informed
Big data has helped patients become more informed about their own healthcare risks, as well as the prognosis based on patients with similar conditions. This helps them make better informed decisions regarding their own care.
Part of the reason is that patients aren’t entirely dependent on their doctor for healthcare advice. They can use a variety of healthcare apps and industry databases to assess their healthcare needs. Even tools such as 23AndMe help them identify genes that may make them susceptible to future healthcare problems, which they can address with their doctor. According to a team of experts from Comply Foam, Many patients using these tools have discovered that they are predisposed to congenital hearing problems, which has caused them to invest in noise canceling headphones to minimize trauma on their ears in noisy environments.
Healthcare providers will need to accept that they don’t have the same level of influence over their patients that they used to. This means that patients will be better advocates for themselves. Over the long run, this will be a good thing, because patients will receive better care by being more knowledgeable and vocal about their healthcare concerns.
Patients will have more options in better connected healthcare networks
Healthcare providers are also using big data to improve collaboration with their peers. They can create more extensive data sets on individual patients. They can also create HIPAA compliant predictive analytics models based on their patient information. This helps them improve medical outcomes significantly.
L. Gordon Moore, MD, Senior Medical Director of Population and Payment Solutions at 3M Healthcare, shared some of these insights with Health IT Analytics.
We can take all the different claims and diagnoses and aggregate them. So now we know that this person has diabetes, and we can also rank them on a scale of severity. They also have congestive heart failure, and we can rank that on the scale, too. Then we can take these things together and say that they’ve been to the emergency department three times in the past year, which puts them in an incredibly high-risk category.
As medical providers work together to create better data driven healthcare solutions, patients will have a wider pool of knowledgeable experts to choose from. This gives patients more flexibility with choosing their healthcare providers.
Using market-based initiatives to combat medical malpractice
This could be one of the most important ways to fight medical negligence and ethical violations. This will help correct some of the ethical concerns that have plagued healthcare practices in many regions. Physicians and other healthcare providers will need to be more careful, since patients will be able to immediately access records of malpractice claims.
With the rise of big data and artificial intelligence, digital technology knows very few borders. The levels of globalization the internet has introduced have had many benefits in this sense, creating a booming digital economy and ecommerce industries. As these have evolved and transformed to form a more complex economy though, the issue of taxation has arisen.
Adaption is key to survival in such a complex digital economy. As Sean Mallon points out, tax authorities have already been using big data for audits, matching 1099s to similar social security numbers, monitoring social media data, and using regional income statistics to watch cash-based businesses. But how do tax authorities use big data to level the playing field globally? The recent proposition of international tax codes is one potential solution. This signals a sense of impending deglobulization and disruption which could bring further challenges.
Big Data Can Help Regulate Existing Taxes on the Digital Economy
Rules around taxation are constantly changing around the world. For this reason, such large digital corporations have been moving profits between jurisdictions to pick and choose those which best benefit them. Recently there has been a growing trend in some countries for the governments to charge tax based on the location of the purchaser instead to avoid taking advantage of low rates in other countries. International tax codes will help tax authorities use big data to prevent digital giants from global tax evasion.
Already there are a number of countries which have introduced new digital tax laws. These range from charging an extra 10 per cent on sales of low value goods in Australia by non-resident ecommerce companies to Norway’s VAT requirements for B2C transactions for e-services with an annual threshold above NOR 50,000 (that have been in place since 2011).
International Tax Code Propositions Will Make Room for Big Data
According to Pierre Moscovici, European commissioner for economic and financial affairs, taxation and customs, a European digital tax is happening. The question of if has now turned into when and how. Many European governments now share the same view that action is required to fix what has become a big problem of many large digital companies paying low levels of tax across the board.
In April another OECD report is expected into the area. Therefore, the EU is currently finalising its plans for tax reform to create a workable and effective international approach to deal with digital taxation. Tax takes have failed to keep up with rising profits, but the digital economy will not wait for reform, so the implementation of international tax codes needs to be sooner rather than later.
One OECD report stated that for consumption purposes, internationally traded services and goods should be taxed according to the rules of the jurisdiction of consumption. These international tax codes will differ for each country and region, meaning digital companies and consumers will need to be aware of such individual taxation rules. As mentioned, many countries outside of the EU already have some in place, while others are in the process of developing ones.
The digital economy is currently undergoing and preparing for some large changes in relation to taxation, so all such businesses need to conform to new regulations. International tax authorities have big data and AI tools already at their fingertips – it’s just a matter of having the laws in place to allow them to use new technology to level the global playing field.
Every marketing decision you make is backed by some kind of data, whether it’s a statistic, a report, or a trend. To make the right decisions, it’s important for that data to be as accurate as possible.
It’s not enough to take the conclusion of your reports at face value. Each program you use to calculate data has limitations, and it takes a keen eye to spot these limitations.
You know what your programs are calculating, but it’s vital to understand a program’s limitations regarding what it’s not calculating. Bad data costs you money and can be caused by program errors as well as errors in perception. Therefore, knowing what your programs aren’t calculating allows you to more accurately perceive what you’re looking at.
Here are some ways flawed data might be influencing your decisions without your knowledge:
1. You’re unaware of how data is sourced and calculated
The more you know about how your data is gathered and calculated, the better. This gives you the ability to spot discrepancies and other possible errors. It also gives you the ability to look at your reports and know exactly what you’re looking at rather than relying on the description.
For instance, when you look at your Google Attribution reports, the data looks clean and precise. It’s hard to imagine a big company like Google could produce reports with flawed data. In truth, the data itself isn’t flawed. The real problem is the limited parameters within which the data is processed.
There’s a discrepancy between what the data actually represents, and what you think it represents. Google Attribution only uses Google campaign data to measure the influence on marketing decisions. If you’re doing anything other than Adwords, it won’t be reflected in Google Attribution. That’s fairly obvious. However, there’s a much bigger issue that most people won’t spot right away; an issue MediaTwo spotted immediately.
“AdWords and Analytics conversion tracking only report up to 90 days before the lookback window expires,” Trey Dickert from MediaTwo explains. “That means that if someone came to a brand’s site 91 days ago, then did a branded search and converted on day 91, it would be reported as if that customer came to the site once and converted on the same day.”
That’s not something to take lightly. The only thing this data will do for you is keep you throwing more money into Google’s marketing services. Meanwhile, you’ll be wondering why you haven’t seen an increase in revenue.
It’s difficult to justify making decisions based on flawed data. If you can’t rely on Google’s data, you’ll need to find another source.
This is something to be aware of with every tool you use, no matter what big names are behind it or how popular it is. No tool is exempt from the possibility of producing flawed data.
Ignorance is bliss, as they say, but it comes at a price.
If you’re having a difficult time figuring out where your programs fall short, craft a small experiment with data input you can control as much as possible. You’ll be able to spot gross inaccuracies much faster with a small amount of data you can manually calculate to compare.
2. Selection bias
How many surveys have you been asked to participate in that produce the officially recognized statistics on topics like health, consumer preferences, and finances? If the answer is never, it shouldn’t surprise you. Surveys are one of the most unintentionally biased forms of data collection we have.
“Selection bias is one of the major flaws associated with the increased availability of big data,” Kevin Sheetz from Powerlytics says. “Many businesses only capture a small piece of the pie when it comes to data available to their segment or industry, and this means their data and subsequent analysis are skewed. Much of the data companies use to make critical business decisions is incomplete, inaccurate, and of poor quality, and, as you can expect, this leads to inaccurate analysis and benchmarking.”
If you’re relying on someone else’s marketing research to determine your whole marketing strategy, keep in mind that their reports might be influenced by skewed data. Do your own research.
Go as close to the source as possible
Just like a private investigator, you want to go as close to the source as possible. Find out where the data you’re using comes from, how it was gathered, and how it was crunched. If it’s potentially flawed, that doesn’t mean it’s completely useless. Even flawed data can provide clues toward trends.
Algorithmic trading is the use of advanced, high-speed performance computer programs to execute trade entries and exits in the financial markets. Statistical models are used to generate the formulae with which the software can make market entries and exits.
The study of technical analysis works with three basic assumptions:
Price discounts everything.
History tends to repeat itself.
Price moves in trends.
Algorithmic Trading Uses Big Data to Enhance Rational Factors in Supply and Demand
Supply and demand are driven by rational and irrational factors. Rational factors include data and economic factors. The data component of price action is what is harnessed by statisticians and mathematicians to produce the models on which the algorithms are made. These algorithms are then coded using the programming languages of the trading platforms to generate the algorithmic trading software.
This software is usually utilized on algorithmic trading platforms, built on facilities which are co-located to the trading servers of the exchanges so as to reduce latency in the time the trade order data travel, thus converting these algorithmic software into high-frequency software which are capable of placing orders and exiting orders at extremely high speeds (<1ms). This algorithmic software tends to produce accurate results, because they draw on data that obeys one of the major tenets of technical analysis, which is that of history repeating itself.
Investment banks and hedge funds tend to deploy algorithmic trading for forex and the stock markets. The fact that they are highly successful is determined to a large extent by their algorithmic-based operations. Algorithmic trading is automated and does not involve the human element of emotion. It is therefore highly desirable for today’s traders because it has been proven to work over the years.
How to Profit From Machine Learning and Big Data in Algorithmic Trading
Algorithm-based trading brings speed, precision and profitable performance to the table. It also brings new dimensions to trading the online markets. Most trading done by retail traders is of the low-frequency variety, which focuses only on execution prices with no regard for latency. This puts the onus of trade performance solely on the trade’s ability to close beyond the opening price — in the direction of the trader’s expectation.
Algorithmic trading for forex, on the other hand, adopts several possible standpoints for profiting. Some of these are as follows:
An algorithmic trading platform will therefore present more sites for possible profiting from the markets than low-frequency or manual trading.
Algorithm-Based Trading vs Manual Trading: Big Data Makes the Difference
This question tends to get asked repeatedly: What really is the big deal in algorithmic trading? To understand how big a deal algorithmic-based trading really is, you need to get some sobering facts. Some of the early proponents and practitioners of technical analysis are not making as much money as they used to. The machines are taking it all at an increasingly alarming rate.
One area where algorithms have proven superior to manual trading is in the elimination of emotions. Emotions are generally damaging in forex. Emotions tend to be irrational responses to perceptions of what is going on in the market. But algorithmic trading makes use of statistical models, which are based on numbers that cannot be disputed. When this component is added to an algorithmic trading platform that injects speed and reduced latency into the process, you have a very powerful weapon with which to get ahead of others in executing your trades.
Algorithmic based trading is therefore more precise, more accurate and faster than manual trading. Once institutional traders found that they could multiply their profits using algorithmic trading, it became a mainstay of online markets. In the last decade, it has simply been a race to develop faster and better algorithms.
How Individual Traders Can Benefit from AI and Big Data in Algorithmic Trading
Using algorithmic trading at home will ordinarily be a very expensive venture for the individual trader if the trader were to put all the required infrastructure together. High-frequency algorithmic trading will require the following:
Hardware such as routers, servers, Network Interface Controllers (NICs) and switches.
Premium subscription to news feed services such as those of Bloomberg and Thomson Reuters.
The capital outlay for such a setup is simply too prohibitive for an individual trader. But does it mean that there is no opportunity to practice algorithmic trading at home? There is indeed a way out.
Subscribing to a managed account trading service which makes use of an algorithmic trading platform and software is the way to go. This way, you get the best of highly accurate, high-speed algorithmic trading without all the high initial costs that come with the setup of such a system.
For instance, there is an algorithmic trading platform where the EA there makes a return of 10% monthly with a loss protection mechanism which practically ensures that the traders who subscribe to this system of algorithmic trading for forex practically trade risk-free. This all comes without the need to pay for expensive setup equipment for algorithmic trading.
Algorithmic trading is now the mainstay of trading in the forex markets, although it is mostly used by the institutional investors. Retail traders are still lagging behind by using manual trading methods. In today’s news, the use of Artificial Intelligence (AI) in everyday life is getting almost daily mention. Therefore, it is only logical that retail traders adapt to the changing face of automated forex trading by using algorithm-based systems which are built with an AI-backbone to perform their trading in a risk-free environment, devoid of human emotions. Such systems easily adapt to changing market conditions and ensure that traders are not left behind as 2018 pushes to the second quarter.
Retailers are saying goodbye to intuitive guessing based on old-school data-gathering methods to convert customers. Welcome to the age of machine learning in retail, where online and brick-and-mortar business leaders can not only automate operational tasks and align product description with retail sales strategies, they now can accurately predict consumer behavior and personalize customer experience better than ever before.
If certain retailers have not considered implementing machine learning into operations, it’s high time that they do. A recent study from Infosys of 1,000 consumers and 50 retailers across the U.S. reveals that consumers now expect personalization, saying that 59% of shoppers who have experienced personalization believe it has a noticeable effect on their purchasing decisions.
Machine learning has surpassed the personalization legwork established by retail giants
For years, many companies have been using more “catch-all” personalization tactics like including a customer’s name in the subject line of an email, but, due to the implementation of machine learning, personalization tactics have grown exponentially from this level of “impersonal.”
As SDC contributor Mohammad Ali states: “Artificial intelligence is undoubtedly transforming the way ecommerce businesses work by offering tailored solutions, personalized customer experience, evolving the sales process smartly to convert leads into paying customers. It is making incredible changes to the way retailers and ecommerce brands deal with their customers, getting an easy access to big data and harnessing their marketing and sales team skills for business growth.”
How machine learning is actually humanizing the retail shopping experience
Machine learning is simply a method of data analysis that allows a computer to self-learn without a human programming its every move. And within the vast amounts of data processed, retailers are able to hone in on who their customers are and, more importantly, what their needs are in a precise moment.
With machine learning, “Retailers can now predict buying behavior with a greater degree of accuracy by understanding what products their shoppers are engaging with and how, whether they are trying on a product or simply picking it up,” writes Talitha Loftus of RetailNext.net. “Machine learning principles can identify human actions of both shoppers and employees, including crouching, bending, reaching overhead, and the like, all the way down to analyzing what aids are being used – carts, bags, brooms, mops, and more.”
These days, almost everyone is armed with some sort of smart device before and during shopping. You can order anything you want through a simple voice command through Amazon Echo. Your previous orders on any e-commerce website are tracked and analyzed. And imagine someday that you walk by a brick-and-mortar retail store you’ve never been in, and your smart phone alerts you to a sale that appeals to you that is going on right inside that store. Those days are not far off.
Where and how to start to implementing machine learning personalization?
It might seem daunting to blend machine learning into a human-driven retail operation to enhance personalization and to better predict buyer behavior. But companies like WorkFusion are out there to help. WorkFusion is an automation platform that offers a number of services, using smart process automation and robot process automation, where essentially an algorithm can understand relevant data points contextually (like a human!). These automations can enrich existing human workforces to improve the retail customer experience in many ways including:
Meaningful website content
Automated product categorization that aligns original product data with the ways the retailer himself would actually sell the product
Improved data quality, speed and return on investment
According to the Harvard Business Review, the age of responsive retail is over — we have entered the era of predictive commerce: “It’s time for retailers to help people find products in their precise moment of need — and perhaps before they even perceive that need — whether or not they’re logged in or ready to click a ‘buy’ button on a screen. This shift will require designing experiences that merge an understanding of human behavior with large-scale automation and data integration.”
Retailers who are open to harnessing these tools effectively will be able to differentiate themselves by creating amazing personalized customer experiences. The rest, stuck in a bygone era, will literally be collecting dust.
In 2018, gender and racial equality are larger concerns than ever before. Fortunately, big data has the potential to address these issues in important new ways. It can help with hiring and compliance in virtually every organization.
Concerns about workplace equality are rising
Workplace equality has been a major concern since the 1980s. Although the gap has closed in recent years, it is still a concern to many people, especially as women continue to make 78 cents on the dollar compared to their male colleagues. This number has been disputed by people that point out that it doesn’t account for hours worked, experience and other factors. However, it still indicates an imbalance in wages between male and female employees.
Business leaders, activists and regulators in every field have started discussing new ways to deal with gender equality. They have started to realize that big data could be a game changer.
How can big data help with workplace equality?
While most experts have emphasized the ways that big data can help with marketing and logistics, they have also discovered ways to use data to improve diversity outcomes. Here are some big data solutions to workplace equality concerns.
Using big data to track diversity metrics
Last year, Google made a surprising announcement. The company said that diversity was one of its foremost concerns. They wanted to track their progress with improving diversity ratios. They created an online tool called the “Diversity Tracker” to monitor the number of employees in various demographic groups. They said that this tool was implemented to improve transparency. Since it was launched, Google has improved its diversity metrics considerably.
“Google should be a place where people from different backgrounds and experiences come to do their best work–a place where every Googler feels they belong. The truth is that we’re not there yet. We know diversity and inclusion are values critical to our success and future innovation. We also know challenging bias–inside and outside our walls–is the right thing to do. That’s why we continue to support efforts that fuel our commitments to progress. These commitments require us to look at bias through a wider lens: at Google, in the industry, and in society. And while progress will take time, our actions today will determine who we are in the future,” Google writes.
Using Hadoop data mining to find a larger base of qualified minority candidates
Companies in some regions have a more difficult time finding minority candidates than others. This is often due to homogeneous demographics of the region question.
Data mining tools make it much easier for companies to identify minority candidates to hire. Recruiters can use tools such as Greenhouse Recruiting Software to find a larger base of potential employees through LinkedIn and other professional networking and employment databases.
Better monitoring internal complaints
The memo that James Damore released sent shockwaves through Google. Other employees were furious about his statements, even though many of them agreed with certain elements of his report.
Most allegations of discrimination do not receive nearly as much attention. They tend to go unnoticed for a number of reasons, ranging from empathy fatigue to apathy. Employees may file bias incident reports, but they often get lost or ignored by diversity officers.
Big data makes it easier to track these reports for auditing purposes. The chief diversity officer can routinely monitor complaints to see how many were filed and what percent were sufficiently addressed.
This information can also be important for external audits. The department of labor may be close attention to them if they are investigating a company for institutional discrimination allegations. The data could also be subpoenaed by employees filing a civil suit against their employer.
So far, tracking these statistics it’s not mandatory. However, that may change in the near future.
Data gathering did not start with computers as many in the younger generation might think. Data has been used in experiments and studies and to prove theories for hundreds of years. The organization of the data has just changed platforms as technology has progressed. The use of data has become much more prevalent, as business leaders now realizing that a data-backed approach is the only way to pitch clients or investors.
The following are the ways data has been organized and used over the years.
Data sets and graphs
The organization of data used to be done in data sets as well as graphs. The graphs were used much like they are today in order to visualize the data if the numbers are not clear for viewers. Finding totals of things would take quite a bit of time as each individual data set would have to be added up. Finding which variable impacted success or failure was much more difficult in these times.
Microsoft Excel changed the way that data was collected and analyzed. The functions of Excel make it easy to gather data to see totals as well as averages simply by inputting the correct formula. Many companies use Excel on a daily basis, as it has become a part of most peoples’ lives since they learned to use it in elementary school.
Information and data collected from the cloud can be quite valuable as well as the easiest to compile. The ability for various people to access the same document in order to input data can be extremely useful for international companies. Data that is kept on the cloud on a virtual private network (VPN) is also secure, as many companies need to worry about keeping customer information safe with massive data breaches happening over the last couple of years.
Polls were the original way of figuring out what candidates would be the frontrunners for specific parties. Polls can be wrong though — not all people participate. As we saw in the 2016 presidential election, Hilary Clinton had over a 90 percent chance of winning, only to lose. Organizing data of what people thought about a certain speech can allow the speech writers to craft the perfect speech for a specific audience. All demographics are different, so presenting the same facts in a different way can be most effective for the data collected.
Used for marketing
A political campaign is much like a marketing campaign with a person instead of a company trying to convince you to do something. Online shopping has given a rise to intense data on shoppers which can do a plethora of things. Instead of trying to convince a person that a product is great, the companies are starting to customize products for different desires. Market research companies do quite a bit of data collection in order to see what the next burger will be at McDonalds or what color smartphone will do better. Marketing strategies have also become more targeted with a higher ROI with the data that is collected from online behaviors.
The targeted marketing approaches above have given rise to consumer based selling. This means that many of the ads that a person sees have to do with searches they have made or previous purchases. Artificial intelligence is playing a huge role in how data is being utilized as far as ads and marketing goes. The more data is analyzed in a correct way, the better the product will be for the consumer.
Data can be used for more than mentioned above, as it is something we tend to make decisions with. Data organization and utilization will continue to grow over the years to the advantage of everyone involved, both companies and consumers.
Apache Spark is a lightning fast solution to handle big data, process humongous data, and derive knowledge from it at record speed. The efficiency that is possible through Apache Spark make it a preferred choice among data scientists and big data enthusiasts.
But, alongside the many advantages and features of Apache Spark that make it appealing, there are some ugly aspects of the technology, too. We have listed some of the challenges that developers face when working on big data with Apache Spark.
Here are some aspects to flip side of Apache Spark so that you can make an informed decision whether or not the platform is ideal for your next big data project.
The absence of an in-house file management system
Apache Spark depends on some other third-party system for its file management capabilities, therefore making it less efficient than other platforms. When it is not merged with the Hadoop Distributed File System (HDFS), it needs to be used with another cloud-based data platform. This is considered as one of its key disadvantages.
A large number of small files
This is another file management aspect that Spark is to be blamed for. When Apache Spark is used along with Hadoop, which it usually is, developers come across issues of small files. HDFS supports a limited number of large files, instead of a large number of small files.
Near real-time processing
When talking about Spark Streaming, the arriving stream is divided into batches of pre-defined intervals and each batch is then processed as a Resilient Distributed Dataset (RDD). After the operations are applied to each batch, the results are returned back in batches. Thus, this treating of data in batches does not qualify to be called a real-time processing, but since the operations are fast, Apache Spark can be called a near real-time data processing platform.
No automatic optimization process
Apache Spark does not have an automatic code optimization process in place, and thus there is a need to optimize the code manually. This comes as a disadvantage of the platform when most technologies and platforms are moving toward automation.
Back pressure is the condition when the data buffer fills completely, and there is a lining up of data at the input and the output channel. When this happens, no data is received or transferred until the buffer is emptied. Apache Spark does not have the required capability to handle this build-up of data implicitly, and thus this needs to be taken care of manually.
Expensive in-memory operations
In places where cost-effectiveness of processing is desirable, an in-memory processing capability can become a bottleneck as memory consumption is high and not handled from the perspective of the user. Apache Spark consumes and fills a lot of RAM to run its processes and analytics, thus being a costly approach to computing.
Developers and enthusiasts almost always recommend using Scala for working with Apache Spark, the reason being that each Spark release brings a thing or two for Scala and Java and updates the Python APIs to include newer things. Python users and developers are always a step behind Scala or Java users when working with Apache Spark. Also, with a pure RDD approach, Python is almost always slower than its Scala or Java counterpart.
Developers complain of out-of-place errors when working with Apache Spark. Some failures are so vague that developers can spend hours simply looking at them and trying to defer what they mean.
With these lagging points, Apache Spark implementation may or may not be your way to go. Research is key in finding the right lightning fast big dats processing platform.
Every business has an audience. But what, exactly, defines “your audience” in this technological day and age? Essentially, it’s the total number of people, no matter the role (readers, listeners, event attendees, viewers etc.) who take advantage of a certain service that has to do with media, advertising campaigns, speeches or any other events represent the audience.
An audience’s opinion obviously defines a brand’s reputation – and you want to keep that opinion positive in order to maintain your bottom line. This is why many researchers have started to focus more on understanding audience marketing from a competitive and economical point of view.
The audience refers to a certain quantity of information that’s structured based on a single criterion. There is no exhaustive analysis that could take into account all the possible data, so it’s best to segment it. Statistics show that an accurate analysis of the audience market is possible by using the services of companies specialized in this field. At the same time, you can make efforts to understand audience marketing through research and analysis by yourself. It requires time and money, but the outcome is surely a favorable one.
Here are some concepts that could help anyone understand why the opinion of their target audience matters this much, and how its impact could make positive changes on business and life.
Audience marketing is now accessible to small businesses
A while ago, people perceived marketing research as a practice that only large organizations needed to ensure success. They thought was that smaller businesses just did not have the resources to keep marketing research going. But today, research has become much more accessible. Audience research is a subsection that gives business owners the possibility to test their ideas before applying them on a large scale.
Taking such measurement can drastically reduce financial losses. The main concern of the audience is that it has profound implications for future marketing strategies and helping the company answer questions related to customer feedback. Audience marketing is essential for future decision-making.
Why audience analysis is essential in predictive commerce
Research and analysis work hand in hand for future marketing campaigns. Learn the difference between SEO, PPC, media planning, social media marketing, paid social marketing, and so on. These are all methods to reach your audience.
But before that, you have to get to know your audience. The main purpose of research and analysis when referring to the audience is to profile and segment it. Most things you do can be categorized into interests. People are interested in a certain topic that they follow. This is the moment when audience marketing intervenes. You will know what subcategories of your target audience are interested in and build your future strategies based on these interests. The impact will be much more visible and the outcome the one you expected.
What would happen if audience marketing is missing? The answer to this question is simple – when no audience has been analyzed beforehand, a marketing strategy has all the chances to fail. Without precisely knowing what the preferences of your audience are, you won’t be able to strive.
Most audience categories are established around aspects like how much money they make each month, what profession they have, how old are they, what their gender or marital status is, and so on. These criteria can also include demographics and psychographics. Collecting information should always be organized around each subsection of the target audience. To gather this information, you can use questionnaires, surveys, online forums, interviews, focus groups, online ratings or even magazine reviews.
The most important step in this process is to segment the audience properly in more than one single category. This step can help a business establish powerful marketing campaigns based on the preferences of customers, leading to a guaranteed profitability boost. Without assessing the correct audience categories, there is a risk of applying a marketing strategy that doesn’t cover the preferences of each person.
Hire the right people to help you
So, after you’ve read all this information about research, analysis, audience marketing and strategies, do you think you could handle everything it involves? The answer is yes. If you’re motivated enough, you can learn more about each stage of audience marketing and do it yourself. The downsides are that it will take much longer to complete a research/analysis when it is the very first time you do it, and the results might not always be relevant to your current situation.
This is the reason why the best option is hiring specialists to research the audience. Specialists can make your job easier by telling you exactly what your audience’s motivation is, how to solve your customer’s issues, how to assess the needs of each individual customer, how to communicate with prospects in an effective manner.
Two criteria influence audience segmentation: demography and psychography, the reason why they are always included in audience research services. You can learn some things by yourself, but a company will do the same things in a shorter amount of time. It is an investment worth making.
In a world where company leaders need to be proactive about maintaining brand reputation, predict buyer behavior and provide personalized consumer experience, it is no longer an option to neglect audience research and analysis in your marketing strategy. Do it on your own or hire the help you need.