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As we run our businesses, what do we trust more: Big Data or our own gut instinct?

Big Data is awesome in its capacity to digest reams of complex information. But it is weak in its ability to weigh nuanced variables.

Human instinct is frail and biased in computational power. Yet it is amazing in its ability to combine nuanced variables and make an exponential leap.

It’s a tough challenge facing businesses today: do we trust the data analytics trend lines, or rely on the hard-earned wisdom of human managers? 

The answer, of course, is usually “some mix of both.” But it’s not that easy. What’s the mix? And how to combine them?

Untold revenue gains rest on answering those questions correctly. Maximizing profit requires making the best decisions (and executing on them, of course).

To provide insight on this thorny issue, I spoke with six Big Data experts at the Strata Data Conference in San Francisco:

Ben Lorica, Chief Data Scientist, O’Reilly Media

Nong Li, CTO, Okera

Jon Bock, VP of Marketing, Streamlio

Jeff Curie, Principal, The Curie Point

Jon Rooney, VP of Marketing, Domino Data Lab

James Kotecki, Director of Marketing, Infinia ML

SEE VIDEO WITH EXPERT ADVICE BELOW

If your business struggles to make the most from Big Data, you’re not alone.

Yet even as users lean on Big Data, they know (even if they won’t admit it) that the results are confusing, problematic, even worthless. Sometimes they’re a fast path into deep weeds.

In a 2023 survey from New Vantage, a remarkable 77 percent of respondents admitted that “business adoption of Big Data and AI plans is creating a challenge for their organization.”

Translated: it’s hard to figure this stuff out.

Gartner recently opined that, “Through 2023, only 20% of analytic insights will deliver business outcomes.”

Translated: We constantly stare at the metrics but it’s not making us much money.

Despite the challenges, harnessing Big Data is absolutely essential. The idea that any business can compete without using analytics is woefully outdated. It’s like a ship without a compass. In the infamous Target predictive analytics example, the retailer predicted customer pregnancy even before other family members knew – that’s the shocking competitive power of analytics.

If you’re a manager, you feel the pressure: you know at this very minute your competitors are staring at the numbers. Weighing them, juggling them. Using hyper-specific reporting to guide plans that will grab market share from you. If you’re not doing likewise, you’re falling behind quickly.

Yet as you’ve surely seen, some of the most critical variables can’t be precisely quantified – which in the eyes of your software means they can’t be calculated at all.

Oh sure, sometimes your metrics are trustable. Your analytics app shows sales of 1,200 widgets this quarter, up from last quarter’s 1,000. So your multi-colored dashboard reports that sales are up 20 percent – that’s a rock solid number.

But straight reporting isn’t the magic of Big Data. Truly leveraging that expensive analytics tool means using it to make complex business decisions. Making those insightful decisions that blast you past your competitors.

The problem is this: the more complex the decision, the more likely it includes variables that can’t be quantified. Let’s look at a typical way that data analytics offers no help.

In this hypothetical example, let’s say your company has a Western Division and an Eastern Division. It’s time to invest and grow the company: do you invest more in Western or Eastern?

Western has always been the legacy cash cow, a steady revenue machine. But now the bright trend lines in your Big Data app show that Eastern is growing quicker. Drilling down, you see that in Eastern, “revenue per sales rep, per quarter” is up 17.8 percent over Western.

That’s a golden metric. Human wisdom alone would never have discovered that info nugget.

Based on the metrics, the budget spigot opens wide and cash pours into Eastern. We can’t wait for the boosted revenue numbers.

Oh goodness. What the Big Data doesn’t reveal – because it can’t be quantified – is that an HR pro in Eastern, Jessica Roberts, is a genius at recruiting top salespeople. Hence that higher revenue per sales rep metric.

However, Jessica, being talented and ambitious, moves to a competitor. Over the next 24 months, Eastern’s revenue trends drearily downward as Jessica’s hires leave and are replaced by an HR department that’s asleep at the wheel.

The question of where to invest was a core issue for the business – but the Big Data software offered no help.

Then again, you might say: it’s not the software that went wrong, it was the people who looked at the software. Ultimately that’s true. Which brings us to one of the thorniest problems in using Big Data.

Let’s accept as a given that most businesses are combining Big Data and human instinct when they make decisions. But to what extent  – and how – to combine these two inputs is a constant question.

Ironically, the decision of how to weigh the data and gut instinct is itself a human decision. The software can’t tell you how seriously to take its results. So modern business management has become a “meta” process, with decision making at two different levels:

The pre-decision making process, which involves a human decision about how to weigh the data versus human input.

The final decision itself.

Bottom line, since all even the best analytics software can do is churn out gorgeous pie charts, the challenge remains. You decide on the balance.

Will you rely largely on the data, with only the merest factor of human wisdom?

This approach was favored by the captain of the Titanic. Chart the most direct approach, raise the speed to the max, arrive as soon as possible. Alas, there was a lurking variable that no thoughtful navigator would have ignored.

Or, will you rely completely on our own hunch, hardly glancing at the data?

Hopefully you’ll find a more careful mix then either of these extremes. One thing is sure: your revenue depends on how you balance these factors.

As I spoke with the experts in the video below, it was clear: combining instinct and data is more art than science. At the least, it requires really knowing your business and really understanding the software.

What’s your opinion? Please use the Comments section below to add your experience/insight about combining Big Data and human wisdom. What’s a Big Data best practice?

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Big Data Survey: Big Data Growing Quickly

Big data has arrived as a key decision making tool in business – that’s the conclusion of a new survey of Big Data professionals conducted by QuinStreet, Inc., Datamation’s publisher.

The survey found that:

• 77 percent of respondents consider Big Data analytics a priority.

• 72 percent cite enhancing the speed and accuracy of business decisions as a top benefit of Big Data analytics.

• 71 percent of mid-sized and large companies have plans for, or are currently involved with, Big Data initiatives.

The graph of survey responses below reveals that transparency and speed are of key importance, with accurate decision making also seen as a highly important benefit. Note, too, that timely integration of data ranked well. Interestingly, some 61 percent see the value of automated decision making, perhaps suggests that human analysis of Big Data will become less of a default choice as tools grow more sophisticated.

Survey Reveals Big Data Vendors Still Emerging

Although the survey reveals keen interest in Big Data, it also shows that the sector isn’t fully mature. Big Data remains an emerging market sector. For instance, the role of vendors and the relative status of vendors is still every much up for grabs. When participants were asked which vendors they were working with (or planned to work with) to address Big Data analytics, a large chunk – 43 percent – said “none.” Surprisingly, only one vendor was selected by more than 10 percent of respondents.

Part of what’s holding back businesses is the big confusion surrounding Big Data. While IT professionals realize they need to get on board with Big Data, many are concerned about issues like project and management costs, along with issues involved with scaling infrastructure and overcoming data silos and application integration.

Will this be easy? Certainly not – particularly in light of the oceans of data that business now creates, from unstructured social media posts to data gleaned from always-on mobile apps. Survey respondents expect their data volumes to grow by 45 percent over the next two years – a stunning upward trajectory. Even more overwhelming, 10 percent of respondents forecast that their data volumes will double (or more) in that time period.

And this survey response is supported by research from IDC, which forecast that by 2023, the world will generate 50 times today’s information. Oddly, IDC also predicts that, given the evolution of Big Data tools, the IT staff needed to manage the tsunami of data will grow by less than 1.5 times. An optimistic staffing forecast, perhaps.

How Big Data Will Grow So Big

To be sure, the challenges of Big Data are numerous, including the need to scale to accommodate the sheer scope of data. Not surprisingly, the most popular survey responses to a question about how firms will scale for Big Data are “establish easy to use tools” and “increasing network bandwidth.”

It’s interesting to note that “building analytics internally” also scored high among respondents. This may reflect that (as noted above) many respondents have yet to settle on a Big Data vendor and so still expect to rely more heavily on internal resources.

In the years ahead, it’s reasonable to assume one other survey response about scaling will change: “migrating to cloud based storage.” While a mere 23 percent of respondents chose this, massive data volume will surely push this number higher in the years ahead.

About the survey: the QuinStreet Enterprise Big Data research study took place between October 22 and November 8, 2013, with 540 Big Data decision makers completing the survey. Only subscribers involved in Big Data purchasing decisions were allowed to take the online survey. The survey yields a margin of error of +/- 4.3 percent at a 95 percent confidence level.

Photo courtesy of Shutterstock.

Why Distinction Matters In Big Data And Data Science?

Data has become a resource of interest globally, and harnessing its true potential is becoming important to organizations. According to IBM, 2.5 quintillion bytes of data is created every day. This means that data never sleeps. The increase in data requires the use of different tools and techniques to meaningfully extract insights. Let us first understand how the use of data is defined in the big data and data science industry. Defining data by Work The big data and data science industry terms and definitions overlap and interweave with one another in the analytics field.  However, these are still distinct and are used based on the nature of work. Data science comprises a number of disciplines. These include business intelligence, computer science, data engineering, and statistics, among others. Data science involves processes to collect, clean and analyze both structured and unstructured data. It makes use of the following: + Cleaning raw data to make it ready for analysis. + Finding patterns in the data and helping decision makers in day-to-day business problems. Data science involves discovering hidden patterns within the data through dependencies between different variables. It is used in different industries to make better decisions by understanding and improving the existing business models. On the other hand, Big Data analytics deals with the processing of a large volume of both structured or unstructured data which cannot be processed with the traditional methods. Big data is characterized by 3Vs: the volume, the variety and the velocity at which the data is processed. The key enablers for the growth of big-data are the increase of storage capacities, an increase of processing power, and the availability of huge amount of data. How is data analyzed? Big data and data science help organizations to understand their consumers, and identify new opportunities. Let’s understand how these are applied in real-world situations.

Hypothesis-based reasoning

: The hypothesis-based reasoning helps in formulating hypothesis about relationships between variables. It requires experimenting with data to test hypothesis and models.

Pattern-based reasoning

: The pattern-based reasoning helps to discover new relationships and the analytical path from the data. It involves drawing inferences based on probability. The conclusion reached from this technique is reasonable, probable and believable. On the contrary, big data analytics involves the following steps.

Data Integration

: Big data analytics starts with ingesting data from different sources. This is the first step towards the analysis. It requires integrating all types of structured, unstructured and semi-structured data. Examples include databases, mainframe, social media, file systems, SaaS applications, and XML.

Discovery

: The step involves understanding the data sets and how they relate to each other. The process consists of exploration and discovery of data.

Iteration

: Uncovering insights from data is an iterative process as the actual relationships are not known. Industry experts suggest small defined-projects to enable learning from the iterations. Classification and Prediction: Once the right data is collected, we go ahead for classifying and predicting the data. Classification models predict categorical data, and prediction models predict continuous data. Qualifications matter A critical component of any organization is its team. Both data science and big data require a diverse set of skills. Data scientist or big data analyst are the hottest job titles in the IT industry. Data scientists are highly educated with 88% have master’s degree and 46% have PhDs. They need to possess an in-depth knowledge of statistics with programming languages such as SAS, and R. Big data analysts must have technical knowledge along with the skills possessed by a data scientist. These include SQL databases and database querying languages, Python, Hadoop, Hive & Pig and cloud tools like Amazon S3. However, in both the fields, domain expertise significantly contributes to the understanding of where the problem lies and how the problems could be measured. Closing Thoughts Big data continues to occupy our day to day lives.  When properly infused and analyzed, big data analytics can provide unique insights hidden inside the data. Both data science and big data tools and techniques require a significant investment of time across an array of tasks. The dynamic nature of the field makes its necessary for organizations to understand both the terms. However, no matter, how many the differences are, one cannot be successful without the other.

Upgrading Psychiatry Treatment Using Ai And Big Data

Initially, psychiatrists found it difficult to implement AI in psychiatry use cases 

  Artificial Intelligence (AI) has invaded the

Use cases of AI and its Applications in Psychiatry Predictive modeling

Predictive modeling is generally building machine learning algorithms that are used to predict future events by utilizing historical data. Predictive modeling in psychiatry is aiding doctors to predict which treatment is likely to work for patients with issues like anxiety and depression. Generally, doctors segment patients on three rows according to their response to the treatment.

Early responders, Patients who responded in the first two years of treatment

Late responders, Patients who responded between two and five years of treatment

Non-responders, Patients who continued to suffer even after five years of treatment

Before the invasion of artificial intelligence, doctors used to manually segment the patients according to clinical intuition, presentation and history to predict which group the patient belonged to. However, most of the human analysis was mere guesses rather than accurate answers. This swayed the treatment from being exact to somewhere close it. Henceforth, utilizing artificial intelligence with predictive modeling would help improve the matching of the patients to the right group, so the right treatment can be started quickly. Classifying and concentrating on non-responders is a critical task. They are patients who need immediate attention. Artificial intelligence segregates them and indicates it to the psychiatrist who can show special care for the needy.  

Computational Phenotyping

Computational phenotyping is utilizing computational techniques such as machine learning to classify illnesses and other clinical concepts from data itself. Traditionally, phenotyping psychiatric disorders 

Phenotyping requires highly skilled psychiatrists to supply correct labels, and hence limits its scalability and accuracy

It relies on existing clinical descriptions and limits the sorts of patterns/subtypes that can be found

  Even though when the initiative began well, the traditional approach to phenotyping psychiatric disorders failed to acknowledge that a psychiatric disorder as a single condition may really have several subtypes with different phenotypes, as seen to be the case with depression and schizophrenia. Recently, computational phenotyping is using 

Patient similarity

While treating patients, doctors often compare the current patient with previous patients with a similar disorder. This is called case-based reasoning. Psychiatrists use a computer algorithm to address the case-based reasoning. Whenever a patient comes, the psychiatrist does an examination of the patient and search for similar past cases in the database. The computer algorithm then provides a list of those potentially similar patients. The psychiatrist will provide some supervision on that result to find those truly similar patients through this specific clinical context. He/she will take them as a group and see which treatment worked best. The psychiatrist recommends the same treatment to the current patient.  

Future Predictions

Artificial Intelligence (AI) has invaded the healthcare sector long back . It is making accountable impacts on treatment and overviewing of patients. However, psychiatry department stands out when it comes to utilising AI applications. It has taken a long way before reaching the current initial stage where AI is being used for analyzing patients but only by a handful of psychiatrists. Medicine is already reaping a fruitful benefit from artificial intelligence and big data . It has shown promising results in diagnosing disease, interpreting images and concentrating on treatment plans. Though psychiatry is in many ways a uniquely human field, requiring emotional intelligence and perception that computers can’t stimulate, experts say that AI could have an impact. The field could profit from artificial intelligence’s ability to analyze data and pick up on patterns and warning signs so subtle humans might never notice them. However, connecting psychiatry with artificial intelligence and big data is not an easy job. Psychiatrists and behavioral health researchers found it difficult to make the connection on how to implement artificial intelligence into actual psychiatry use cases. Today, both medicine and technology are breaking their barriers to make a change.Predictive modeling is generally building machine learning algorithms that are used to predict future events by utilizing historical data. Predictive modeling in psychiatry is aiding doctors to predict which treatment is likely to work for patients with issues like anxiety and depression. Generally, doctors segment patients on three rows according to their response to the treatment.Before the invasion of artificial intelligence, doctors used to manually segment the patients according to clinical intuition, presentation and history to predict which group the patient belonged to. However, most of the human analysis was mere guesses rather than accurate answers. This swayed the treatment from being exact to somewhere close it. Henceforth, utilizing artificial intelligence with predictive modeling would help improve the matching of the patients to the right group, so the right treatment can be started quickly. Classifying and concentrating on non-responders is a critical task. They are patients who need immediate attention. Artificial intelligence segregates them and indicates it to the psychiatrist who can show special care for the needy.Computational phenotyping is utilizing computational techniques such as machine learning to classify illnesses and other clinical concepts from data itself. Traditionally, phenotyping psychiatric disorders involved using supervised learning and relied on domain experts with two main chúng tôi though when the initiative began well, the traditional approach to phenotyping psychiatric disorders failed to acknowledge that a psychiatric disorder as a single condition may really have several subtypes with different phenotypes, as seen to be the case with depression and schizophrenia. Recently, computational phenotyping is using unsupervised learning to find novel patterns with regards to grouping psychiatric disorders based on observation of prognostic similarity. This unsupervised learning approach of utilizing computational power and machine learning clustering algorithms shows great potential for finding patterns in Electronic Health Records that would otherwise be hidden and that can lead to a greater understanding of psychiatric conditions and treatments. The process of phenotyping involves raw patient data from different sources such as demographic information, diagnosis, medication, procedure, lab tests and clinical notes. Computational phenotyping turns the raw patient data into psychiatric concepts or phenotypes by utilizing computational power and clustering machine learning algorithm While treating patients, doctors often compare the current patient with previous patients with a similar disorder. This is called case-based reasoning. Psychiatrists use a computer algorithm to address the case-based reasoning. Whenever a patient comes, the psychiatrist does an examination of the patient and search for similar past cases in the database. The computer algorithm then provides a list of those potentially similar patients. The psychiatrist will provide some supervision on that result to find those truly similar patients through this specific clinical context. He/she will take them as a group and see which treatment worked best. The psychiatrist recommends the same treatment to the current patient.Artificial intelligence is believed to make more changes in psychiatry soon. Already, mobile apps and online bot consulting are being highly utilized by people. The future that researchers and scientists look for in artificial intelligence is human-like AI robots that comfort mentally unstable patients.

What Is Big Data Analytics?

Introduction to Big Data Analytics

Hadoop, Data Science, Statistics & others

We can Define Big Data as Three Vs

Volume: The amount of data that is being generated every second. Every day organizations like social media, e-commerce businesses, and airlines collect a huge amount of data.

Variety: Data can take various forms, including structured data such as numeric data, unstructured data such as text, images, videos, financial transactions, etc., or semi-structured data like JSON or XML.

What are we Doing with this Big Data?

We can use this big data to process and draw some meaningful insights out of it. There are various frameworks available to process big data. The list below provides the popular framework that big data developers and analysts use widely.

Apache Hadoop: We can write map-reduce the program to process the data.

Spark: We can write a Spark program to process the data; we can also process a live data stream using Spark.

Apache Flink: This framework is also utilized for processing data streams.

And many more like Storm and Samza.

Big Data Analytics

Big Data analytics is collecting, organizing, and analyzing a large amount of data to uncover hidden patterns, correlations, and other meaningful insights. It helps an organization to understand the information in their data and use it to provide new opportunities to improve their business, leading to more efficient operations, higher profits, and happier customers.

To analyze such a large volume of data, Big Data analytics applications enable big data analysts, data scientists, predictive modelers, statisticians, and other analytical performers to analyze the growing importance of structured and unstructured data. Performing these tasks involves the utilization of specialized software tools and applications. Using these tools, one can perform various data operations such as data mining, text mining, predictive analysis, forecasting, etc. High-performance analytics relies on carrying out these processes individually as integral components. Using Big Data analytic tools and software enables an organization to process a large amount of data and provide meaningful insights that deliver better business decisions in the future.

Key Technologies Behind Big Data Analytics

Analytics comprises various technologies that help you get the most valued information from the data.

1. Hadoop 2. Data Mining

Once the data is stored in the data management system, you can use data mining techniques to discover the patterns for further analysis and answer complex business questions. Data mining removes all the repetitive and noisy data and points out only the relevant information used to accelerate the pace of making informed decisions.

3. Text Mining 4. Predictive Analytics

Predictive analytics uses data, statistical algorithms, and machine learning techniques to identify future outcomes based on historical data. It’s all about providing the best future outcomes so organizations can feel confident in their business decisions.

Benefits of Big Data Analytics

Big Data Analytics has been popular among various organizations. Organizations like the e-commerce, social media, healthcare, Banking, and Entertainment industries widely use analytics to understand multiple patterns, collect and utilize customer insights, fraud detection, monitor financial market activities, etc.

E-commerce industries like Amazon, Flipkart, Myntra, and many other online shopping sites use big data.

They collect customer data in several ways like

Collect information about the items searched by the customer.

Information regarding their preferences.

Information about the popularity of the products and many other data.

Using these kinds of data, organizations derive some patterns and provide the best customer service, like

We showcase the popular products being sold.

Show the products that are related to the products that a customer bought.

We ensure secure money transfers and actively detect any fraudulent transactions that occur.

Forecast the demand for the products and many more.

Conclusion

Big Data is a game-changer. Many organizations use more analytics to drive strategic actions and offer a better customer experience. A slight change in efficiency or the smallest savings can lead to a huge profit, which is why most organizations are moving towards big data.

Recommend Articles

This has been a guide to Big Data Analytics. Here we have discussed basic concepts like what is Big data Analytics, its benefits, and the key technology behind Big data Analytics. You may also look at the following articles to learn more –

Best Power Banks 2023: Reviews And Buying Advice

That’s where we come in. We’ve tested a ton of power banks from different manufacturers spanning the range of available price points and specs. We’ve curated a list of our favorites to help you find the best value. You can rest assured that our recommendations are all great picks as the PCWorld staff puts each power bank through a battery—pun intended—of tests. You can learn more about our evaluation process below our picks.

While our recommendations reflect a range of needs, if you spend a lot of time commuting you might also consider our roundup of USB car chargers. Additionally, check out our roundup of best power stations if you’re looking for something more substantial that can power your appliances while off-grid or in an emergency.

Mophie Powerstation XXL – Best overall

Pros

Incredibly efficient

Sleek fabric design

Small footprint

Cons

The indicator lights are an afterthought

Best Prices Today:

Building on the success of the Powerstation Plus XL (our previous pick for best overall power bank), Mophie’s Powerstation XXL matches its predecessor in efficiency, achieving 92.51 percent of its stated maximum current, plus it’s stylish, portable, and affordably priced. It’s a no-brainer recommendation if you’re in the market for a dependable power source on-the-go.

Read our full

Tronsmart Presto PBT10 – Most portable

Pros

Small and lightweight

Efficient

QC 3.0 compatible

Cons

Port alignment inside isn’t exact, but it still works

Best Prices Today:

Small enough to fit into a coat pocket or the side pocket on a backpack, the Tronsmart Presto is an obvious companion when you’re on-the-go. It’s a 10,000mAh (37Wh) pack, with two USB-A ports, one with QC3.0 for fast charging.

Read our full

Otterbox Fast Charge 20,000mAh – Best USB-C power bank with Power Delivery 

Pros

Well designed

Reasonably priced

Supports multiple charging standards

Cons

Would love to see another port or two on it

Best Prices Today:

With above-average efficiency, a rugged case, and Power Delivery, Otterbox’s Fast Charge power bank is a great option for your bag. It could easily be our pick for best overall power bank, if not for the fact that our top pick—Mophie’s Powerstation XXL features more ports.

Read our full

Einova Laptop Power Bank – Most stylish

Pros

The display is actually useful

Fabric covering is a nice touch

Above-average efficiency

Cons

Both USB ports should have the same charging speeds

Best Prices Today:

With its stylish fabric-clad body, its discreet yet useful battery-status display, and its three USB ports (one of which is USB-C), Einova’s power bank adds flair and above-average battery efficiency to your everyday carry.

Read our full

Powercore Fusion 5000 2-in-1 Portable Charger and Wall Charger – Best budget option

Pros

Built-in wall adapter

Two USB ports

Cons

Not the most efficient battery pack we’ve tested

Best Prices Today:

The combination wall charger and portable power bank format makes the Powercore Fusion 5000 exceedingly convenient. The bank itself can be recharged via either method—power outlet or MicroUSB port. It’s limited to two USB-A ports for device charging, but that doesn’t dimish its great handiness-to-cost ratio.

Sherpa 100AC Portable Power Bank – Best for road warriors

Pros

Informative display

Airline approved

Numerous ports

Cons

Pricey

Slow wireless charging

Best Prices Today:

If you spend a lot of time on the road and value device preparedness, the Sherpa 100AC makes a trusty, if pricey, companion. Yes, at $299.95, you’re looking at a big investment. But that buys you two USB-C ports capable of fast-charging speeds, two standard USB ports, a Qi wireless charging pad, a standard U.S. 110V outlet, a full complement of cables, and a nifty status display and buttons for controlling various functions of the pack. The pack itself can be charged in just a couple hours.

Belkin 10K USB-C Power Bank with Integrated Cables – Most novel convenience

Pros

Built-in cables

18W output

USB-C and Lightning supported

Cons

Average efficiency

A little pricey

Best Prices Today:

Belkin’s Charge Plus 10K USB-C Power Bank is a capable mobile companion in its own right—both its USB-C and Thunderbolt ports are capable of delivering 18W of battery power to your device, or 23W total if both ports are used simultaneously. But it’s the built-in cables for each port that really sets this power bank apart. Of course, you’ll pay a slight premium for the convenience.

Read our full

RAVPower Portable Power Station 252.7Wh Power House – Best power station

Pros

Small form factor

Plenty of ports

Comes with a case and built-in flashlight

Cons

Built for quick trips, not for extended use

Best Prices Today:

There are times when a mere power bank isn’t enough—the circumstances call for a power station. Say you’re spending the weekend off the grid. Or you want to be prepared for a future emergency. The RAVPower Portable Power Station 252.7Wh Power House will get the job done. With a capacity near 252.7Wh, a nice complement of ports, a built-in flashlight, and an included carrying case, this highly portable power station makes a great travel companion for road trips. (For more options, see our roundup of best portable power stations.)

Read our full

Determining whether a power bank lives up to a company’s promise entails more than simply connecting it to a phone and charging. Testing battery packs is done over weeks, not days, and requires extra equipment in order to ensure the batteries work as expected.

1. Upon receiving each battery pack, it’s fully charged, using indicator lights as a means to track charge level.

2. Then to track efficiency, we use an AVHzY USB Power Meter in tandem with a DROK Micro Load Tester.

As we use the DROK load tester to drain the pack of power, we are able to test against a battery’s stated maximum current, and verify that proper shutdown mechanisms are in place should something go wrong during a charging session (such as a device drawing over the maximum amps).

3. Next, we recharged the battery, this time using the AVHzY to track it and chart the amount of time it takes to reach full charge.

The AVHzY meter solves a shortcoming we had with our previous method with the PortaPow. Previously we had to use a GoPro camera to track each battery through its charge cycle, as the PortaPow monitor would continue to collect data after the battery was fully charged (trickle charging is normal, and unfortunately interferes with our testing).

If a battery was capable of charging through USB-C, we use that instead of Micro-USB.

4. The AVHzY also has a feature built in that checks a charging port for all of its supported charging standards. We are able to run that test and get an instant readout to confirm support for QC 3.0, for example, without needing to have compatible phones or devices on hand.

What to look for in a portable power bank

Without fancy testing equipment, you never truly know if you’re getting what you paid for with a battery pack. Vendors, especially in Amazon listings, like to throw around a lot of terms and certifications.

Here are a few tips to help you make a decision:

For those with a compatible device, make sure the battery pack is Quick Charge 2.0, 3.0, or PD certified. Depending on your smartphone, this can make a big difference in performance. If you own a QC 2.0 device, however, ask yourself if paying extra for a QC 3.0 capable pack is worth it.

Don’t put 100 percent confidence in a company’s claims of a pack being able to charge, say, a Galaxy S8 or iPhone X six times over. Battery capacity and efficiency varies based on a number of factors. Read this Macworld report on USB-C packs to learn more about batteries and capacity.

Look at the specs of the battery, and ensure that its input isn’t limited to slow charging such as 5V/1A. The faster the input, the faster your battery pack rechargers, the faster you’re ready to hit the road.

FAQ

1.

What devices can a power bank charge?

Power banks are most commonly intended to charge mobile devices such as cellphones, tablets, cameras, and portable speakers. They can also be used to charge laptops provided the ports allow for it and the power charging rate is compatible.

Power banks are generally not suited to charging larger electronic devices in your home. If you’re looking for a backup power source during an emergency or while camping, you should instead opt for a power station.

2.

What is mAh capacity?

The mAh (milliampere per hour) capacity rating refers to the available storage capacity for a battery. A higher number means that the battery can store more energy and has a longer battery life when charging a device. So for example, an iPhone 13 Pro Max’s battery is rated at 4,352 mAh. This means that a power bank with 10,000 mAh can fully recharge that phone a little over twice before running out of power.

3.

What battery capacity should you look for?

For a portable power bank that you can easily travel with, you should look for a minimum of 10,000 mAh. Most models nowadays even offer 20,000 mAh for reasonable prices. The more mAh, the better, just be sure to weigh your capacity needs against the physical size of the power bank.

Generally, the more mAh a power bank has, the larger its physical size and the heavier it is as well. Therefore, it is recommended when looking at a power bank, that you first consider the amount of power you need to charge your devices and then adjust your expectations based on the size and weight you are willing to carry around with you.

4.

How fast can a power bank charge my devices?

How fast your power bank can charge a device is dependent upon the available output of the power bank and the available input of the device. Without getting too much into the electrical details, a standard 3,000mAh device such as a smartphone can be expected to charge in about 90 minutes by most power banks. However, many power banks nowadays come with quick charge technology, which helps speed up the process even more. Larger devices such as tablets and laptops may take much longer to charge as they require much higher inputs than smartphones.

Always double-check that the power bank you want has an output that matches or exceeds the input of your device to ensure the fastest possible charging.

5.

What is the lifespan of a power bank?

Power bank lifespan is determined by a few factors such as how often you charge the power bank, the quality of the power bank itself, and the conditions in which the power bank is kept. That being said, you should expect a high-quality power bank that is charged every few days and kept in a cool dry place to last several years or more.

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